On August 21, 2017, there’s going to be a total eclipse of the Sun visible on a line across the US. But when exactly will the eclipse occur at a given location? Being able to predict astronomical events has historically been one of the great triumphs of exact science. But in 2017, how well can it actually be done?

The answer, I think, is well enough that even though the edge of totality moves at just over 1000 miles per hour it should be possible to predict when it will arrive at a given location to within perhaps a second. And as a demonstration of this, we’ve created a website to let anyone enter their geo location (or address) and then immediately compute when the eclipse will reach them—as well as generate many pages of other information.

These days it’s easy to find out when the next solar eclipse will be; indeed built right into the Wolfram Language there’s just a function that tells you (in this form the output is the “time of greatest eclipse”):

✕ SolarEclipse[] |

It’s also easy to find out, and plot, where the region of totality will be:

✕
GeoListPlot[SolarEclipse["TotalPhasePolygon"]] |

Or to determine that the whole area of totality will be about 16% of the area of the US:

✕ GeoArea[SolarEclipse["TotalPhasePolygon"]]/GeoArea[Entity["Country", "UnitedStates"]] |

But computing eclipses is not exactly a new business. In fact, the Antikythera device from 2000 years ago even tried to do it—using 37 metal gears to approximate the motion of the Sun and Moon (yes, with the Earth at the center). To me there’s something unsettling—and cautionary—about the fact that the Antikythera device stands as such a solitary piece of technology, forgotten but not surpassed for more than 1600 years.

But right there on the bottom of the device there’s an arm that moves around, and when it points to an Η or Σ marking, it indicates a possible Sun or Moon eclipse. The way of setting dates on the device is a bit funky (after all, the modern calendar wouldn’t be invented for another 1500 years), but if one takes the simulation on the Wolfram Demonstrations Project (which was calibrated back in 2012 when the Demonstration was created), and turns the crank to set the device for August 21, 2017, here’s what one gets:

And, yes, all those gears move so as to line the Moon indicator up with the Sun—and to make the arm on the bottom point right at an Η—just as it should for a solar eclipse. It’s amazing to see this computation successfully happen on a device designed 2000 years ago.

Of course the results are a lot more accurate today. Though, strangely, despite all the theoretical science that’s been done, the way we actually compute the position of the Sun and Moon is conceptually very much like the gears—and effectively epicycles—of the Antikythera device. It’s just that now we have the digital equivalent of hundreds of thousands of gears.

A total solar eclipse occurs when the Moon gets in front of the Sun from the point of view of a particular location on the Earth. And it so happens that at this point in the Earth’s history the Moon can just block the Sun because it has almost exactly the same angular diameter in the sky as the Sun (about 0.5° or 30 arc-minutes).

So when does the Moon get between the Sun and the Earth? Well, basically every time there’s a new moon (i.e. once every lunar month). But we know there isn’t an eclipse every month. So how come?

✕ Graphics[{Style[Disk[{0, 0}, .3/5], Yellow], Style[Disk[{.8, 0}, .1/5], Gray], Style[Disk[{1, 0}, .15/5], Blue]}] |

Well, actually, in the analogous situation of Ganymede and Jupiter, there is an eclipse every time Ganymede goes around Jupiter (which happens to be about once per week). Like the Earth, Jupiter’s orbit around the Sun lies in a particular plane (the “Plane of the Ecliptic”). And it turns out that Ganymede’s orbit around Jupiter also lies in essentially the same plane. So every time Ganymede reaches the “new moon” position (or, in official astronomy parlance, when it’s aligned “in syzygy”—pronounced sizz-ee-gee), it’s in the right place to cast its shadow onto Jupiter, and to eclipse the Sun wherever that shadow lands. (From Jupiter, Ganymede appears about 3 times the size of the Sun.)

But our moon is different. Its orbit doesn’t lie in the plane of the ecliptic. Instead, it’s inclined at about 5°. (How it got that way is unknown, but it’s presumably related to how the Moon was formed.) But that 5° is what makes eclipses so comparatively rare: they can only happen when there’s a “new moon configuration” (syzygy) right at a time when the Moon’s orbit passes through the Plane of the Ecliptic.

To show what’s going on, let’s draw an exaggerated version of everything. Here’s the Moon going around the Earth, colored red whenever it’s close to the Plane of the Ecliptic:

✕ Graphics3D[{With[{dt = 0, \[Theta] = 20 Degree}, Table[{With[{p = {Sin[2 Pi (t + dt)/27.3] Cos[\[Theta]], Cos[2 Pi (t + dt)/27.3] Cos[\[Theta]], Cos[2 Pi (t + dt)/27.3] Sin[\[Theta]]}}, {Style[ Line[{{0, 0, 0}, p}], Opacity[.1]], Style[Sphere[p, .05], Blend[{Red, GrayLevel[.8, .02]}, Sqrt[Abs[Cos[2 Pi t/27.2]]]]]}], Style[Sphere[{0, 0, 0}, .1], Blue]}, {t, 0, 26}]], EdgeForm[Red], Style[InfinitePlane[{0, 0, 0}, {{1, 0, 0}, {0, 1, 0}}], Directive[Red, Opacity[.02]]]}, Lighting -> "Neutral", Boxed -> False] |

Now let’s look at what happens over the course of about a year. We’re showing a dot for where the Moon is each day. And the dot is redder if the Moon is closer to the Plane of the Ecliptic that day. (Note that if this was drawn to scale, you’d barely be able to see the Moon’s orbit, and it wouldn’t ever seem to go backwards like it does here.)

✕ With[{dt = 1}, Graphics[{Style[Disk[{0, 0}, .1], Darker[Yellow]], Table[{With[{p = .2 {Sin[2 Pi t/27.3], Cos[2 Pi t/27.3]} + {Sin[ 2 Pi t/365.25], Cos[2 Pi t/365.25]}}, {Style[ Line[{{Sin[2 Pi t/365.25], Cos[2 Pi t/365.25]}, p}], Opacity[.3]], Style[Disk[p, .01], Blend[{Red, GrayLevel[.8]}, Sqrt[Abs[Cos[2 Pi (t + dt)/27.2]]]]]}], Style[Disk[{Sin[2 Pi t/365.25], Cos[2 Pi t/365.25]}, .005], Blue]}, {t, 360}]}]] |

Now we can start to see how eclipses work. The basic point is that there’s a solar eclipse whenever the Moon is both positioned between the Earth and the Sun, and it’s in the Plane of the Ecliptic. In the picture, those two conditions correspond to the Moon being as far as possible towards the center, and as red as possible. So far we’re only showing the position of the (exaggerated) moon once per day. But to make things clearer, let’s show it four times a day—and now prune out cases where the Moon isn’t at least roughly lined up with the Sun:

✕ With[{dt=1},Graphics[{Style[Disk[{0,0},.1],Darker[Yellow]],Table[{With[{p=.2 {Sin[2 Pi t/27.3],Cos[2 Pi t/27.3]}+{Sin[2 Pi t/365.25],Cos[2 Pi t/365.25]}},If[Norm[p]>.81,{},{Style[Line[{{Sin[2 Pi t/365.25],Cos[2 Pi t/365.25]},p}],Opacity[.3]],Style[Disk[p,.01],Blend[{Red,GrayLevel[.8]},Sqrt[Abs[Cos[2 Pi (t+dt)/27.2]]]]]}]],Style[Disk[{Sin[2 Pi t/365.25],Cos[2 Pi t/365.25]},.005],Blue]},{t,1,360,.25}]}]] |

And now we can see that at least in this particular case, there are two points (indicated by arrows) where the Moon is lined up and in the plane of the ecliptic (so shown red)—and these points will then correspond to solar eclipses.

In different years, the picture will look slightly different, essentially because the Moon is starting at a different place in its orbit at the beginning of the year. Here are schematic pictures for a few successive years:

✕ GraphicsGrid[ Partition[ Table[With[{dt = 1}, Graphics[{Table[{With[{p = .2 {Sin[2 Pi t/27.3], Cos[2 Pi t/27.3]} + {Sin[2 Pi t/365.25], Cos[2 Pi t/365.25]}}, If[Norm[p] > .81, {}, {Style[Line[{{0, 0}, p}], Blend[{Red, GrayLevel[.8]}, Sqrt[Abs[Cos[2 Pi (t + dt)/27.2]]]]], Style[Line[{{Sin[2 Pi t/365.25], Cos[2 Pi t/365.25]}, p}], Opacity[.3]], Style[Disk[p, .01], Blend[{Red, GrayLevel[.8]}, Sqrt[Abs[Cos[2 Pi (t + dt)/27.2]]]]]}]], Style[Disk[{Sin[2 Pi t/365.25], Cos[2 Pi t/365.25]}, .005], Blue]}, {t, 1 + n *365.25, 360 + n*365.25, .25}], Style[Disk[{0, 0}, .1], Darker[Yellow]]}]], {n, 0, 5}], 3]] |

It’s not so easy to see exactly when eclipses occur here—and it’s also not possible to tell which are total eclipses where the Moon is exactly lined up, and which are only partial eclipses. But there’s at least an indication, for example, that there are “eclipse seasons” in different parts of the year where eclipses happen.

OK, so what does the real data look like? Here’s a plot for 20 years in the past and 20 years in the future, showing the actual days in each year when total and partial solar eclipses occur (the small dots everywhere indicate new moons):

✕ coord[date_] := {DateValue[date, "Year"], date - NextDate[DateObject[{DateValue[date, "Year"]}], "Instant"]} ListPlot[coord /@ SolarEclipse[{Now - Quantity[20, "Years"], Now + Quantity[20, "Years"], All}], AspectRatio -> 1/3, Frame -> True] |

The reason for the “drift” between successive years is just that the lunar month (29.53 days) doesn’t line up with the year, so the Moon doesn’t go through a whole number of orbits in the course of a year, with the result that at the beginning of a new year, the Moon is in a different phase. But as the picture makes clear, there’s quite a lot of regularity in the general times at which eclipses occur—and for example there are usually 2 eclipses in a given year—though there can be more (and in 0.2% of years there can be as many as 5, as there last were in 1935 ).

To see more detail about eclipses, let’s plot the time differences (in fractional years) between all successive solar eclipses for 100 years in the past and 100 years in the future:

✕ ListLinePlot[Differences[SolarEclipse[{Now - Quantity[100, "Years"], Now + Quantity[100, "Years"], All}]]/Quantity[1, "Years"], Mesh -> All, PlotRange -> All, Frame -> True, AspectRatio -> 1/3, FrameTicks -> {None, Automatic}] |

And now let’s plot the same time differences, but just for total solar eclipses:

✕ ListLinePlot[Differences[SolarEclipse[{Now - Quantity[100, "Years"], Now + Quantity[100, "Years"], All}, EclipseType -> "Total"]]/Quantity[1, "Years"], Mesh -> All, PlotRange -> All, Frame -> True, AspectRatio -> 1/3, FrameTicks -> {None, Automatic}] |

There’s obviously a fair amount of overall regularity here, but there are also lots of little fine structure and irregularities. And being able to correctly predict all these details has basically taken science the better part of a few thousand years.

It’s hard not to notice an eclipse, and presumably even from the earliest times people did. But were eclipses just reflections—or omens—associated with random goings-on in the heavens, perhaps in some kind of soap opera among the gods? Or were they things that could somehow be predicted?

A few thousand years ago, it wouldn’t have been clear what people like astrologers could conceivably predict. When will the Moon be at a certain place in the sky? Will it rain tomorrow? What will the price of barley be? Who will win a battle? Even now, we’re not sure how predictable all of these are. But the one clear case where prediction and exact science have triumphed is astronomy.

At least as far as the Western tradition is concerned, it all seems to have started in ancient Babylon—where for many hundreds of years, careful observations were made, and, in keeping with the ways of that civilization, detailed records were kept. And even today we still have thousands of daily official diary entries written in what look like tiny chicken scratches preserved on little clay tablets (particularly from Ninevah, which happens now to be in Mosul, Iraq). “Night of the 14th: Cold north wind. Moon was in front of α Leonis. From 15th to 20th river rose 1/2 cubit. Barley was 1 kur 5 siit. 25th, last part of night, moon was 1 cubit 8 fingers behind ε Leonis. 28th, 74° after sunrise, solar eclipse…”

If one looks at what happens on a particular day, one probably can’t tell much. But by putting observations together over years or even hundreds of years it’s possible to see all sorts of repetitions and regularities. And back in Babylonian times the idea arose of using these to construct an ephemeris—a systematic table that said where a particular heavenly body such as the Moon was expected to be at any particular time.

(Needless to say, reconstructing Babylonian astronomy is a complicated exercise in decoding what’s by now basically an alien culture. A key figure in this effort was a certain Otto Neugebauer, who happened to work down the hall from me at the Institute for Advanced Study in Princeton in the early 1980s. I would see him almost every day—a quiet white-haired chap, with a twinkle in his eye—and just sometimes I’d glimpse his huge filing system of index cards which I now realize was at the center of understanding Babylonian astronomy.)

One thing the Babylonians did was to measure surprisingly accurately the repetition period for the phases of the Moon—the so-called synodic month (or “lunation period”) of about 29.53 days. And they noticed that 235 synodic months was very close to 19 years—so that about every 19 years dates and phases of the Moon repeat their alignment, forming a so-called Metonic cycle (named after Meton of Athens, who described it in 432 BC).

It probably helps that the random constellations in the sky form a good pattern against which to measure the precise position of the Moon (it reminds me of the modern fashion of wearing fractals to make motion capture for movies easier). But the Babylonians noticed all sorts of details of the motion of the Moon. They knew about its “anomaly”: its periodic speeding up and slowing down in the sky (now known to be a consequence of its slightly elliptical orbit). And they measured the average period of this—the so-called anomalistic month—to be about 27.55 days. They also noticed that the Moon went above and below the Plane of the Ecliptic (now known to be because of the inclination of its orbit)—with an average period (the so-called draconic month) that they measured as about 27.21 days.

And by 400 BC they’d noticed that every so-called saros of about 18 years 11 days all these different periods essentially line up (223 synodic months, 239 anomalistic months and 242 draconic months)—with the result that the Moon ends up at about the same position relative to the Sun. And this means that if there was an eclipse at one saros, then one can make the prediction that there’s going to be an eclipse at the next saros too.

When one’s absolutely precise about it, there are all sorts of effects that prevent precise repetition at each saros. But over timescales of more than 1300 years, there are in fact still strings of eclipses separated from each other by one saros. (Over the course of such a saros series, the locations of the eclipses effectively scan across the Earth; the upcoming eclipse is number 22 in a series of 77 that began in 1639 AD with an eclipse near the North Pole and will end in 3009 AD with an eclipse near the South Pole.)

Any given moment in time will be in the middle of quite a few saros series (right now it’s 40)—and successive eclipses will always come from different series. But knowing about the saros cycle is a great first step in predicting eclipses—and it’s for example what the Antikythera device uses. In a sense, it’s a quintessential piece of science: take many observations, then synthesize a theory from them, or a least a scheme for computation.

It’s not clear what the Babylonians thought about abstract, formal systems. But the Greeks were definitely into them. And by 300 BC Euclid had defined his abstract system for geometry. So when someone like Ptolemy did astronomy, they did it a bit like Euclid—effectively taking things like the saros cycle as axioms, and then proving from them often surprisingly elaborate geometrical theorems, such as that there must be at least two solar eclipses in a given year.

Ptolemy’s Almagest from around 150 AD is an impressive piece of work, containing among many other things some quite elaborate procedures—and explicit tables—for predicting eclipses. (Yes, even in the later printed version, numbers are still represented confusingly by letters, as they always were in ancient Greek.)

In Ptolemy’s astronomy, Earth was assumed to be at the center of everything. But in modern terms that just meant he was choosing to use a different coordinate system—which didn’t affect most of the things he wanted to do, like working out the geometry of eclipses. And unlike the mainline Greek philosophers he wasn’t so much trying to make a fundamental theory of the world, but just wanted whatever epicycles and so on he needed to explain what he observed.

For more than a thousand years Ptolemy’s theory of the Moon defined the state of the art. In the 1300s Ibn al-Shatir revised Ptolemy’s models, achieving somewhat better accuracy. In 1472 Regiomontanus (Johannes Müller), systematizer of trigonometry, published more complete tables as part of his launch of what was essentially the first-ever scientific publishing company. But even in 1543 when Nicolaus Copernicus introduced his Sun-centered model of the solar system, the results he got were basically the same as Ptolemy’s, even though his underlying description of what was going on was quite different.

It’s said that Tycho Brahe got interested in astronomy in 1560 at age 13 when he saw a solar eclipse that had been predicted—and over the next several decades his careful observations uncovered several effects in the motion of the Moon (such as speeding up just before a full moon)—that eventually resulted in perhaps a factor 5 improvement in the prediction of its position. To Tycho eclipses were key tests, and he measured them carefully, and worked hard to be able to predict their timing more accurately than to within a few hours. (He himself never saw a total solar eclipse, only partial ones.)

Armed with Tycho’s observations, Johannes Kepler developed his description of orbits as ellipses—introducing concepts like inclination and eccentric anomaly—and in 1627 finally produced his *Rudolphine Tables*, which got right a lot of things that had been got wrong before, and included all sorts of detailed tables of lunar positions, as well as vastly better predictions for eclipses.

Using Kepler’s *Rudolphine Tables* (and a couple of pages of calculations)—the first known actual map of a solar eclipse was published in 1654. And while there are some charming inaccuracies in overall geography, the geometry of the eclipse isn’t too bad.

Whether it was Ptolemy’s epicycles or Kepler’s ellipses, there were plenty of calculations to do in determining the motions of heavenly bodies (and indeed the first known mechanical calculator—excepting the Antikythera device—was developed by a friend of Kepler’s, presumably for the purpose). But there wasn’t really a coherent underlying theory; it was more a matter of describing effects in ways that could be used to make predictions.

So it was a big step forward in 1687 when Isaac Newton published his Principia, and claimed that with his laws for motion and gravity it should be possible—essentially from first principles—to calculate everything about the motion of the Moon. (Charmingly, in his “Theory of the World” section he simply asserts as his Proposition XXII “That all the motions of the Moon… follow from the principles which we have laid down.”)

Newton was proud of the fact that he could explain all sorts of known effects on the basis of his new theory. But when it came to actually calculating the detailed motion of the Moon he had a frustrating time. And even after several years he still couldn’t get the right answer—in later editions of the Principia adding the admission that actually “The apse of the Moon is about twice as swift” (i.e. his answer was wrong by a factor of 2).

Still, in 1702 Newton was happy enough with his results that he allowed them to be published, in the form of a 20-page booklet on the “Theory of the Moon”, which proclaimed that “By this Theory, what by all Astronomers was thought most difficult and almost impossible to be done, the Excellent Mr. Newton hath now effected, viz. to determine the Moon’s Place even in her Quadratures, and all other Parts of her Orbit, besides the Syzygys, so accurately by Calculation, that the Difference between that and her true Place in the Heavens shall scarce be two Minutes…”

Newton didn’t explain his methods (and actually it’s still not clear exactly what he did, or how mathematically rigorous it was or wasn’t). But his booklet effectively gave a step-by-step algorithm to compute the position of the Moon. He didn’t claim it worked “at the syzygys” (i.e. when the Sun, Moon and Earth are lined up for an eclipse)—though his advertised error of two arc-minutes was still much smaller than the angular size of the Moon in the sky.

But it wasn’t eclipses that were the focus then; it was a very practical problem of the day: knowing the location of a ship out in the open ocean. It’s possible to determine what latitude you’re at just by measuring how high the Sun gets in the sky. But to determine longitude you have to correct for the rotation of the Earth—and to do that you have to accurately keep track of time. But back in Newton’s day, the clocks that existed simply weren’t accurate enough, especially when they were being tossed around on a ship.

But particularly after various naval accidents, the problem of longitude was deemed important enough that the British government in 1714 established a “Board of Longitude” to offer prizes to help get it solved. One early suggestion was to use the regularity of the moons of Jupiter discovered by Galileo as a way to tell time. But it seemed that a simpler solution (not requiring a powerful telescope) might just be to measure the position of our moon, say relative to certain fixed stars—and then to back-compute the time from this.

But to do this one had to have an accurate way to predict the motion of the Moon—which is what Newton was trying to provide. In reality, though, it took until the 1760s before tables were produced that were accurate enough to be able to determine time to within a minute (and thus distance to within 15 miles or so). And it so happens that right around the same time a marine chronometer was invented that was directly able to keep good time.

One of Newton’s great achievements in the Principia was to solve the so-called two-body problem, and to show that with an inverse square law of gravity the orbit of one body around another must always be what Kepler had said: an ellipse.

In a first approximation, one can think of the Moon as just orbiting the Earth in a simple elliptical orbit. But what makes everything difficult is that that’s just an approximation, because in reality there’s also a gravitational pull on the Moon from the Sun. And because of this, the Moon’s orbit is no longer a simple fixed ellipse—and in fact it ends up being much more complicated. There are a few definite effects one can describe and reason about. The ellipse gets stretched when the Earth is closer to the Sun in its own orbit. The orientation of the ellipse precesses like a top as a result of the influence of the Sun. But there’s no way in the end to work out the orbit by pure reasoning—so there’s no choice but to go into the mathematics and start solving the equations of the three-body problem.

In many ways this represented a new situation for science. In the past, one hadn’t ever been able to go far without having to figure out new laws of nature. But here the underlying laws were supposedly known, courtesy of Newton. Yet even given these laws, there was difficult mathematics involved in working out the behavior they implied.

Over the course of the 1700s and 1800s the effort to try to solve the three-body problem and determine the orbit of the Moon was at the center of mathematical physics—and attracted a veritable who’s who of mathematicians and physicists.

An early entrant was Leonhard Euler, who developed methods based on trigonometric series (including much of our current notation for such things), and whose works contain many immediately recognizable formulas:

In the mid-1740s there was a brief flap—also involving Euler’s “competitors” Clairaut and d’Alembert—about the possibility that the inverse-square law for gravity might be wrong. But the problem turned out to be with the calculations, and by 1748 Euler was using sums of about 20 trigonometric terms and proudly proclaiming that the tables he’d produced for the three-body problem had predicted the time of a total solar eclipse to within minutes. (Actually, he had said there’d be 5 minutes of totality, whereas in reality there was only 1—but he blamed this error on incorrect coordinates he’d been given for Berlin.)

Mathematical physics moved rapidly over the next few decades, with all sorts of now-famous methods being developed, notably by people like Lagrange. And by the 1770s, for example, Lagrange’s work was looking just like it could have come from a modern calculus book (or from a Wolfram|Alpha step-by-step solution):

Particularly in the hands of Laplace there was increasingly obvious success in deriving the observed phenomena of what he called “celestial mechanics” from mathematics—and in establishing the idea that mathematics alone could indeed generate new results in science.

At a practical level, measurements of things like the position of the Moon had always been much more accurate than calculations. But now they were becoming more comparable—driving advances in both. Meanwhile, there was increasing systematization in the production of ephemeris tables. And in 1767 the annual publication began of what was for many years the standard: the British Nautical Almanac.

The almanac quoted the position of the Moon to the arc-second, and systematically achieved at least arc-minute accuracy. The primary use of the almanac was for navigation (and it was what started the convention of using Greenwich as the “prime meridian” for measuring time). But right at the front of each year’s edition were the predicted times of the eclipses for that year—in 1767 just two solar eclipses:

At a mathematical level, the three-body problem is about solving a system of three ordinary differential equations that give the positions of the three bodies as a function of time. If the positions are represented in standard 3D Cartesian coordinates

*r _{i}*={

The {*x*,*y*,*z*} coordinates here aren’t, however, what traditionally show up in astronomy. For example, in describing the position of the Moon one might use longitude and latitude on a sphere around the Earth. Or, given that one knows the Moon has a roughly elliptical orbit, one might instead choose to describe its motions by variables that are based on deviations from such an orbit. In principle it’s just a matter of algebraic manipulation to restate the equations with any given choice of variables. But in practice what comes out is often long and complex—and can lead to formulas that fill many pages.

But, OK, so what are the best kind of variables to use for the three-body problem? Maybe they should involve relative positions of pairs of bodies. Or relative angles. Or maybe positions in various kinds of rotating coordinate systems. Or maybe quantities that would be constant in a pure two-body problem. Over the course of the 1700s and 1800s many treatises were written exploring different possibilities.

But in essentially all cases the ultimate approach to the three-body problem was the same. Set up the problem with the chosen variables. Identify parameters that, if set to zero, would make the problem collapse to some easy-to-solve form. Then do a series expansion in powers of these parameters, keeping just some number of terms.

By the 1860s Charles Delaunay had spent 20 years developing the most extensive theory of the Moon in this way. He’d identified five parameters with respect to which to do his series expansions (eccentricities, inclinations, and ratios of orbit sizes)—and in the end he generated about 1800 pages like this (yes, he really needed Mathematica!):

But the sad fact was that despite all this effort, he didn’t get terribly good answers. And eventually it became clear why. The basic problem was that Delaunay wanted to represent his results in terms of functions like sin and cos. But in his computations, he often wanted to do series expansions with respect to the frequencies of those functions. Here’s a minimal case:

✕ Series[Sin[(ω + δ)*t], {δ, 0, 3}] |

And here’s the problem. Take a look even at the second term. Yes, the δ parameter may be small. But how about the *t* parameter, standing for time? If you don’t want to make predictions very far out, that’ll stay small. But what if you want to figure out what will happen further in the future?

Well eventually that term will get big. And higher-order terms will get even bigger. But unless the Moon is going to escape its orbit or something, the final mathematical expressions that represent its position can’t have values that are too big. So in these expressions the so-called secular terms that increase with t must somehow cancel out.

But the problem is that at any given order in the series expansion, there’s no guarantee that will happen in a numerically useful way. And in Delaunay’s case—even though with immense effort he often went to 7th order or beyond—it didn’t.

One nice feature of Delaunay’s computation was that it was in a sense entirely algebraic: everything was done symbolically, and only at the very end were actual numerical values of parameters substituted in.

But even before Delaunay, Peter Hansen had taken a different approach—substituting numbers as soon as he could, and dropping terms based on their numerical size rather than their symbolic form. His presentations look less pure (notice things like all those *t**−*1800, where *t* is the time in years), and it’s more difficult to tell what’s going on. But as a practical matter, his results were much better, and in fact were used for many national almanacs from about 1862 to 1922, achieving errors as small as 1 or 2 arc-seconds at least over periods of a decade or so. (Over longer periods, the errors could rapidly increase because of the lack of terms that had been dropped as a result of what amounted to numerical accidents.)

Both Delaunay and Hansen tried to represent orbits as series of powers and trigonometric functions (so-called Poisson series). But in the 1870s, George Hill in the US Nautical Almanac Office proposed instead using as a basis numerically computed functions that came from solving an equation for two-body motion with a periodic driving force of roughly the kind the Sun exerts on the Moon’s orbit. A large-scale effort was mounted, and starting in 1892 Ernest W. Brown (who had moved to the US, but had been a student of George Darwin, Charles Darwin’s physicist son) took charge of the project and in 1918 produced what would stand for many years as the definitive “Tables of the Motion of the Moon”.

Brown’s tables consist of hundreds of pages like this—ultimately representing the position of the Moon as a combination of about 1400 terms with very precise coefficients:

He says right at the beginning that the tables aren’t particularly intended for unique events like eclipses, but then goes ahead and does a “worked example” of computing an eclipse from 381 BC, reported by Ptolemy:

It was an impressive indication of how far things had come. But ironically enough the final presentation of Brown’s tables had the same sum-of-trigonometric-functions form that one would get from having lots of epicycles. At some level it’s not surprising, because any function can ultimately be represented by epicycles, just as it can be represented by a Fourier or other series. But it’s a strange quirk of history that such similar forms were used.

It’s all well and good that one can find approximations to the three-body problem, but what about just finding an outright solution—like as a mathematical formula? Even in the 1700s, there’d been some specific solutions found—like Euler’s collinear configuration, and Lagrange’s equilateral triangle. But a century later, no further solutions had been found—and finding a complete solution to the three-body problem was beginning to seem as hopeless as trisecting an angle, solving the quintic, or making a perpetual motion machine. (That sentiment was reflected for example in a letter Charles Babbage wrote Ada Lovelace in 1843 mentioning the “horrible problem [of] the three bodies”—even though this letter was later misinterpreted by Ada’s biographers to be about a romantic triangle, not the three-body problem of celestial mechanics.)

In contrast to the three-body problem, what seemed to make the two-body problem tractable was that its solutions could be completely characterized by “constants of the motion”—quantities that stay constant with time (in this case notably the direction of the axis of the ellipse). So for many years one of the big goals with the three-body problem was to find constants of the motion.

