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AI Math Space

AI Cracks Centuries-Old 'Three Body Problem' In Under a Second (livescience.com) 146

Long-time Slashdot reader taiwanjohn shared this article from Live Science: The mind-bending calculations required to predict how three heavenly bodies orbit each other have baffled physicists since the time of Sir Isaac Newton. Now artificial intelligence (A.I.) has shown that it can solve the problem in a fraction of the time required by previous approaches.

Newton was the first to formulate the problem in the 17th century, but finding a simple way to solve it has proved incredibly difficult. The gravitational interactions between three celestial objects like planets, stars and moons result in a chaotic system -- one that is complex and highly sensitive to the starting positions of each body. Current approaches to solving these problems involve using software that can take weeks or even months to complete calculations. So researchers decided to see if a neural network -- a type of pattern recognizing A.I. that loosely mimics how the brain works -- could do better.

The algorithm they built provided accurate solutions up to 100 million times faster than the most advanced software program, known as Brutus. That could prove invaluable to astronomers trying to understand things like the behavior of star clusters and the broader evolution of the universe, said Chris Foley, a biostatistician at the University of Cambridge and co-author of a paper to the arXiv database, which has yet to be peer-reviewed.

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AI Cracks Centuries-Old 'Three Body Problem' In Under a Second

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  • Just sayin'..
    • Re: (Score:3, Interesting)

      Comment removed based on user account deletion
      • Actually, O'contraire! Tesla owner. They drive for miles and miles, and will not hit anything. I take mine to San Diego from Orange County 3x a week, and for 90% of the way down, my input isn't needed (freeway vs surface). 40k miles with 1/2 of them on autopilot, it's become way better than it was and gets even better every update. If you think the rate of advancements or possibilities in this field are going to keep AI out of X task because people can do it better, wait a few years and think again.
        • Reading the paper posted on arxiv, it is a little suspicious that they talk about black holes in the context of Newtonian mechanics (they only arise in the relativistic setting). It makes you think they might not know so well that they're doing...
        • Comment removed based on user account deletion
      • by v1 ( 525388 )

        Sure it can. Just not very long without hitting anything.

        a fact which is also true for many humans as well it would seem

    • by mosel-saar-ruwer ( 732341 ) on Saturday November 09, 2019 @03:48PM (#59398124)
      Current approaches to solving these problems involve using software that can take weeks or even months to complete calculations.

      The algorithm they built provided accurate solutions up to 100 million times faster than the most advanced software program, known as Brutus.

      So they ran Brutus for several months to get what they believe to be the 'correct' answer, and then the new algorithm accomplished something-or-other else in an elapsed time of, say (three months) / (100,000,000).

      Lets call that 90 days, and assume 60 seconds in a minute, 60 minutes in an hour, and 24 hours in a day.

      Then we're looking at an elapsed time of:

      (90 * 24 * 60 * 60 seconds) / (100,000,000)

      = (7,776,000 seconds) / (100,000,000)

      = (7,776 seconds) / (100,000)

      = 0.07776 seconds

      Two points:

      1) Unless they had access to something like the IBM Summit supercomputer, and somehow they seeded the various aspects of the parallelization algorithm to each of the nodes of Summit before shouting "Go!", I'm calling bu11sh!t on any meaningful work being accomplished in under 8/100ths of a second.

      2) Even using something as grotesquely inaccurate as 64-bit floating numbers [I'm gonna assume that they didn't divvy up the $$$s necessary to run this on 128-bit math hardware], presumably Brutus and this New Algorithm didn't come to precisely the same answer - each would have been off by a few bits from one another.

      So we're dealing with three different quantities:

      A) The closest 64-bit approximations to what's actually happening in the equations.

      B) Brutus's answers

      C) The New Algorithm's answers

      But this is CHAOS, ergo [ipso facto] we don't even know which of Brutus or the New Algorithm is producing the more accurate answer, much less which will diverge from the correct answer more rapidly.

      PS: I'm not saying that these guys aren't onto something, but whatever they're onto probably isn't gonna be classical DEDUCTIVISTIC mathematics - it's probably gonna be some new EMPIRICAL black-magic voodoo mathematics [which works when it works, but when it fails, could very well fail spectacularly].

      And I don't wanna leave the impression that I would necessarily be opposed to black-magic voodoo mathematics - it could be a fascinating new endeavor for our soon-to-arrive CRISPR'ed Gattaca Super-Babies to ponder [if they can ever pull themselves away from the lure of the 24x7 (((pr0n))) being streamed to them on their 3D contact lenses].

      And I probably shouldn't joke about 24x7 (((pr0n))) being streamed on 3D contact lenses - that might be here by 2030 [if not sooner].

