[HN Gopher] Roboticists discover alternative physics
___________________________________________________________________
Roboticists discover alternative physics
Author : doener
Score : 174 points
Date : 2022-07-31 09:29 UTC (3 days ago)
(HTM) web link (phys.org)
(TXT) w3m dump (phys.org)
| lowbloodsugar wrote:
| Having been programming a while, when confronted with "Well, it
| got the answer 4.7, and we figured out 2, and we can't figure
| what the other 2.7 are", I wouldn't leap to "Wow, the AI has
| found a whole new physics". I'd go with "bug", until I can very
| concretely describe otherwise.
| jug wrote:
| I've thought about this myself before so this was an interesting
| read! I've thought in particular about quantum theories and how
| they are so hard to reconcile with gravity, and the hierarchy
| problem. Maybe we do simply struggle because this reality is so
| distant from the one we experience that we have trouble
| understanding it. Or maybe it is because we formalized it
| empirically but we got off on the wrong foot unlike Einstein's
| part that seems to come together better. Max Planck built his
| framework, not because he understood why quantum physics is the
| way it is, but because he observed how it worked. Since it works,
| we build upon that, what else can we do... So it looks like it's
| right to build on but we're having the damndest time to move
| forward. This can be very hard to later backtrack on!
| ndsipa_pomu wrote:
| I'm not seeing the significance of that. Surely there's plenty of
| alternate descriptions of physics/systems that can be equally
| predictive and certainly there's lots of equivalences to shift
| from one framework to another.
| samloveshummus wrote:
| It's true that there are many mathematically equivalent ways to
| describe physical systems. But the important point is that some
| are more useful than others. For example, Lagrangian mechanics
| and Hamiltonian mechanics are equivalent to Newtonian
| mechanics, but they can give much better intuition for certain
| problems. Feynman diagrams are equivalent to grinding out the
| QFT algebra by hand a la Schwinger, but they give a completely
| different intuition for the underlying Physics.
|
| More importantly, though, they could use this NN on systems
| that have not yet successfully been modeled, perhaps complex
| dynamical systems, to discover good parameters and conserved
| quantities.
| MauranKilom wrote:
| > More importantly, though, they could use this NN on systems
| that have not yet successfully been modeled, perhaps complex
| dynamical systems, to discover good parameters and conserved
| quantities.
|
| That would only make sense to try if the model could do this
| for systems we already understand. By the sound of the
| article, it can't even do that. Despite many efforts the
| researchers couldn't even understand the second pair of
| parameters. That doesn't correspond to my understanding of
| "good parameters".
| sbf501 wrote:
| > For example, Lagrangian mechanics and Hamiltonian mechanics
| are equivalent to Newtonian mechanics, but they can give much
| better intuition for certain problems. Feynman diagrams are
| equivalent to grinding out the QFT algebra by hand a la
| Schwinger, but they give a completely different intuition for
| the underlying Physics.
|
| I just read about Langrangian and Hamiltonian mechanics. I
| didn't encounter those at all in my EE physics, and they are
| fascinating. Great examples! Are you a physics professor, or
| is this stuff undergrad physics majors learn?
| a9h74j wrote:
| Used to be third-year in the major under Classical
| Mechanics.
|
| There's a good series of videos on YT, with the title
| _Variational Calculus and the Euler-Lagrange equation_ on
| channel Structural Dynamics. I have only seen the first
| few. This first video should give you the full playlist:
|
| https://www.youtube.com/watch?v=VCHFCXgYdvY
| sbf501 wrote:
| Sounds like Alan Rickman! Thanks!
| theptip wrote:
| Lagrangian Dynamics was a 3rd or 4th year elective in my
| undergrad physics. You need it for string theory (which was
| masters level IIRC).
| jack_riminton wrote:
| Aren't they just placeholders that help to predict a physical
| outcome rather than objective measurements?
| rch wrote:
| From the paper:
|
| > code to reproduce our training and evaluation results is
| available at the Zenodo repository and GitHub
| (https://github.com/BoyuanChen/neural-state-variables).
| radarsat1 wrote:
| This reminded me a great deal of a paper I read like 10 years
| ago, which I just looked up because I wanted to see if it was
| cited here, "Distilling Free-Form Natural Laws from Experimental
| Data" [0]. Lo and behold it is not only cited, but is written by
| the same authors. So the novelty here, as discussed in the paper
| [1], is that they are doing something similar but from video
| instead of from sensor streams. Which is quite interesting, as it
| opens up the available information to systems that are hard to
| 'sense' apart from pointing a camera at them, like the lava lamp
| example.