In 1887, though, Heinrich Bruns showed that there couldn’t be any such constants of the motion, at least expressible as algebraic functions of the standard {*x*,*y*,*z*} position and velocity coordinates of the three bodies. Then in the mid-1890s Henri Poincaré showed that actually there couldn’t be any constants of the motion that were expressible as any analytic functions of the positions, velocities and mass ratios.

One reason that was particularly disappointing at the time was that it had been hoped that somehow constants of the motion would be found in *n*-body problems that would lead to a mathematical proof of the long-term stability of the solar system. And as part of his work, Poincaré also saw something else: that at least in particular cases of the three-body problem, there was arbitrarily sensitive dependence on initial conditions—implying that even tiny errors in measurement could be amplified to arbitrarily large changes in predicted behavior (the classic “chaos theory” phenomenon).

But having discovered that particular solutions to the three-body problem could have this kind of instability, Poincaré took a different approach that would actually be characteristic of much of pure mathematics going forward: he decided to look not at individual solutions, but at the space of all possible solutions. And needless to say, he found that for the three-body problem, this was very complicated—though in his efforts to analyze it he invented the field of topology.

Poincaré’s work all but ended efforts to find complete solutions to the three-body problem. It also seemed to some to explain why the series expansions of Delaunay and others hadn’t worked out—though in 1912 Karl Sundman did show that at least in principle the three-body problem could be solved in terms of an infinite series, albeit one that converges outrageously slowly.

But what does it mean to say that there can’t be a solution to the three-body problem? Galois had shown that there couldn’t be a solution to the generic quintic equation, at least in terms of radicals. But actually it’s still perfectly possible to express the solution in terms of elliptic or hypergeometric functions. So why can’t there be some more sophisticated class of functions that can be used to just “solve the three-body problem”?

Here are some pictures of what can actually happen in the three-body problem, with various initial conditions:

✕ eqns = {Subscript[m, 1]* Derivative[2][Subscript[r, 1]][ t] == -((Subscript[m, 1]* Subscript[m, 2]*(Subscript[r, 1][t] - Subscript[r, 2][t]))/ Norm[Subscript[r, 1][t] - Subscript[r, 2][t]]^3) - (Subscript[ m, 1]*Subscript[m, 3]*(Subscript[r, 1][t] - Subscript[r, 3][t]))/ Norm[Subscript[r, 1][t] - Subscript[r, 3][t]]^3, Subscript[m, 2]* Derivative[2][Subscript[r, 2]][ t] == -((Subscript[m, 1]* Subscript[m, 2]*(Subscript[r, 2][t] - Subscript[r, 1][t]))/ Norm[Subscript[r, 2][t] - Subscript[r, 1][t]]^3) - (Subscript[ m, 2]*Subscript[m, 3]*(Subscript[r, 2][t] - Subscript[r, 3][t]))/ Norm[Subscript[r, 2][t] - Subscript[r, 3][t]]^3, Subscript[m, 3]* Derivative[2][Subscript[r, 3]][ t] == -((Subscript[m, 1]* Subscript[m, 3]*(Subscript[r, 3][t] - Subscript[r, 1][t]))/ Norm[Subscript[r, 3][t] - Subscript[r, 1][t]]^3) - (Subscript[ m, 2]*Subscript[m, 3]*(Subscript[r, 3][t] - Subscript[r, 2][t]))/ Norm[Subscript[r, 3][t] - Subscript[r, 2][t]]^3}; (SeedRandom[#]; {Subscript[m, 1], Subscript[m, 2], Subscript[m, 3]} = RandomReal[{0, 1}, 3]; inits = Table[{Subscript[r, i][0] == RandomReal[{-1, 1}, 3], Subscript[r, i]'[0] == RandomReal[{-1, 1}, 3]}, {i, 3}]; sols = NDSolve[{eqns, inits}, {Subscript[r, 1], Subscript[r, 2], Subscript[r, 3]}, {t, 0, 100}]; ParametricPlot3D[{Subscript[r, 1][t], Subscript[r, 2][t], Subscript[r, 3][t]} /. sols, {t, 0, 100}, Ticks -> None]) & /@ {776, 5742, 6711, 2300, 5281, 9225} |

And looking at these immediately gives some indication of why it’s not easy to just “solve the three-body problem”. Yes, there are cases where what happens is fairly simple. But there are also cases where it’s not, and where the trajectories of the three bodies continue to be complicated and tangled for a long time.

So what’s fundamentally going on here? I don’t think traditional mathematics is the place to look. But I think what we’re seeing is actually an example of a general phenomenon I call computational irreducibility that I discovered in the 1980s in studying the computational universe of possible programs.

Many programs, like many instances of the three-body problem, behave in quite simple ways. But if you just start looking at all possible simple programs, it doesn’t take long before you start seeing behavior like this:

✕ ArrayPlot[ CellularAutomaton[{#, 3, 1}, {{2}, 0}, 100], ImageSize -> {Automatic, 100}] & /@ {5803305107286, 2119737824118, 5802718895085, 4023376322994, 6252890585925} |

How can one tell what’s going to happen? Well, one can just keep explicitly running each program and seeing what it does. But the question is: is there some systematic way to jump ahead, and to predict what will happen without tracing through all the steps?

The answer is that in general there isn’t. And what I call the Principle of Computational Equivalence suggests that pretty much whenever one sees complex behavior, there won’t be.

Here’s the way to think about this. The system one’s studying is effectively doing a computation to work out what its behavior will be. So to jump ahead we’d in a sense have to do a more sophisticated computation. But what the Principle of Computational Equivalence says is that actually we can’t—and that whether we’re using our brains or our mathematics or a Turing machine or anything else, we’re always stuck with computations of the same sophistication.

So what about the three-body problem? Well, I strongly suspect that it’s an example of computational irreducibility: that in effect the computations it’s doing are as sophisticated as any computations that we can do, so there’s no way we can ever expect to systematically jump ahead and solve the problem. (We also can’t expect to just define some new finite class of functions that can just be evaluated to give the solution.)

I’m hoping that one day someone will rigorously prove this. There’s some technical difficulty, because the three-body problem is usually formulated in terms of real numbers that immediately have an infinite number of digits—but to compare with ordinary computation one has to require finite processes to set up initial conditions. (Ultimately one wants to show for example that there’s a “compiler” that can go from any program, say for a Turing machine, and can generate instructions to set up initial conditions for a three-body problem so that the evolution of the three-body problem will give the same results as running that program—implying that the three-body problem is capable of universal computation.)

I have to say that I consider Newton in a sense very lucky. It could have been that it wouldn’t have been possible to work out anything interesting from his theory without encountering the kind of difficulties he had with the motion of the Moon—because one would always be running into computational irreducibility. But in fact, there was enough computational reducibility and enough that could be computed easily that one could see that the theory was useful in predicting features of the world (and not getting wrong answers, like with the apse of the Moon)—even if there were some parts that might take two centuries to work out, or never be possible at all.

Newton himself was certainly aware of the potential issue, saying that at least if one was dealing with gravitational interactions between many planets then “to define these motions by exact laws admitting of easy calculation exceeds, if I am not mistaken, the force of any human mind”. And even today it’s extremely difficult to know what the long-term evolution of the solar system will be.

It’s not particularly that there’s sensitive dependence on initial conditions: we actually have measurements that should be precise enough to determine what will happen for a long time. The problem is that we just have to do the computation—a bit like computing the digits of π—to work out the behavior of the *n*-body problem that is our solar system.

Existing simulations show that for perhaps a few tens of millions of years, nothing too dramatic can happen. But after that we don’t know. Planets could change their order. Maybe they could even collide, or be ejected from the solar system. Computational irreducibility implies that at least after an infinite time it’s actually formally undecidable (in the sense of Gödel’s Theorem or the Halting Problem) what can happen.

One of my children, when they were very young, asked me whether when dinosaurs existed the Earth could have had two moons. For years when I ran into celestial mechanics experts I would ask them that question—and it was notable how difficult they found it. Most now say that at least at the time of the dinosaurs we couldn’t have had an extra moon—though a billion years earlier it’s not clear.

We used to only have one system of planets to study. And the fact that there were (then) 9 of them used to be a classic philosopher’s example of a truth about the world that just happens to be the way it is, and isn’t “necessarily true” (like 2+2=4). But now of course we know about lots of exoplanets. And it’s beginning to look as if there might be a theory for things like how many planets a solar system is likely to have.

At some level there’s presumably a process like natural selection: some configurations of planets aren’t “fit enough” to be stable—and only those that are survive. In biology it’s traditionally been assumed that natural selection and adaptation is somehow what’s led to the complexity we see. But actually I suspect much of it is instead just a reflection of what generally happens in the computational universe—both in biology and in celestial mechanics. Now in celestial mechanics, we haven’t yet seen in the wild any particularly complex forms (beyond a few complicated gap structures in rings, and tumbling moons and asteroids). But perhaps elsewhere we’ll see things like those obviously tangled solutions to the three-body problem—that come closer to what we’re used to in biology.

It’s remarkable how similar the issues are across so many different fields. For example, the whole idea of using “perturbation theory” and series expansions that has existed since the 1700s in celestial mechanics is now also core to quantum field theory. But just like in celestial mechanics there’s trouble with convergence (maybe one should try renormalization or resummation in celestial mechanics). And in the end one begins to realize that there are phenomena—no doubt like turbulence or the three-body problem—that inevitably involve more sophisticated computations, and that need to be studied not with traditional mathematics of the kind that was so successful for Newton and his followers but with the kind of science that comes from exploring the computational universe.

But let’s get back to the story of the motion of the Moon. Between Brown’s tables, and Poincaré’s theoretical work, by the beginning of the 1900s the general impression was that whatever could reasonably be computed about the motion of the Moon had been computed.

Occasionally there were tests. Like for example in 1925, when there was a total solar eclipse visible in New York City, and the *New York Times* perhaps overdramatically said that “scientists [were] tense… wondering whether they or moon is wrong as eclipse lags five seconds behind”. The fact is that a prediction accurate to 5 seconds was remarkably good, and we can’t do all that much better even today. (By the way, the actual article talks extensively about “Professor Brown”—as well as about how the eclipse might “disprove Einstein” and corroborate the existence of “coronium”—but doesn’t elaborate on the supposed prediction error.)

As a practical matter, Brown’s tables were not exactly easy to use: to find the position of the Moon from them required lots of mechanical desk calculator work, as well as careful transcription of numbers. And this led Leslie Comrie in 1932 to propose using a punch-card-based IBM Hollerith automatic tabulator—and with the help of Thomas Watson, CEO of IBM, what was probably the first “scientific computing laboratory” was established—to automate computations from Brown’s tables.

(When I was in elementary school in England in the late 1960s—before electronic calculators—I always carried around, along with my slide rule, a little book of “4-figure mathematical tables”. I think I found it odd that such a book would have an author—and perhaps for that reason I still remember the name: “L. J. Comrie”.)

By the 1950s, the calculations in Brown’s tables were slowly being rearranged and improved to make them more suitable for computers. But then with John F. Kennedy’s 1962 “We choose to go the Moon”, there was suddenly urgent interest in getting the most accurate computations of the Moon’s position. As it turned out, though, it was basically just a tweaked version of Brown’s tables, running on a mainframe computer, that did the computations for the Apollo program.

At first, computers were used in celestial mechanics purely for numerical computation. But by the mid-1960s there were also experiments in using them for algebraic computation, and particularly to automate the generation of series expansions. Wallace Eckert at IBM started using FORMAC to redo Brown’s tables, while in Cambridge David Barton and Steve Bourne (later the creator of the “Bourne shell” (sh) in Unix) built their own CAMAL computer algebra system to try extending the kind of thing Delaunay had done. (And by 1970, Delaunay’s 7th-order calculations had been extended to 20th order.)

When I myself started to work on computer algebra in 1976 (primarily for computations in particle physics), I’d certainly heard about CAMAL, but I didn’t know what it had been used for (beyond vaguely “celestial mechanics”). And as a practicing theoretical physicist in the late 1970s, I have to say that the “problem of the Moon” that had been so prominent in the 1700s and 1800s had by then fallen into complete obscurity.

I remember for example in 1984 asking a certain Martin Gutzwiller, who was talking about quantum chaos, what his main interest actually was. And when he said “the problem of the Moon”, I was floored; I didn’t know there still was any problem with the Moon. As it turns, in writing this post I found out that Gutzwiller was actually the person who took over from Eckert and spent nearly two decades working on trying to improve the computations of the position of the Moon.

Traditional approaches to the three-body problem come very much from a mathematical way of thinking. But modern computational thinking immediately suggests a different approach. Given the differential equations for the three-body problem, why not just directly solve them? And indeed in the Wolfram Language there’s a built-in function `NDSolve` for numerically solving systems of differential equations.

So what happens if one just feeds in equations for a three-body problem? Well, here are the equations:

✕
eqns = {Subscript[m, 1] (Subscript[r, 1]^\[Prime]\[Prime])[t] == -(( Subscript[m, 1] Subscript[m, 2] (Subscript[r, 1][t] - Subscript[r, 2][t]))/ Norm[Subscript[r, 1][t] - Subscript[r, 2][t]]^3) - ( Subscript[m, 1] Subscript[m, 3] (Subscript[r, 1][t] - Subscript[r, 3][t]))/ Norm[Subscript[r, 1][t] - Subscript[r, 3][t]]^3, Subscript[m, 2] (Subscript[r, 2]^\[Prime]\[Prime])[t] == -(( Subscript[m, 1] Subscript[m, 2] (Subscript[r, 2][t] - Subscript[r, 1][t]))/ Norm[Subscript[r, 2][t] - Subscript[r, 1][t]]^3) - ( Subscript[m, 2] Subscript[m, 3] (Subscript[r, 2][t] - Subscript[r, 3][t]))/ Norm[Subscript[r, 2][t] - Subscript[r, 3][t]]^3, Subscript[m, 3] (Subscript[r, 3]^\[Prime]\[Prime])[t] == -(( Subscript[m, 1] Subscript[m, 3] (Subscript[r, 3][t] - Subscript[r, 1][t]))/ Norm[Subscript[r, 3][t] - Subscript[r, 1][t]]^3) - ( Subscript[m, 2] Subscript[m, 3] (Subscript[r, 3][t] - Subscript[r, 2][t]))/ Norm[Subscript[r, 3][t] - Subscript[r, 2][t]]^3}; |

Now as an example let’s set the masses to random values:

✕ {Subscript[m, 1], Subscript[m, 2], Subscript[m, 3]} = RandomReal[{0, 1}, 3] |

And let’s define the initial position and velocity for each body to be random as well:

✕ inits = Table[{Subscript[r, i][0] == RandomReal[{-1, 1}, 3], Derivative[1][Subscript[r, i]][0] == RandomReal[{-1, 1}, 3]}, {i, 3}] |

Now we can just use `NDSolve` to get the solutions (it gives them as implicit approximate numerical functions of *t*):

✕ sols = NDSolve[{eqns, inits}, {Subscript[r, 1], Subscript[r, 2], Subscript[r, 3]}, {t, 0, 100}] |

And now we can plot them. And now we’ve got a solution to a three-body problem, just like that!

✕ ParametricPlot3D[Evaluate[{Subscript[r, 1][t], Subscript[r, 2][t], Subscript[r, 3][t]} /. First[sols]], {t, 0, 100}] |

Well, obviously this is using the Wolfram Language and a huge tower of modern technology. But would it have been possible even right from the beginning for people to generate direct numerical solutions to the three-body problem, rather than doing all that algebra? Back in the 1700s, Euler already knew what’s now called Euler’s method for finding approximate numerical solutions to differential equations. So what if he’d just used that method to calculate the motion of the Moon?

The method relies on taking a sequence of discrete steps in time. And if he’d used, say, a step size of a minute, then he’d have had to take 40,000 steps to get results for a month, but he should have been able to successfully reproduce the position of the Moon to about a percent. If he’d tried to extend to 3 months, however, then he would already have had at least a 10% error.

Any numerical scheme for solving differential equations in practice eventually builds up some kind of error—but the more one knows about the equations one’s solving, and their expected solutions, the more one’s able to preprocess and adapt things to minimize the error. `NDSolve` has enough automatic adaptivity built into it that it’ll do pretty well for a surprisingly long time on a typical three-body problem. (It helps that the Wolfram Language and `NDSolve` can handle numbers with arbitrary precision, not just machine precision.)

But if one looks, say, at the total energy of the three-body system—which one can prove from the equations should stay constant—then one will typically see an error slowly build up in it. One can avoid this if one effectively does a change of variables in the equations to “factor out” energy. And one can imagine doing a whole hierarchy of algebraic transformations that in a sense give the numerical scheme as much help as possible.

And indeed since at least the 1980s that’s exactly what’s been done in practical work on the three-body problem, and the Earth-Moon-Sun system. So in effect it’s a mixture of the traditional algebraic approach from the 1700s and 1800s, together with modern numerical computation.

OK, so what’s involved in solving the real problem of the Earth-Moon-Sun system? The standard three-body problem gives a remarkably good approximation to the physics of what’s happening. But it’s obviously not the whole story.

For a start, the Earth isn’t the only planet in the solar system. And if one’s trying to get sufficiently accurate answers, one’s going to have to take into account the gravitational effect of other planets. The most important is Jupiter, and its typical effect on the orbit of the Moon is at about the 10^{-5} level—sufficiently large that for example Brown had to take it into account in his tables.

The next effect is that the Earth isn’t just a point mass, or even a precise sphere. Its rotation makes it bulge at the equator, and that affects the orbit of the Moon at the 10^{-6} level.

Orbits around the Earth ultimately depend on the full mass distribution and gravitational field of the Earth (which is what Sputnik-1 was nominally launched to map)—and both this, and the reverse effect from the Moon, come in at the 10^{-8} level. At the 10^{-9} level there are then effects from tidal deformations (“solid tides”) on the Earth and moon, as well as from gravitational redshift and other general relativistic phenomena.

To predict the position of the Moon as accurately as possible one ultimately has to have at least some model for these various effects.

But there’s a much more immediate issue to deal with: one has to know the initial conditions for the Earth, Sun and Moon, or in other words, one has to know as accurately as possible what their positions and velocities were at some particular time.

And conveniently enough, there’s now a really good way to do that, because Apollo 11, 14 and 15 all left laser retroreflectors on the Moon. And by precisely timing how long it takes a laser pulse from the Earth to round-trip to these retroreflectors, it’s now possible in effect to measure the position of the Moon to millimeter accuracy.

OK, so how do modern analogs of the Babylonian ephemerides actually work? Internally they’re dealing with the equations for all the significant bodies in the solar system. They do symbolic preprocessing to make their numerical work as easy as possible. And then they directly solve the differential equations for the system, appropriately inserting models for things like the mass distribution in the Earth.

They start from particular measured initial conditions, but then they repeatedly insert new measurements, trying to correct the parameters of the model so as to optimally reproduce all the measurements they have. It’s very much like a typical machine learning task—with the training data here being observations of the solar system (and typically fitting just being least squares).

But, OK, so there’s a model one can run to figure out something like the position of the Moon. But one doesn’t want to have to explicitly do that every time one needs to get a result; instead one wants in effect just to store a big table of pre-computed results, and then to do something like interpolation to get any particular result one needs. And indeed that’s how it’s done today.

Back in the 1960s NASA started directly solving differential equations for the motion of planets. The Moon was more difficult to deal with, but by the 1980s that too was being handled in a similar way. Ongoing data from things like the lunar retroreflectors was added, and all available historical data was inserted as well.

The result of all this was the JPL Development Ephemeris (JPL DE). In addition to new observations being used, the underlying system gets updated every few years, typically to get what’s needed for some spacecraft going to some new place in the solar system. (The latest is DE432, built for going to Pluto.)

But how is the actual ephemeris delivered? Well, for every thousand years covered, the ephemeris has about 100 megabytes of results, given as coefficients for Chebyshev polynomials, which are convenient for interpolation. And for any given quantity in any given coordinate system over a particular period of time, one accesses the appropriate parts of these results.

OK, but so how does one find an eclipse? Well, it’s an iterative process. Start with an approximation, perhaps from the saros cycle. Then interpolate the ephemeris and look at the result. Then keep iterating until one finds out just when the Moon will be in the appropriate position.

But actually there’s some more to do. Because what’s originally computed are the positions of the barycenters (centers of mass) of the various bodies. But now one has to figure out how the bodies are oriented.

The Earth rotates, and we know its rate quite precisely. But the Moon is basically locked with the same face pointing to the Earth, except that in practice there are small “librations” where the Moon wobbles a little back and forth—and these turn out to be particularly troublesome to predict.

OK, so let’s say one knows where the Earth, Moon and Sun are. How does one then figure out where on the Earth the eclipse will actually hit? Well, there’s some further geometry to do. Basically, the Moon generates a cone of shadow in a direction defined by the location of the Sun, and what’s then needed is to figure out how the surface of the Earth intersects that cone.

In 1824 Friedrich Bessel suggested in effect inverting the problem by using the shadow cone to define a coordinate system in which to specify the positions of the Sun and Moon. The resulting so-called Besselian elements provide a convenient summary of the local geometry of an eclipse—with respect to which its path can be defined.

OK, but so how does one figure out at what time an eclipse will actually reach a given point on Earth? Well, first one has to be clear on one’s definition of time. And there’s an immediate issue with the speed of light and special relativity. What does it mean to say that the positions of the Earth and Sun are such-and-such at such-and-such a time? Because it takes light about 8 minutes to get to the Earth from the Sun, we only get to see where the Sun was 8 minutes ago, not where it is now.

And what we need is really a classic special relativity setup. We essentially imagine that the solar system is filled with a grid of clocks that have been synchronized by light pulses. And what a modern ephemeris does is to quote the results for positions of bodies in the solar system relative to the times on those clocks. (General relativity implies that in different gravitational fields the clocks will run at different rates, but for our purposes this is a tiny effect. But what isn’t a tiny effect is including retardation in the equations for the *n*-body problem—making them become delay differential equations.)

But now there’s another issue. If one’s observing the eclipse, one’s going to be using some timepiece (phone?) to figure out what time it is. And if it’s working properly that timepiece should show official “civil time” that’s based on UTC—which is what NTP internet time is synchronized to. But the issue is that UTC has a complicated relationship to the time used in the astronomical ephemeris.

The starting point is what’s called UT1: a definition of time in which one day is the average time it takes the Earth to rotate once relative to the Sun. But the point is that this average time isn’t constant, because the rotation of the Earth is gradually slowing down, primarily as a result of interactions with the Moon. But meanwhile, UTC is defined by an atomic clock whose timekeeping is independent of any issues about the rotation of the Earth.

There’s a convention for keeping UT1 aligned with UTC: if UT1 is going to get more than 0.9 seconds away from UTC, then a leap second is added to UTC. One might think this would be a tiny effect, but actually, since 1972, a total of 27 leap seconds have been added. Exactly when a new leap second will be needed is unpredictable; it depends on things like what earthquakes have occurred. But we need to account for leap seconds if we’re going to get the time of the eclipse correct to the second relative to UTC or internet time.

There are a few other effects that are also important in the precise observed timing of the eclipse. The most obvious is geo elevation. In doing astronomical computations, the Earth is assumed to be an ellipsoid. (There are many different definitions, corresponding to different geodetic “datums”—and that’s an issue in defining things like “sea level”, but it’s not relevant here.) But if you’re at a different height above the ellipsoid, the cone of shadow from the eclipse will reach you at a different time. And the size of this effect can be as much as 0.3 seconds for every 1000 feet of height.

All of the effects we’ve talked about we’re readily able to account for. But there is one remaining effect that’s a bit more difficult. Right at the beginning or end of totality one typically sees points of light on the rim of the Moon. Known as Baily’s beads, these are the result of rays of light that make it to us between mountains on the Moon. Figuring out exactly when all these rays are extinguished requires taking geo elevation data for the Moon, and effectively doing full 3D ray tracing. The effect can last as long as a second, and can cause the precise edge of totality to move by as much as a mile. (One can also imagine effects having to do with the corona of the Sun, which is constantly changing.)

But in the end, even though the shadow of the Moon on the Earth moves at more than 1000 mph, modern science successfully makes it possible to compute when the shadow will reach a particular point on Earth to an accuracy of perhaps a second. And that’s what our precisioneclipse.com website is set up to do.

I saw my first partial solar eclipse more than 50 years ago. And I’ve seen one total solar eclipse before in my life—in 1991. It was the longest eclipse (6 minutes 53 seconds) that’ll happen for more than a century.

There was a certain irony to my experience, though, especially in view of our efforts now to predict the exact arrival time of next week’s eclipse. I’d chartered a plane and flown to a small airport in Mexico (yes, that’s me on the left with the silly hat)—and my friends and I had walked to a beautiful deserted beach, and were waiting under a cloudless sky for the total eclipse to begin.

I felt proud of how prepared I was—with maps marking to the minute when the eclipse should arrive. But then I realized: there we were, out on a beach with no obvious signs of modern civilization—and nobody had brought any properly set timekeeping device (and in those days my cellphone was just a phone, and didn’t even have signal there).

And so it was that I missed seeing a demonstration of an impressive achievement of science. And instead I got to experience the eclipse pretty much the way people throughout history have experienced eclipses—even if I did know that the Moon would continue gradually eating into the Sun and eventually cover it, and that it wouldn’t make the world end.

There’s always something sobering about astronomical events, and about realizing just how tiny human scales are compared to them. Billions of eclipses have happened over the course of the Earth’s history. Recorded history has covered only a few thousand of them. On average, there’s an eclipse at any given place on Earth roughly every 400 years; in Jackson, WY, where I’m planning to see next week’s eclipse, it turns out the next total eclipse will be 727 years from now—in 2744.

In earlier times, civilizations built giant monuments to celebrate the motions of the Sun and moon. For the eclipse next week what we’re making is a website. But that website builds on one of the great epics of human intellectual history—stretching back to the earliest times of systematic science, and encompassing contributions from a remarkable cross-section of the most celebrated scientists and mathematicians from past centuries.

It’ll be about 9538 days since the eclipse I saw in 1991. The Moon will have traveled some 500 million miles around the Earth, and the Earth some 15 billion miles around the Sun. But now—in a remarkable triumph of science—we’re computing to the second when they’ll be lined up again.

]]>How far can one get in teaching computational thinking to high-school students in two weeks? Judging by the results of this year’s Wolfram High-School Summer Camp the answer is: remarkably far.

I’ve been increasingly realizing what an immense and unique opportunity there now is to teach computational thinking with the whole stack of technology we’ve built up around the Wolfram Language. But it was a thrill to see just how well this seems to actually work with real high-school students—and to see the kinds of projects they managed to complete in only two weeks.

We’ve been doing our high-school summer camp for 5 years now (as well as our 3-week Summer School for more experienced students for 15 years). And every time we do the camp, we figure out a little more. And I think that by now we really have it down—and we’re able to take even students who’ve never really been exposed to computation before, and by the end of the camp have them doing serious computational thinking—and fluently implementing their ideas by writing sometimes surprisingly sophisticated Wolfram Language code (as well as creating well-written notebooks and “computational essays” that communicate about what they’ve done).

Over the coming year, we’re going to be dramatically expanding our Computational Thinking Initiative, and working to bring analogs of the Summer Camp experience to as many students as possible. But the Summer Camp provides fascinating and important data about what’s possible.

So how did the Summer Camp actually work? We had a lot of applicants for the 40 slots we had available this year. Some had been pointed to the camp by parents, teachers, or previous attendees. But a large fraction had just seen mention of it in the Wolfram|Alpha sidebar. There were students from a range of kinds of schools around the US, and overseas (though we still have to figure out how to get more applicants from underserved populations). Our team had done interviews to pick the final students—and I thought the ones they’d selected were terrific.

The students’ past experience was quite diverse. Some were already accomplished programmers (almost always self-taught). Others had done a CS class or two. But quite a few had never really done anything computational before—even though they were often quite advanced in various STEM areas such as math. But almost regardless of background, it was striking to me how new the core concepts of computational thinking seemed to be to so many of the students.

How does one take an idea or a question about almost anything, and find a way to formulate it for a computer? To be fair, it’s only quite recently, with all the knowledge and automation that we’ve been able to build into the Wolfram Language, that it’s become realistic for kids to do these kinds of things for real. So it’s not terribly surprising that in their schools or elsewhere our students hadn’t really been exposed to such things before. But it’s now possible—and that means there’s a great new opportunity to seriously teach computational thinking to kids, and to position them to pursue the amazing range of directions that computational thinking is opening up.

