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Teaching physics to neural networks removes 'chaos blindness'
- keenmaster 6y agoI’ve said this before, but I think that a lack of physical modeling might be the key barrier for AV technology. Human drivers have a mental model of physics that they’ve honed for 17-18 hours a day since they were born.
- deleted 6y ago[deleted]
- CardenB 6y agoYou are likely correct. I think most researchers would agree, however. The bigger issue is actually learning how to form complex models. People want networks to just learn this implicitly, believing that we would likely impose counterproductive models. Other people simply struggle to incorporate models into the training process.
- piyh 6y ago2 minute papers has good videos on neural nets learning physical modeling https://www.youtube.com/watch?v=2Bw5f4vYL98 https://www.youtube.com/watch?v=2Bw5f4vYL98
- CyberDildonics 6y agoThis isn't something that has never been thought of. Jim Keller described many problems like changing lanes as a matter of ballistics.
- mhh__ 6y agoVehicle dynamics is a fairly accurate science these days (50/50 for the tires)
- jefft255 6y agoI'm working on autonomous off-road vehicles, and while this is (probably) true for autonomous cars, dynamics modeling for wheeled robots on rough terrain is another beast where these approaches could very much help.
- mhh__ 6y agoIs the issue in the surface modelling? I don't think I've ever seen a physical tire model for loose terrain
- jefft255 6y agoPeople in space robotics have been working on that (moon and mars rovers need to deal with this). Perception is also a bottleneck; you have to see rocks, root, grass, mud and predict the effects on the dynamics.
- solotronics 6y agoRacing teams and big car manufacturers have incredibly accurate models of vehicle dynamics.
- mhh__ 6y agoBut not outside those teams. If you want to put something together in a few weeks your options are relatively limited in that collecting accurate data is fairly hard. The actual dynamics of the car is fairly simple but the forces applied to it are quite hard to model (I don't know how much Michelin charge to use TameTire but I'm guessing not cheap)
- keenmaster 6y agoSure, but to be clear, I meant physical modeling which includes real-time modeling of all salient objects and surfaces in the immediate and foreseeable environment. I mean going as far as creating a physical model for deer, their range of behavior and speed, weight distribution, predictive modeling for subsequent behavior, etc...
- Fricken 6y agoDon't sell biology short like that. Human driver are born with a mental model of physics that's been honed 24 hours a day since before they were diatoms.
- mtgp1000 6y agoI don't think that's quite right. I believe that humans are essentially born as blank neural networks; it's the structure, and the graph of connections between brain structures and sensory inputs, that is effectively primed for learning certain tasks that we find to be intuitive. A baby is not born with the knowledge of body movement, for example, but through natural exploration of the body and environment, almost all physically capable humans learn to walk.
- SamReidHughes 6y agoI disagree with this, sort of completely, after having been attacked by an animal. There is millions of years of evolution that will wake up and save you from getting hurt as it recognizes what's happening and instantly formulates a response to it.
- redisman 6y ago> A baby is not born with the knowledge of body movement Anyone who's witnessed a birth can tell you this is wrong.
- inetknght 6y agoPerhaps born is the wrong word. That freshly-born baby did have nine months of gestation during which it's undoubtedly been exploring how to move about and sense its very limited environment.
- TechBro8615 6y ago“Pushed to prod”
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- MereInterest 6y agoSide note, is "AV" to mean "autonomous vehicles" (assumed from context) a common usage? I've only ever heard it mean "audio/visual".
- keenmaster 6y agoYes. That usage of the acronym works when there are enough context clues. I think it will supplant the "audio/visual" meaning as autonomous vehicles become more salient. Here is text from an old job posting at Ford quoted on TechCrunch: "We are seeking exceptional candidates to join our growing Autonomous Vehicle (AV) business team!" https://techcrunch.com/2019/03/13/ford-is-expanding-its-self-driving-vehicle-program-to-austin/ https://techcrunch.com/2019/03/13/ford-is-expanding-its-self...
- yongjik 6y ago> autonomous vehicles ...oh that makes so much more sense! -.-
- deleted 6y ago[deleted]
- mywittyname 6y agoWhy do you need a neural network when you have the Hamiltonian mechanics of the system modeled? I've always understood Langrangian/Hamiltonian mechanics to be methods of modeling the behavior of a system through the decomposition of the external constraints and forces acting on a body. In other words you can understand a complex model by doing some calculus on the less complex constituents of the model. I'm probably misunderstanding what the accomplished, but it sounds like they've increased the accuracy of a neural network model of a system, notably for edge cases, by training it on complete a complete model of said system.
- joshlk 6y agoFor some systems even with the Lagrangian/Hamiltonian setup your solving differential equations with numerical techniques that has error. It might be that the neural networks has less error than the standard techniques. This is a guess.
- seesawtron 6y agoHamiltonian NNs are not a new thing. There was a NIPS 2019 paper [0] that attempted to do that same for some toy problems. In general the idea of including model or context-based information into neural networks goes along the line of Kahneman's System I and System II of the human mind. System I is the "emotional" brain that is fast and makes decisions quickly while System II is the "rational" brain that is slow and expensive and takes time to compute a response. Researchers have been trying to develop ML models that utilize this dichotomy by building corresponding dual modules but the major challenge remains in efficiently embedding the assumptions of the world dynamics into the models. [0] https://arxiv.org/abs/1906.01563 https://arxiv.org/abs/1906.01563 [1] https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow
- deleted 6y ago[deleted]
- noobermin 6y agoTo be frank, this should be the reference, compare to numerical integration and see which is better.