|
| I did see a presentation a few years ago on a similar topic by
| Erwin Couwans, author of Bullet physics engine, who was
| discussing doing neural inference of physics. He basically wanted
| to replace Bullet with a neural network, which I thought was kind
| of funny at the time, but cool if it worked. (Physical laws at
| this level of mechanics are mostly quite well understood, but the
| devil is in the details and actually solving time-discretized
| non-smooth dynamics, as well as performing good contact detection
| is less than obvious the more you get into it, not to mention
| friction, so I could see why a "learned" solution could be
| attractive, but at the time I found it funny that he was
| proposing to learn the physics that were already simulated by
| Bullet.) Looking back, it appears that those ideas culminated in
| a paper [2] two years ago, which I'll have to read now -- from
| the abstract, the differentiability of the simulation has
| benefits not only for learning the physics (mostly friction
| models it appears), but for learning controllers for plants
| within the simulated physics. It seems to be not cited in the
| linked article but maybe it is a slightly different, but related,
| topic. In any case, fascinating stuff.
|
| [0]: https://www.science.org/doi/10.1126/science.1165893
|
| [1]: https://arxiv.org/abs/2112.10755
|
| [2]: https://arxiv.org/abs/2011.04217
| dls2016 wrote:
| Piggy backing here... but this dimension-reduction or system-
| identification stuff always reminds me of Takens' theorem [0].
| He even did some neural network stuff back in the "dark ages"
| of the topic [1].
|
| [0] https://en.wikipedia.org/wiki/Takens%27s_theorem
|
| [1] https://clgiles.ist.psu.edu/papers/NC-2000-learning-chaos-
| nn...
| adamnemecek wrote:
| This is not surprising considering this is just a fixed point
| or diagonalization. I have written up a bit on this topic
|
| https://github.com/adamnemecek/adjoint
| dls2016 wrote:
| Did you mean to reply to my other post? If so, you may be
| interested in:
| https://en.wikipedia.org/wiki/Pontryagin_duality
| qikInNdOutReply wrote:
| Out of interest, how would such a system derive material
| properties from a still 3d scene?
|
| We know the leaves of a house plant will bow in the wind,
| because you have observed it in the wild, as a child several
| times. Even given non-still image training material, the nn
| would have to learn all of the physical properties of all the
| things from scratch, like a human.
|
| To train a physics model like this to derive the properties of
| material from the world is going to be bound to hilarious
| failures. A mountain with smoke hanging over it, is obviously a
| house plant.
| digdugdirk wrote:
| I'd imagine you could do a frame by frame to identify points
| of interest and then "predict" the track they should follow
| into the next frame, training the model on each point of
| interest as you go.
|
| In this case, you wouldn't be aiming to identify the material
| properties specifically, but given a model that's tightly
| fitted enough, there should be constants that stand in for
| the missing material properties when it's all said and done.
|
| Just an initial thought, I'd love to hear why I'm wrong from
| those in the field.
| t_mann wrote:
| Thanks for those links! Back in school, I loved wondering about
| whether things like the inverse square law for gravity could be
| (have been) discovered from raw experimental data via ML - cool
| to see that it's actually been done already.
| ok_dad wrote:
| That's interesting thanks for sharing. I always was bearish on
| neural networks when advertised as "AI", but now I think that
| perhaps when used as a tool rather than a panacea they could be
| very useful. Humans learn to operate heavy machinery in
| industrial settings through experience and use via our own
| neural networks, and we have incomplete understanding of
| physics, so why not use a neural network to learn the
| physically best way to drive an industrial machine and use the
| physics as backup for safety? Seems to work well based on all
| these stories so far, so maybe learning neural networks are the
| next differential equations for engineering.
| radarsat1 wrote:
| The trick is to remember that a neural network is a function
| approximator. A good deal of AI research is in the business
| of casting "intelligence" as a "function" so that you can
| pose a problem and figure out how to feed it data, ie., well-
| defined input and output. That's why different function
| approximators can be used for AI, such as decision trees,
| etc., not just neural networks. _What_ to model, and _how_ to
| model it, are orthogonal problems, both interesting in their
| own right. It just happens that NN are particularly good at
| handling high-dimensional inputs, which are necessary for
| perception tasks, as well as for handling large vocabulary
| language modeling, so they are doing rather well lately.
|
| On the other hand, there are lots of places where "functions"
| are useful, that have nothing to do with "intelligence", but
| where actually writing the function with full fidelity can be
| difficult or intractable. These are also opportunities where
| learnable function approximation can provide some great
| benefit, provided you can figure out how to pose the problem
| such that data is available for the learning part.
|
| A good example is in physically-based rendering, you have
| lots of complicated aspects of light transport for which we
| can model the physics quite well, but when you get into
| complicated reflections and scattering, etc., this can all be
| modeled by a complicated function called the BRDF [0]. Hand-
| writing a good BRDF is possible, and quite typical of high-
| end renderers in fact, but it's no surprise that there's been
| research in replacing it with a "neural BRDF" [e.g. 1].