It’s important, by the way, to distinguish between “computational thinking” and straight “coding”. Computational thinking is about formulating things in computational terms. Coding is about the actual mechanics of telling a computer what to do. One of our great goals with the Wolfram Language is to automate the process of coding as much as possible so people can concentrate on pure computational thinking. When one’s using lower-level languages, like C++ and Java, there’s no choice but to be involved with the detailed mechanics of coding. But with the Wolfram Language the exciting thing is that it’s possible to teach pure high-level computational thinking, without being forced to deal with the low-level mechanics of coding.

What does this mean in practice? I think it’s very empowering for students: as soon as they “get” a concept, they can immediately apply it, and do real things with it. And it was pretty neat at the Summer Camp to see how easily even students who’d never written programs before were able to express surprisingly sophisticated computational ideas in the Wolfram Language. Sometimes it seemed like students who’d learned a low-level language before were actually at a disadvantage. Though for me it was interesting a few times to witness the “aha” moment when a student realized that they didn’t have to break down their computations into tiny steps the way they’d been taught—and that they could turn some big blob of code they’d written into one simple line that they could immediately understand and extend.

The Summer Camp program involves several hours each day of lectures and workshops aimed at bringing students up to speed with computational thinking and how to express it in the Wolfram Language. But the real core of the program is every student doing an individual, original, computational thinking project.

And, yes, this is a difficult thing to orchestrate. But over the years we’ve been doing our Summer School and Summer Camp we’ve developed a very successful way of setting this up. There are a bunch of pieces to it, and the details depend on the level of the students. But here let’s talk about high-school students, and this year’s Summer Camp.

Right before the camp we (well, actually, I) came up with a list of about 70 potential projects. Some are quite specific, some are quite open-ended, and some are more like “metaprojects” (e.g. pick a dataset in the Wolfram Data Repository and analyze it). Some are projects that could at least in some form already have been done quite a few years ago. But many projects have only just become possible—this year particularly as a result of all our recent advances in machine learning.

I tried to have a range of nominal difficulty levels for the projects. I say “nominal” because even a project that can in principle be done in an easy way can also always be done in a more elaborate and sophisticated way. I wanted to have projects that ranged from the extremely well defined and precise (implement a precise algorithm of this particular type), to ones that involved wrangling data or machine learning training, to ones that were basically free-form and where the student got to define the objective.

Many of the projects in this list might seem challenging for high-school students. But my calculation (which in fact worked out well) was that with the technology we now have, all of them are within range.

It’s perhaps interesting to compare the projects with what I suggested for this year’s Summer School. The Summer School caters to more experienced students—typically at the college, graduate school or postdoc level. And so I was able to suggest projects that require deeper mathematical or software engineering knowledge—or are just bigger, with a higher threshold to achieve a reasonable level of success.

Before students start picking projects, it’s important that they understand what a finished project should look like, and what’s involved in doing it. So at the very beginning of the camp, the instructors went through projects from previous camps, and discussed what the “output” of a project should be. Maybe it’ll be an active website; maybe an interactive Demonstration; maybe it’ll be a research paper. It’s got to be possible to make a notebook that describes the project and its results, and to make a post about it for Wolfram Community.

After talking about the general idea of projects, and giving examples of previous ones, the instructors did a quick survey of this year’s suggestions list, filling in a few details of what the imagined projects actually were. After this, the students were asked to pick their top three projects from our list, and then invent two more potential projects of their own.

It’s always an interesting challenge to find the right project for each student—and it’s something I’ve personally been involved in at our Summer Camp for the past several years. (And, yes, it helps that I have decades of experience in organizing professional and research projects and figuring out the best people to do them.)

It’s taken us a few iterations, but here’s the approach we’ve found works well. First, we randomly break the students up into groups of a dozen or so. Then we meet with each group, going around the room and asking each student a little about themselves, their interests and goals—and their list of projects.

After we’re finished with each group, we meet separately and try to come up with a project for each student. Sometimes it’ll be one of the projects straight from our list. Sometimes it’ll be a project that the student themself suggested. And sometimes it’ll be some creative combination of these, or even something completely different based on what they said they were interested in.

After we think we’ve come up with a good project, the next step is to meet individually with each student and actually suggest it to them. It’s very satisfying that a lot of the time the students seem really enthused about the projects we end up suggesting. But sometimes it becomes clear that a project just isn’t a good fit—and then sometimes we modify it in real time, but more often we circle back later with a different suggestion.

Once the projects are set, we assign an appropriate mentor to each student, taking into account both the student and the subject of the project. And then things are off and running. We have various checkpoints, like that students have to write up descriptions of their projects and post them on the internal Summer Camp site.

I personally wasn’t involved in the actual execution of the projects (though I did have a chance to check in on a few of them). So it was pretty interesting for me to see at the end of the camp what had actually happened. It’s worth mentioning that our scheme is that mentors can make suggestions about projects, but all the final code in a project should be created by the student. And if one version of the project ends up being too difficult, it’s up to the mentor to simplify it. So however the final project comes out, it really is the student’s work.

Much of the time, the Summer Camp will be the first time students have ever done an original project. It could potentially seem daunting. But I think the fact that we give so many examples of other projects, and that everyone else at the camp is also doing a project, really helps. And in the end experiencing the whole process of going from the idea for a project to a real, finished project is incredibly educational—and seems to have a big effect on many of our students.

OK, so that’s the theory. So what actually happened at this year’s Summer Camp? Well, here are all the projects the students did, with the titles they gave them:

It’s a very interesting, impressive, and diverse list. But let me pick out a few semi-randomly to discuss in a bit more detail. Consider these as “case studies” for what high-school students can accomplish with the Wolfram Language in a couple of weeks at a summer camp.

One young man at our camp had quite a lot of math background, and told me he was interested in airplanes and flying, and had designed his own remote-control plane. I started thinking about all sorts of drone survey projects. But he didn’t have a drone with him—and we had to come up with a project that could actually be done in a couple of weeks. So I ended up suggesting the following: given two points on Earth, find how an airplane can get from one to the other by the shortest path that never needs to go above a certain altitude. (And, yes, a small-scale version of this would be relevant to things like drone surveying too.)

Here’s how the student did this project. First, he realized that one could think of possible flight paths as edges on a graph whose nodes are laid out on a grid on the Earth. Then he used the built-in `GeoElevationData` to delete nodes that couldn’t be visited because the elevation at that point was above the cutoff. Then he just used `FindShortestPath` to find the shortest path in the graph from the start to the end.

I thought this was a pretty clever solution. It was a nice piece of computational thinking to realize that the elements of paths could be thought of as edges on a graph with nodes removed. Needless to say, there were some additional details to get a really good result. First, the student added in diagonal connections on the grid, with appropriate weightings to still get the correct shortest path computation. And then he refined the path by successively merging line segments to better approximate a great-circle path, at each step using computational geometry to check that the path wouldn’t go through a “too-high” region.

You never know what people are going to come to Summer Camp with. A young man from New Zealand came to our camp with some overnight audio recordings from outside his house featuring occasional periods of (quite strange-sounding) squawking that were apparently the calls of one or more kiwi birds. What the young man wanted to do was automatic “kiwi voice recognition”, finding the calls, and perhaps distinguishing different birds.

I said I thought this wouldn’t be a particularly easy project, but he should try it anyway. Looking at what happened, it’s clear the project started out well. It was easy to pull out all intervals in his audio that weren’t just silence. But that broke up everything, including kiwi calls, into very small blocks. He solved that by the following interesting piece of code, that uses pattern matching to combine symbolic audio objects:

At this point it might just have worked to use unsupervised machine learning and `FeatureSpacePlot` to distinguish kiwi from non-kiwi sound clips. But machine learning is still quite a hit-or-miss business—and in this case it wasn’t a hit. So what did the student do? Well, he built himself a tiny lightweight user interface in a notebook, then started manually classifying sound clips. (Various instructors commented that it was fortunate he brought headphones…)

After classifying 200 clips, he used `Classify` to automatically classify all the other clips. He did a variety of transformations to the data—applying signal processing, generating a spectrogram, etc. And in the end he got his kiwi classifier to 82% accuracy: enough to make a reasonable first pass on finding kiwi calls—and going down a path to computational ornithology.

One young woman said she’d recently gotten a stress fracture in her foot that she was told was related to the force she was putting on it while running. She asked if she could make a computational model of what was going on. I have to say I was pessimistic about being able to do that in two weeks—and I suggested instead a project that I thought would be more manageable, involving studying possible gaits (walk, trot, etc.) for creatures with different numbers of legs. But I encouraged her to spend a little time seeing if she could do her original project—and I suggested that if she got to the stage of actually modeling bones, she could use our built-in anatomical data.

The next I knew it was a day before the end of the Summer Camp, and I was looking at what had happened with the projects… and I was really impressed! She’d found a paper with an appropriate model, understood it, and implemented it, and now she had an interactive demonstration of the force on a foot during walking or running. She’d even used the anatomical data to show a 3D image of what was happening.

She explained that when one walks there are two peaks in the force, but when one runs, there’s only one. And when I set her interactive demonstration for my own daily walking regimen I found out that (as she said was typical) I put a maximum force of about twice my weight on my foot when I walk.

At first I couldn’t tell if he was really serious… but one young man insisted he wanted to use machine learning to tell when a piece of fruit is ripe. As it happens, I had used pretty much this exact example in a blog post some time ago discussing the use of machine learning in smart contracts. So I said, “sure, why don’t you try it”. I saw the student a few times during the Summer Camp, curiously always carrying a banana. And what I discovered at the end of the camp was that that very banana was a key element of his project.

At first he searched the web for images of bananas described as “underripe”, “overripe”, etc., then arranged them using `FeatureSpacePlot`:

Then he realized that he could get more quantitative by first looking at where in color space the pixels of the banana image lay. The result was that he was actually able to define a “banana ripeness scale”, where, as he described it: “A value of one was assigned to bananas that were on the brink of spoilage. A value of zero was assigned to a green banana fresh off a tree. A value of 0.5 was assigned to the ‘perfect’ banana.” It’s a nice example of how something everyday and qualitative can be made computational.

For his project, the student made a “Banana Classifier” app that he deployed through the Wolfram Cloud. And he even had an actual banana to test it on!

One of my suggested projects was to implement “international or historical numeral systems”—the analogs of things like Roman numerals but for different cultures and times. One young woman fluent in Korean said she’d like to do this project, starting with Korean.

As it happens, our built-in `IntegerName` function converts to traditional Korean numerals. So she set herself the task of converting from Korean numerals. It’s an interesting algorithmic exercise, and she solved it with some nice, elegant code.

By that point, she was on a roll… so she decided to go on to Burmese, and Thai. She tried to figure out Burmese from web sources… only to discover they were inconsistent… with the result that she ended up contacting a person who had an educational video about Burmese numerals, and eventually unscrambled the issue, wrote code to represent it, and then corrected the Wikipedia page about Burmese numerals. All in all, a great example of real-world algorithm curation. Oh, and she set up the conversions as a Wolfram Language microsite on the web.

Can machine learning tell if something is funny? One young man at the Summer Camp wanted to find out. So for his project he used our Reddit API connection to pull jokes from the Jokes subreddit, and (presumably) non-jokes from the AskReddit subreddit. It took a bit of cleanup and data wrangling… but then he was able to feed his training data straight into the `Classify` function, and generated a classifier from which he then built a website.

It’s a little hard to know how well it works outside of “Reddit-style humor”—but his anecdotal study at the Summer Camp suggested about a 90% success rate.

Different projects involve different kinds of challenges. Sometimes the biggest challenge is just to define the project precisely enough. Other times it’s to get—or clean—the data that’s needed. Still other times, it’s to find a way to interpret voluminous output. And yet other times, it’s to see just how elegantly some particular idea can be implemented.

One math-oriented young woman at the camp picked “implementing checksum algorithms” from my list. Such algorithms (used for social security numbers, credit card numbers, etc.) are very nicely and precisely defined. But how simply and elegantly can they be implemented in the Wolfram Language? It’s a good computational thinking exercise—that requires really understanding both the algorithms and the language. And for me it’s nice to be able to immediately read off from the young woman’s code just how these checksum algorithms work…

How should one plot a function in 4D? I had a project in my list about this, though I have to admit I hadn’t really figured out how it should be done. But, fortunately, a young man at the Summer Camp was keen to try to work on it. And with an interesting mixture of computational and mathematical thinking, he created `ParametricPlot4D`—then did a bunch of math to figure out how to render the results in what seemed like two useful ways: as an orthogonal projection, and as a stereographic projection. A `Manipulate` makes the results interactive—and they look pretty neat…

In addition to my explicit list of project suggestions, I had a “meta suggestion”: take any dataset, for example from the new Wolfram Data Repository, and try to analyze and understand it. One student took a dataset about meteorite impacts; another about the recent Ebola outbreak in Africa. One young woman said she was interested in actuarial science—so I suggested that she look at something quintessentially actuarial: mortality data.

I suggested that maybe she could look at the (somewhat macabrely named) Death Master File. I wasn’t sure how far she’d get with it. But at the end of the camp I found out that she’d processed 90 million records—and successfully reduced them to derive aggregate survival curves for 25 different states and make an interactive Demonstration of the results. (Letting me conclude, for example, that my current probability of living to age 100 is 28% higher in Massachusetts than in Indiana…)

Each year when I make up a list of projects for the Summer Camp I wonder if there’ll be particular favorites. My goal is actually to avoid this, and to have as uniform a distribution of interest in the projects as possible. But this year “Use Machine Learning to Identify Polyhedra” ended up being a minor favorite. And one consequence was that a student had already started working on the project even before we’d talked to him—even though by that time the project was already assigned to someone else.

But actually the “recovery” was better than the original. Because we figured out a really nice alternative project that was very well suited to the student. The project was to take images of regular tilings, say from a book, and to derive a computational representation of them, suitable, say, for `LatticeData`.

The student came up with a pretty sophisticated approach, largely based on image processing, but with a dash of computational geometry, combinatorics and even some cluster analysis thrown in. First, he used fairly elaborate image processing to identify the basic unit in the tiling. Then he figured out how this unit was arranged to form the final tiling. It ended up being about 102 lines of fairly dense algorithmic code—but the result was a quite robust “tiling OCR” system, that he also deployed on the web.

In my list I had a project “Identify buildings from satellite images”. A few students thought it sounded interesting, but as I thought about it some more, I got concerned that it might be really difficult. Still, one of our students was a capable young man who already seemed to know a certain amount about machine learning. So I encouraged him to give it a try. He ended up doing an impressive job.

He started by getting training data by comparing satellite images with street maps that marked buildings (and, conveniently, starting with the upcoming version of the Wolfram Language, not only streets maps but also satellite images are built in):

Then he used `NetChain` to build a neural net (based on the classic LeNet network, but modified). And then he started trying to classify parts of images as “building” or “not building”.

The results weren’t at all bad. But so far they were only answering the question “is there a building in that square?”, not “where is there a building?”. So then—in a nice piece of computational thinking—the student came up with a further idea: just have a window pan across the image, at each step estimating the probability of building vs. not-building. The result was a remarkably accurate heat map of where buildings might be.

It’d be a nice machine learning result for anyone. But as something done by a high-school student in two weeks I think it’s really impressive. And another great example of what’s now possible at an educational level with our whole Wolfram Language technology stack.

OK, so our Summer Camp was a success, and, with luck, the students from it are now successfully “launched” as independent computational thinkers. (The test, as far as I’m concerned, is whether when confronted with something in their education or their lives, they routinely turn to computational thinking, and just “write a program to solve the problem”. I’m hopeful that many of them now will. And, by the way, they immediately have “marketable skills”—like being able to do all sorts of data-science-related things.)

But how can we scale up what we’ve achieved with the Summer Camp? Well, we have a whole Computational Thinking Initiative that we’ve been designing to do just that. We’ll be rolling out different parts over the next little while, but one aspect will be doing other camps, and enabling other people to also do camps.

We’ve now got what amounts to an operations manual for how to “do a camp”. But suffice it to say that the core of it is to have instructors with good knowledge of the Wolfram Language (e.g. to the level of our Certified Instructor program), access to a bunch of great students, and use of a suitable venue. Two weeks seems to be a good length, though longer would work too. (Shorter will probably not be sufficient for students without prior experience to get to the point of doing a real project.)

Our camp is for high-school students (mainly aged 15 through 17). I think it would also be possible to do a successful camp for advanced middle-school students (maybe aged 12 and 13). And, of course, our long-running Summer School provides a very successful model for older students.

Beyond camps, we’ve had for some time a mentorships program which we will be streamlining and scaling up—helping students to work on longer-term projects. We’re also planning a variety of events and venues in which students can showcase their computational thinking work.

But for now it’s just exciting to see what was achieved in two weeks at this year’s Summer Camp. Yes, with the tech stack we now have, high-school students really can do serious computational thinking—that will make them not only immediately employable, but also positioned for what I think will be some of the most interesting career directions of the next few decades.

]]>“We’ve just got to decide: is a chemical like a city or like a number?” I spent my day yesterday—as I have for much of the past 30 years—designing new features of the Wolfram Language. And yesterday afternoon one of my meetings was a fast-paced discussion about how to extend the chemistry capabilities of the language.

At some level the problem we were discussing was quintessentially practical. But as so often turns out to be the case for things we do, it ultimately involves some deep intellectual issues. And to actually get the right answer—and to successfully design language features that will stand the test of time—we needed to plumb those depths, and talk about things that usually wouldn’t be considered outside of some kind of philosophy seminar.

Part of the issue, of course, is that we’re dealing with things that haven’t really ever come up before. Traditional computer languages don’t try to talk directly about things like chemicals; they just deal with abstract data. But in the Wolfram Language we’re trying to build in as much knowledge about everything as possible, and that means we have to deal with actual things in the world, like chemicals.

We’ve built a whole system in the Wolfram Language for handling what we call *entities*. An entity could be a city (like New York City), or a movie, or a planet—or a zillion other things. An entity has some kind of name (“New York City”). And it has definite properties (like population, land area, founding date, …).

We’ve long had a notion of chemical entities—like water, or ethanol, or tungsten carbide. Each of these chemical entities has properties, like molecular mass, or structure graph, or boiling point.

And we’ve got many hundreds of thousands of chemicals where we know lots of properties. But all of these are in a sense *concrete chemicals*: specific compounds that we could put in a test tube and do things with.

But what we were trying to figure out yesterday is how to handle abstract chemicals—chemicals that we just abstractly construct, say by giving an abstract graph representing their chemical structures. Should these be represented by entities, like water or New York City? Or should they be considered more abstract, like lists of numbers, or, for that matter, mathematical graphs?

Well, of course, among the abstract chemicals we can construct are chemicals that we already represent by entities, like sucrose or aspirin or whatever. But here there’s an immediate distinction to make. Are we talking about individual molecules of sucrose or aspirin? Or about these things as bulk materials?

At some level it’s a confusing distinction. Because, we might think, once we know the molecular structure, we know everything—it’s just a matter of calculating it out. And some properties—like molar mass—are basically trivial to calculate from the molecular structure. But others—like melting point—are very far from trivial.

OK, but is this just a temporary problem that one shouldn’t base a long-term language design on? Or is it something more fundamental that will never change? Well, conveniently enough, I happen to have done a bunch of basic science that essentially answers this: and, yes, it’s something fundamental. It’s connected to what I call computational irreducibility. And for example, the precise value of, say, the melting point for an infinite amount of some material may actually be fundamentally uncomputable. (It’s related to the undecidability of the tiling problem; fitting in tiles is like seeing how molecules will arrange to make a solid.)

So by knowing this piece of (rather leading-edge) basic science, we know that we can meaningfully make a distinction between bulk versions of chemicals and individual molecules. Clearly there’s a close relation between, say, water molecules, and bulk water. But there’s still something fundamentally and irreducibly different about them, and about the properties we can compute for them.

Alright, so let’s talk about individual molecules. Obviously they’re made of atoms. And it seems like at least when we talk about atoms, we’re on fairly solid ground. It might be reasonable to say that any given molecule always has some definite collection of atoms in it—though maybe we’ll want to consider “parametrized molecules” when we talk about polymers and the like.

But at least it seems safe to consider types of atoms as entities. After all, each type of atom corresponds to a chemical element, and there are only a limited number of those on the periodic table. Now of course in principle one can imagine additional “chemical elements”; one could even think of a neutron star as being like a giant atomic nucleus. But again, there’s a reasonable distinction to be made: almost certainly there are only a limited number of fundamentally stable types of atoms—and most of the others have ridiculously short lifetimes.

There’s an immediate footnote, however. A “chemical element” isn’t quite as definite a thing as one might imagine. Because it’s always a mixture of different isotopes. And, say, from one tungsten mine to another, that mixture might change, giving a different effective atomic mass.

And actually this is a good reason to represent types of atoms by entities. Because then one just has to have a single entity representing tungsten that one can use in talking about molecules. And only if one wants to get properties of that type of atom that depend on qualifiers like which mine it’s from does one have to deal with such things.

In a few cases (think heavy water, for example), one will need to explicitly talk about isotopes in what is essentially a chemical context. But most of the time, it’s going to be enough just to specify a chemical element.

To specify a chemical element you just have to give its atomic number *Z*. And then textbooks will tell you that to specify a particular isotope you just have to say how many neutrons it contains. But that ignores the unexpected case of tantalum. Because, you see, one of the naturally occurring forms of tantalum (^{180m}Ta) is actually an excited state of the tantalum nucleus, which happens to be very stable. And to properly specify this, you have to give its excitation level as well as its neutron count.

In a sense, though, quantum mechanics saves one here. Because while there are an infinite number of possible excited states of a nucleus, quantum mechanics says that all of them can be characterized just by two discrete values: spin and parity.

Every isotope—and every excited state—is different, and has its own particular properties. But the world of possible isotopes is much more orderly than, say, the world of possible animals. Because quantum mechanics says that everything in the world of isotopes can be characterized just by a limited set of discrete quantum numbers.

We’ve gone from molecules to atoms to nuclei, so why not talk about particles too? Well, it’s a bigger can of worms. Yes, there are the well-known particles like electrons and protons that are pretty easy to talk about—and are readily represented by entities in the Wolfram Language. But then there’s a zoo of other particles. Some of them—just like nuclei—are pretty easy to characterize. You can basically say things like: “it’s a particular excited state of a charm-quark-anti-charm-quark system” or some such. But in particle physics one’s dealing with quantum field theory, not just quantum mechanics. And one can’t just “count elementary particles”; one also has to deal with the possibility of virtual particles and so on. And in the end the question of what kinds of particles can exist is a very complicated one—rife with computational irreducibility. (For example, what stable states there can be of the gluon field is a much more elaborate version of something like the tiling problem I mentioned in connection with melting points.)

Maybe one day we’ll have a complete theory of fundamental physics. And maybe it’ll even be simple. But exciting as that will be, it’s not going to help much here. Because computational irreducibility means that there’s essentially an irreducible distance between what’s underneath, and what phenomena emerge.

And in creating a language to describe the world, we need to talk in terms of things that can actually be observed and computed about. We need to pay attention to the basic physics—not least so we can avoid setups that will lead to confusion later. But we also need to pay attention to the actual history of science, and actual things that have been measured. Yes, there are, for example, an infinite number of possible isotopes. But for an awful lot of purposes it’s perfectly useful just to set up entities for ones that are known.

But is it the same in chemistry? In nuclear physics, we think we know all the reasonably stable isotopes that exist—so any additional and exotic ones will be very short-lived, and therefore probably not important in practical nuclear processes. But it’s a different story in chemistry. There are tens of millions of chemicals that people have studied (and, for example, put into papers or patents). And there’s really no limit on the molecules that one might want to consider, and that might be useful.

But, OK, so how can we refer to all these potential molecules? Well, in a first approximation we can specify their chemical structures, by giving graphs in which every node is an atom, and every edge is a bond.

What really is a “bond”? While it’s incredibly useful in practical chemistry, it’s at some level a mushy concept—some kind of semiclassical approximation to a full quantum mechanical story. There are some standard extra bits: double bonds, ionization states, etc. But in practice chemistry is very successfully done just by characterizing molecular structures by appropriately labeled graphs of atoms and bonds.

OK, but should chemicals be represented by entities, or by abstract graphs? Well, if it’s a chemical one’s already heard of, like carbon dioxide, an entity seems convenient. But what if it’s a new chemical that’s never been discussed before? Well, one could think about inventing a new entity to represent it.

Any self-respecting entity, though, better have a name. So what would the name be? Well, in the Wolfram Language, it could just be the graph that represents the structure. But maybe one wants something that seems more like an ordinary textual name—a string. Well, there’s always the IUPAC way of naming chemicals with names like 1,1′-{[3-(dimethylamino)propyl]imino}bis-2-propanol. Or there’s the more computer-friendly SMILES version: CC(CN(CCCN(C)C)CC(C)O)O. And whatever underlying graph one has, one can always generate one of these strings to represent it.

There’s an immediate problem, though: the string isn’t unique. In fact, however one chooses to write down the graph, it can’t always be unique. A particular chemical structure corresponds to a particular graph. But there can be many ways to draw the graph—and many different representations for it. And in fact even the (“graph isomorphism”) problem of determining whether two representations correspond to the same graph can be difficult to solve.

OK, so let’s imagine we represent a chemical structure by a graph. At first, it’s an abstract thing. There are atoms as nodes in the graph, but we don’t know how they’d be arranged in an actual molecule (and e.g. how many angstroms apart they’d be). Of course, the answer isn’t completely well defined. Are we talking about the lowest-energy configuration of the molecule? (What if there are multiple configurations of the same energy?) Is the molecule supposed to be on its own, or in water, or whatever? How was the molecule supposed to have been made? (Maybe it’s a protein that folded a particular way when it came off the ribosome.)

Well, if we just had an entity representing, say, “naturally occurring hemoglobin”, maybe we’d be better off. Because in a sense that entity could encapsulate all these details.

But if we want to talk about chemicals that have never actually been synthesized it’s a bit of a different story. And it feels as if we’d be better off just with an abstract representation of any possible chemical.

But let’s talk about some other cases, and analogies. Maybe we should just treat everything as an entity. Like every integer could be an entity. Yes, there are an infinite number of them. But at least it’s clear what names they should be given. With real numbers, things are already messier. For example, there’s no longer the same kind of uniqueness as with integers: 0.99999… is really the same as 1.00000…, but it’s written differently.

What about sequences of integers, or, for that matter, mathematical formulas? Well, every possible sequence or every possible formula could conceivably be a different entity. But this wouldn’t be particularly useful, because much of what one wants to do with sequences or formulas is to go inside them, and transform their structure. But what’s convenient about entities is that they’re each just “single things” that one doesn’t have to “go inside”.

So what’s the story with “abstract chemicals”? It’s going to be a mixture. But certainly one’s going to want to “go inside” and transform the structure. Which argues for representing the chemical by a graph.

But then there’s potentially a nasty discontinuity. We’ve got the entity of carbon dioxide, which we already know lots of properties about. And then we’ve got this graph that abstractly represents the carbon dioxide molecule.

We might worry that this would be confusing both to humans and programs. But the first thing to realize is that we can distinguish what these two things are representing. The entity represents the bulk naturally occurring version of the chemical—whose properties have potentially been measured. The graph represents an abstract theoretical chemical, whose properties would have to be computed.

But obviously there’s got to be a bridge. Given a concrete chemical entity, one of the properties will be the graph that represents the structure of the molecule. And given a graph, one will need some kind of `ChemicalIdentify` function, that—a bit like `GeoIdentify` or maybe `ImageIdentify`—tries to identify from the graph what chemical entity (if any) has a molecular structure that corresponds to that graph.

As I write out some of the issues, I realize how complicated all this may seem. And, yes, it is complicated. But in our meeting yesterday, it all went very quickly. Of course it helps that everyone there had seen similar issues before: this is the kind of thing that’s all over the foundations of what we do. But each case is different.

And somehow this case got a bit deeper and more philosophical than usual. “Let’s talk about naming stars”, someone said. Obviously there are nearby stars that we have explicit names for. And some other stars may have been identified in large-scale sky surveys, and given identifiers of some kind. But there are lots of stars in distant galaxies that will never have been named. So how should we represent them?

That led to talking about cities. Yes, there are definite, chartered cities that have officially been assigned names–and we probably have essentially all of these right now in the Wolfram Language, updated regularly. But what about some village that’s created for a single season by some nomadic people? How should we represent it? Well, it has a certain location, at least for a while. But is it even a definite single thing, or might it, say, devolve into two villages, or not a village at all?

One can argue almost endlessly about identity—and even existence—for many of these things. But ultimately it’s not the philosophy of such things that we’re interested in: we’re trying to build software that people will find useful. And so what matters in the end is what’s going to be useful.

Now of course that’s not a precise thing to know. But it’s like for language design in general: think of everything people might want to do, then see how to set up primitives that will let people do those things. Does one want some chemicals represented by entities? Yes, that’s useful. Does one want a way to represent arbitrary chemical structures by graphs? Yes, that’s useful.