      • by Your.Master ( 1088569 ) on Saturday November 09, 2019 @04:54PM (#59398232)

        1) You don't think you can have a meaningful calculation in 80 milliseconds? The actual paper suggests, by the way, that the time taken was on the order of 1 millisecond, no need to reverse engineer. It's a fixed-time result. Which means that I agree it can't be totally general in an analytic sense.
        2) In physics the inputs are *always* precision-bounded, so outputs are always in comparison, even in chaotic systems. Chaos here is a relative term, not absolute. Almost any chaotic system is non-chaotic when the changes in initial conditions are constrained enough. Given the Heisenberg uncertainty principle, and well, practical reality, a truly absolutely chaotic system is one we cannot make predictions on, so it's ultimately indistinguishable from truly random and therefore irrelevant to something we think we can calculate. In any case, chaotic systems are sensitive to initial inputs, but the accumulated error is *not* initial inputs, it's outputs (and intermediate internal states). The arxiv article contains analysis of error accumulation.

      • I once worked on a system that took 20ms to provide answers, and that involved running jobs on a small cluster. It was intended to replace existing systems that take about 6-12 months to process.

        Yes, its answers were accurate and precise. The algorithm was similar in concept to adding an index to a database table. This is exactly how major jumps in computer science occur: take an existing problem, rearrange its parameters into a different problem, and try to solve that one faster than the original.

        Sometimes

        • by jythie ( 914043 )
          Heh, I got started by working on a problem like that with a similar outcome. Months spent trying to find way to shave off a millisecond or two by using increasingly complex optimizations, and a grad student came in and noted that memory had gotten REALLY cheap and replaced the whole mess with what pretty much came down to a fancy index file.

          In this case though, what they really developed is essentially a template system. The big number cruncher is still needed to build the patterns, and then the neural n
        • by ceoyoyo ( 59147 )

          Part of my PhD was a O(N log N) algorithm to replace a O(N^2) one. It brought a computation we wanted to do down from about 2 million years to twenty or 30 milliseconds.

        • Also relevant, is that a neural network is effectively a type of lookup table. There is a lot of information pre-compiled into the weights of the completely trained neural network. This could explain how an NN approach can come up with results a lot faster than a naive brute-force computation.

      • It would help you to try to understand what they actually did. They ran a whole bunch of Brutus run to generate some training data. They then fit a neural net proxy model to the Brutus data. In physics we calm this generating a reduced model. So the neural network they trained is just a proxy model of Brutus. So in the hierarchy of what’s correct, Brutus is the most correct given the accuracy of the numerical methods used. The neural net is a close second since it’s an approximation of the first
    • They can drive just fine, it's those human drivers and pedestrians that are the problem.
    • A robot cat. And he can drive, just not very well.

      https://giphy.com/gifs/snl-nbc... [giphy.com]

  • by HiThere ( 15173 ) <charleshixsn.earthlink@net> on Saturday November 09, 2019 @02:46PM (#59397908)

    That sounds like an extremely useful algorithm, but if I read the summary correctly, it's still just an approximation. It's not a solution.

    Actually, after reading the abstract it sounds even more limited than that, though it's hard to say just what the limits are. "Our results provide evidence that, for computationally challenging regions of phase-space, a trained ANN can replace existing numerical solvers, enabling fast and scalable simulations of many-body systems to shed light on outstanding phenomena such as the formation of black-hole binary systems or the origin of the core collapse in dense star clusters. "

    • But do the scientists know the actual algorithm that the computer used or is it a black box? If the later then we're on the way to trusting our future to Skynet.

      • by Mal-2 ( 675116 )

        The state of the network *is* the actual algorithm, so yes, they know that. What they don't know is the logic by which the network arrived at that configuration.

    • by Crashmarik ( 635988 ) on Saturday November 09, 2019 @03:10PM (#59398018)

      Exactly. Cracking the three body problem involves a closed form solution of the equations of motion. This is just a new computational method.

    • by Solandri ( 704621 ) on Saturday November 09, 2019 @04:21PM (#59398190)
      I get the impression all their ANN is doing is plotting the different parameters of the three bodies (mass, distance from each other, and initial velocity vectors) in a big static multi-dimensional chart, and spitting out the approximate value of the closest solution or interpolated solution. Kinda like how (as a one-dimensional example) if you know that a body falls 1 unit in 1 second, 4 units in 2 seconds, and 16 units in 4 seconds, then you can guess that it'll fall 9 units in 3 seconds.

      Your answer is probably correct. But since the 3-body problem is chaotic, if there's a localized hole or attractor [wikipedia.org] which wasn't present in the data used to train the ANN, then the results it gives will be completely wrong in that region of the solution space.
  • ... presently, the algorithm can only solve for two variables in this [wikipedia.org] three body problem: Fast, Cheap, Good
    • by Mal-2 ( 675116 )

      In some fields even that would be a massive improvement. It's a computer, not a messiah. It's not going to break the laws of physics.