|
| That's just to give an example of a place where a single,
| very targeted and small (but complicated) part of a larger
| framework can stand to benefit from data-driven modeling, and
| a neural network can be one good way to do that. Another
| example is similar usage in computation fluid dynamics [2],
| where we can hand write a pretty good model, but to capture
| what is missing, it can be useful to have an approximator.
| The problem there is that having approximated the function
| well, it doesn't necessarily lead to better human
| understanding of the phenomenon. Discovering the true, sparse
| latent variables in a way that is interpretable, instead of a
| black box, is a useful step towards that. (Which I guess the
| current article is aiming at, but I haven't read it in full
| yet.) But sometimes all you want is results, as in the case
| of synthesizing a good controller. For example in CFD, if you
| can use the blackbox model to generate a good stabilizer for
| an ocean platform, you don't really care about the physics,
| as long as it's accurate enough to be trustworthy. So the
| utility of these methods, like most things, is relative to
| what your goals are.
|
| [0]: https://pbr-
| book.org/3ed-2018/Color_and_Radiometry/Surface_R...
|
| [1]: https://arxiv.org/abs/2111.03797
|
| [2]: https://github.com/loliverhennigh/Computational-Fluid-
| Dynami...
| ok_dad wrote:
| Cool, thanks for expanding! I have no applications for it,
| but I love math and math-adjacent things :)
| a-dub wrote:
| > A particularly interesting question was whether the set of
| variable was unique for every system, or whether a different set
| was produced each time the program was restarted.
|
| indeed! i'm assuming it finds the same variables over multiple
| runs with different rng seeds. if that is true, it seems the next
| interesting question is if multiple kinematic problems can be
| used for the training of the same net. one key feature of physics
| is that it was invented to describe general principles that apply
| to multiple problems, so it doesn't surprise me that it might
| find some more optimal representation that doesn't necessarily
| generalize to other physical problems.
| [deleted]
| alzyx wrote:
| since it's paywalled and genlib doesnt seem to already have it,
| here's the ArXiv link: https://arxiv.org/pdf/2112.10755.pdf
| a_nop wrote:
| I'll be tickled when they discover the AI is measuring the lens
| distortion of the camera or some other artifact of the signal
| chain.
| Tao3300 wrote:
| I was also wondering if one or more of these variables pertain
| to perception. Digital artifacts in the video being mistaken
| for _machine elves_.
| bufferoverflow wrote:
| Or the noise specific to that camera sensor.
| a-dub wrote:
| more cameras. more physical systems. different settings. more
| data. :)
| lisper wrote:
| Worth noting: the concept of "energy" was controversial for a
| very long time.
|
| https://en.wikipedia.org/wiki/Conservation_of_energy#History
| comboy wrote:
| Well, it does seem like a "made up", not directly measurable
| variable. We just have it for convenience of understanding the
| world. Or am I missing something?
| alok-g wrote:
| According to Noether's theorem, law of conservation of energy
| is same as symmetry of laws (equations) of physics with
| respect to time.
| lisper wrote:
| Nothing is "directly measurable". _Everything_ in science is
| a story that we make up to explain our _personal experience_
| , including "measurement" and "observation".
| jovial_cavalier wrote:
| State variables of dynamic systems can be arbitrary. The fact
| that two that were selected by an automatic process happened to
| correspond to the two angles is probably more interesting than
| the other two being uncorrelated with the momenta.
| mike_hock wrote:
| I guess it's because it's a serendipitous discovery by
| roboticists rather than an experiment explicitly set up to
| discover "alternative physics," but wouldn't it be much more
| efficient to feed the NN actual physical measurements rather
| than a video if discovering physical models was the goal?
|
| It's amazing that the AI found two variables that corresponded
| to actual physical phenomena just from video footage but it
| seems highly unlikely that it found an alternative description
| of physics given how far off the answer was. The NN could be
| modeling any kind of weird "fantasy" for how the visual signal
| it was being fed could be produced that might not even
| correspond to 3D space in a meaningful way.
| aaaaaaaaaaab wrote:
| Ugh... Sorry but this is not physics.
| thriftwy wrote:
| Why not? It will surely have the same bounding box as our own
| physics, but the laws may be represented in completely
| different fashion. They would mostly map to each other, though.
|
| Or maybe not. Maybe the "AI physics" has predictions for
| phenomena which our physics never bothered to figure out,
| whereas it would ignore large chunks of our physics area as
| irrelevant.
| aaaaaaaaaaab wrote:
| This is more like the empirical "laws" that various
| engineering disciplines use. I.e. some number crunching that
| has no theoretical foundations whatsoever, but seems to be
| working anyway.