But to see what to actually do, one has to understand quite deeply what’s really being represented in each case, and how everything is related. And that’s where the philosophy has to meet the chemistry, and the math, and the physics, and so on.

I’m happy to say that by the end of our hour-long meeting yesterday (informed by about 40 years of relevant experience I’ve had, and collectively 100+ years from people in the meeting), I think we’d come up with the essence of a really nice way to handle chemicals and chemical structures. It’s going to be a while before it’s all fully worked out and implemented in the Wolfram Language. But the ideas are going to help inform the way we compute and reason about chemistry for many years to come. And for me, figuring out things like this is an extremely satisfying way to spend my time. And I’m just glad that in my long-running effort to advance the Wolfram Language I get to do so much of it.

]]>A week ago a new train station, named “Cambridge North”, opened in Cambridge, UK. Normally such an event would be far outside my sphere of awareness. (I think I last took a train to Cambridge in 1975.) But last week people started sending me pictures of the new train station, wondering if I could identify the pattern on it:

And, yes, it does indeed look a lot like patterns I’ve spent years studying—that come from simple programs in the computational universe. My first—and still favorite—examples of simple programs are one-dimensional cellular automata like this:

The system evolves line by line from the top, determining the color of each cell according to the rule underneath. This particular cellular automata I called “rule 182”, because the bit pattern in the rule corresponds to the number 182 in binary. There are altogether 256 possible cellular automata like this, and this is what all of them do:

Many of them show fairly simple behavior. But the huge surprise I got when I first ran all these cellular automata in the early 1980s is that even though all the rules are very simple to state, some of them generate very complex behavior. The first in the list that does that—and still my favorite example—is rule 30:

If one runs it for 400 steps one gets this:

And, yes, it’s remarkable that starting from one black cell at the top, and just repeatedly following a simple rule, it’s possible to get all this complexity. I think it’s actually an example of a hugely important phenomenon, that’s central to how complexity gets made in nature, as well as to how we can get a new level of technology. And in fact, I think it’s important enough that I spent more than a decade writing a 1200-page book (that just celebrated its 15th anniversary) based on it.

And for years I’ve actually had rule 30 on my business cards:

But back to the Cambridge North train station. Its pattern is obviously not completely random. But if it was made by a rule, what kind of rule? Could it be a cellular automaton?

I zoomed in on a photograph of the pattern:

Suddenly, something seemed awfully familiar: the triangles, the stripes, the L shapes. Wait a minute… it couldn’t actually be my favorite rule of all time, rule 30?

Clearly the pattern is tipped 45° from how I’d usually display a cellular automaton. And there are black triangles in the photograph, not white ones like in rule 30. But if one black-white inverts the rule (so it’s now rule 135), one gets this:

And, yes, it’s the same kind of pattern as in the photograph! But if it’s rule 30 (or rule 135) what’s its initial condition? Rule 30 can actually be used as a cryptosystem—because it can be hard (maybe even NP complete) to reconstruct its initial condition.

But, OK, if it’s my favorite rule, I wondered if maybe it’s also my favorite initial condition—a single black cell. And, yes, it is! The train station pattern comes exactly from the (inverted) right-hand edge of my favorite rule 30 pattern!

Here’s the Wolfram Language code. First run the cellular automaton, then rotate the pattern:

It’s a little trickier to pull out precisely the section of the pattern that’s used. Here’s the code (the `PlotRange` is what determines the part of the pattern that’s shown):

OK, so where is this pattern actually used at the train station? Everywhere!

It’s made of perforated aluminum. You can actually look through it, reminiscent of an old latticed window. From inside, the pattern is left-right reversed—so if it’s rule 135 from outside, it’s rule 149 from inside. And at night, the pattern is black-white inverted, because there’s light coming from inside—so from the outside it’s “rule 135 by day, and rule 30 at night”.

What are some facts about the rule 30 pattern? It’s extremely hard to rigorously prove things about it (and that’s interesting in itself—and closely related to the fundamental phenomenon of computational irreducibility). But, for example—like, say, the digits of π—many aspects of it seem random. And, for instance, black and white squares appear to occur with equal frequency—meaning that at the train station the panels let in about 50% of the outside light.

If one looks at sequences of n cells, it seems that all 2^{n} configurations will occur on average with equal frequency. But not everything is random. And so, for example, if one looks at 3×2 blocks of cells, only 24 of the 32 possible ones ever occur. (Maybe some people waiting for trains will figure out which blocks are missing…)

When we look at the pattern, our visual system particularly picks out the black triangles. And, yes, it seems as if triangles of any size can ultimately occur, albeit with frequency decreasing exponentially with size.

If one looks carefully at the right-hand edge of the rule 30 pattern, one can see that it repeats. However, the repetition period seems to increase exponentially as one goes in from the edge.

At the train station, there are lots of identical panels. But rule 30 is actually an inexhaustible source of new patterns. So what would happen if one just continued the evolution, and rendered it on successive panels? Here’s the result. It’s a pity about the hint of periodicity on the right-hand edge, and the big triangle on panel 5 (which might be a safety problem at the train station).

Fifteen more steps in from the edge, there’s no hint of that anymore:

What about other initial conditions? If the initial conditions repeat, then so will the pattern. But otherwise, so far as one can tell, the pattern will look essentially the same as with a single-cell initial condition.

One can try other rules too. Here are a few from the same simplest 256-rule set as rule 30:

Moving deeper from the edge the results look a little different (for aficionados, rule 89 is a transformed version of rule 45, rule 182 of rule 90, and rule 193 of rule 110):

And starting from random initial conditions, rather than a single black cell, things again look different:

And here are a few more rules, started from random initial conditions:

Here’s a website (made in a couple of minutes with a tiny piece of Wolfram Language code) that lets you experiment (including with larger rule numbers, based on longer-range rules). (And if you want to explore more systematically, here’s a Wolfram Notebook to try.)

It’s amazing what’s out there in the computational universe of possible programs. There’s an infinite range of possible patterns. But it’s cool that the Cambridge North train station uses my all-time favorite discovery in the computational universe—rule 30! And it looks great!

There’s something curiously timeless about algorithmically generated forms. A dodecahedron from ancient Egypt still looks crisp and modern today. As do periodic tilings—or nested forms—even from centuries ago:

But can one generate richer forms algorithmically? Before I discovered rule 30, I’d always assumed that any form generated from simple rules would always somehow end up being obviously simple. But rule 30 was a big shock to my intuition—and from it I realized that actually in the computational universe of all possible rules, it’s actually very easy to get rich and complex behavior, even from simple underlying rules.

And what’s more, the patterns that are generated often have remarkable visual interest. Here are a few produced by cellular automata (now with 3 possible colors for each cell, rather than 2):

There’s an amazing diversity of forms. And, yes, they’re often complicated. But because they’re based on simple underlying rules, they always have a certain logic to them: in a sense each of them tells a definite “algorithmic story”.

One thing that’s notable about forms we see in the computational universe is that they often look a lot like forms we see in nature. And I don’t think that’s a coincidence. Instead, I think what’s going on is that rules in the computational universe capture the essence of laws that govern lots of systems in nature—whether in physics, biology or wherever. And maybe there’s a certain familiarity or comfort associated with forms in the computational universe that comes from their similarity to forms we’re used to in nature.

But is what we get from the computational universe art? When we pick out something like rule 30 for a particular purpose, what we’re doing is conceptually a bit like photography: we’re not creating the underlying forms, but we are selecting the ones we choose to use.

In the computational universe, though, we can be more systematic. Given some aesthetic criterion, we can automatically search through perhaps even millions or billions of possible rules to find optimal ones: in a sense automatically “discovering art” in the computational universe.

We did an experiment on this for music back in 2007: WolframTones. And what’s remarkable is that even by sampling fairly small numbers of rules (cellular automata, as it happens), we’re able to produce all sorts of interesting short pieces of music—that often seem remarkably “creative” and “inventive”.

From a practical point of view, automatic discovery in the computational universe is important because it allows for mass customization. It makes it easy to be “original” (and “creative”)—and to find something different every time, or to fit constraints that have never been seen before (say, a pattern in a complicated geometric region).

The Cambridge North train station uses a particular rule from the computational universe to make what amounts to an ornamental pattern. But one can also use rules from the computational universe for other things in architecture. And one can even imagine a building in which everything—from overall massing down to details of moldings—is completely determined by something close to a single rule.

One might assume that such a building would somehow be minimalist and sterile. But the remarkable fact is that this doesn’t have to be true—and that instead there are plenty of rich, almost “organic” forms to be “mined” from the computational universe.

Ever since I started writing about one-dimensional cellular automata back in the early 1980s, there’s been all sorts of interesting art done with them. Lots of different rules have been used. Sometimes they’ve been what I called “class 4” rules that have a particularly organic look. But often it’s been other rules—and rule 30 has certainly made its share of appearances—whether it’s on floors, shirts, tea cosies, kinetic installations, or, recently, mass-customized scarves (with the knitting machine actually running the cellular automaton):

*Starting now, in celebration of its 15th anniversary, A New Kind of Science will be freely available in its entirety, with high-resolution images, on the web or for download.*

It’s now 15 years since I published my book A New Kind of Science—more than 25 since I started writing it, and more than 35 since I started working towards it. But with every passing year I feel I understand more about what the book is really about—and why it’s important. I wrote the book, as its title suggests, to contribute to the progress of science. But as the years have gone by, I’ve realized that the core of what’s in the book actually goes far beyond science—into many areas that will be increasingly important in defining our whole future.

So, viewed from a distance of 15 years, what is the book really about? At its core, it’s about something profoundly abstract: the theory of all possible theories, or the universe of all possible universes. But for me one of the achievements of the book is the realization that one can explore such fundamental things concretely—by doing actual experiments in the computational universe of possible programs. And in the end the book is full of what might at first seem like quite alien pictures made just by running very simple such programs.

Back in 1980, when I made my living as a theoretical physicist, if you’d asked me what I thought simple programs would do, I expect I would have said “not much”. I had been very interested in the kind of complexity one sees in nature, but I thought—like a typical reductionistic scientist—that the key to understanding it must lie in figuring out detailed features of the underlying component parts.

In retrospect I consider it incredibly lucky that all those years ago I happened to have the right interests and the right skills to actually try what is in a sense the most basic experiment in the computational universe: to systematically take a sequence of the simplest possible programs, and run them.

I could tell as soon as I did this that there were interesting things going on, but it took a couple more years before I began to really appreciate the force of what I’d seen. For me it all started with one picture:

Or, in modern form:

I call it rule 30. It’s my all-time favorite discovery, and today I carry it around everywhere on my business cards. What is it? It’s one of the simplest programs one can imagine. It operates on rows of black and white cells, starting from a single black cell, and then repeatedly applies the rules at the bottom. And the crucial point is that even though those rules are by any measure extremely simple, the pattern that emerges is not.

It’s a crucial—and utterly unexpected—feature of the computational universe: that even among the very simplest programs, it’s easy to get immensely complex behavior. It took me a solid decade to understand just how broad this phenomenon is. It doesn’t just happen in programs (“cellular automata”) like rule 30. It basically shows up whenever you start enumerating possible rules or possible programs whose behavior isn’t obviously trivial.

Similar phenomena had actually been seen for centuries in things like the digits of pi and the distribution of primes—but they were basically just viewed as curiosities, and not as signs of something profoundly important. It’s been nearly 35 years since I first saw what happens in rule 30, and with every passing year I feel I come to understand more clearly and deeply what its significance is.

Four centuries ago it was the discovery of the moons of Jupiter and their regularities that sowed the seeds for modern exact science, and for the modern scientific approach to thinking. Could my little rule 30 now be the seed for another such intellectual revolution, and a new way of thinking about everything?

In some ways I might personally prefer not to take responsibility for shepherding such ideas (“paradigm shifts” are hard and thankless work). And certainly for years I have just quietly used such ideas to develop technology and my own thinking. But as computation and AI become increasingly central to our world, I think it’s important that the implications of what’s out there in the computational universe be more widely understood.

Here’s the way I see it today. From observing the moons of Jupiter we came away with the idea that—if looked at right—the universe is an ordered and regular place, that we can ultimately understand. But now, in exploring the computational universe, we quickly come upon things like rule 30 where even the simplest rules seem to lead to irreducibly complex behavior.

One of the big ideas of A New Kind of Science is what I call the Principle of Computational Equivalence. The first step is to think of every process—whether it’s happening with black and white squares, or in physics, or inside our brains—as a computation that somehow transforms input to output. What the Principle of Computational Equivalence says is that above an extremely low threshold, all processes correspond to computations of equivalent sophistication.

It might not be true. It might be that something like rule 30 corresponds to a fundamentally simpler computation than the fluid dynamics of a hurricane, or the processes in my brain as I write this. But what the Principle of Computational Equivalence says is that in fact all these things are computationally equivalent.

It’s a very important statement, with many deep implications. For one thing, it implies what I call computational irreducibility. If something like rule 30 is doing a computation just as sophisticated as our brains or our mathematics, then there’s no way we can “outrun” it: to figure out what it will do, we have to do an irreducible amount of computation, effectively tracing each of its steps.

The mathematical tradition in exact science has emphasized the idea of predicting the behavior of systems by doing things like solving mathematical equations. But what computational irreducibility implies is that out in the computational universe that often won’t work, and instead the only way forward is just to explicitly run a computation to simulate the behavior of the system.

One of the things I did in A New Kind of Science was to show how simple programs can serve as models for the essential features of all sorts of physical, biological and other systems. Back when the book appeared, some people were skeptical about this. And indeed at that time there was a 300-year unbroken tradition that serious models in science should be based on mathematical equations.

But in the past 15 years something remarkable has happened. For now, when new models are created—whether of animal patterns or web browsing behavior—they are overwhelmingly more often based on programs than on mathematical equations.

Year by year, it’s been a slow, almost silent, process. But by this point, it’s a dramatic shift. Three centuries ago pure philosophical reasoning was supplanted by mathematical equations. Now in these few short years, equations have been largely supplanted by programs. For now, it’s mostly been something practical and pragmatic: the models work better, and are more useful.

But when it comes to understanding the foundations of what’s going on, one’s led not to things like mathematical theorems and calculus, but instead to ideas like the Principle of Computational Equivalence. Traditional mathematics-based ways of thinking have made concepts like force and momentum ubiquitous in the way we talk about the world. But now as we think in fundamentally computational terms we have to start talking in terms of concepts like undecidability and computational irreducibility.

Will some type of tumor always stop growing in some particular model? It might be undecidable. Is there a way to work out how a weather system will develop? It might be computationally irreducible.

These concepts are pretty important when it comes to understanding not only what can and cannot be modeled, but also what can and cannot be controlled in the world. Computational irreducibility in economics is going to limit what can be globally controlled. Computational irreducibility in biology is going to limit how generally effective therapies can be—and make highly personalized medicine a fundamental necessity.

And through ideas like the Principle of Computational Equivalence we can start to discuss just what it is that allows nature—seemingly so effortlessly—to generate so much that seems so complex to us. Or how even deterministic underlying rules can lead to computationally irreducible behavior that for all practical purposes can seem to show “free will”.

A central lesson of A New Kind of Science is that there’s a lot of incredible richness out there in the computational universe. And one reason that’s important is that it means that there’s a lot of incredible stuff out there for us to “mine” and harness for our purposes.

Want to automatically make an interesting custom piece of art? Just start looking at simple programs and automatically pick out one you like—as in our WolframTones music site from a decade ago. Want to find an optimal algorithm for something? Just search enough programs out there, and you’ll find one.

We’ve normally been used to creating things by building them up, step by step, with human effort—progressively creating architectural plans, or engineering drawings, or lines of code. But the discovery that there’s so much richness so easily accessible in the computational universe suggests a different approach: don’t try building anything; just define what you want, and then search for it in the computational universe.

Sometimes it’s really easy to find. Like let’s say you want to generate apparent randomness. Well, then just enumerate cellular automata (as I did in 1984), and very quickly you come upon rule 30—which turns out to be one of the very best known generators of apparent randomness (look down the center column of cell values, for examples). In other situations you might have to search 100,000 cases (as I did in finding the simplest axiom system for logic, or the simplest universal Turing machine), or you might have to search millions or even trillions of cases. But in the past 25 years, we’ve had incredible success in just discovering algorithms out there in the computational universe—and we rely on many of them in implementing the Wolfram Language.

At some level it’s quite sobering. One finds some tiny program out in the computational universe. One can tell it does what one wants. But when one looks at what it’s doing, one doesn’t have any real idea how it works. Maybe one can analyze some part—and be struck by how “clever” it is. But there just isn’t a way for us to understand the whole thing; it’s not something familiar from our usual patterns of thinking.

Of course, we’ve often had similar experiences before—when we use things from nature. We may notice that some particular substance is a useful drug or a great chemical catalyst, but we may have no idea why. But in doing engineering and in most of our modern efforts to build technology, the great emphasis has instead been on constructing things whose design and operation we can readily understand.

In the past we might have thought that was enough. But what our explorations of the computational universe show is that it’s not: selecting only things whose operation we can readily understand misses most of the immense power and richness that’s out there in the computational universe.

What will the world look like when more of what we have is mined from the computational universe? Today the environment we build for ourselves is dominated by things like simple shapes and repetitive processes. But the more we use what’s out there in the computational universe, the less regular things will look. Sometimes they may look a bit “organic”, or like what we see in nature (since after all, nature follows similar kinds of rules). But sometimes they may look quite random, until perhaps suddenly and incomprehensibly they achieve something we recognize.

For several millennia we as a civilization have been on a path to understand more about what happens in our world—whether by using science to decode nature, or by creating our own environment through technology. But to use more of the richness of the computational universe we must at least to some extent forsake this path.

In the past, we somehow counted on the idea that between our brains and the tools we could create we would always have fundamentally greater computational power than the things around us—and as a result we would always be able to “understand” them. But what the Principle of Computational Equivalence says is that this isn’t true: out in the computational universe there are lots of things just as powerful as our brains or the tools we build. And as soon as we start using those things, we lose the “edge” we thought we had.

Today we still imagine we can identify discrete “bugs” in programs. But most of what’s powerful out there in the computational universe is rife with computational irreducibility—so the only real way to see what it does is just to run it and watch what happens.

We ourselves, as biological systems, are a great example of computation happening at a molecular scale—and we are no doubt rife with computational irreducibility (which is, at some fundamental level, why medicine is hard). I suppose it’s a tradeoff: we could limit our technology to consist only of things whose operation we understand. But then we would miss all that richness that’s out there in the computational universe. And we wouldn’t even be able to match the achievements of our own biology in the technology we create.

There’s a common pattern I’ve noticed with intellectual fields. They go for decades and perhaps centuries with only incremental growth, and then suddenly, usually as a result of a methodological advance, there’s a burst of “hypergrowth” for perhaps 5 years, in which important new results arrive almost every week.

I was fortunate enough that my own very first field—particle physics—was in its period of hypergrowth right when I was involved in the late 1970s. And for myself, the 1990s felt like a kind of personal period of hypergrowth for what became A New Kind of Science—and indeed that’s why I couldn’t pull myself away from it for more than a decade.

But today, the obvious field in hypergrowth is machine learning, or, more specifically, neural nets. It’s funny for me to see this. I actually worked on neural nets back in 1981, before I started on cellular automata, and several years before I found rule 30. But I never managed to get neural nets to do anything very interesting—and actually I found them too messy and complicated for the fundamental questions I was concerned with.

And so I “simplified them”—and wound up with cellular automata. (I was also inspired by things like the Ising model in statistical physics, etc.) At the outset, I thought I might have simplified too far, and that my little cellular automata would never do anything interesting. But then I found things like rule 30. And I’ve been trying to understand its implications ever since.

In building Mathematica and the Wolfram Language, I’d always kept track of neural nets, and occasionally we’d use them in some small way for some algorithm or another. But about 5 years ago I suddenly started hearing amazing things: that somehow the idea of training neural nets to do sophisticated things was actually working. At first I wasn’t sure. But then we started building neural net capabilities in the Wolfram Language, and finally two years ago we released our ImageIdentify.com website—and now we’ve got our whole symbolic neural net system. And, yes, I’m impressed. There are lots of tasks that had traditionally been viewed as the unique domain of humans, but which now we can routinely do by computer.

But what’s actually going on in a neural net? It’s not really to do with the brain; that was just the inspiration (though in reality the brain probably works more or less the same way). A neural net is really a sequence of functions that operate on arrays of numbers, with each function typically taking quite a few inputs from around the array. It’s not so different from a cellular automaton. Except that in a cellular automaton, one’s usually dealing with, say, just 0s and 1s, not arbitrary numbers like 0.735. And instead of taking inputs from all over the place, in a cellular automaton each step takes inputs only from a very well-defined local region.

Now, to be fair, it’s pretty common to study “convolutional neural nets”, in which the patterns of inputs are very regular, just like in a cellular automaton. And it’s becoming clear that having precise (say 32-bit) numbers isn’t critical to the operation of neural nets; one can probably make do with just a few bits.

But a big feature of neural nets is that we know how to make them “learn”. In particular, they have enough features from traditional mathematics (like involving continuous numbers) that techniques like calculus can be applied to provide strategies to make them incrementally change their parameters to “fit their behavior” to whatever training examples they’re given.

It’s far from obvious how much computational effort, or how many training examples, will be needed. But the breakthrough of about five years ago was the discovery that for many important practical problems, what’s available with modern GPUs and modern web-collected training sets can be enough.

Pretty much nobody ends up explicitly setting or “engineering” the parameters in a neural net. Instead, what happens is that they’re found automatically. But unlike with simple programs like cellular automata, where one’s typically enumerating all possibilities, in current neural nets there’s an incremental process, essentially based on calculus, that manages to progressively improve the net—a little like the way biological evolution progressively improves the “fitness” of an organism.

It’s plenty remarkable what comes out from training a neural net in this way, and it’s plenty difficult to understand how the neural net does what it does. But in some sense the neural net isn’t venturing too far across the computational universe: it’s always basically keeping the same basic computational structure, and just changing its behavior by changing parameters.

But to me the success of today’s neural nets is a spectacular endorsement of the power of the computational universe, and another validation of the ideas of A New Kind of Science. Because it shows that out in the computational universe, away from the constraints of explicitly building systems whose detailed behavior one can foresee, there are immediately all sorts of rich and useful things to be found.

Is there a way to bring the full power of the computational universe—and the ideas of A New Kind of Science—to the kinds of things one does with neural nets? I suspect so. And in fact, as the details become clear, I wouldn’t be surprised if exploration of the computational universe saw its own period of hypergrowth: a “mining boom” of perhaps unprecedented proportions.

In current work on neural nets, there’s a definite tradeoff one sees. The more what’s going on inside the neural net is like a simple mathematical function with essentially arithmetic parameters, the easier it is to use ideas from calculus to train the network. But the more what’s going is like a discrete program, or like a computation whose whole structure can change, the more difficult it is to train the network.

It’s worth remembering, though, that the networks we’re routinely training now would have looked utterly impractical to train only a few years ago. It’s effectively just all those quadrillions of GPU operations that we can throw at the problem that makes training feasible. And I won’t be surprised if even quite pedestrian (say, local exhaustive search) techniques will fairly soon let one do significant training even in cases where no incremental numerical approach is possible. And perhaps even it will be possible to invent some major generalization of things like calculus that will operate in the full computational universe. (I have some suspicions, based on thinking about generalizing basic notions of geometry to cover things like cellular automaton rule spaces.)

What would this let one do? Likely it would let one find considerably simpler systems that could achieve particular computational goals. And maybe that would bring within reach some qualitatively new level of operations, perhaps beyond what we’re used to being possible with things like brains.

There’s a funny thing that’s going on with modeling these days. As neural nets become more successful, one begins to wonder: why bother to simulate what’s going on inside a system when one can just make a black-box model of its output using a neural net? Well, if we manage to get machine learning to reach deeper into the computational universe, we won’t have as much of this tradeoff any more—because we’ll be able to learn models of the mechanism as well as the output.

I’m pretty sure that bringing the full computational universe into the purview of machine learning will have spectacular consequences. But it’s worth realizing that computational universality—and the Principle of Computational Equivalence—make it less a matter of principle. Because they imply that even neural nets of the kinds we have now are universal, and are capable of emulating anything any other system can do. (In fact, this universality result was essentially what launched the whole modern idea of neural nets, back in 1943.)

And as a practical matter, the fact that current neural net primitives are being built into hardware and so on will make them a desirable foundation for actual technology systems, though, even if they’re far from optimal. But my guess is that there are tasks where for the foreseeable future access to the full computational universe will be necessary to make them even vaguely practical.

What will it take to make artificial intelligence? As a kid, I was very interested in figuring out how to make a computer know things, and be able to answer questions from what it knew. And when I studied neural nets in 1981, it was partly in the context of trying to understand how to build such a system. As it happens, I had just developed SMP, which was a forerunner of Mathematica (and ultimately the Wolfram Language)—and which was very much based on symbolic pattern matching (“if you see this, transform it to that”). At the time, though, I imagined that artificial intelligence was somehow a “higher level of computation”, and I didn’t know how to achieve it.

I returned to the problem every so often, and kept putting it off. But then when I was working on A New Kind of Science it struck me: if I’m to take the Principle of Computational Equivalence seriously, then there can’t be any fundamentally “higher level of computation”—so AI must be achievable just with the standard ideas of computation that I already know.

And it was this realization that got me started building Wolfram|Alpha. And, yes, what I found is that lots of those very “AI-oriented things”, like natural language understanding, could be done just with “ordinary computation”, without any magic new AI invention. Now, to be fair, part of what was happening was that we were using ideas and methods from A New Kind of Science: we weren’t just engineering everything; we were often searching the computational universe for rules and algorithms to use.

So what about “general AI”? Well, I think at this point that with the tools and understanding we have, we’re in a good position to automate essentially anything we can define. But definition is a more difficult and central issue than we might imagine.

The way I see things at this point is that there’s a lot of computation even near at hand in the computational universe. And it’s powerful computation. As powerful as anything that happens in our brains. But we don’t recognize it as “intelligence” unless it’s aligned with our human goals and purposes.

Ever since I was writing A New Kind of Science, I’ve been fond of quoting the aphorism “the weather has a mind of its own”. It sounds so animistic and pre-scientific. But what the Principle of Computational Equivalence says is that actually, according to the most modern science, it’s true: the fluid dynamics of the weather is the same in its computational sophistication as the electrical processes that go on in our brains.

But is it “intelligent”? When I talk to people about *A New Kind of Science*, and about AI, I’ll often get asked when I think we’ll achieve “consciousness” in a machine. Life, intelligence, consciousness: they are all concepts that we have a specific example of, here on Earth. But what are they in general? All life on Earth shares RNA and the structure of cell membranes. But surely that’s just because all life we know is part of one connected thread of history; it’s not that such details are fundamental to the very concept of life.

And so it is with intelligence. We have only one example we’re sure of: us humans. (We’re not even sure about animals.) But human intelligence as we experience it is deeply entangled with human civilization, human culture and ultimately also human physiology—even though none of these details are presumably relevant in the abstract definition of intelligence.

We might think about extraterrestrial intelligence. But what the Principle of Computational Equivalence implies is that actually there’s “alien intelligence” all around us. But somehow it’s just not quite aligned with human intelligence. We might look at rule 30, for example, and be able to see that it’s doing sophisticated computation, just like our brains. But somehow it just doesn’t seem to have any “point” to what it’s doing.

We imagine that in doing the things we humans do, we operate with certain goals or purposes. But rule 30, for example, just seems to be doing what it’s doing—just following some definite rule. In the end, though, one realizes we’re not so very different. After all, there are definite laws of nature that govern our brains. So anything we do is at some level just playing out those laws.

Any process can actually be described either in terms of mechanism (“the stone is moving according to Newton’s laws”), or in terms of goals (“the stone is moving so as to minimize potential energy”). The description in terms of mechanism is usually what’s most useful in connecting with science. But the description in terms of goals is usually what’s most useful in connecting with human intelligence.

And this is crucial in thinking about AI. We know we can have computational systems whose operations are as sophisticated as anything. But can we get them to do things that are aligned with human goals and purposes?

In a sense this is what I now view as the key problem of AI: it’s not about achieving underlying computational sophistication, but instead it’s about communicating what we want from this computation.

I’ve spent much of my life as a computer language designer—most importantly creating what is now the Wolfram Language. I’d always seen my role as a language designer being to imagine the possible computations people might want to do, then—like a reductionist scientist—trying to “drill down” to find good primitives from which all these computations could be built up. But somehow from A New Kind of Science, and from thinking about AI, I’ve come to think about it a little differently.