  • If you have three bodies moving around at a given precision, there are a finite number of positions in which they can find themselves. For example, if the bodies are at position P1 (which is a matrix with an x and y for each body respectively, and velocity), then you can calculate they will be at position P2 in five seconds. Then, for any other position of the bodies, if you are able to calculate that they will be in position P2, then you automatically know that 5 seconds later they will be at P1 without an
    • by Mal-2 ( 675116 )

      AIs will suck at extrapolation for the same reason Taylor series are useless for extrapolation -- because they're focusing entirely on fitting the existing data to the curve and interpolating. But maybe (like a Taylor series) the fitted region will provide a term in a larger series as the borders of the approximation get expanded, meaning AIs can build on the models discovered by other AIs, find out where they overlap and can be stitched together to provide a bit more generality, and then generate new model

  • by gweihir ( 88907 ) on Saturday November 09, 2019 @03:49PM (#59398126)

    They do not. All we have is statistical classification, automation and some rule-based inference. There is absolutely no intelligence or insight in machines today and that will remain true for the foreseeable future.

    • They don't. That's why they call it AI. The A is important.
      • by gweihir ( 88907 )

        Well, not really. Maybe calling it FI (Fake Intelligence) would be an appropriate description. Of course, anybody in the know has started real intelligence created artificially AGI (Artificial General Intelligence), but most people do not understand that.

        Of course, if you look at "artificial xyz", it usually is not very good and quite often not a real replacement for the real thing, so you do have a point.

    • Re: (Score:2, Insightful)

      Comment removed based on user account deletion
      • by gweihir ( 88907 )

        Actually, computers cannot do reasoning or knowledge at all. Both things require understanding and insight. What they can do is data storage and real simplistic logical and arithmetic operations really fast.

    • There is absolutely no intelligence or insight in machines today and that will remain true for the foreseeable future.

      You'e exactly right. However and somehow, they're still smarter than some people I know. Different domains of expertise, I guess.

      • by gweihir ( 88907 )

        Well, the thing about people is that while everybody does actually have intelligence, most people rarely use it and when they do, they do so only in a limited fashion and only applied to certain types of problems. For most things, most people just copy what others do without ever thinking about it. Then you have the about 10-15% "independent thinkers" and these people are able and willing to apply their intelligence to anything and there you can see what is actually possible. Unfortunately, being able to th

    • by Kjella ( 173770 )

      What we're really going to discover is how little is actually intelligence and how much is passed down skill and knowledge. Even the things that are original thought will often end up being things other smart or skilled people have thought of before that we're simply not aware of or that they've never managed to articulate to others. I wish I knew all a Michelin star chef knew about cooking but I don't, in fact any moderately trained AI is probably better than me because it'll mimic skills far beyond my own

  • My prediction is that this will turn out as utter bullshit. Something will be misinterpreted, misunderstood, or miscommunicated.

    I haven't looked at the problem for ages, but if you are interested, there is a nice paper here: https://iopscience.iop.org/art... [iop.org] . It discusses calculation of positions of planets etc in the solar system over the last 200 million years. It's just _slightly_ more difficult than the 3-body problem. There are probably newer papers.
    • That is my sense also, but I must admit I am massively ignorant in this area.
    • Trying to project the motions of the solar system over tens of millions if years, let alone hundreds of millions of years is an exercise in futility. We know that we don't know where all the masses "within" the solar system are (see, for example, the ongoing debate over whether or not Brown & Batygin's 2016 model for "planet nine" exists, explains the orbital motions of large-orbit KBOs, is detectable, and has been detected ; all of those points are in debate (with active experiments searching for sight
  • This is not "solving the 3-body problem" in any way that you would recognize. Solving it, in mathematical terms, would require a closed-form solution. This appears to just be a different way to do the integration of a numerical solution. That's not "solved". In any case, most practical applications of 3-body gravitational dynamics - like navigating to the moon with errors on the order of 100's of feet - was "solved" in the same sense back in the 60's in seconds, and could be solved in the same sense now in

  • Most recently [newscientist.com], a thousand solutions...
  • Once again, the reporting is overly flamboyant, but the actual article is quite nice.
    The abstract is at: https://arxiv.org/abs/1910.072... [arxiv.org]
    The article is at: https://arxiv.org/pdf/1910.072... [arxiv.org]

    Science and mathematics have done a great job of understanding the interactions of two bodies, or else one body (parameter, variable, whatever) in time. But, 3 bodies or N-body interactions do not have discrete equations of state. The best you can do is take a system of multiple equations describing the 2-body intera

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