| forgotusername6 wrote:
| This reminds me of my tribology course. The professor said
| at the beginning that there weren't many real equations but
| here are some lookup tables.
| dmos62 wrote:
| What is physics, if not a transition between states?
| Whether the merchanism that predicts those transitions is
| machine- or human-interpretable is orthogonal.
| goatlover wrote:
| An attempt to describe fundamental nature of the word,
| best as we can get at it. Unless you're an antirealist,
| or you think the universe is just information.
| [deleted]
| thriftwy wrote:
| Even if universe is still information, knowing laws which
| govern change of that information is no lesser valuable.
| Daub wrote:
| Agreed, if only for the reason that the AI seems to be making
| observations of the physical world based on videos thereof.
| samloveshummus wrote:
| I don't see why that's relevant, a video camera is just
| another instrument that records data, not essentially
| different from the detectors at the LHC, albeit completely
| un-optimized - which is necessary for this experiment to
| work.
| Shorel wrote:
| Apart from some very slow atom and particle simulations...
|
| Is not most physics based on very extreme oversimplifications?
|
| They are still very useful because errors average out, but for
| example: we don't know why fluid simulations work, in fact
| aerodynamics is an entire empirical field.
|
| This was seen in F1 this year. All cars bouncing on straights,
| it was even given the name porpoising by the pilots. The teams
| never expected it, because the simulations never discovered
| anything about it.
|
| Also: all the work of AlphaFold predicting protein structures.
|
| Physics are awesome at predicting planetary motion, but stuff
| at smaller scales require a lot of empirical engineering to
| figure out.
| mhh__ wrote:
| The porpoising is apparently because the more expensive
| higher speed wind tunnel analysis is banned.
|
| Engineering needs engineering,. Physics does not. The
| interesting thing about aero in physics is the emergence of
| extremely complicated phenomena, not the equations as per se.
| JPLeRouzic wrote:
| > _but stuff at smaller scales require a lot of empirical
| engineering to figure out._
|
| And that made the fortune of CAD software designers.
| samloveshummus wrote:
| I'm not sure why you don't think it's Physics. It's about
| formulating laws that describe the behaviour of physical
| systems - that's the essence of what Physics is. I have a PhD
| in high energy theory and this really seems like Physics to me.
| mhh__ wrote:
| The difference is parsimony
| mjburgess wrote:
| Sure, if the pseudoscientific description of _factor
| analysis_ applied to images is correct, then it 's phyiscs.
|
| As-is, it's pseudoscience. What happens when you do a factor
| analysis on images? You get some measure of the axes of
| geometrical variance across those images.
|
| Are those axes "related" to any physical variables, sure --
| but almost never directly. To suppose the system itself had
| these properties is to suppose, for example, constellations
| actually exist and cause your personality traits.
|
| Everything we want to know is _what_ phyical properties of
| the system give rise to the observed consistent correlations
| in geometrical properties. *THAT* is physics.
|
| Showing these geometrical properties exist and are consistent
| is just what we're trying to explain.
|
| You cannot go from images to the domain of physics -- there
| are an infinite number of theories consistent with these
| images domains. And this is pseudoscience.
| samloveshummus wrote:
| It's really easy to test whether or not it works - see how
| well the model predicts on out-of-training sample data.
| That wouldn't work with astrology.
|
| There's no such thing as "physical properties of the
| system" other than measurable quantities that can be used
| to make predictions, which is what this does. There's no
| reason to be sure that temperature, for example, is a
| "real" physical property of a system rather than just one
| of many variables that would help us model it and
| understand it.
|
| Do you think it's pseudoscientific because there's no
| theory-ladenness in the predictions?
| fat_cantor wrote:
| None of this works without astrology, since it was the
| guiding theory behind Brahe and Kepler's measurements.
| The out-of-sample training data that Newton used for
| confirmation was comet orbits. Would ML really have
| created an elegant, closed-form theory about the
| elliptical shape of orbits and the power-law dependence
| of the period? Without these insights, there would be no
| inverse-square law in the first place, and perhaps we
| would only have an effective theory.
| ElectricalUnion wrote:
| I think you're confusing Astronomy (studying celestial
| objects) with Astrology (divinatory practices related to
| celestial objects).
|
| Granted, the reason why people did Astronomy was because
| they believed in Astrology, but it's no longer been the
| case since a while.
| fat_cantor wrote:
| The purpose of Brahe's measurements, and the reason he
| hired Kepler, was to gather data for astrological
| predictions. The principles of astrology led them to look
| for simple, basic principles in a way that a computer
| would not, unless directly programmed to do so. The
| astronomical measurements alone were not enough.