Now what I more see myself as doing is making a bridge between our patterns of human thinking, and what the computational universe is capable of. There are all sorts of amazing things that can in principle be done by computation. But what the language does is to provide a way for us humans to express what we want done, or want to achieve—and then to get this actually executed, as automatically as possible.

Language design has to start from what we know and are familiar with. In the Wolfram Language, we name the built-in primitives with English words, leveraging the meanings that those words have acquired. But the Wolfram Language is not like natural language. It’s something more structured, and more powerful. It’s based on the words and concepts that we’re familiar with through the shared corpus of human knowledge. But it gives us a way to build up arbitrarily sophisticated programs that in effect express arbitrarily complex goals.

Yes, the computational universe is capable of remarkable things. But they’re not necessarily things that we humans can describe or relate to. But in building the Wolfram Language my goal is to do the best I can in capturing everything we humans want—and being able to express it in executable computational terms.

When we look at the computational universe, it’s hard not to be struck by the limitations of what we know how to describe or think about. Modern neural nets provide an interesting example. For the `ImageIdentify` function of the Wolfram Language we’ve trained a neural net to identify thousands of kinds of things in the world. And to cater to our human purposes, what the network ultimately does is to describe what it sees in terms of concepts that we can name with words—tables, chairs, elephants, etc.

But internally what the network is doing is to identify a series of features of any object in the world. Is it green? Is it round? And so on. And what happens as the neural network is trained is that it identifies features it finds useful for distinguishing different kinds of things in the world. But the point is that almost none of these features are ones to which we happen to have assigned words in human language.

Out in the computational universe it’s possible to find what may be incredibly useful ways to describe things. But they’re alien to us humans. They’re not something we know how to express, based on the corpus of knowledge our civilization has developed.

Now of course new concepts are being added to the corpus of human knowledge all the time. Back a century ago, if someone saw a nested pattern they wouldn’t have any way to describe it. But now we’d just say “it’s a fractal”. But the problem is that in the computational universe there’s an infinite collection of “potentially useful concepts”—with which we can never hope to ultimately keep up.

When I wrote A New Kind of Science I viewed it in no small part as an effort to break away from the use of mathematics—at least as a foundation for science. But one of the things I realized is that the ideas in the book also have a lot of implications for pure mathematics itself.

What is mathematics? Well, it’s a study of certain abstract kinds of systems, based on things like numbers and geometry. In a sense it’s exploring a small corner of the computational universe of all possible abstract systems. But still, plenty has been done in mathematics: indeed, the 3 million or so published theorems of mathematics represent perhaps the largest single coherent intellectual structure that our species has built.

Ever since Euclid, people have at least notionally imagined that mathematics starts from certain axioms (say, a+b=b+a, a+0=a, etc.), then builds up derivations of theorems. Why is math hard? The answer is fundamentally rooted in the phenomenon of computational irreducibility—which here is manifest in the fact that there’s no general way to shortcut the series of steps needed to derive a theorem. In other words, it can be arbitrarily hard to get a result in mathematics. But worse than that—as Gödel’s Theorem showed—there can be mathematical statements where there just aren’t any finite ways to prove or disprove them from the axioms. And in such cases, the statements just have to be considered “undecidable”.

And in a sense what’s remarkable about math is that one can usefully do it at all. Because it could be that most mathematical results one cares about would be undecidable. So why doesn’t that happen?

Well, if one considers arbitrary abstract systems it happens a lot. Take a typical cellular automaton—or a Turing machine—and ask whether it’s true that the system, say, always settles down to periodic behavior regardless of its initial state. Even something as simple as that will often be undecidable.

So why doesn’t this happen in mathematics? Maybe there’s something special about the particular axioms used in mathematics. And certainly if one thinks they’re the ones that uniquely describe science and the world there might be a reason for that. But one of the whole points of the book is that actually there’s a whole computational universe of possible rules that can be useful for doing science and describing the world.

And in fact I don’t think there’s anything abstractly special about the particular axioms that have traditionally been used in mathematics: I think they’re just accidents of history.

What about the theorems that people investigate in mathematics? Again, I think there’s a strong historical character to them. For all but the most trivial areas of mathematics, there’s a whole sea of undecidability out there. But somehow mathematics picks the islands where theorems can actually be proved—often particularly priding itself on places close to the sea of undecidability where the proof can only be done with great effort.

I’ve been interested in the whole network of published theorems in mathematics (it’s a thing to curate, like wars in history, or properties of chemicals). And one of the things I’m curious about is whether something there’s an inexorable sequence to the mathematics that’s done, or whether, in a sense, random parts are being picked.

And here, I think, there’s a considerable analogy to the kind of thing we were discussing before with language. What is a proof? Basically it’s a way of explaining to someone why something is true. I’ve made all sorts of automated proofs in which there are hundreds of steps, each perfectly verifiable by computer. But—like the innards of a neural net—what’s going on looks alien and not understandable by a human.

For a human to understand, there have to be familiar “conceptual waypoints”. It’s pretty much like with words in languages. If some particular part of a proof has a name (“Smith’s Theorem”), and has a known meaning, then it’s useful to us. But if it’s just a lump of undifferentiated computation, it won’t be meaningful to us.

In pretty much any axiom system, there’s an infinite set of possible theorems. But which ones are “interesting”? That’s really a human question. And basically it’s going to end up being ones with “stories”. In the book I show that for the simple case of basic logic, the theorems that have historically been considered interesting enough to be given names happen to be precisely the ones that are in some sense minimal.

But my guess is that for richer axiom systems pretty much anything that’s going to be considered “interesting” is going to have to be reached from things that are already considered interesting. It’s like building up words or concepts: you don’t get to introduce new ones unless you can directly relate them to existing ones.

In recent years I’ve wondered quite a bit about how inexorable or not progress is in a field like mathematics. Is there just one historical path that can be taken, say from arithmetic to algebra to the higher reaches of modern mathematics? Or are there an infinite diversity of possible paths, with completely different histories for mathematics?

The answer is going to depend on—in a sense—the “structure of metamathematical space”: just what is the network of true theorems that avoid the sea of undecidability? Maybe it’ll be different for different fields of mathematics, and some will be more “inexorable” (so it feels like the math is being “discovered”) than others (where it seems more like the math is arbitrary, and “invented”).

But to me one of the most interesting things is how close—when viewed in these kinds of terms—questions about the nature and character of mathematics end up being to questions about the nature and character of intelligence and AI. And it’s this kind of commonality that makes me realize just how powerful and general the ideas in A New Kind of Science actually are.

There are some areas of science—like physics and astronomy—where the traditional mathematical approach has done quite well. But there are others—like biology, social science and linguistics—where it’s had a lot less to say. And one of the things I’ve long believed is that what’s needed to make progress in these areas is to generalize the kinds of models one’s using, to consider a broader range of what’s out there in the computational universe.

And indeed in the past 15 or so years there’s been increasing success in doing this. And there are lots of biological and social systems, for example, where models have now been constructed using simple programs.

But unlike with mathematical models which can potentially be “solved”, these computational models often show computational irreducibility, and are typically used by doing explicit simulations. This can be perfectly successful for making particular predictions, or for applying the models in technology. But a bit like for the automated proofs of mathematical theorems one might still ask, “is this really science?”.

Yes, one can simulate what a system does, but does one “understand” it? Well, the problem is that computational irreducibility implies that in some fundamental sense one can’t always “understand” things. There might be no useful “story” that can be told; there may be no “conceptual waypoints”—only lots of detailed computation.

Imagine that one’s trying to make a science of how the brain understands language—one of the big goals of linguistics. Well, perhaps we’ll get an adequate model of the precise rules which determine the firing of neurons or some other low-level representation of the brain. And then we look at the patterns generated in understanding some whole collection of sentences.

Well, what if those patterns look like the behavior of rule 30? Or, closer at hand, the innards of some recurrent neural network? Can we “tell a story” about what’s happening? To do so would basically require that we create some kind of higher-level symbolic representation: something where we effectively have words for core elements of what’s going on.

But computational irreducibility implies that there may ultimately be no way to create such a thing. Yes, it will always be possible to find patches of computational reducibility, where some things can be said. But there won’t be a complete story that can be told. And one might say there won’t be a useful reductionistic piece of science to be done. But that’s just one of the things that happens when one’s dealing with (as the title says) a new kind of science.

People have gotten very worried about AI in recent years. They wonder what’s going to happen when AIs “get much smarter” than us humans. Well, the Principle of Computational Equivalence has one piece of good news: at some fundamental level, AIs will never be “smarter”—they’ll just be able to do computations that are ultimately equivalent to what our brains do, or, for that matter, what all sorts of simple programs do.

As a practical matter, of course, AIs will be able to process larger amounts of data more quickly than actual brains. And no doubt we’ll choose to have them run many aspects of the world for us—from medical devices, to central banks to transportation systems, and much more.

So then it’s important to figure how we’ll tell them what to do. As soon as we’re making serious use of what’s out there in the computational universe, we’re not going to be able to give a line-by-line description of what the AIs are going to do. Rather, we’re going to have to define goals for the AIs, then let them figure out how best to achieve those goals.

In a sense we’ve already been doing something like this for years in the Wolfram Language. There’s some high-level function that describes something you want to do (“lay out a graph”, “classify data”, etc.). Then it’s up to the language to automatically figure out the best way to do it.

And in the end the real challenge is to find a way to describe goals. Yes, you want to search for cellular automata that will make a “nice carpet pattern”, or a “good edge detector”. But what exactly do those things mean? What you need is a language that a human can use to say as precisely as possible what they mean.

It’s really the same problem as I’ve been talking about a lot here. One has to have a way for humans to be able to talk about things they care about. There’s infinite detail out there in the computational universe. But through our civilization and our shared cultural history we’ve come to identify certain concepts that are important to us. And when we describe our goals, it’s in terms of these concepts.

Three hundred years ago people like Leibniz were interested in finding a precise symbolic way to represent the content of human thoughts and human discourse. He was far too early. But now I think we’re finally in a position to actually make this work. In fact, we’ve already gotten a long way with the Wolfram Language in being able to describe real things in the world. And I’m hoping it’ll be possible to construct a fairly complete “symbolic discourse language” that lets us talk about the things we care about.

Right now we write legal contracts in “legalese” as a way to make them slightly more precise than ordinary natural language. But with a symbolic discourse language we’ll be able to write true “smart contracts” that describe in high-level terms what we want to have happen—and then machines will automatically be able to verify or execute the contract.

But what about the AIs? Well, we need to tell them what we generally want them to do. We need to have a contract with them. Or maybe we need to have a constitution for them. And it’ll be written in some kind of symbolic discourse language, that both allows us humans to express what we want, and is executable by the AIs.

There’s lots to say about what should be in an AI Constitution, and how the construction of such things might map onto the political and cultural landscape of the world. But one of the obvious questions is: can the constitution be simple, like Asimov’s Laws of Robotics?

And here what we know from A New Kind of Science tells us the answer: it can’t be. In a sense the constitution is an attempt to sculpt what can happen in the world and what can’t. But computational irreducibility says that there will be an unbounded collection of cases to consider.

For me it’s interesting to see how theoretical ideas like computational irreducibility end up impinging on these very practical—and central—societal issues. Yes, it all started with questions about things like the theory of all possible theories. But in the end it turns into issues that everyone in society is going to end up being concerned about.

Will we reach the end of science? Will we—or our AIs—eventually invent everything there is to be invented?

For mathematics, it’s easy to see that there’s an infinite number of possible theorems one can construct. For science, there’s an infinite number of possible detailed questions to ask. And there’s also an infinite array of possible inventions one can construct.

But the real question is: will there always be interesting new things out there?

Well, computational irreducibility says there will always be new things that need an irreducible amount of computational work to reach from what’s already there. So in a sense there’ll always be “surprises”, that aren’t immediately evident from what’s come before.

But will it just be like an endless array of different weirdly shaped rocks? Or will there be fundamental new features that appear, that we humans consider interesting?

It’s back to the very same issue we’ve encountered several times before: for us humans to find things “interesting” we have to have a conceptual framework that we can use to think about them. Yes, we can identify a “persistent structure” in a cellular automaton. Then maybe we can start talking about “collisions between structures”. But when we just see a whole mess of stuff going on, it’s not going to be “interesting” to us unless we have some higher-level symbolic way to talk about it.

In a sense, then, the rate of “interesting discovery” isn’t going to be limited by our ability to go out into the computational universe and find things. Instead, it’s going to be limited by our ability as humans to build a conceptual framework for what we’re finding.

It’s a bit like what happened in the whole development of what became A New Kind of Science. People had seen related phenomena for centuries if not millennia (distribution of primes, digits of pi, etc.). But without a conceptual framework they just didn’t seem “interesting”, and nothing was built around them. And indeed as I understand more about what’s out there in the computational universe—and even about things I saw long ago there—I gradually build up a conceptual framework that lets me go further.

By the way, it’s worth realizing that inventions work a little differently from discoveries. One can see something new happen in the computational universe, and that might be a discovery. But an invention is about figuring out how something can be achieved in the computational universe.

And—like in patent law—it isn’t really an invention if you just say “look, this does that”. You have to somehow understand a purpose that it’s achieving.

In the past, the focus of the process of invention has tended to be on actually getting something to work (“find the lightbulb filament that works”, etc.). But in the computational universe, the focus shifts to the question of what you want the invention to do. Because once you’ve described the goal, finding a way to achieve it is something that can be automated.

That’s not to say that it will always be easy. In fact, computational irreducibility implies that it can be arbitrarily difficult. Let’s say you know the precise rules by which some chemicals can interact. Can you find a chemical synthesis pathway that will let you get to some particular chemical structure? There may be a way, but computational irreducibility implies that there may be no way to find out how long the pathway may be. And if you haven’t found a pathway you may never be sure if it’s because there isn’t one, or just because you didn’t reach it yet.

If one thinks about reaching the edge of science, one cannot help but wonder about the fundamental theory of physics. Given everything we’ve seen in the computational universe, is it conceivable that our physical universe could just correspond to one of those programs out there in the computational universe?

Of course, we won’t really know until or unless we find it. But in the years since A New Kind of Science appeared, I’ve become ever more optimistic about the possibilities.

Needless to say, it would be a big change for physics. Today there are basically two major frameworks for thinking about fundamental physics: general relativity and quantum field theory. General relativity is a bit more than 100 years old; quantum field theory maybe 90. And both have achieved spectacular things. But neither has succeeded in delivering us a complete fundamental theory of physics. And if nothing else, I think after all this time, it’s worth trying something new.

But there’s another thing: from actually exploring the computational universe, we have a huge amount of new intuition about what’s possible, even in very simple models. We might have thought that the kind of richness we know exists in physics would require some very elaborate underlying model. But what’s become clear is that that kind of richness can perfectly well emerge even from a very simple underlying model.

What might the underlying model be like? I’m not going to discuss this in great detail here, but suffice it to say that I think the most important thing about the model is that it should have as little as possible built in. We shouldn’t have the hubris to think we know how the universe is constructed; we should just take a general type of model that’s as unstructured as possible, and do what we typically do in the computational universe: just search for a program that does what we want.

My favorite formulation for a model that’s as unstructured as possible is a network: just a collection of nodes with connections between them. It’s perfectly possible to formulate such a model as an algebraic-like structure, and probably many other kinds of things. But we can think of it as a network. And in the way I’ve imagined setting it up, it’s a network that’s somehow “underneath” space and time: every aspect of space and time as we know it must emerge from the actual behavior of the network.

Over the past decade or so there’s been increasing interest in things like loop quantum gravity and spin networks. They’re related to what I’ve been doing in the same way that they also involve networks. And maybe there’s some deeper relationship. But in their usual formulation, they’re much more mathematically elaborate.

From the point of view of the traditional methods of physics, this might seem like a good idea. But with the intuition we have from studying the computational universe—and using it for science and technology—it seems completely unnecessary. Yes, we don’t yet know the fundamental theory of physics. But it seems sensible to start with the simplest hypothesis. And that’s definitely something like a simple network of the kind I’ve studied.

At the outset, it’ll look pretty alien to people (including myself) trained in traditional theoretical physics. But some of what emerges isn’t so alien. A big result I found nearly 20 years ago (that still hasn’t been widely understood) is that when you look at a large enough network of the kind I studied you can show that its averaged behavior follows Einstein’s equations for gravity. In other words, without putting any fancy physics into the underlying model, it ends up automatically emerging. I think it’s pretty exciting.

People ask a lot about quantum mechanics. Yes, my underlying model doesn’t build in quantum mechanics (just as it doesn’t build in general relativity). Now, it’s a little difficult to pin down exactly what the essence of “being quantum mechanical” actually is. But there are some very suggestive signs that my simple networks actually end up showing what amounts to quantum behavior—just like in the physics we know.

OK, so how should one set about actually finding the fundamental theory of physics if it’s out there in the computational universe of possible programs? Well, the obvious thing is to just start searching for it, starting with the simplest programs.

I’ve been doing this—more sporadically than I would like—for the past 15 years or so. And my main discovery so far is that it’s actually quite easy to find programs that aren’t obviously not our universe. There are plenty of programs where space or time are obviously completely different from the way they are in our universe, or there’s some other pathology. But it turns out it’s not so difficult to find candidate universes that aren’t obviously not our universe.

But we’re immediately bitten by computational irreducibility. We can simulate the candidate universe for billions of steps. But we don’t know what it’s going to do—and whether it’s going to grow up to be like our universe, or completely different.

It’s pretty unlikely that in looking at that tiny fragment of the very beginning of a universe we’re going to ever be able to see anything familiar, like a photon. And it’s not at all obvious that we’ll be able to construct any kind of descriptive theory, or effective physics. But in a sense the problem is bizarrely similar to the one we have even in systems like neural networks: there’s computation going on there, but can we identify “conceptual waypoints” from which we can build up a theory that we might understand?

It’s not at all clear our universe has to be understandable at that level, and it’s quite possible that for a very long time we’ll be left in the strange situation of thinking we might have “found our universe” out in the computational universe, but not being sure.

Of course, we might be lucky, and it might be possible to deduce an effective physics, and see that some little program that we found ends up reproducing our whole universe. It would be a remarkable moment for science. But it would immediately raise a host of new questions—like why this universe, and not another?

Right now us humans exist as biological systems. But in the future it’s certainly going to be technologically possible to reproduce all the processes in our brains in some purely digital—computational—form. So insofar as those processes represent “us”, we’re going to be able to be “virtualized” on pretty much any computational substrate. And in this case we might imagine that the whole future of a civilization could wind up in effect as a “box of a trillion souls”.

Inside that box there would be all kinds of computations going on, representing the thoughts and experiences of all those disembodied souls. Those computations would reflect the rich history of our civilization, and all the things that have happened to us. But at some level they wouldn’t be anything special.

It’s perhaps a bit disappointing, but the Principle of Computational Equivalence tells us that ultimately these computations will be no more sophisticated than the ones that go on in all sorts of other systems—even ones with simple rules, and no elaborate history of civilization. Yes, the details will reflect all that history. But in a sense without knowing what to look for—or what to care about—one won’t be able to tell that there’s anything special about it.

OK, but what about for the “souls” themselves? Will one be able to understand their behavior by seeing that they achieve certain purposes? Well, in our current biological existence, we have all sorts of constraints and features that give us goals and purposes. But in a virtualized “uploaded” form, most of these just go away.

I’ve thought quite a bit about how “human” purposes might evolve in such a situation, recognizing, of course, that in virtualized form there’s little difference between human and AI. The disappointing vision is that perhaps the future of our civilization consists in disembodied souls in effect “playing videogames” for the rest of eternity.

But what I’ve slowly realized is that it’s actually quite unrealistic to project our view of goals and purposes from our experience today into that future situation. Imagine talking to someone from a thousand years ago and trying to explain that people in the future would be walking on treadmills every day, or continually sending photographs to their friends. The point is that such activities don’t make sense until the cultural framework around them has developed.

It’s the same story yet again as with trying to characterize what’s interesting or what’s explainable. It relies on the development of a whole network of conceptual waypoints.

Can we imagine what the mathematics of 100 years from now will be like? It depends on concepts we don’t yet know. So similarly if we try to imagine human motivation in the future, it’s going to rely on concepts we don’t know. Our best description from today’s viewpoint might be that those disembodied souls are just “playing videogames”. But to them there might be a whole subtle motivation structure that they could only explain by rewinding all sorts of steps in history and cultural development.

By the way, if we know the fundamental theory of physics then in a sense we can make the virtualization complete, at least in principle: we can just run a simulation of the universe for those disembodied souls. Of course, if that’s what’s happening, then there’s no particular reason it has to be a simulation of our particular universe. It could as well be any universe from out in the computational universe.

Now, as I’ve mentioned, even in any given universe one will never in a sense run out of things to do, or discover. But I suppose I myself at least find it amusing to imagine that at some point those disembodied souls might get bored with just being in a simulated version of our physical universe—and might decide it’s more fun (whatever that means to them) to go out and explore the broader computational universe. Which would mean that in a sense the future of humanity would be an infinite voyage of discovery in the context of none other than A New Kind of Science!

Long before we have to think about disembodied human souls, we’ll have to confront the issue of what humans should be doing in a world where more and more can be done automatically by AIs. Now in a sense this issue is nothing new: it’s just an extension of the long-running story of technology and automation. But somehow this time it feels different.

And I think the reason is in a sense just that there’s so much out there in the computational universe, that’s so easy to get to. Yes, we can build a machine that automates some particular task. We can even have a general-purpose computer that can be programmed to do a full range of different tasks. But even though these kinds of automation extend what we can do, it still feels like there’s effort that we have to put into them.

But the picture now is different—because in effect what we’re saying is that if we can just define the goal we want to achieve, then everything else will be automatic. All sorts of computation, and, yes, “thinking”, may have to be done, but the idea is that it’s just going to happen, without human effort.

At first, something seems wrong. How could we get all that benefit, without putting in more effort? It’s a bit like asking how nature could manage to make all the complexity it does—even though when we build artifacts, even with great effort, they end up far less complex. The answer, I think, is it’s mining the computational universe. And it’s exactly the same thing for us: by mining the computational universe, we can achieve essentially an unbounded level of automation.

If we look at the important resources in today’s world, many of them still depend on actual materials. And often these materials are literally mined from the Earth. Of course, there are accidents of geography and geology that determine by whom and where that mining can be done. And in the end there’s a limit (if often very large) to the amount of material that’ll ever be available.

But when it comes to the computational universe, there’s in a sense an inexhaustible supply of material—and it’s accessible to anyone. Yes, there are technical issues about how to “do the mining”, and there’s a whole stack of technology associated with doing it well. But the ultimate resource of the computational universe is a global and infinite one. There’s no scarcity and no reason to be “expensive”. One just has to understand that it’s there, and take advantage of it.

Probably the greatest intellectual shift of the past century has been the one towards the computational way of thinking about things. I’ve often said that if one picks almost any field “X”, from archaeology to zoology, then by now there either is, or soon will be, a field called “computational X”—and it’s going to be the future of the field.

I myself have been deeply involved in trying to enable such computational fields, in particular through the development of the Wolfram Language. But I’ve also been interested in what is essentially the meta problem: how should one teach abstract computational thinking, for example to kids? The Wolfram Language is certainly important as a practical tool. But what about the conceptual, theoretical foundations?

Well, that’s where A New Kind of Science comes in. Because at its core it’s discussing the pure abstract phenomenon of computation, independent of its applications to particular fields or tasks. It’s a bit like with elementary mathematics: there are things to teach and understand just to introduce the ideas of mathematical thinking, independent of their specific applications. And so it is too with the core of A New Kind of Science. There are things to learn about the computational universe that give intuition and introduce patterns of computational thinking—quite independent of detailed applications.

One can think of it as a kind of “pre computer science” , or “pre computational X”. Before one gets into discussing the specifics of particular computational processes, one can just study the simple but pure things one finds in the computational universe.

And, yes, even before kids learn to do arithmetic, it’s perfectly possible for them to fill out something like a cellular automaton coloring book—or to execute for themselves or on a computer a whole range of different simple programs. What does it teach? Well, it certainly teaches the idea that there can be definite rules or algorithms for things—and that if one follows them one can create useful and interesting results. And, yes, it helps that systems like cellular automata make obvious visual patterns, that for example one can even find in nature (say on mollusc shells).

As the world becomes more computational—and more things are done by AIs and by mining the computational universe—there’s going to an extremely high value not only in understanding computational thinking, but also in having the kind of intuition that develops from exploring the computational universe and that is, in a sense, the foundation for *A New Kind of Science*.

My goal over the decade that I spent writing *A New Kind of Science* was, as much as possible, to answer all the first round of “obvious questions” about the computational universe. And looking back 15 years later I think that worked out pretty well. Indeed, today, when I wonder about something to do with the computational universe, I find it’s incredibly likely that somewhere in the main text or notes of the book I already said something about it.

But one of the biggest things that’s changed over the past 15 years is that I’ve gradually begun to understand more of the implications of what the book describes. There are lots of specific ideas and discoveries in the book. But in the longer term I think what’s most significant is how they serve as foundations, both practical and conceptual, for a whole range of new things that one can now understand and explore.

But even in terms of the basic science of the computational universe, there are certainly specific results one would still like to get. For example, it would be great to get more evidence for or against the Principle of Computational Equivalence, and its domain of applicability.

Like most general principles in science, the whole epistemological status of the Principles of Computational Equivalence is somewhat complicated. Is it like a mathematical theorem that can be proved? Is it like a law of nature that might (or might not) be true about the universe? Or is it like a definition, say of the very concept of computation? Well, much like, say, the Second Law of Thermodynamics or Evolution by Natural Selection, it’s a combination of these.

But one thing that’s significant is that it’s possible to get concrete evidence for (or against) the Principle of Computational Equivalence. The principle says that even systems with very simple rules should be capable of arbitrarily sophisticated computation—so that in particular they should be able to act as universal computers.

And indeed one of the results of the book is that this is true for one of the simplest possible cellular automata (rule 110). Five years after the book was published I decided to put up a prize for evidence about another case: the simplest conceivably universal Turing machine. And I was very pleased that in just a few months the prize was won, the Turing machine was proved universal, and there was another piece of evidence for the Principle of Computational Equivalence.

There’s a lot to do in developing the applications of *A New Kind of Science*. There are models to be made of all sorts of systems. There’s technology to be found. Art to be created. There’s also a lot to do in understanding the implications.

But it’s important not to forget the pure investigation of the computational universe. In the analogy of mathematics, there are applications to be pursued. But there’s also a “pure mathematics” that’s worth pursuing in its own right. And so it is with the computational universe: there’s a huge amount to explore just at an abstract level. And indeed (as the title of the book implies) there’s enough to define a whole new kind of science: a pure science of the computational universe. And it’s the opening of that new kind of science that I think is the core achievement of *A New Kind of Science*—and the one of which I am most proud.

For the 10th anniversary of *A New Kind of Science*, I wrote three posts:

- It’s Been 10 Years: What’s Happened with
*A New Kind of Science*? - Living a Paradigm Shift: Looking Back on Reactions to
*A New Kind of Science* - Looking to the Future of
*A New Kind of Science*

*The complete high-resolution A New Kind of Science is now available on the web. There are also a limited number of print copies of the book still available (all individually coded!).*

*( An Elementary Introduction to the Wolfram Language is available in print, as an ebook, and free on the web—as well as in Wolfram Programming Lab in the Wolfram Open Cloud. There’s also now a free online hands-on course based on it.)*

A year ago I published a book entitled *An Elementary Introduction to the Wolfram Language*—as part of my effort to teach computational thinking to the next generation. I just published the second edition of the book—with (among other things) a significantly extended section on modern machine learning.

I originally expected my book’s readers would be high schoolers and up. But it’s actually also found a significant audience among middle schoolers (11- to 14-year-olds). So the question now is: can one teach the core concepts of modern machine learning even to middle schoolers? Well, the interesting thing is that—thanks to the whole technology stack we’ve now got in the Wolfram Language—the answer seems to be “yes”!

Here’s what I did in the book:

After this main text, the book has Exercises, Q&A and Tech Notes.

What was my thinking behind this machine learning section? Well, first, it has to fit into the flow of the book—using only concepts that have already been introduced, and, when possible, reinforcing them. So it can talk about images, and real-world data, and graphs, and text—but not functional programming or external data resources.

With modern machine learning, it’s easy to show “wow” examples—like our imageidentify.com website from 2015 (based on the Wolfram Language `ImageIdentify` function). But my goal in the book was also to communicate a bit of the background and intuition of how machine learning works, and where it can be used.