| mjburgess wrote:
| It's pseudoscience because there's nothing in the
| geometrical properties of those images called "gravity",
| etc. One can generate those pixel patterns from an
| infinite number of theories with an infinite variety of
| causally efficacious parameters.
|
| From the article, it doesn't work. They found on known
| physics it gives 4.7 dimensions, of which only two are
| explicable -- 4 is correct; the others have no known
| physical interpretation. No surprises: those two are just
| the geometric properties of the system (angles) which are
| actually properties of the image. The others are pure
| bullshit.
|
| Since, of course, the real physical parameters of the
| system we take to have generated those images are not
| present in them. The images are distal effects of these
| things
|
| Only in cases where the geometric properties of the
| target system are causally relevant to its actual causal
| properties will this work -- ie., only when "angles
| matter"
|
| Thinking you can infer laws of nature from images is
| pseudoscience, and these guys need to think more
| carefully about why we experiment in the first place
|
| eg., Consider that if mass is a relevant causal property,
| there'd be no way of inferring it from images: two
| objects can be visually identical whilst having radically
| different masses... making images *OBVIOUSLY* not a
| measure of mass...
|
| this project almost defines the modern kind of
| schizophrenic pseudoscience born of this wave of AI
| samloveshummus wrote:
| Who cares if there's no gravity? Gravity wasn't sent down
| to us from heaven on a stone tablet, it's just a concept
| that lets us make predictions. At school I was taught it
| was a force and at university I was taught it was a
| pseudoforce resulting from fixing a non-inertial frame.
| Both approaches give correct answers, even though they're
| conceptually very different. There's no objective way to
| say which is right; they're just different approaches to
| modelling.
|
| And maybe the 4.7 is actually more correct? The
| 4-parameter model is an approximation that neglects
| friction and air resistance. Moreover the double pendulum
| is a chaotic system and chaotic systems sometimes have
| dynamics described by laws with non-integer exponents
| such as Lyapunov dimension. I'm just spitballing, but the
| point is that it's not a priori ridiculous.
|
| It's definitely possible to estimate mass from images.
| How do you think we know the masses of asteroids and
| planets? No-one put them on a scale, we just record their
| motion and work out which value fits best.
| goatlover wrote:
| > Who cares if there's no gravity? Gravity wasn't sent
| down to us from heaven on a stone tablet, it's just a
| concept that lets us make predictions.
|
| A concept that says gravity is the result of bending
| spacetime, with the speed of light being constant. It's
| not just a model, it's saying the universe is 4D
| spacetime, which explains why GR is so predictive.
| samloveshummus wrote:
| It is just a model though! Everything in science is just
| a model. We better hope it's just a model, because it's
| incompatible with quantum field theory, which is another
| very accurate model. The only consistent model that
| bridges the two, superstring theory, says that spacetime
| and gravity could fundamentally be many things, from
| closed strings travelling between D3-branes to the
| holographic projection of a conformal theory - and you
| still get the same predictions.
| YeGoblynQueenne wrote:
| It also incidentally underscores the amazing predictive
| powers of Noam Chomsky, when he thinks he's describing
| something that common sense indicates is dumb, and then a
| few years later someone actually goes out and does it,
| and does it in earnest, and unironically, and tries to
| promote it as an actual advance:
|
| _So for example, take an extreme case, suppose that
| somebody says he wants to eliminate the physics
| department and do it the right way. The "right" way is to
| take endless numbers of videotapes of what's happening
| outside the video, and feed them into the biggest and
| fastest computer, gigabytes of data, and do complex
| statistical analysis -- you know, Bayesian this and that
| [Editor's note: A modern approach to analysis of data
| which makes heavy use of probability theory.] -- and
| you'll get some kind of prediction about what's gonna
| happen outside the window next. In fact, you get a much
| better prediction than the physics department will ever
| give. Well, if success is defined as getting a fair
| approximation to a mass of chaotic unanalyzed data, then
| it's way better to do it this way than to do it the way
| the physicists do, you know, no thought experiments about
| frictionless planes and so on and so forth. But you won't
| get the kind of understanding that the sciences have
| always been aimed at -- what you'll get at is an
| approximation to what's happening._
|
| http://chomsky.info/20121101/
| samloveshummus wrote:
| But he's saying it is bad to do it that way instead of
| doing traditional physics because you get no
| understanding, which is true, but in this study they're
| not using it as a "physics engine" to pilot aircraft or
| whatever, they're using it as a trick to generate novel
| hypotheses, which could then be theorised and
| investigated properly, not as a replacement to theory.
| mjburgess wrote:
| Its worse than that. It doesnt actually work.
|
| Unless you have videos of experiments designed to observe
| measurement devices we have created, on systems we have
| designed, it's all useless.
|
| The only useful thing in figuring out how nature works is
| creating truely novel experimental circumstances and
| measuring them with novel devices created for that
| purpose.
|
| You cannot do science as a statitics of images; that's
| pseudoscience. And chomsky is here only half-right; it's
| actually much worse than he's sayign.
| quinnjh wrote:
| I show someone a photo of a bowling ball and a styrofoam
| ball of the same shape and size. If someone thinks they
| can infer from a simple visual scan of the scene
| (analyzing the factors you see) are they delusional?
|
| Perhaps they could leverage their lifelong training set
| which correlates scenes that look like they have bowling
| balls with scenarios that have a high mass movable
| sphere.
|
| Perhaps we could have a good laugh together by painting a
| bowling ball to look like styrofoam and painting
| styrofoam to look like a bowling ball- then we could
| watch the silly ai/human apply an incorrect mental model
| and fail to grasp the causal reality! Ohohoho
| CrimpCity wrote:
| I think I understand your criticism. There is no inherent
| ground truth in the image. The mass example is great
| since a 2D plane can't capture the quantity mass it's
| literally impossible the dimensions don't work. At best a
| 2D plane could show you correlations of mass (mass vs
| something plotted out). Hence this is just modern AI aka
| pattern-matching on steroids.
|
| I think a counter argument would be that if there is SOME
| signal in the photos AND there's enough training data
| that does have the correct ground truth signal that the
| scientists are matching up then you can have SOME level
| of accuracy. If the training set can reasonably cover the
| space of possibility that we're interested in then we can
| get reasonable interpretations.
|
| However in this case the insane number of physical
| phenomena will always be larger than any training set so
| this approach should NEVER generalize there will always
| be way too much noise which is what the scientists have
| figured out here. So I agree with you that it's extremely
| limited but I don't think I'd call it pseudoscience there
| might be very limited domains where for example the only
| data we have available are images and so such a tool may
| be appropriate.
|
| I definitely share your frustration though since any half
| way decent scientist should have just done a thought
| experiment instead and figured that this wouldn't work
| well. This smells like BS academic marketing where they
| always inflate their own impact and significance.
| [deleted]
| YeGoblynQueenne wrote:
| >> It's about formulating laws that describe the behaviour of
| physical systems - that's the essence of what Physics is.
|
| I didn't see any attempt to formulate laws. The researchers
| trained a neural net model to predict the next event in a
| sequence. That is not a natural law, it's a maximum
| probability estimator.
|
| To clarify, a natural law would be a formula with variables
| that one can plug in numbers to, in order to predict the
| behaviour of a system. For example, Newton's law of
| gravitation is a natural law, Kepler's laws of planetary
| motion are natural laws, the laws of thermondynamics are
| natural laws. But a neural net model trained to predict the
| next frame in a video? How is that a "law"?
| [deleted]
| samloveshummus wrote:
| I don't see any fundamental difference. A deep neural
| network is a universal function approximator. It uses
| different language from what we're used to (weights and
| activations instead of analytic functions and calculus) but
| that's not a big deal. The point is that it uses only a
| handful of latent variables to describe the state of the
| system at a given time, and these can be used to predict
| the system's behaviour, which is fundamentally the same
| thing that a scientist would try to do.
| YeGoblynQueenne wrote:
| So, to be clear on what you are saying, if I understand
| corectly you are saying that training a neural net to
| approximate a function is formulating a law, like for
| example a natural law? Is that right?
|
| As a for instance, if I train a neural net to predict the
| motions of the planets, the trained model is a law of
| planetary motion, like Kepler's laws of planetary motion?
| Is that correct?
| [deleted]
| YeGoblynQueenne wrote:
| So these guys trained "an AI" to identify hidden variables by
| looking at videos of physical phenomena, without prior knowledge
| of physics (except what was implicitly and obscurely encoded in
| "the AI's" neural net architecture, and its training set and
| hyperparameters, by the researchers).
|
| The "AI" came up with a slightly off number and the researchers
| struggle to understand why. They struggle because "the AI" is a
| black box that lacks explainability and cannot produce an
| explanatory model, only a predictive model [1]. The simplest
| conclusion is that their "AI" has not learned any useful models,
| and has only learned to accurately predict their test set, to
| which the authors had acces throughout the training of "the AI"
| [2]. The simplest explanation is that their model isn't very good
| at doing what they wanted it to do, but they opt to explain it as
| a scientific mystery, instead. Why?
|
| It seems the only reason to try and see a scientific mystery
| where a simple failure would suffice as an explanation is because
| the model is "an AI". There seems to be some kind of expectation
| that "an AI" _must_ have some deeper understanding of physics
| than humans, even when we don 't understand what it's doing, even
| whe it's not doing very well.
|
| In short, this seems to be based on very wrong assumptions and to
| be coming up with very wrong conclusions, but then again it makes
| for a great headline and so here we are, discussing it on HN.