I start off by explaining that machine learning is different from traditional “programming”, because it’s based on learning from examples, rather than on explicitly specifying computational steps. The first thing I discuss is something that doesn’t really need all the fanciness of modern neural-net machine learning: it’s recognizing what languages text fragments are from:

Kids (and other people) can sort of imagine (or discuss in a classroom) how something like this might work—looking words up in dictionaries, etc. And I think it’s useful to give a first example that doesn’t seem like “pure magic”. (In reality, `LanguageIdentify` uses a combination of traditional lookup, and modern machine learning techniques.)

But then I give a much more “magic” example—of `ImageIdentify`:

I don’t immediately try to explain how it works, but instead go on to something different: sentiment analysis. Kids have lots of fun trying out sentiment analysis. But the real point here is that it shows the idea of making a “classifier”: there are an infinite number of possible inputs, but only (in this case) 3 possible outputs:

Having seen this, we’re ready to give a little more indication of how something like this works. And what I do is to show the function `Classify` classifying handwritten digits into 0s and 1s. I’m not saying what’s going on inside, but people can get the idea that `Classify` is given a bunch of examples, and then it’s using those to classify a particular input as being 0 or 1:

OK, but how does it do this? In reality one’s dealing with ideas about attractors—and inputs that lie in the basins of attraction for particular outputs. But in a first approximation, one can say that inputs that are “nearer to”, say, the 0 examples are taken to be 0s, and inputs that are nearer to the 1 examples are taken to be 1s.

People don’t usually have much difficulty with that explanation—unless they start to think too hard about what “nearest” might really mean in this context. But rather than concentrating on that, what I do in the book is just to talk about the case of numbers, where it’s really easy to see what “nearest” means:

`Nearest` isn’t the most exciting function to play with: one potentially puts a lot of things in, and then just one “nearest thing” comes out. Still, `Nearest` is nice because its functionality is pretty easy to understand (and one can have reasonable guesses about algorithms it could use).

Having seen `Nearest` for numbers, I show `Nearest` for colors. In the book, I’ve already talked about how colors are represented by red-green-blue triples of numbers, so this isn’t such a stretch—but seeing `Nearest` operate on colors begins to make it a little more plausible that it could operate on things like images too.

Next I show the case of words. In the book, I’ve already done quite a bit with strings and words. In the main text I don’t talk about the precise definition of “nearness” for words, but again, kids easily get the basic idea. (In a Tech Note, I do talk about `EditDistance`, another good algorithmic operation that people can think about and try out.)

OK, so how does one get from here to something like `ImageIdentify`? The approach I used is to talk next about OCR and `TextRecognize`. This doesn’t seem as “magic” as `ImageIdentify` (and lots of people know about “OCR’ing documents”), but it’s a good place to get a further idea of what `ImageIdentify` is doing.

Turning a piece of text into an image, and then back into the same text again, doesn’t seem that impressive or useful. But it gets more interesting if one blurs the text out (and, yes, blurring an image is something I talked about earlier in the book):

Given the blurred image, the question is: can one still recognize the text? At this stage in the book I haven’t talked about `/@` (`Map`) or `%` (last output) yet, so I have to write the code out a bit more verbosely. But the result is:

And, yes, when the image isn’t too blurred, `TextRecognize` can recognize the text, but when the text gets too blurred, it stops being able to. I like this example, because it shows something impressive—but not “magic”—happening. And I think it’s useful to show both where machine learning-based functions succeed, and where they fail. By the way, the result here is different from the one in the book—because the text font is different, and those details matter when one’s on the edge of what can be recognized. (If one was doing this in a class, for example, one might try some different fonts and sizes, and discuss why some survive more blurring than others.)

`TextRecognize` shows how one can effectively do something like `ImageIdentify`, but with just 26 letterforms (well, actually, `TextRecognize` handles many more glyphs than that). But now in the book I show `ImageIdentify` again, blurring like we did with letters:

It’s fun to see what it does, but it’s also helpful. Because it gives a sense of the “attractor” around the “cheetah” concept: stay fairly close and the cheetah can still be recognized; go too far away and it can’t. (A slightly tricky issue is that we’re continually producing new, better neural nets for `ImageIdentify`—so even between when the book was finished and today there’ve been some new nets—and it so happens they give different results for the not-a-cheetah cases. Presumably the new results are “better”, though it’s not clear what that means, given that we don’t have an official right-answer “blurred cheetah” category, and who’s to say whether the blurriest image is more like a whortleberry or a person.)

I won’t go through my whole discussion of machine learning from the book here. Suffice it to say that after discussing explicitly trained functions like `TextRecognize` and `ImageIdentify`, I start discussing “unsupervised learning”, and things like clustering in feature space. I think our new `FeatureSpacePlot` is particularly helpful.

It’s fairly clear what it means to arrange colors:

But then one can “do the same thing” with images of letters. (In the book the code is a little longer, because I haven’t talked about `/@` yet.)

And what’s nice about this is that—as well as being useful in its own right—it also reinforces the idea of how something like `TextRecognize` might work by finding the “nearest letter” to whatever input it’s given.

My final example in the section uses photographs. `FeatureSpacePlot` does a nice job of separating images of different kinds of things—again giving an idea of how `ImageIdentify` might work:

Obviously in just 10 pages in an elementary book I’m not able to give a complete exposition of modern machine learning. But I was pleased to see how many of the core concepts I was able to touch on.

Of course, the fact that this was possible at all depends critically on our whole Wolfram Language technology stack. Whether it’s the very fact that we have machine learning in the language, or the fact that we can seamlessly work with images or text or whatever, or the whole (28-year-old!) Wolfram Notebook system that lets us put all these pieces together—all these pieces are critical to making it possible to bring modern machine learning to people like middle schoolers.

And what I really like is that what one gets to do isn’t toy stuff: one can take what I’m discussing in the book, and immediately apply it in real-world situations. At some level the fact that this works is a reflection of the whole automation emphasis of the Wolfram Language: there’s very sophisticated stuff going on inside, but it’s automated at all levels, so one doesn’t need to be an expert and understand the details to be able to use it—or to get a good intuition about what can work and what can’t.

OK, so how would one go further in teaching machine learning?

One early thing might be to start talking about probabilities. `ImageIdentify` has various possible choices of identifications, but what probabilities does it assign to them?

This can lead to a useful discussion about prior probabilities, and about issues like trading off specificity for certainty.

But the big thing to talk about is training. (After all, “machine learning trainer” will surely be a big future career for some of today’s middle schoolers…) And the good news is that in the Wolfram Language environment, it’s possible to make training work with only a modest amount of data.

Let’s get some examples of images of characters from *Guardians of the Galaxy* by searching the web (we’re using an external search API, so you unfortunately can’t do exactly this on the Open Cloud):

Now we can use these images as training material to create a classifier:

And, sure enough, it can identify Rocket:

And, yes, it thinks a real raccoon is him too:

How does it do it? Well, let’s look at `FeatureSpacePlot`:

Some of this looks good—but some looks confusing. Because it’s arranging some of the images not according to who they’re of, but just according to their background colors. And here we begin to see some of the subtlety of machine learning. The actual classifier we built works only because in the training examples for each character there were ones with different backgrounds—so it can figure out that background isn’t the only distinguishing feature.

Actually, there’s another critical thing as well: `Classify` isn’t starting from scratch in classifying the images. Because it’s already been pre-trained to pick out “good features” that help distinguish real-world images. In fact, it’s actually using everything it learned from the creation of `ImageIdentify`—and the tens of millions of images it saw in connection with that—to know up front what features it should pay attention to.

It’s a bit weird to see, but internally `Classify` is characterizing each image as a list of numbers, each associated with a different “feature”:

One can do an extreme version of this in which one insists that each image is reduced to just two numbers—and that’s essentially how `FeatureSpacePlot` determines where to position an image:

OK, but what’s going on under the hood? Well, it’s complicated. But in the Wolfram Language it’s easy to see—and getting a look at it helps in terms of getting an intuition about how neural nets really work. So, for example, here’s the low-level Wolfram Language symbolic representation of the neural net that powers `ImageIdentify`:

And there’s actually even more: just click and keep drilling down:

And yes, this is hard to understand—certainly for middle schoolers, and even for professionals. But if we take this whole neural net object, and apply it to a picture of a tiger, it’ll do what `ImageIdentify` does, and tell us it’s a tiger:

But here’s a neat thing, made possible by a whole stack of functionality in the Wolfram Language: we can actually go “inside” the neural net, to get a sense of what’s happening. As an example, let’s just take the first 3 “layers” of the network, apply them to the tiger, and visualize what comes out:

Basically what’s happening is that the network has made lots of copies of the original image, and then processed each of them to pick out a different aspect of the image. (What’s going on actually seems to be remarkably similar to the first few levels of visual processing in the brain.)

What if we go deeper into the network? Here’s what happens at layer 10. The images are more abstracted, and presumably pick out higher-level features:

Go to level 20, and the network is “thinking about” lots of little images:

But by level 28, it’s beginning to “come to some conclusions”, with only a few of its possible channels of activity “lighting up”:

Finally, by level 31, all that’s left is an array of numbers, with a few peaks visible:

And applying the very last layer of the network (a “softmax” layer) only a couple of peaks are left:

And the highest one is exactly the one that corresponds to the concept of “tiger”:

I’m not imagining that middle schoolers will follow all these details (and no, nobody should be learning neural net layer types like they learn parts of the water cycle). But I think it’s really useful to see “inside” `ImageIdentify`, and get even a rough sense of how it works. To someone like me it still seems a little like magic that it all comes together as it does. But what’s great is that now with our latest Wolfram Language tools one can easily look inside, and start getting an intuition about what’s going on.

The idea of the Wolfram Language `Classify` function is to do machine learning at the highest possible level—as automatically as possible, and building on as much pre-training as possible. But if one wants to get a more complete feeling for what machine learning is like, it’s useful to see what happens if one instead tries to just train a neural net from scratch.

There is an immediate practical issue though: to get a neural net, starting from scratch, to actually do anything useful, one typically has to give it a very large amount of training data—which is hard to collect and wrangle. But the good news here is that with the recent release of the Wolfram Data Repository we have a growing collection of ready-to-use training sets immediately available for use in the Wolfram Language.

Like here’s the classic MNIST handwritten digit training set, with its 60,000 training examples:

One thing one can do with a training set like this is just feed a random sample of it into `Classify`. And sure enough this gives one a classifier function that’s essentially a simple version of `TextRecognize` for handwritten digits:

And even with just 1000 training examples, it does pretty well:

And, yes, we can use `FeatureSpacePlot` to see how the different digits tend to separate in feature space:

But, OK, what if we want to actually train a neural net from scratch, with none of the fancy automation of `Classify`? Well, first we have to set up a raw neural net. And conveniently, the Wolfram Language has a bunch of classic neural nets built in. Here one’s called LeNet:

It’s much simpler than the `ImageIdentify` net, but it’s still pretty complicated. But we don’t have to understand what’s inside it to start training it. Instead, in the Wolfram Language, we can just use `NetTrain` (which, needless to say, automatically applies all the latest GPU tricks and so on):

It’s pretty neat to watch the training happening, and to see the orange line of the neural net’s error rate for fitting the examples keep going down. After about 20 seconds, `NetTrain` decides it’s gone far enough, and generates a finally trained net—which works pretty well:

If you stop the training early, it won’t do quite so well:

In the professional world of machine learning, there’s a whole art and science of figuring out the best parameters for training. But with what we’ve got now in the Wolfram Language, nothing is stopping a middle schooler from doing their own experiments, visualizing and analyzing the results, and getting as good an intuition as anyone.

OK, so if we want to really get down to the lowest level, we have to talk about what neural nets are made of. I’m not sure how much of this is middle-school stuff—but as soon as one knows about graphs of functions, one can already explain quite a bit. Because, you see, the “layers” in a neural net are actually just functions, that take numbers in, and put numbers out.

Take layer 2 of LeNet. It’s essentially just a simple `Ramp` function, which we can immediately plot (and, yes, it looks like a ramp):

Neural nets don’t typically just deal with individual numbers, though. They deal with arrays (or “tensors”) of numbers—represented in the Wolfram Language as nested lists. And each layer takes an array of numbers in, and puts an array of numbers out. Here’s a typical single layer:

This particular layer is set up to take 2 numbers as input, and put 4 numbers out:

It might seem to be doing something quite “random”, and actually it is. Because the actual function the layer is implementing is determined by yet another array of numbers, or “weights”—which `NetInitialize` here just sets randomly. Here’s what it set them to in this particular case:

Why is any of this useful? Well, the crucial point is that what `NetTrain` does is to progressively tweak the weights in each layer of a neural network to try to get the overall behavior of the net to match the training examples you gave.

There are two immediate issues, though. First, the structure of the network has to be such that it’s possible to get the behavior you want by using some appropriate set of weights. And second, there has to be some way to progressively tweak weights so as to get to appropriate values.

Well, it turns out a single `LinearLayer` like the one above can’t do anything interesting. Here’s a contour plot of (the first element of) its output, as a function of its two inputs. And as the name `LinearLayer` might suggest, we always get something flat and linear out:

But here’s the big discovery that makes neural nets useful: if we chain together several layers, it’s easy to get something much more complicated. (And, yes, in the Wolfram Language outputs from one layer get knitted into inputs to the next layer in a nice, automatic way.) Here’s an example with 4 layers—two linear layers and two ramps:

And now when we plot the function, it’s more complicated:

We can actually also look at an even simpler case—of a neural net with 3 layers, and just one number as final output. (For technical reasons, it’s nice to still have 2 inputs, though we’ll always set one of those inputs to the constant value of 1.)

Here’s what this particular network does as a function of its input:

Inside the network, there’s an array of 3 numbers being generated—and it turns out that “3” causes there to be at most 3 (+1) distinct linear parts in the function. Increase the 3 to 100, and things can get more complicated:

Now, the point is that this is in a sense a “random function”, determined by the particular random weights picked by `NetInitialize`. If we run `NetInitialize` a bunch of times, we’ll get a bunch of different results:

But the big question is: can we find an instance of this “random function” that’s useful for whatever we’re trying to do? Or, more particularly, can we find a random function that reproduces particular training examples?

Let’s imagine that our training examples give the values of the function at the dots in this plot (by the way, the setup here is more like machine learning in the style of `Predict` than `Classify`):

Here’s an instance of our network again:

And here’s a plot of what it initially does over the range of the training examples (and, yes, it’s obviously completely wrong):

Well, let’s just try training our network on our training data using `NetTrain`:

After about 20 seconds of training on my computer, there’s some vague sign that we’re beginning to reproduce at least some aspects of the original training data. But it’s at best slow going—and it’s not clear what’s eventually going to happen.

It’s a frontier question in neural net research just what structure of net will work best in any particular case (yes, we’re working on this question). But here let’s just try a slightly more complicated network:

Random instances of this network don’t give very different results from our last network (though the presence of that `Tanh` layer makes the functions a bit smoother):

But now let’s do some training (data was defined above):

And here’s the result—which is surprisingly decent:

In fact, if we compare it to our original training data we see that the training values lie right on the function that the neural net produced:

Here’s what happened during the training process. The neural net effectively “tried out” a bunch of different possibilities, finally settling on the result here:

In what sense is the result “correct”? Well, it fits the training examples, and that’s really all we can ask. Because that’s all the input we gave. How it “interpolates” between the training examples is really its own business. We’d like it to learn to “generalize” from the data it’s given—but it can’t really deduce much about the whole distribution of the data from the few points it’s being given here, so the kind of smooth interpolation it’s doing is as good as anything.

Outside the range of the training values, the neural net does what seem to be fairly random things—but again, there’s no “right answer” so one can’t really fault it:

But the fact that with the arbitrariness and messiness of our original neural net, we were able to successfully train it at all is quite remarkable. Neural nets of pretty much the type we’re talking about here had actually been studied for more than 60 years—but until the modern “deep learning revolution” nobody knew that it was going to be practical to train them for real problems.

But now—particularly with everything we have now in the Wolfram Language—it’s easy for anyone to do this.

Modern machine learning is very new—so even many of the obvious experiments haven’t been tried yet. But with our whole Wolfram Language setup there’s a lot that even middle schoolers can do. For example (and I admit I’m curious about this as I write this post): one can ask just how much something like the tiny neural net we were studying can learn.

Here’s a plot of the lengths of the first 60 Roman numerals:

After a small amount of training, here’s what the network managed to reproduce:

And one might think that maybe this is the best it’ll ever do. But I was curious if it could eventually do better—and so I just let it train for 2 minutes on my computer. And here’s the considerably better result that came out:

I think I can see why this particular thing works the way it does. But seeing it suggests all sorts of new questions to pursue. But to me the most exciting point is the overarching one of just how wide open this territory is—and how easy it is now to explore it.

Yes, there are plenty of technical details—some fundamental, some superficial. But transcending all of these, there’s intuition to be developed. And that’s something that can perfectly well start with the middle schoolers…

]]>I’m pleased to announce that as of today, the Wolfram Data Repository is officially launched! It’s been a long road. I actually initiated the project a decade ago—but it’s only now, with all sorts of innovations in the Wolfram Language and its symbolic ways of representing data, as well as with the arrival of the Wolfram Cloud, that all the pieces are finally in place to make a true computable data repository that works the way I think it should.

It’s happened to me a zillion times: I’m reading a paper or something, and I come across an interesting table or plot. And I think to myself: “I’d really like to get the data behind that, to try some things out”. But how can I get the data?

If I’m lucky there’ll be a link somewhere in the paper. But it’s usually a frustrating experience to follow it. Because even if there’s data there (and often there actually isn’t), it’s almost never in a form where one can readily use it. It’s usually quite raw—and often hard to decode, and perhaps even intertwined with text. And even if I can see the data I want, I almost always find myself threading my way through footnotes to figure out what’s going on with it. And in the end I usually just decide it’s too much trouble to actually pull out the data I want.

And I suppose one might think that this is just par for the course in working with data. But in modern times, we have a great counterexample: the Wolfram Language. It’s been one of my goals with the Wolfram Language to build into it as much data as possible—and make all of that data immediately usable and computable. And I have to say that it’s worked out great. Whether you need the mass of Jupiter, or the masses of all known exoplanets, or Alan Turing’s date of birth—or a trillion much more obscure things—you just ask for them in the language, and you’ll get them in a form where you can immediately compute with them.

Here’s the mass of Jupiter (and, yes, one can use “Wolfram|Alpha-style” natural language to ask for it):

Dividing it by the mass of the Earth immediately works:

Here’s a histogram of the masses of known exoplanets, divided by the mass of Jupiter:

And here, for good measure, is Alan Turing’s date of birth, in an immediately computable form:

Of course, it’s taken many years and lots of work to make everything this smooth, and to get to the point where all those thousands of different kinds of data are fully integrated into the Wolfram Language—and Wolfram|Alpha.

But what about other data—say data from some new study or experiment? It’s easy to upload it someplace in some raw form. But the challenge is to make the data actually useful.

And that’s where the new Wolfram Data Repository comes in. Its idea is to leverage everything we’ve done with the Wolfram Language—and Wolfram|Alpha, and the Wolfram Cloud—to make it as easy as possible to make data as broadly usable and computable as possible.

There are many parts to this. But let me state our basic goal. I want it to be the case that if someone is dealing with data they understand well, then they should be able to prepare that data for the Wolfram Data Repository in as little as 30 minutes—and then have that data be something that other people can readily use and compute with.

It’s important to set expectations. Making data fully computable—to the standard of what’s built into the Wolfram Language—is extremely hard. But there’s a lower standard that still makes data extremely useful for many purposes. And what’s important about the Wolfram Data Repository (and the technology around it) is it now makes that standard easy to achieve—with the result that it’s now practical to publish data in a form that can really be used by many people.

Each item published in the Wolfram Data Repository gets its own webpage. Here, for example, is the page for a public dataset about meteorite landings:

At the top is some general information about the dataset. But then there’s a piece of a Wolfram Notebook illustrating how to use the dataset in the Wolfram Language. And by looking at this notebook, one can start to see some of the real power of the Wolfram Data Repository.

One thing to notice is that it’s very easy to get the data. All you do is ask for `ResourceData["Meteorite Landings"]`. And whether you’re using the Wolfram Language on a desktop or in the cloud, this will give you a nice symbolic representation of data about 45716 meteorite landings (and, yes, the data is carefully cached so this is as fast as possible, etc.):

And then the important thing is that you can immediately start to do whatever computation you want on that dataset. As an example, this takes the `"Coordinates"` element from all rows, then takes a random sample of 1000 results, and geo plots them:

Many things have to come together for this to work. First, the data has to be reliably accessible—as it is in the Wolfram Cloud. Second, one has to be able to tell where the coordinates are—which is easy if one can see the dataset in a Wolfram Notebook. And finally, the coordinates have to be in a form in which they can immediately be computed with.

This last point is critical. Just storing the textual form of a coordinate—as one might in something like a spreadsheet—isn’t good enough. One has to have it in a computable form. And needless to say, the Wolfram Language has such a form for geo coordinates: the symbolic construct `GeoPosition[{`*lat*`,` *lon*`}]`.

There are other things one can immediately see from the meteorites dataset too. Like notice there’s a `"Mass"` column. And because we’re using the Wolfram Language, masses don’t have to just be numbers; they can be symbolic `Quantity` objects that correctly include their units. There’s also a `"Year"` column in the data, and again, each year is represented by an actual, computable, symbolic `DateObject` construct.

There are lots of different kinds of possible data, and one needs a sophisticated data ontology to handle them. But that’s exactly what we’ve built for the Wolfram Language, and for Wolfram|Alpha, and it’s now been very thoroughly tested. It involves 10,000 kinds of units, and tens of millions of “core entities”, like cities and chemicals and so on. We call it the Wolfram Data Framework (WDF)—and it’s one of the things that makes the Wolfram Data Repository possible.

Today is the initial launch of the Wolfram Data Repository, and to get ready for this launch we’ve been adding sample content to the repository for several months. Some of what we’ve added are “obvious” famous datasets. Some are datasets that we found for some reason interesting, or curious. And some are datasets that we created ourselves—and in some cases that I created myself, for example, in the course of writing my book *A New Kind of Science*.

There’s plenty already in the Wolfram Data Repository that’ll immediately be useful in a variety of applications. But in a sense what’s there now is just an example of what can be there—and the kinds of things we hope and expect will be contributed by many other people and organizations.

The fact that the Wolfram Data Repository is built on top of our Wolfram Language technology stack immediately gives it great generality—and means that it can handle data of any kind. It’s not just tables of numerical data as one might have in a spreadsheet or simple database. It’s data of any type and structure, in any possible combination or arrangement.

There are time series:

There are training sets for machine learning:

There’s gridded data:

There’s the text of many books:

There’s geospatial data:

Many of the data resources currently in the Wolfram Data Repository are quite tabular in nature. But unlike traditional spreadsheets or tables in databases, they’re not restricted to having just one level of rows and columns—because they’re represented using symbolic Wolfram Language `Dataset` constructs, which can handle arbitrarily ragged structures, of any depth.

But what about data that normally lives in relational or graph databases? Well, there’s a construct called `EntityStore` that was recently added to the Wolfram Language. We’ve actually been using something like it for years inside Wolfram|Alpha. But what `EntityStore` now does is to let you set up arbitrary networks of entities, properties and values, right in the Wolfram Language. It typically takes more curation than setting up something like a `Dataset`—but the result is a very convenient representation of knowledge, on which all the same functions can be used as with built-in Wolfram Language knowledge.

Here’s a data resource that’s an entity store:

This adds the entity stores to the list of entity stores to be used automatically:

Now here are 5 random entities of type `"MoMAArtist"` from the entity store:

For each artist, one can extract a dataset of values:

This queries the entity store to find artists with the most recent birth dates:

The Wolfram Data Repository is built on top of a new, very general thing in the Wolfram Language called the “resource system”. (Yes, expect all sorts of other repository and marketplace-like things to be rolling out shortly.)

The resource system has “resource objects”, that are stored in the cloud (using `CloudObject`), then automatically downloaded and cached on the desktop if necessary (using `LocalObject`). Each `ResourceObject` contains both primary content and metadata. For the Wolfram Data Repository, the primary content is data, which you can access using `ResourceData`.

The Wolfram Data Repository that we’re launching today is a public resource, that lives in the public Wolfram Cloud. But we’re also going to be rolling out private Wolfram Data Repositories, that can be run in Enterprise Private Clouds—and indeed inside our own company we’ve already set up several private data repositories, that contain internal data for our company.

There’s no limit in principle on the size of the data that can be stored in the Wolfram Data Repository. But for now, the “plumbing” is optimized for data that’s at most about a few gigabytes in size—and indeed the existing examples in the Wolfram Data Repository make it clear that an awful lot of useful data never even gets bigger than a few megabytes in size.

The Wolfram Data Repository is primarily intended for the case of definitive data that’s not continually changing. For data that’s constantly flowing in—say from IoT devices—we released last year the Wolfram Data Drop. Both Data Repository and Data Drop are deeply integrated into the Wolfram Language, and through our resource system, there’ll be some variants and combinations coming in the future.

Our goal with the Wolfram Data Repository is to provide a central place for data from all sorts of organizations to live—in such a way that it can readily be found and used.

Each entry in the Wolfram Data Repository has an associated webpage, which describes the data it contains, and gives examples that can immediately be run in the Wolfram Cloud (or downloaded to the desktop).

On the webpage for each repository entry (and in the `ResourceObject` that represents it), there’s also metadata, for indexing and searching—including standard Dublin Core bibliographic data. To make it easier to refer to the Wolfram Data Repository entries, every entry also has a unique DOI.

The way we’re managing the Wolfram Data Repository, every entry also has a unique readable registered name, that’s used both for the URL of its webpage, and for the specification of the `ResourceObject` that represents the entry.

It’s extremely easy to use data from the Wolfram Data Repository inside a Wolfram Notebook, or indeed in any Wolfram Language program. The data is ultimately stored in the Wolfram Cloud. But you can always download it—for example right from the webpage for any repository entry.

The richest and most useful form in which to get the data is the Wolfram Language or the Wolfram Data Framework (WDF)—either in ASCII or in binary. But we’re also setting it up so you can download in other formats, like JSON (and in suitable cases CSV, TXT, PNG, etc.) just by pressing a button.

Of course, even formats like JSON don’t have native ways to represent entities, or quantities with units, or dates, or geo positions—or all those other things that WDF and the Wolfram Data Repository deal with. So if you really want to handle data in its full form, it’s much better to work directly in the Wolfram Language. But then with the Wolfram Language you can always process some slice of the data into some simpler form that does makes sense to export in a lower-level format.

The Wolfram Data Repository as we’re releasing it today is a platform for publishing data to the world. And to get it started, we’ve put in about 500 sample entries. But starting today we’re accepting contributions from anyone. We’re going to review and vet contributions much like we’ve done for the past decade for the Wolfram Demonstrations Project. And we’re going to emphasize contributions and data that we feel are of general interest.

But the technology of the Wolfram Data Repository—and the resource system that underlies it—is quite general, and allows people not just to publish data freely to the world, but also to share data in a more controlled fashion. The way it works is that people prepare their data just like they would for submission to the public Wolfram Data Repository. But then instead of actually submitting it, they just deploy it to their own Wolfram Cloud accounts, giving access to whomever they want.

And in fact, the general workflow is that even when people are submitting to the public Wolfram Data Repository, we’re going to expect them to have first deployed their data to their own Wolfram Cloud accounts. And as soon as they do that, they’ll get webpages and everything—just like in the public Wolfram Data Repository.

OK, so how does one create a repository entry? You can either do it programmatically using Wolfram Language code, or do it more interactively using Wolfram Notebooks. Let’s talk about the notebook way first.

You start by getting a template notebook. You can either do this through the menu item `File > New > Data Resource`, or you can use `CreateNotebook["DataResource"]`. Either way, you’ll get something that looks like this:

Basically it’s then a question of “filling out the form”. A very important section is the one that actually provides the content for the resource:

Yes, it’s Wolfram Language code—and what’s nice is that it’s flexible enough to allow for basically any content you want. You can either just enter the content directly in the notebook, or you can have the notebook refer to a local file, or to a cloud object you have.

An important part of the Construction Notebook (at least if you want to have a nice webpage for your data) is the section that lets you give examples. When the examples are actually put up on the webpage, they’ll reference the data resource you’re creating. But when you’re filling in the Construction Notebook the resource hasn’t been created yet. The symbolic character of the Wolfram Language comes to the rescue, though. Because it lets you reference the content of the data resource symbolically as `$$Data` in the inputs that’ll be displayed, but lets you set `$$Data` to actual data when you’re working in the Construction Notebook to build up the examples.