|
| ____________________
|
| [1] To clarify: a predictive model is one that can predict novel
| events. An explainable model is one that can explain how it made
| a prediction. An explanatory model is one that explains how the
| world works and why certain predictions are true, or not.
| Predictive and explainable models are useful, but most scientists
| aim to build explanatory models, because an explanatory model is
| necessarily also explainable and ultimately better at
| predictions, while a predictive model is not necessarily
| explainable or explanatory and an explainable model is not
| necessarily predictive or explanatory. Deep neural nets can only
| build predictive models, which are sometimes explainable, but
| they can't build explanatory models. That's because they can only
| identify correlations in data, but not explain those correlations
| with generalised theories, based on previous knowledge.
|
| To put it plainly, neural nets can't come up with scientific
| theories, but they can estimate probabilities of things
| happening. But scientists can and want to come up with scientific
| theories, not just predictions.
|
| [2] When the experimenter has access to the test data and can
| tune the learner's model until it scores highly on the test data,
| that leads to a model that overfits to the test data. Such a
| model is useless for prediction over unseen data (i.e. data not
| available to the researchers during training).
| drran wrote:
| > To put it plainly, neural nets can't come up with scientific
| theories, but they can estimate probabilities of things
| happening. But scientists can and want to come up with
| scientific theories, not just predictions.
|
| Current science is famous for "shut up and calculate"
| principle.
|
| - Foo, bar, baz, ...
|
| - But why?
|
| - Shut up and calculate!
| YeGoblynQueenne wrote:
| As I understand it, that just applies to quantum mechanics.
| For example, in computer science, we certainly try to prove
| the properties of algorithms, such as their computational
| complexity or their soundness and completeness etc.
| nootropicat wrote:
| >To clarify: a predictive model is one that can predict novel
| events. An explainable model is one that can explain how it
| made a prediction.
|
| Sorry but that distinction doesn't mean anything falsifiable.
| Did you mean an explanatory model is one that's easier to
| understand for a human? If so, that's certainly useful, but it
| may well turn out that the simplest working model of physics is
| incomprehensible.
| YeGoblynQueenne wrote:
| No, I distinguish between "explainable" and "explanatory". To
| give an example, the theory of epicycles is a predictive
| model, that is also explainable, but it is not explanatory.
| Kepler's laws of planetary motion are an explanatory model
| that is also explainable and predictive.
|
| The theory of epicycles is predictive because it predicts the
| motions of the planets, as they are observed in the night
| sky. It is explainable because anyone can perform the
| necessary calculations and understand how the predicted
| motions are, well, predicted. The theory of epicycles is not
| an explanatory model because it does not explain why the
| planets should move in circular orbits with epicyclical sub-
| orbits. Kepler's laws are explanatory because they explain
| planetary motion as a result of Newton's law of universal
| gravitation, are predictive because they can be used to
| predict the motion of the planets and are explainable because
| anyone can plug in the numbers and see how the results are
| calculated.
|
| So an "explanatory" model is a theory that explains why
| things happen they way they happen. A predictive model only
| predicts that some things will happen. An explainable model
| explains why it made a prediction, but it does not explain
| why this prediction should hold or with what frequency.
|
| Another way to see this is that an explanatory model explains
| past observations and predicts future observations, while a
| predictive model only predicts future observations and has no
| explanatory power, cannot explain why past phenomena were
| observed.
|
| An "explainable" model is just a model that people can
| understand. It's not more complicated than that.
| woah wrote:
| > Kepler's laws are explanatory because they explain
| planetary motion as a result of Newton's law of universal
| gravitation
|
| So "explanatory" means that the theory references some
| other theory?
| YeGoblynQueenne wrote:
| Oh yes, that's absolutely necessary. That's how science
| works, right? Every new bit of knowledge builds upon
| older knowledge. It's theories all the way down, until we
| hit arbitrary axioms on which all our knowledge is based,
| though we hope those are somehow based on solid
| observations. And that's how we understand the world.
|
| But just to be clear, I understand there's a colloquial
| meaning of "theory" as in what people mean when they say
| "that's just a theory". What I mean by "theory" is an
| epistemic object with either explanatory, or predictive
| power, or both. A theory can include multiple laws and
| hypotheses etc. And a theory "is just a theory" only
| until we can refute it, or find a better theory that does
| a better job at explaining things.
| sidlls wrote:
| In the same way definitions of words reference other
| words, yes.
| YeGoblynQueenne wrote:
| >> If so, that's certainly useful, but it may well turn out
| that the simplest working model of physics is
| incomprehensible.
|
| I just re-read your comment and I notcied that.