Alright, so once you’ve filled out the Construction Notebook, what do you do? There are two initial choices: set up the resource locally on your computer, or set it up in the cloud:

And then, if you’re ready, you can actually submit your resource for publication in the public Wolfram Data Repository (yes, you need to get a Publisher ID, so your resource can be associated with your organization rather than just with your personal account):

It’s often convenient to set up resources in notebooks. But like everything else in our technology stack, there’s a programmatic Wolfram Language way to do it too—and sometimes this is what will be best.

Remember that everything that is going to be in the Wolfram Data Repository is ultimately a `ResourceObject`. And a `ResourceObject`—like everything else in the Wolfram Language—is just a symbolic expression, which happens to contain an association that gives the content and metadata of the resource object.

Well, once you’ve created an appropriate `ResourceObject`, you can just deploy it to the cloud using `CloudDeploy`. And when you do this, a private webpage associated with your cloud account will automatically be created. That webpage will in turn correspond to a `CloudObject`. And by setting the permissions of that cloud object, you can determine who will be able to look at the webpage, and who will be able to get the data that’s associated with it.

When you’ve got a `ResourceObject`, you can submit it to the public Wolfram Data Repository just by using `ResourceSubmit`.

By the way, all this stuff works not just for the main Wolfram Data Repository in the public Wolfram Cloud, but also for data repositories in private clouds. The administrator of an Enterprise Private Cloud can decide how they want to vet data resources that are submitted (and how they want to manage things like name collisions)—though often they may choose just to publish any resource that’s submitted.

The procedure we’ve designed for vetting and editing resources for the public Wolfram Data Repository is quite elaborate—though in any given case we expect it to run quickly. It involves doing automated tests on the incoming data and examples—and then ensuring that these continue working as changes are made, for example in subsequent versions of the Wolfram Language. Administrators of private clouds definitely don’t have to use this procedure—but we’ll be making our tools available if they want to.

OK, so let’s say there’s a data resource in the Wolfram Data Repository. How can it actually be used to create a data-backed publication? The most obvious answer is just for the publication to include a link to the webpage for the data resource in the Wolfram Data Repository. And once people go to the page, it immediately shows them how to access the data in the Wolfram Language, use it in the Wolfram Open Cloud, download it, or whatever.

But what about an actual visualization or whatever that appears in the paper? How can people know how to make it? One possibility is that the visualization can just be included among the examples on the webpage for the data resource. But there’s also a more direct way, which uses Source Links in the Wolfram Cloud.

Here’s how it works. You create a Wolfram Notebook that takes data from the Wolfram Data Repository and creates the visualization:

Then you deploy this visualization to the Wolfram Cloud—either using Wolfram Language functions like `CloudDeploy` and `EmbedCode`, or using menu items. But when you do the deployment, you say to include a source link (`SourceLink->Automatic` in the Wolfram Language). And this means that when you get an embeddable graphic, it comes with a source link that takes you back to the notebook that made the graphic:

So if someone is reading along and they get to that graphic, they can just follow its source link to see how it was made, and to see how it accesses data from the Wolfram Data Repository. With the Wolfram Data Repository you can do data-backed publishing; with source links you can also do full notebook-backed publishing.

Now that we’ve talked a bit about how the Wolfram Data Repository works, let’s talk again about why it’s important—and why having data in it is so valuable.

The #1 reason is simple: it makes data immediately useful, and computable.

There’s nice, easy access to the data (just use `ResourceData["..."]`). But the really important—and unique—thing is that data in the Wolfram Data Repository is stored in a uniform, symbolic way, as WDF, leveraging everything we’ve done with data over the course of so many years in the Wolfram Language and Wolfram|Alpha.

Why is it good to have data in WDF? First, because in WDF the meaning of everything is explicit: whether it’s an entity, or quantity, or geo position, or whatever, it’s a symbolic element that’s been carefully designed and documented. (And it’s not just a disembodied collection of numbers or strings.) And there’s another important thing: data in WDF is already in precisely the form it’s needed for one to be able to immediately visualize, analyze or otherwise compute with it using any of the many thousands of functions that are built into the Wolfram Language.

Wolfram Notebooks are also an important part of the picture—because they make it easy to show how to work with the data, and give immediately runnable examples. Also critical is the fact that the Wolfram Language is so succinct and easy to read—because that’s what makes it possible to give standalone examples that people can readily understand, modify and incorporate into their own work.

In many cases using the Wolfram Data Repository will consist of identifying some data resource (say through a link from a document), then using the Wolfram Language in Wolfram Notebooks to explore the data in it. But the Wolfram Data Repository is fully integrated into the Wolfram Language, so it can be used wherever the language is used. Which means the data from the Wolfram Data Repository can be used not just in the cloud or on the desktop, but also in servers and so on. And, for example, it can also be used in APIs or scheduled tasks, using the exact same `ResourceData` functions as ever.

The most common way the Wolfram Data Repository will be used is one resource at a time. But what’s really great about the uniformity and standardization that WDF provides is that it allows different data resources to be used together: those dates or geo positions mean the same thing even in different data resources, so they can immediately be put together in the same analysis, visualization, or whatever.

The Wolfram Data Repository builds on the whole technology stack that we’ve been assembling for the past three decades. In some ways it’s just a sophisticated piece of infrastructure that makes a lot of things easier to do. But I can already tell that its implications go far beyond that—and that it’s going to have a qualitative effect on the extent to which people can successfully share and reuse a wide range of kinds of data.

It’s a big win to have data in the Wolfram Data Repository. But what’s involved in getting it there? There’s almost always a certain amount of data curation required.

Let’s take a look again at the meteorite landings dataset I showed earlier in this post. It started from a collection of data made available in a nicely organized way by NASA. (Quite often one has to scrape webpages or PDFs; this is a case where the data happens to be set up to be downloadable in a variety of convenient formats.)

As is fairly typical, the basic elements of the data here are numbers and strings. So the first thing to do is to figure out how to map these to meaningful symbolic constructs in WDF. For example, the “mass” column is labeled as being “(g)”, i.e. in grams—so each element in it should get converted to `Quantity[`*value*`,"Grams"]`. It’s a little trickier, though, because for some rows—corresponding to some meteorites—the value is just blank, presumably because it isn’t known.

So how should that be represented? Well, because the Wolfram Language is symbolic it’s pretty easy. And in fact there’s a standard symbolic construct `Missing[...]` for indicating missing data, which is handled consistently in analysis and visualization functions.

As we start to look further into the dataset, we see all sorts of other things. There’s a column labeled “year”. OK, we can convert that into `DateObject[{`*value*`}]`—though we need to be careful about any BC dates (how would they appear in the raw data?).

Next there are columns “reclat” and “reclong”, as well as a column called “GeoLocation” that seems to combine these, but with numbers quoted a different precision. A little bit of searching suggests that we should just take reclat and reclong as the latitude and longitude of the meteorite—then convert these into the symbolic form `GeoPosition[{`*lat*`,`*lon*`}]`.

To do this in practice, we’d start by just importing all the data:

OK, let’s extract a sample row:

Already there’s something unexpected: the date isn’t just the year, but instead it’s a precise time. So this needs to be converted:

Now we’ve got to reset this to correspond only to a date at a granularity of a year:

Here is the geo position:

And we can keep going, gradually building up code that can be applied to each row of the imported data. In practice there are often little things that go wrong. There’s something missing in some row. There’s an extra piece of text (a “footnote”) somewhere. There’s something in the data that got misinterpreted as a delimiter when the data was provided for download. Each one of these needs to be handled—preferably with as much automation as possible.

But in the end we have a big list of rows, each of which needs to be assembled into an association, then all combined to make a `Dataset` object that can be checked to see if it’s good to go into the Wolfram Data Repository.

The example above is fairly typical of basic curation that can be done in less than 30 minutes by any decently skilled user of the Wolfram Language. (A person who’s absorbed my book *An Elementary Introduction to the Wolfram Language* should, for example, be able to do it.)

It’s a fairly simple example—where notably the original form of the data was fairly clean. But even in this case it’s worth understanding what hasn’t been done. For example, look at the column labeled `"Classification"` in the final dataset. It’s got a bunch of strings in it. And, yes, we can do something like make a word cloud of these strings:

But to really make these values computable, we’d have to do more work. We’d have to figure out some kind of symbolic representation for meteorite classification, then we’d have to do curation (and undoubtedly ask some meteorite experts) to fit everything nicely into that representation. The advantage of doing this is that we could then ask questions about those values (“what meteorites are above L3?”), and expect to compute answers. But there’s plenty we can already do with this data resource without that.

My experience in general has been that there’s a definite hierarchy of effort and payoff in getting data to be computable at different levels—starting with the data just existing in digital form, and ending with the data being cleanly computable enough that it can be fully integrated in the core Wolfram Language, and used for repeated, systematic computations.

Let’s talk about this hierarchy a bit.

The zeroth thing, of course, is that the data has to exist. And the next thing is that it has to be in digital form. If it started on handwritten index cards, for example, it had better have been entered into a document or spreadsheet or something.

But then the next issue is: how are people supposed to get access to that document or spreadsheet? Well, a good answer is that it should be in some kind of accessible cloud—perhaps referenced with a definite URI. And for a lot of data repositories that exist out there, just making the data accessible like this is the end of the story.

But one has to go a lot further to make the data actually useful. The next step is typically to make sure that the data is arranged in some definite structure. It might be a set of rows and columns, or it might be something more elaborate, and, say, hierarchical. But the point is to have a definite, known structure.

In the Wolfram Language, it’s typically trivial to take data that’s stored in any reasonable format, and use `Import` to get it into the Wolfram Language, arranged in some appropriate way. (As I’ll talk about later, it might be a `Dataset`, it might be an `EntityStore`, it might just be a list of `Image` objects, or it might be all sorts of other things.)

But, OK, now things start getting more difficult. We need to be able to recognize, say, that such-and-such a column has entries representing countries, or pairs of dates, or animal species, or whatever. `SemanticImport` uses machine learning and does a decent job of automatically importing many kinds of data. But there are often things that have to be fixed. How exactly is missing data represented? Are there extra annotations that get in the way of automatic interpretation? This is where one starts needing experts, who really understand the data.

But let’s say one’s got through this stage. Well, then in my experience, the best thing to do is to start visualizing the data. And very often one will immediately see things that are horribly wrong. Some particular quantity was represented in several inconsistent ways in the data. Maybe there was some obvious transcription or other error. And so on. But with luck it’s fairly easy to transform the data to handle the obvious issues—though to actually get it right almost always requires someone who is an expert on the data.

What comes out of this process is typically very useful for many purposes—and it’s the level of curation that we’re expecting for things submitted to the Wolfram Data Repository.

It’ll be possible to do all sorts of analysis and visualization and other things with data in this form.

But if one wants, for example, to actually integrate the data into Wolfram|Alpha, there’s considerably more that has to be done. For a start, everything that can realistically be represented symbolically has to be represented symbolically. It’s not good enough to have random strings giving values of things—because one can’t ask systematic questions about those. And this typically requires inventing systematic ways to represent new kinds of concepts in the world—like the `"Classification"` for meteorites.

Wolfram|Alpha works by taking natural language input. So the next issue is: when there’s something in the data that can be referred to, how do people refer to it in natural language? Often there’ll be a whole collection of names for something, with all sorts of variations. One has to algorithmically capture all of the possibilities.

Next, one has to think about what kinds of questions will be asked about the data. In Wolfram|Alpha, the fact that the questions get asked in natural language forces a certain kind of simplicity on them. But it makes one also need to figure out just what the linguistics of the questions can be (and typically this is much more complicated than the linguistics for entities or other definite things). And then—and this is often a very difficult part—one has to figure out what people want to compute, and how they want to compute it.

At least in the world of Wolfram|Alpha, it turns out to be quite rare for people just to ask for raw pieces of data. They want answers to questions—that have to be computed with models, or methods, or algorithms, from the underlying data. For meteorites, they might want to know not the raw information about when a meteorite fell, but compute the weathering of the meteorite, based on when it fell, what climate it’s in, what it’s made of, and so on. And to have data successfully be integrated into Wolfram|Alpha, those kinds of computations all need to be there.

For full Wolfram|Alpha there’s even more. Not only does one have to be able to give a single answer, one has to be able to generate a whole report, that includes related answers, and presents them in a well-organized way.

It’s ultimately a lot of work. There are very few domains that have been added to Wolfram|Alpha with less than a few skilled person-months of work. And there are plenty of domains that have taken person-years or tens of person-years. And to get the right answers, there always has to be a domain expert involved.

Getting data integrated into Wolfram|Alpha is a significant achievement. But there’s further one can go—and indeed to integrate data into the Wolfram Language one has to go further. In Wolfram|Alpha people are asking one-off questions—and the goal is to do as well as possible on individual questions. But if there’s data in the Wolfram Language, people won’t just ask one-off questions with it: they’ll also do large-scale systematic computations. And this demands a much greater level of consistency and completeness—which in my experience rarely takes less than person-years per domain to achieve.

But OK. So where does this leave the Wolfram Data Repository? Well, the good news is that all that work we’ve put into Wolfram|Alpha and the Wolfram Language can be leveraged for the Wolfram Data Repository. It would take huge amounts of work to achieve what’s needed to actually integrate data into Wolfram|Alpha or the Wolfram Language. But given all the technology we have, it takes very modest amounts of work to make data already very useful. And that’s what the Wolfram Data Repository is about.

With the Wolfram Data Repository (and Wolfram Notebooks) there’s finally a great way to do true data-backed publishing—and to ensure that data can be made available in an immediately useful and computable way.

For at least a decade there’s been lots of interest in sharing data in areas like research and government. And there’ve been all sorts of data repositories created—often with good software engineering—with the result that instead of data just sitting on someone’s local computer, it’s now pretty common for it to be uploaded to a central server or cloud location.

But the problem has been that the data in these repositories is almost always in a quite raw form—and not set up to be generally meaningful and computable. And in the past—except in very specific domains—there’s been no really good way to do this, at least in any generality. But the point of the Wolfram Data Repository is to use all the development we’ve done on the Wolfram Language and WDF to finally be able to provide a framework for having data in an immediately computable form.

The effect is dramatic. One goes from a situation where people are routinely getting frustrated trying to make use of data to one in which data is immediately and readily usable. Often there’s been lots of investment and years of painstaking work put into accumulating some particular set of data. And it’s often sad to see how little the data actually gets used—even though it’s in principle accessible to anyone. But I’m hoping that the Wolfram Data Repository will provide a way to change this—by allowing data not just to be accessible, but also computable, and easy for anyone to immediately and routinely use as part of their work.

There’s great value to having data be computable—but there’s also some cost to making it so. Of course, if one’s just collecting the data now, and particularly if it’s coming from automated sources, like networks of sensors, then one can just set it up to be in nice, computable WDF right from the start (say by using the data semantics layer of the Wolfram Data Drop). But at least for a while there’s going to still be a lot of data that’s in the form of things like spreadsheets and traditional databases—-that don’t even have the technology to support the kinds of structures one would need to directly represent WDF and computable data.

So that means that there’ll inevitably have to be some effort put into curating the data to make it computable. Of course, with everything that’s now in the Wolfram Language, the level of tools available for curation has become extremely high. But to do curation properly, there’s always some level of human effort—and some expert input—that’s required. And a key question in understanding the post-Wolfram-Data-Repository data publishing ecosystem is who is actually going to do this work.

In a first approximation, it could be the original producers of the data—or it could be professional or other “curation specialists”—or some combination. There are advantages and disadvantages to all of these possibilities. But I suspect that at least for things like research data it’ll be most efficient to start with the original producers of the data.

The situation now with data curation is a little similar to the historical situation with document production. Back when I was first doing science (yes, in the 1970s) people handwrote papers, then gave them to professional typists to type. Once typed, papers would be submitted to publishers, who would then get professional copyeditors to copyedit them, and typesetters to typeset them for printing. It was all quite time consuming and expensive. But over the course of the 1980s, authors began to learn to type their own papers on a computer—and then started just uploading them directly to servers, in effect putting them immediately in publishable form.

It’s not a perfect analogy, but in both data curation and document editing there are issues of structure and formatting—and then there are issues that require actual understanding of the content. (Sometimes there are also more global “policy” issues too.) And for producing computable data, as for producing documents, almost always the most efficient thing will be to start with authors “typing their own papers”—or in the case of data, putting their data into WDF themselves.

Of course, to do this requires learning at least a little about computable data, and about how to do curation. And to assist with this we’re working with various groups to develop materials and provide training about such things. Part of what has to be communicated is about mechanics: how to move data, convert formats, and so on. But part of it is also about principles—and about how to make the best judgement calls in setting up data that’s computable.

We’re planning to organize “curate-a-thons” where people who know the Wolfram Language and have experience with WDF data curation can pair up with people who understand particular datasets—and hopefully quickly get all sorts of data that they may have accumulated over decades into computable form—and into the Wolfram Data Repository.

In the end I’m confident that a very wide range of people (not just techies, but also humanities people and so on) will be able to become proficient at data curation with the Wolfram Language. But I expect there’ll always be a certain mixture of “type it yourself” and “have someone type it for you” approaches to data curation. Some people will make their data computable themselves—or will have someone right there in their lab or whatever who does. And some people will instead rely on outside providers to do it.

Who will these providers be? There’ll be individuals or companies set up much like the ones who provide editing and publishing services today. And to support this we’re planning a “Certified Data Curator” program to help define consistent standards for people who will work with the originators of a wide range of different kinds of data putting it into computable form.

But in additional to individuals or specific “curation companies”, there are at least two other kinds of entities that have the potential to be major facilitators of making data computable.

The first is research libraries. The role of libraries at many universities is somewhat in flux these days. But something potentially very important for them to do is to provide a central place for organizing—and making computable—data from the university and beyond. And in many ways this is just a modern analog of traditional library activities like archiving and cataloging.

It might involve the library actually having a private cloud version of the Wolfram Data Repository—and it might involve the library having its own staff to do curation. Or it might just involve the library providing advice. But I’ve found there’s quite a bit of enthusiasm in the library community for this kind of direction (and it’s perhaps an interesting sign that at our company people involved in data curation have often originally been trained in library science).

In addition to libraries, another type of organization that should be involved in making data computable is publishing companies. Some might say that publishing companies have had it a bit easy in the last couple of decades. Back in the day, every paper they published involved all sorts of production work, taking it from manuscript to final typeset version. But for years now, authors have been delivering their papers in digital forms that publishers don’t have to do much work on.

With data, though, there’s again something for publishers to do, and again a place for them to potentially add great value. Authors can pretty much put raw data into public repositories for themselves. But what would make publishers visibly add value is for them to process (or “edit”) the data—putting in the work to make it computable. The investment and processes will be quite similar to what was involved on the text side in the past—it’s just that now instead of learning about phototypesetting systems, publishers should be learning about WDF and the Wolfram Language.

It’s worth saying that as of today all data that we accept into the Wolfram Data Repository is being made freely available. But we’re anticipating in the near future we’ll also incorporate a marketplace in which data can be bought and sold (and even potentially have meaningful DRM, at least if it’s restricted to being used in the Wolfram Language). It’ll also be possible to have a private cloud version of the Wolfram Data Repository—in which whatever organization that runs it can set up whatever rules it wants about contributions, subscriptions and access.

One feature of traditional paper publishing is the sense of permanence it provides: once even just a few hundred printed copies of a paper are on shelves in university libraries around the world, it’s reasonable to assume that the paper is going to be preserved forever. With digital material, preservation is more complicated.

If someone just deploys a data resource to their Wolfram Cloud account, then it can be available to the world—but only so long as the account is maintained. The Wolfram Data Repository, though, is intended to be something much more permanent. Once we’ve accepted a piece of data for the repository, our goal is to ensure that it’ll continue to be available, come what may. It’s an interesting question how best to achieve that, given all sorts of possible future scenarios in the world. But now that the Wolfram Data Repository is finally launched, we’re going to be working with several well-known organizations to make sure that its content is as securely maintained as possible.

The Wolfram Data Repository—and private versions of it—is basically a powerful, enabling technology for making data available in computable form. And sometimes all one wants to do is to make the data available.

But at least in academic publishing, the main point usually isn’t the data. There’s usually a “story to be told”—and the data is just backup for that story. Of course, having that data backing is really important—and potentially quite transformative. Because when one has the data, in computable form, it’s realistic for people to work with it themselves, reproducing or checking the research, and directly building on it themselves.

But, OK, how does the Wolfram Data Repository relate to traditional academic publishing? For our official Wolfram Data Repository we’re going to have definite standards for what we accept—and we’re going to concentrate on data that we think is of general interest or use. We have a whole process for checking the structure of data, and applying software quality assurance methods, as well as expert review, to it.

And, yes, each entry in the Wolfram Data Repository gets a DOI, just like a journal article. But for our official Wolfram Data Repository we’re focused on data—and not the story around it. We don’t see it as our role to check the methods by which the data was obtained, or to decide whether conclusions drawn from it are valid or not.

But given the Wolfram Data Repository, there are lots of new opportunities for data-backed academic journals that do in effect “tell stories”, but now have the infrastructure to back them up with data that can readily be used.

I’m looking forward, for example, to finally making the journal *Complex Systems* that I founded 30 years ago a true data-backed journal. And there are many existing journals where it makes sense to use versions of the Wolfram Data Repository (often in a private cloud) to deliver computable data associated with journal articles.

But what’s also interesting is that now that one can take computable data for granted, there’s a whole new generation of “Journal of Data-Backed ____” journals that become possible—that not only use data from the Wolfram Data Repository, but also actually present their results as Wolfram Notebooks that can immediately be rerun and extended (and can also, for example, contain interactive elements).

I’ve been talking about the Wolfram Data Repository in the context of things like academic journals. But it’s also important in corporate settings. Because it gives a very clean way to have data shared across an organization (or shared with customers, etc.).

Typically in a corporate setting one’s talking about private cloud versions. And of course these can have their own rules about how contributions work, and who can access what. And the data can not only be immediately used in Wolfram Notebooks, but also in automatically generated reports, or instant APIs.

It’s been interesting to see—during the time we’ve been testing the Wolfram Data Repository—just how many applications we’ve found for it within our own company.

There’s information that used to be on webpages, but is now in our private Wolfram Data Repository, and is now immediately usable for computation. There’s information that used to be in databases, and which required serious programming to access, but is now immediately accessible through the Wolfram Language. And there are all sorts of even quite small lists and so on that used to exist only in textual form, but are now computable data in our data repository.

It’s always been my goal to have a truly “computable company”—and putting in place our private Wolfram Data Repository is an important step in achieving this.

In addition to public and corporate uses, there are also great uses of Wolfram Data Repository technology for individuals—and particularly for individual researchers. In my own case, I’ve got huge amounts of data that I’ve collected or generated over the course of my life. I happen to be pretty organized at keeping things—but it’s still usually something of an adventure to remember enough to “bring back to life” data I haven’t dealt with in a decade or more. And in practice I make much less use of older data than I should—even though in many cases it took me immense effort to collect or generate the data in the first place.

But now it’s a different story. Because all I have to do is to upload data once and for all to the Wolfram Data Repository, and then it’s easy for me to get and use the data whenever I want to. Some data (like medical or financial records) I want just for myself, so I use a private cloud version of the Wolfram Data Repository. But other data I’ve been getting uploaded into the public Wolfram Data Repository.

Here’s an example. It comes from a page in my book *A New Kind of Science*:

The page says that by searching about 8 trillion possible systems in the computational universe I found 199 that satisfy some particular criterion. And in the book I show examples of some of these. But where’s the data?

Well, because I’m fairly organized about such things, I can go into my file system, and find the actual Wolfram Notebook from 2001 that generated the picture in the book. And that leads me to a file that contains the raw data—which then takes a very short time to turn into a data resource for the Wolfram Data Repository:

We’ve been systematically mining data from my research going back into the 1980s—even from Mathematica Version 1 notebooks from 1988 (which, yes, still work today). Sometimes the experience is a little less inspiring. Like to find a list of people referenced in the index of *A New Kind of Science*, together with their countries and dates, the best approach seemed to be to scrape the online book website:

And to get a list of the books I used while working on *A New Kind of Science* required going into an ancient FileMaker database. But now all the data—nicely merged with Open Library information deduced from ISBNs—is in a clean WDF form in the Wolfram Data Repository. So I can do such things as immediately make a word cloud of the titles of the books:

Many things have had to come together to make today’s launch of the Wolfram Data Repository possible. In the modern software world it’s easy to build something that takes blobs of data and puts them someplace in the cloud for people to access. But what’s vastly more difficult is to have the data actually be immediately useful—and making that possible is what’s required the whole development of our Wolfram Language and Wolfram Cloud technology stack, which are now the basis for the Wolfram Data Repository.

But now that the Wolfram Data Repository exists—and private versions of it can be set up—there are lots of new opportunities. For the research community, the most obvious is finally being able to do genuine data-backed publication, where one can routinely make underlying data from pieces of research available in a way that people can actually use. There are variants of this in education—making data easy to access and use for educational exercises and projects.

In the corporate world, it’s about making data conveniently available across an organization. And for individuals, it’s about maintaining data in such a way that it can be readily used for computation, and built on.

But in the end, I see the Wolfram Data Repository as a key enabling technology for defining how one can work with data in the future—and I’m excited that after all this time it’s finally now launched and available to everyone.

*To comment, please visit the copy of this post at the Wolfram Blog »*

I’m pleased to announce the release today of Version 11.1 of the Wolfram Language (and Mathematica). As of now, Version 11.1 is what’s running in the Wolfram Cloud—and desktop versions are available for immediate download for Mac, Windows and Linux.

What’s new in Version 11.1? Well, actually a remarkable amount. Here’s a summary:

There’s a lot here. One might think that a .1 release, nearly 29 years after Version 1.0, wouldn’t have much new any more. But that’s not how things work with the Wolfram Language, or with our company. Instead, as we’ve built our technology stack and our procedures, rather than progressively slowing down, we’ve been continually accelerating. And now even something as notionally small as the Version 11.1 release packs an amazing amount of R&D, and new functionality.

There’s one very obvious change in 11.1: the documentation looks different. We’ve spiffed up the design, and on the web we’ve made everything responsive to window width—so it looks good even when it’s in a narrow sidebar in the cloud, or on a phone.

We’ve also introduced some new design elements—like the mini-view of the Details section. Most people like to see examples as soon as they get to a function page. But it’s important not to forget the Details—and the mini-view provides what amounts to a little “ad” for them.

Here’s a word cloud of new functions in Version 11.1:

Altogether there are an impressive 132 new functions—together with another 98 that have been significantly enhanced. These functions represent the finished output of our R&D pipeline in just the few months that have elapsed since Version 11.0 was released.

When we bring out a major “integer” release—like Version 11—we’re typically introducing a bunch of complete, new frameworks. In (supposedly) minor .1 releases like Version 11.1, we’re not aiming for complete new frameworks. Instead, there’s typically new functionality that’s adding to existing frameworks—together with a few (sometimes “experimental”) hints of major new frameworks to come. Oh, and if a complete, new framework does happen to be finished in time for a .1 release, it’ll be there too.

One very hot area in which Version 11.1 makes some big steps forward is neural nets. It’s been exciting over the past few years to see this area advance so quickly in the world at large, and it’s been great to see the Wolfram Language at the very leading edge of what’s being done.

Our goal is to define a very high-level interface to neural nets, that’s completely integrated into the Wolfram Language. Version 11.1 adds some new recently developed building blocks—in particular 30 new types of neural net layers (more than double what was there in 11.0), together with automated support for recurrent nets. The concept is always to let the neural net be specified symbolically in the Wolfram Language, then let the language automatically fill in the details, interface with low-level libraries, etc. It’s something that’s very convenient for ordinary feed-forward networks (tensor sizes are all knitted together automatically, etc.)—but for recurrent nets (with variable-length sequences, etc.) it’s something that’s basically essential if one’s going to avoid lots of low-level programming.

Another crucial feature of neural nets in the Wolfram Language is that it’s set up to be automatic to encode images, text or whatever in an appropriate way. In Version 11.1, `NetEncoder` and `NetDecoder` cover a lot of new cases—extending what’s integrated into the Wolfram Language.

It’s worth saying that underneath the whole integrated symbolic interface, the Wolfram Language is using a very efficient low-level library—currently MXNet—which takes care of optimizing ultimate performance for the latest CPU and GPU configurations. By the way, another feature enhanced in 11.1 is the ability to store complete neural net specifications, complete with encoders, etc. in a portable and reusable .wlnet file.

There’s a lot of power in treating neural nets as symbolic objects. In 11.1 there are now functions like `NetMapOperator` and `NetFoldOperator` that symbolically build up new neural nets. And because the neural nets are symbolic, it’s easy to manipulate them, for example breaking them apart to monitor what they’re doing inside, or systematically comparing the performance of different structures of net.