|
| I think by "the simplest working model of physics" you mean
| quantum mechanics? I hope I clarified how I mean
| "explainable" vs. "explanatory" but yeah, I think that's
| absolutely spot on. Quantum mechanics is predictive, but not
| explanatory. I do think it's "explainable" though in the
| sense I sort-of define it, because it's a bunch of formulae
| that anyone can plug in the numbers to, and see how they come
| up with results. I don't reckon there's many theories in the
| sciences that are not explainable. The lack of explainability
| only becomes an issue with black box models like neural nets.
|
| Perhaps I should have used the word "comprehensible" or
| "interpretable" instead of "explainable" since it causes
| confusion by being too close to "explanatory". But those
| words have their own problems ("comprehensible"... by whom?)
|
| As an aside, I'm on the camp that hopes that quantum
| mechanics is somehow fundamentally wrong and we'll figure it
| out in the next big paradigm shift. It bothers me deeply that
| we seem to have hit a wall where we can predict, but don't
| have a clue and can't understand why. I think every other big
| leap in scientific ... understanding (hint) has come from the
| ability to make explanatory theories, that tell us how things
| work _and why_.
| l33tman wrote:
| The generic response to your type of reasoning is that you
| might need to rethink what you think "why" and "how" and
| "have a clue" are defined :)
|
| The human mind really wants to understand things in terms
| of the paltry slow coarse-grained 3D world we inhabit,
| despite 100 years of QM and QFT-based experiments showing
| that the world simply doesn't work that way. Can't other
| realms have other "why"'s? Tens of thousands of
| professional solid state physicists and QM researchers
| would probably not refer to their field as "not having a
| clue" for example, any more than anybody working in a field
| dominated by Newtonian mechanics would have a clue at
| least.
|
| In fact, if you make a simulated world in a computer game,
| it's trivial to come up with algorithms that do alternative
| physics in the game that can create interesting behaviour,
| despite being far from explanatory for a being inside the
| sim.. I'm rather amazed the reality we inhabit is so easy
| to comprehend as it is even despite QM/QFT being pretty far
| from Newtonian.
| hsnewman wrote:
| I posted this 4 days ago.
| motoxpro wrote:
| And multiple other people posted it 7 days ago, when it came
| out.
| CrimpCity wrote:
| This is cool and I personally believe this type of work may lead
| to breakthroughs in messy data rich fields like biology where we
| can arrive at a higher levels of abstraction maybe not exactly to
| "laws" like physics but highly correlative rules around
| phenomena. I think this is more on the side of knowledge creation
| and is human friendly as opposed to being more of a black box
| prediction like deep neural networks. Though I think both things
| are complimentary since human curiosity isn't satisfied by
| prediction alone.
|
| If anyone else is interested in this line of work I recommend
| checking out Kathleen Champion, Steve Brunton, and J. Nathan
| Kutz's work on Discovering governing equations from data by
| sparse identification of nonlinear dynamical
| systems(https://www.pnas.org/doi/full/10.1073/pnas.1517384113).
|
| Also this intro video is great! https://youtu.be/Z-l7G8zq8I0
| jamesakirk wrote:
| Thank you for mentioning J. Nathan Kutz! Reading through this
| article, I saw similarities to Dynamic Mode Decomposition (I am
| not literate enough on the topic to elaborate). His Coursera
| courses and book were a fascinating dive into orthogonal basis
| functions, lower-rank approximations like PCA... I'm not sharp
| enough anymore (over a decade since grad school) to fully grok
| it, but damn his work is so cool!
| [deleted]
| taneq wrote:
| > OK so we know the answer to this question is 4, let's check our
| new software against that.
|
| > [software returns 4.7]
|
| > Oh my GOD, it's discovered new physics!
| coliveira wrote:
| Good summary. There's no end to the kind of delusion some AI
| proponents will cling too. In a few days they'll say this is
| another proof we live in a simulation!
| Rerarom wrote:
| The new physics is not in the 4.7 but that in that the model's
| third and fourth variables seem to be new, compared to the
| known models.
| YeGoblynQueenne wrote:
| They seem to be uncorrelated with real-world physics. Whether
| they are "new" is anybody's guess. They're variables
| identified in a virtual environment, rather than the real
| world so there's very little chance they correspond to
| something in the real world, let alone physics, much less
| "new physics".
| a9h74j wrote:
| I saw it commented somewhere that epicycles (of
| astronomical fame) are essentially additional terms in a
| Fourier series.
|
| I suppose anything nonlinear could invite multiple terms
| incidental to a particular local fit.
| toxicFork wrote:
| At least it wasn't 42
| JackFr wrote:
| But can they play the tape backwards and come up with entropy.
| [deleted]
| ijidak wrote:
| > the AI produced the answer: 4.7
|
| This gave me a Hitchhikers Guide chuckle.
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