In some sense, neural net layers are like the machine code of a neural net programming system. In 11.1 there’s a convenient function—`NetModel`—that provides pre-built trained or untrained neural net models. As of today, there are a modest number of famous neural nets included, but we plan to add more every week—surfing the leading edge of what’s being developed in the neural net research community, as well as adding some ideas of our own.

Here’s a simple example of `NetModel` at work:

Now apply the network to some actual data—and see it gets the right answer:

But because the net is specified symbolically, it’s easy to “go inside” and “see what it’s thinking”. Here’s a tiny (but neat) piece of functional programming that visualizes what happens at every layer in the net—and, yes, in the end the first square lights up red to show that the output is 0:

Neural nets are an important method for machine learning. But one of the core principles of the Wolfram Language is to provide highly automated functionality, independent of underlying methods. And in 11.1 there’s a bunch more of this in the area of machine learning. (As it happens, much of it uses the latest deep learning neural net methods, but for users what’s important is what it does, not how it does it.)

My personal favorite new machine learning function in 11.1 is `FeatureSpacePlot`. Give it any collection of objects, and it’ll try to lay them out in an appropriate “feature space”. Like here are the flags of countries in Europe:

What’s particularly neat about `FeatureSpacePlot` is that it’ll immediately use sophisticated pre-trained feature extractors for specific classes of input—like photographs, texts, etc. And there’s also now a `FeatureNearest` function that’s the analog of `Nearest`, but operates in feature space. Oh, and all the stuff with `NetModel` and pre-trained net models immediately flows into these functions, so it becomes trivial, say, to experiment with “meaning spaces”:

Particularly with `NetModel`, there are all sorts of very useful few-line neural net programs that one can construct. But in 11.1 there are also some major new, more infrastructural, machine learning capabilities. Notable examples are `ActiveClassification` and `ActivePrediction`—which build classifiers and predictors by actively sampling a space, learning how to do this as efficiently as possible. There will be lots of end-user applications for `ActiveClassification` and `ActivePrediction`, but for us internally the most immediately interesting thing is that we can use these functions to optimize all sorts of meta-algorithms that are built into the Wolfram Language.

Version 11.0 began the process of making audio—like images—something completely integrated into the Wolfram Language. Version 11.1 continues that process. For example, for desktop systems, it adds `AudioCapture` to immediately capture audio from a microphone on your computer. (Yes, it’s nontrivial to automatically handle out-of-core storage and processing of large audio samples, etc.) Here’s an example of me saying “hello”:

Play Audio

You can immediately take this, and, say, make a cepstrogram (yes, that’s another new audio function in 11.1):

Version 11.1 has quite an assortment of new features for images and visualization. `CurrentImage` got faster and better. `ImageEffect` has lots of new effects added. There are new functions and options to support the latest in computational photography and computational microscopy. And images got even more integrated as first-class objects—that one can for example now immediately do arithmetic with:

Something else with images—that I’ve long wanted—is the ability to take a bitmap image, and find an approximate vector graphics representation of it:

`TextRecognize` has also become significantly stronger—in particular being able to pick out structure in text, like paragraphs and columns and the like.

Oh, and in visualization, there are things like `GeoBubbleChart`, here showing the populations of the largest cities in the US:

There’s lots of little (but nice) stuff too. Like support for arbitrary callouts in pie charts, optimized labeling of discrete histograms and full support of scaling functions for `Plot3D`, etc.

There’s always new data flowing into the Wolfram Knowledgebase, and there’ve also been plenty of completely new things added since 11.0: 130,000+ new types of foods, 250,000+ atomic spectral lines, 12,000+ new mountains, 10,000+ new notable buildings, 300+ types of neurons, 650+ new waterfalls, 200+ new exoplanets (because they’ve recently been discovered), and lots else (not to mention 7,000+ new spelling words). There’s also, for example, much higher resolution geo elevation data—so now a 3D-printable Mount Everest can have much more detail:

Something new in Version 11.1 are integrated external services—that allow built-in functions that work by calling external APIs. Two examples are `WebSearch` and `WebImageSearch`. Here are thumbnail images found by searching the web for “colorful birds”:

For the heck of it, let’s see what `ImageIdentify` thinks they are (oh, and in 11.1. `ImageIdentify` is much more accurate, and you can even play with the network inside it by using `NetModel`):

Since `WebSearch` and `WebImageSearch` use external APIs, users need to pay for them separately. But we’ve set up what we call Service Credits to make this seamless. (Everything’s in the language, of course, so there’s for example `$ServiceCreditsAvailable`.)

There will be quite a few more examples of integrated services in future versions, but in 11.1, beyond web searching, there’s also `TextTranslation`. `WordTranslation` (new in 11.0) handles individual word translation for hundreds of languages; now in 11.1 `TextTranslation` uses external services to also translate complete pieces of text between several tens of languages:

A significant part of our R&D organization is devoted to continuing our three-decade effort to push the frontiers of mathematical and algorithmic computation. So it should come as no surprise that Version 11.1 has all sorts of advances in these areas. There’s space-filling curves, fractal meshes, ways to equidistribute points on a sphere:

There are new kinds of spatial, robust and multivariate statistics. There are Hankel transforms, built-in modular inverses, and more. Even in differentiation, there’s something new: *n*^{th} order derivatives, for symbolic *n*:

Here’s something else about differentiation: there are now functions `RealAbs` and `RealSign` that are versions of `Abs` and `Sign` that are defined only by the real axis, and so can freely be differentiated, without having to give any assumptions about variables.

In Version 10.1, we introduced the function `AnglePath`, that computes a path from successive segments with specified lengths and angles. At some level, `AnglePath` is like an industrial-scale version of Logo (or Scratch) “turtle geometry”. But `AnglePath` has turned out to be surprisingly broadly useful, so for Version 11.1, we’ve generalized it to `AnglePath3D` (and, yes, there are all sorts of subtleties about frames and Euler angles and so on).

When we say “June 23, 1988”, what do we mean? The beginning of that day? The whole 24-hour period from midnight to midnight? Or what? In Version 11.1 we’ve introduced the notion of granularity for dates—so you can say whether a date is supposed to represent a day, a year, a second, a week starting Sunday—or for that matter just an instant in time.

It’s a nice application of the symbolic character of the Wolfram Language—and it solves all sorts of problems in dealing with dates and times. In a way, it’s a little like precision for numbers, but it’s really its own thing. Here for example is how we now represent “the current week”:

Here’s the current decade:

This is the next month from now:

This says we want to start from next month, then add 7 weeks—getting another month:

And here’s the result to the granularity of a month:

Talking of dates, by the way, one of the things that’s coming across the system is the use of `Dated` as a qualifier, for example for properties of entities of the knowledgebase (so this asks for the population of New York City in 1970):

I’m very proud of how smooth the Wolfram Language is to use—and part of how that’s been achieved is that for 30 years we’ve been continually polishing it. We’re always making sure everything fits perfectly together—and we’re always adding little conveniences.

One of our principles is that if there’s a lump of computational work that people repeatedly do, then so long as there’s a good name for it (that people will readily remember, and readily recognize when they see it in a piece of code), it should be inserted as a built-in function. A very simple example in Version 11.1 is `ReverseSort`:

(One might think: what’s the point of this—it’s just `Reverse[Sort[...]]`. But it’s very common to want to map what’s now `ReverseSort` over a bunch of objects, and it’s smoother to be able to say `ReverseSort /@ ...` rather than `Reverse[Sort[#]]& /@ ...` or `Reverse@*Sort /@ ...`).

Another little convenience: `Nearest` now has special ways to specify useful things to return. For example, this gives the distances from 2.7 to the 5 nearest values:

`CellularAutomaton` is a very broad function. Version 11.1 makes it easier to use for common cases by allowing rules to be specified by associations with labeled elements:

We’re always trying to make sure that patterns we’ve established get used as broadly as possible. Like in 11.1, you can use `UpTo` in lots of new places, like in `ImageSize` specifications.

We also always trying to make sure that things are as general as possible. Like `IntegerString` now works not only with the standard representation of integers, but also with traditional ones used for different purposes around the world:

And `IntegerName` can also now handle different types and languages of names:

And there are lots more examples—each making the experience of using the Wolfram Language just a little bit smoother.

If you make a definition list ` x=7`, or

`PersistentValue` lets you specify a name (like `"foo"`), and a `"persistence location"`. (It also allows options like `PersistenceTime` and `ExpirationDate`.) The persistence location can just be `"KernelSession"`—which means that the value lasts only for a single kernel session. But it can also be `"FrontEndSession"`, or `"Local"` (meaning that it should be the same whenever you use the same computer), or `"Cloud"` (meaning that it’s globally synchronized across the cloud).

`PersistentValue` is pretty general. It lets you have values in different places (like different private clouds, for example); then there’s a `$PersistencePath` that defines the order to look at them in, and a `MergingFunction` that specifies how (if at all) the values should be merged.

One of the goals of the Wolfram Language is to be able to interact as broadly as possible with all computational ecosystems. Version 11.1 adds support for the M4A audio format, the .ubj binary JSON format, as well as .ini files and Java .properties files. There’s also a new function, `BinarySerialize`, that converts any Wolfram Language expression into a new binary (“WXF”) form, optimized for speed or size:

`BinaryDeserialize` gets it back:

Version 11.0 introduced `WolframScript`—a command-line interface to the Wolfram Language, running either locally or in the cloud. With `WolframScript` you can create standalone Wolfram Language programs that run from the shell. There are several enhancements to `WolframScript` itself in 11.1, but there’s also now a new `New` > `Script` menu item that gives you a notebook interface for creating .wls (=“Wolfram Language Script”) files to be run by `WolframScript`:

One of the major ways the Wolfram Language has advanced in recent times has been in its deployability. We’ve put a huge amount of work into making sure that the Wolfram Language can be robustly deployed at scale (and there are now lots of examples of successes out in the world).

We make updates to the Wolfram Cloud very frequently (and invisibly), steadily enhancing server performance and user interface capabilities. Along with Version 11.1 we’ve made some major updates. There are a few signs of this in the language.

Like there’s now an option `AutoCopy` that can be set for any cloud object—and that means that every time the object is accessed, one should get a fresh copy of it. This is very useful if, for example, you want to have a notebook that lots of people can separately modify. (“Explore these ideas; here’s a notebook to start from…”, etc.)

`CloudDeploy[APIFunction[...]]` makes it extremely easy to deploy web APIs. In Version 11.1 there are some options to automate aspects of how those APIs behave. For example, there’s `AllowedCloudExtraParameters`, which lets you say that APIs can have parameters like `"_timeout"` or `"_geolocation"` automated. There’s also `AllowedCloudParameterExtensions` (no, it’s not the longest name in the system; that honor currently goes to `MultivariateHypergeometricDistribution`). What `AllowedCloudParameterExtensions` does is to let you say not just ` x=value`, but

Another thing about Version 11.1 is that it’s got various features added to support private instances of the Wolfram Cloud—and our major new Wolfram Enterprise Private Cloud product (with a first version released late last year). For example, in addition to `$WolframID` for the Wolfram Cloud, there’s also `$CloudUserID` that’s generalized to allow authentication on private clouds. And inside the system, there are all sorts of new capabilities associated with “multicloud authentication” and so on. (Yes, it’s a complicated area—but the symbolic character of the Wolfram Language lets one handle it rather beautifully.)

OK, so I’ve summarized some of what’s in 11.1. There’s a lot more I could say. New functions, and new capabilities—each of which is going to be exciting to somebody. But to me it’s actually pretty amazing that I can write this long a post about a .1 release! It’s a great testament to the strength of the R&D pipeline—and to how much can be done with the framework we’ve built in the Wolfram Language over the past 30 years.

We always work on a portfolio of projects—from small ones that get delivered very quickly, to ones that may take a decade or more to mature. Version 11.1 has the results of several multi-year projects (e.g. in machine learning, computational geometry, etc.), and a great many shorter projects. It’s exciting for us to be able to deliver the fruits of our efforts, and I look forward to hearing what people do with Version 11.1—and to seeing successive versions continue to be developed and delivered.

*To comment, please visit the copy of this post at the Wolfram Blog »*

“In the next hour I’m going to try to make a new discovery in mathematics.” So I began a few days ago at two different hour-long *Math Encounters* events at the National Museum of Mathematics (“MoMath”) in New York City. I’ve been a trustee of the museum since before it opened in 2012, and I was looking forward to spending a couple of hours trying to “make some math” there with a couple of eclectic audiences from kids to retirees.

People usually assume that new discoveries aren’t things one can ever see being made in real time. But the wonderful thing about the computational tools I’ve spent decades building is that they make it so fast to implement ideas that it becomes realistic to make discoveries as a kind of real-time performance art.

Try the experiments for yourself in the Wolfram Open Cloud »

But mathematics is an old field. Haven’t all the “easy” discoveries already been made? Absolutely not! Mathematics has progressed along definite lines, steadily adding theorems about all sorts of things. Many great mathematicians (Gauss, Ramanujan, etc.) have done experimental mathematics to find out what’s true. But in general, experimental mathematics hasn’t been pursued nearly as much as it could or should have been. And that means that there’s a huge amount of “low-hanging fruit” still to be picked—even if one’s only got a couple of hours to spend doing it.

My rule for live experiments is that to keep everything fresh I think of the topic only a few minutes before I start. But since this was my first-ever such event at the museum, I thought I should have a topic that’s somehow a big one for me. So I decided it should be something related to cellular automata—which were the very first examples I explored in the multi-decade journey that led to *A New Kind of Science*.

While their setup is nice and easy to understand, cellular automata are fundamentally systems from the computational universe, not “mathematical” systems. But what I thought I’d do for my first experiment was to look at some cellular automata that somehow have a traditional mathematical interpretation.

After introducing cellular automata (and the Wolfram Language), I started off talking about Pascal’s triangle—formed by making each number to be the sum of left and right neighbors at each step. Here’s the code I wrote to make Pascal’s triangle (yes, replacing 0 by “” is a bit hacky, but it makes everything much easier to read):

If one does the same thing mod 2, one gets a rather clear pattern:

And one can think of this as a cellular automaton, with this rule:

Here’s what happens if one runs this, starting from a single 1:

And here’s the same result, from the “mathematical” code:

OK, so what happens if we change the math a bit? Instead of using mod 2, let’s use mod 5:

It’s still a regular pattern. But here was my idea for the experiment: explore what happens if the rule involves mathematical operations other than pure addition.

So what about multiplication? I was mindful of the fact that all the 0s in the initial conditions would tend to make a lot of 0s. So I thought: let’s try adding constants before doing the multiplication. And here’s the first thing I tried:

I was pretty surprised. I wasn’t expecting anything that complicated. But, OK, I thought, let’s back off and try an even simpler rule: let’s use mod 3 instead of mod 5. (Mod 2 would already have been covered by my exhaustive study of the “elementary cellular automata”.)

Here’s the result I got:

I immediately said, “I wonder how fast that pattern grows.” I guessed it might be a logarithm or a square root.

But before going on, I wanted to scope out what else was there in the space of rules like this. Just to check, I ran the mod 2 case. As expected, nothing interesting.

OK, now the mod 3 case:

An interesting little collection. But then it was time to analyze the growth of those patterns.

The first step, as suggested by someone in the audience, was just to rotate the list every step, to make the straight edge be vertical:

Then we picked every other step, to get rid of the horizontal stripes:

And—when in doubt—just run it longer, here for 3000 steps. Well, my guess about square root or logarithm was wrong: this looks roughly linear, albeit irregular.

I was disappointed that this was so gray and hard to read. Trying colors didn’t help, though; the pattern is just rather sparse.

Well, then I tried to just plot the position of the right-hand edge. Here’s the code I came up with:

Here’s a fit:

OK, how can one get some better analysis? First, I took differences to see the growth at each step: always either 0 or 2 cells. Then I looked for runs of growth or no growth. And then I looked specifically for runs of growth, and saw how long the successive runs were.

What is this? Being New York, there were lots of finance people in the audience—including in the front row a world expert on power laws. So the obvious question was, did the spikes have a power-law distribution of sizes? The results, based on the data I had, were inconclusive:

But instead of looking further at this particular rule, I decided to take a quick look at the case of higher moduli. These are the results I got for mod 4:

There was one that looked interesting here:

Would it end up having lots of different possible structures? Trying it with random initial conditions made it look like it was never going to have anything other than repetitive behavior:

Well, by this point our time was basically up. But it was hard to stop. I quickly tried the case of mod 5—and discovered all sorts of interesting behavior:

I just had to check out a couple of these. One that has an overall nested pattern, but with lots of complicated stuff going on “in the background”:

And one with a mixture of regular and irregular growth:

It was time to stop. But I was pretty satisfied. Live experiments are always risky. And we might have found nothing interesting. But instead we found something really interesting: rich and complex behavior based on iterating rules given by simple algebraic formulas. In a sense what we found is an example of a bridge between traditional mathematical constructs (like algebraic formulas), and pure computational systems, with arbitrary computational rules. In an hour we certainly didn’t finish—but we found a seed for all sorts of future research—on what we might call “MoMath Cellular Automata.”

After a break, it was time for experiment #2. This time I decided to do something more related to numbers. I started by talking about reversal-addition systems—where at each step one adds a number to the number obtained by reversing its digits. I showed the result for base 10, starting from the number 123:

Then I said, “Instead of reversing the digits, let’s just rotate them to the left. And let’s make the system simpler, by using base 2 instead of base 10.”

This was the sequence of numbers obtained, starting from 1:

Someone asked whether it was a recognizable sequence. `FindSequenceFunction` didn’t think so:

Then the question was, what’s the overall pattern? Here’s the result for 100 steps:

It looks remarkably complex. And doing 1000 steps doesn’t make it look any simpler:

What about starting with something other than 1?

All pretty similar. I wondered if rotating right, rather than left, would make a difference. It really didn’t:

I thought maybe it’d be interesting to have a fixed number of digits, so I tried reducing mod 2^{20}, to keep only the last 20 digits:

Then I decided to make complete transition graphs for all 2^{n} states in each case. Curious-looking pictures, but not immediately illuminating.

By now I was wondering: “Is there a still simpler system involving digit rotation that does something interesting?” I wondered what would happen if instead of adding in the original number at each step, I just multiplied by 2, and added some constant. This didn’t immediately lead to anything interesting:

So then I wondered about multiplying by 3:

Again, nothing too exciting. But—just to be complete—I thought I’d better run the experiment of looking at a sequence of other multipliers.

Similar behavior until—aha!—something weird and complicated happens when one gets to multiplier 13.

There was an immediate guess from the audience that primes might be special. But that theory was quickly exploded by the case of multiplier 21.

OK, so then the hunt was on for what was special about the multipliers that led to complex behavior. But first we had to figure out how to recognize complex behavior. I thought I’d try something newfangled: using machine learning to make a feature space plot of the images for different multipliers.

It was somewhat interesting—and a nice application of machine learning—but not immediately too useful. (To make it better, one would have to think harder about the feature extractor to use.)

So how could one tell from that which were the complex patterns? A histogram of entropies wasn’t obviously illuminating:

As I was writing this blog post, I thought I should find the entropy distribution more accurately; even including 1000 possible multipliers, it still doesn’t seem terribly helpful:

An expert in telecom math in the front row suggested taking a Fourier transform. I said I wasn’t hopeful:

Yes, there are better ways to do the Fourier transform. But someone else (a hedge-fund CEO, as it happened) suggested looking at the occurrences of particular 2×2 blocks in each pattern. For the case of multiplier 13, lots of blocks occur:

But for the case of multiplier 5, where the pattern is simple, most blocks never occur:

So this suggested that we just generate a list of how many of the 16 possible blocks actually do occur, for each multiplier:

Here’s a plot:

Where are the 16s?

`FindSequenceFunction` didn’t have any luck with these numbers. Plotting the “block count” for longer gave this:

Definitely some structure. But it’s not clear what it is.

And once again, we were out of time—having found an interesting kind of system with the curious property that it’s usually complex in its behavior, but for some special cases, isn’t.

I’ve done many live experiments over the years—though it’s been a while since they were about math. And as the Wolfram Language has evolved, it’s become easier and easier to do the experiments nicely and smoothly—without time wasted on glitches and debugging.

Wolfram Notebooks have the nice little feature that they (by default) keep a Notebook History (see the Cell menu)—that shows when each cell in the notebook has been modified. Here are the results for Experiment #1 and Experiment #2. Mostly they show rather linear progress, with comparatively little backtracking. (There’s a gap in Experiment #2, which came because my network connection suddenly stopped working. Conveniently, there were some networking experts in the audience—and eventually it was determined that the USB-C connection from my fine new computer to the projector had somehow misnegotiated itself as an Ethernet connection…)

Every year at our Summer School I start out by doing a live experiment or two—because I think live experiments are a great way to show just how accessible discovery can be if one approaches it the right way, and with the right tools. I’m expecting that live experiments will be an important part of the process of educating people about computational thinking too.

With the Wolfram Language, one can do live experiments—and live coding—about all sorts of things. (We even tried doing a prototype Live Coding Competition at our last Wolfram Technology Conference; it worked well, and we’ll probably develop it into a whole program.)

But whether they’re live or not, computer experiments are an incredibly powerful methodology for making discoveries—not least in mathematics.

Of course, it’s easy to generate all kinds of random facts about mathematics. The issue is: how does one generate “interesting” facts? In a first approximation, for a fact to be interesting to us humans, it has to relate to things we care about. Those things could be technological applications, observations about the real world—or just pieces of mathematics that have, for whatever reason, historically been studied (think Fermat’s Last Theorem, for example).

I like to think that my book *A New Kind of Science* significantly broadened the kinds of “math-like facts” that one might consider “interesting”—by providing a general intellectual framework (about computation, complexity, and so on) into which those facts can be fit.

But part of the skill needed to do good experimental mathematics is to look for facts that somehow can ultimately be related to larger frameworks, and ultimately to the traditions of mathematics. Like in any area of research, it takes experience and intuition—and luck can help too.

But in experimental mathematics, it’s extremely easy to get started: there’s plenty of fertile territory to be explored, even with quite elementary mathematical ideas. We just happen to live at a time when the tools to make this kind of exploration feasible first exist. (Of course, I’ve spent a lot of my life building them…)

How should experimental mathematics be done? Perhaps there could be “math-a-thons” (or “discover-a-thons”), analogous to hackathons, where the output is math papers, not software projects.

More than 30 years ago I started the journal *Complex Systems*—and one of my long-term goals was to make it a repository for results in experimental mathematics. It certainly has published plenty of them, but the standard form of modern academic papers isn’t optimized for experimental mathematics. Instead, one can imagine some kind of “Discoveries in Experimental Mathematics,” that is much more oriented towards straightforward reports of the results of experiments.

In some ways it would be a return to an earlier style of scientific publishing—like all those papers from the 1800s reporting sighting of strange new animals or physical phenomena. But what’s new today is that with the Wolfram Language—and particularly with Notebook documents—it’s possible not just to report on what one’s seen, but instead to give a completely reproducible version of it, that anyone else can also explore. (And if there’s a giant computation involved, to store the results in a cloud object.)

I’m hoping that finally it’ll now be possible to establish a serious ecosystem for experimental mathematics. A place where results can be found, presented clearly with good visualization and so on, and published in a form where others can build on them. It’s been a long time coming, but I think it’s going to be an important direction for mathematics going forward.

And it was fun for me (and I hope for the audience too) to spend a couple of hours prototyping it live and in public a few days ago.

Download the complete notebooks:

Session #1/Experiment #1 »

Session #2/Experiment #2 »

Try the experiments for yourself in the Wolfram Open Cloud »

]]>Computational thinking needs to be an integral part of modern education—and today I’m excited to be able to launch another contribution to this goal: Wolfram|Alpha Open Code.

Every day, millions of students around the world use Wolfram|Alpha to compute answers. With Wolfram|Alpha Open Code they’ll now not just be able to get answers, but also be able to get code that lets them explore further and immediately apply computational thinking.

It takes a lot of sophisticated technology to make this possible. But to the user, it’s simple. Do a computation with Wolfram|Alpha. Now in almost every section of the output you’ll see an “Open Code” link. Click it and Wolfram|Alpha will generate code for you, then open it in a fully runnable and editable notebook that you can immediately use in the Wolfram Open Cloud:

The sections of the notebook parallel the sections of your Wolfram|Alpha output. But now each section contains not results, but instead core Wolfram Language code needed to get those results. You can run any piece of code by clicking the [>] button (or typing Shift+Enter):

But the really important thing is that right there on the web you can change and extend the code, and then instantly run it again:

If all someone wants is a single, quick result, then classic Wolfram|Alpha should be all they’ll need. But as soon as they want to go further—that’s where Wolfram|Alpha Open Code comes in.

Let’s say you just got a mathematical result from Wolfram|Alpha:

But then you wonder: “what happens for a whole range of exponents?” Well, it’s going to get pretty complicated to tell Wolfram|Alpha what you want just using natural language. But it’s easy to say what to do by giving a tiny bit of Wolfram Language code (and, yes, you can interactively spin those 3D surfaces around):

You could give code to interactively change the parameters too:

Starting with Wolfram|Alpha, then extending using the Wolfram Language, is very powerful. Here’s what happens with some real-world data. Start in Wolfram|Alpha, then get the underlying Wolfram Language code (it can be made shorter, but then it’s a little less clear what’s going on):

Evaluate the code to get a time series. Then plot it. And divide by the corresponding result for the US:

An important feature of notebooks is that they’re full, computable documents—and you can add whatever you want to them. You can do a whole series of computations. You can put in text to annotate what you’re doing. You can add section headings. You can edit out parts you don’t need. And so on. And of course you can do all of this in the cloud, using any modern web browser.

Wolfram|Alpha Open Code is going to be really useful to a lot of people—not just students. But when I invented it my immediate objective was very much educational: I wanted to be able to give the millions of students who use Wolfram|Alpha every day a taste of the power of code, and what can be achieved if one learns about code and computational thinking.

Computational thinking is a critically important skill for the future. And after 30 years of development we’re at the exciting point with the Wolfram Language of being able to directly teach serious computational thinking to a very wide range of students. I see Wolfram|Alpha Open Code as opening a window into the world of computational thinking for all the students who use Wolfram|Alpha.

There’s no learning curve to climb with Wolfram|Alpha: you just type in your question, directly in natural language. But now with Wolfram|Alpha Open Code you can explicitly see how your question gets interpreted computationally. And as soon as you want to go further, you’re immediately doing computational thinking, and writing code. You’re not doing an abstract coding exercise, or creating code in some toy context. You’re immediately using code to formulate computational ideas and get results about something you’re working on.

Of course, what makes this feasible is the character of the Wolfram Language—and its uniquely high-level knowledge-based nature. Because that’s what allows real computations that you want to do to be expressed in small amounts of code that can readily be understood and modified or extended.

Yes, the Wolfram Language has a definite structure and syntax, based on definite principles. But that’s a lot of what makes it easy to understand and to write. And in a notebook you’re always getting suggestions about what to type—and if your browser language is set to something other than English you’ll often get annotations in that language too. And the code you get from using Wolfram|Alpha Open Code will continually illustrate the core principles of the Wolfram Language.

Over the course of the past year, we’ve introduced two important paths into computational thinking, both supported by Wolfram Programming Lab, and available free in the Wolfram Open Cloud.

The first path is to start from Explorations: small projects created using code, that a student can immediately dive into, and then modify and interact with. The second path is to systematically learn the Wolfram Language, for example using my book *An Elementary Introduction to the Wolfram Language*.

And now Wolfram|Alpha Open Code provides a third path: start from a question that a student has asked, and then automatically generate custom code that provides a starting point for further work and thinking.

It’s a nice complement to the other two paths—and perhaps it’ll often provide encouragement to pursue one or the other of them. But it’s a perfectly good path all by itself—and students can go a long way following it.

Of course, under the hood, there’s a remarkable amount of sophisticated technology that’s being used. There’s the whole natural-language understanding system of Wolfram|Alpha that’s understanding the original question. There’s the Wolfram|Alpha computational knowledge system that’s formulating what pieces of code to generate. Then there’s the Wolfram Open Cloud, providing an interactive notebook environment on the web capable of running the code. And at the center of all of it is the Wolfram Language, with its whole integrated design and vast built-in capabilities and knowledge.

It’s taken 30 years of development to get to this point. But now we’ve been able to put everything together to create a very powerful path for students to get into computational thinking.

And I have to say that for me it’s exciting to think about kids out there using Wolfram|Alpha just for homework, but then pressing the Open Code button, and suddenly being transported into the world of code and computational thinking—and perhaps starting on a lifelong journey.

I’m thrilled to be able to provide the tools that make this possible. Try it out. Tell us what you think. Share what you do, and show others what’s possible.

*To comment, please visit the copy of this post at the Wolfram Blog »*