- athesyn 6y agoThis sounds pretty terrifying.
- jefft255 6y agoBut... why?
- civil_engineer 6y agoCareful there, athesyn. No need to offend our computer overlords.
- vajrabum 6y agoI believe this refers to work presented in this journal article. https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.062207 https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.0... Abstract: Artificial neural networks are universal function approximators. They can forecast dynamics, but they may need impractically many neurons to do so, especially if the dynamics is chaotic. We use neural networks that incorporate Hamiltonian dynamics to efficiently learn phase space orbits even as nonlinear systems transition from order to chaos. We demonstrate Hamiltonian neural networks on a widely used dynamics benchmark, the Hénon-Heiles potential, and on nonperturbative dynamical billiards. We introspect to elucidate the Hamiltonian neural network forecasting.
- cmehdy 6y agoThis sounds like the opposite of what Richard Sutton seemed to advocate for in his "Bitter Lesson"[0]. I don't know nearly enough to advocate for one thing or the other, but it is fascinating to see that those approaches seem to compete as we venture into the unknown. [0] http://incompleteideas.net/IncIdeas/BitterLesson.html http://incompleteideas.net/IncIdeas/BitterLesson.html
- fizixer 6y agoThey're not the opposite, and both are correct. Sutton is saying 'over a slightly longer time'. You can wait 20 more years and super-duper-deep-NN-on-steroids, and hardware a million times as big and powerful, would rediscover all of theoretical physics. Or you could inject some theoretical physics acquired by humans and make DNNs smarter today.
- cmehdy 6y agoI assume your 20 years is a guesstimate, and I do think it misses the point of what Sutton's writing is. The trap here is that there's always to be more computing in the future, so where do we draw the line? The idea is to think differently now, for the pursuit of actual progress down the road. Which, by the way, is exactly what people were doing about 40 years ago and what put down more than the foundations for all the tricks we're pulling these days.
- fizixer 6y agoI see what Sutton said as a "statistical learning and artificial intelligence" researcher in line with what the authors of the physics paper presented as "an application of learning research to computational science and engineering, CSE, surrounding physics". CSE researchers did not sit down and wait for AI researchers to learn the bitter lesson before they resumed their work. CSE research goes on independent of whether AI/GOFAI/ML has a winter, a summer, an ice age, or a global warming. It just so happens that in light of the recent progress of AI/ML, specifically 2012 to 2019, they see the utility of incorporating a tiny bit of ML to their vast array of methods. The paper shared in this thread is merely another attempt to advance such an incorporation. If it doesn't pan out, they go back to doing CSE on physics without any AI or ML.
- jariel 6y agoCan someone with AI knowledge please clarify - does this mean we can build 'rules based systems' into AI to synthesise intelligence from both domains? If so, this would be dramatic, no? If you could teach a translation service 'grammar' and then also leverage the pattern matching, could this be a 'fundamental' new idea in AI application? Or is this just something specific?
- samcodes 6y agoThey model a system which they know to be constrained by a closed-form equation called the Hamiltonian. They (cleverly, IMO) force the network’s predictions to be constrained by the Hamiltonian, by choosing the right output and loss function. I don’t see a way to generalize this to the procedural rule-based systems you describe, unless they too are governed by a fairly simple continuous function Like the Hamiltonian. I don’t know if it was “dramatic”, but it made me really happy.
- awinter-py 6y ago> the NAIL team incorporated Hamiltonian structure into neural networks ML non-expert here. Is this the same as having an extra column of your input data that's a hamiltonian of the raw input? Or a kind of neuron that can compute a hamiltonian on an observation? Or something more complicated. is this like a specialized 'functional region' in a biological brain? (broca's area, cerebellum)
- vutekst 6y agoAlso ML non-expert here. I think this is about a different kind of neuron(your 2nd suggestion). The paper another commenter linked says: Hamiltonian neural network (HNN) intakes position and momenta {q,p}, outputs the scalar function H, takes its gradient to find its position and momentum rates of change, and minimizes the loss <latex equation for a modified loss function that differs from traditional NN> which enforces Hamilton's equations of motion. https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.062207#fulltext https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.0...
- zone411 6y agoI haven't used HNNs in practice but it seems that the main difference from common NNs is that the loss function incorporates gradients. It's not a new type of a neuron.
- castratikron 6y agoSo can you teach a NN an equation of motion, and if so would it execute faster than numerically integrating said equation? Could have impacts in physics simulations although the accuracy might not be as good
- _iyig 6y agoBrings to mind this classic from the Jargon File: http://www.catb.org/~esr/jargon/html/koans.html http://www.catb.org/~esr/jargon/html/koans.html In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6. “What are you doing?”, asked Minsky. “I am training a randomly wired neural net to play Tic-Tac-Toe” Sussman replied. “Why is the net wired randomly?”, asked Minsky. “I do not want it to have any preconceptions of how to play”, Sussman said. Minsky then shut his eyes. “Why do you close your eyes?”, Sussman asked his teacher. “So that the room will be empty.” At that moment, Sussman was enlightened.
- nefasti 6y agoI don’t get it :(
- epaga 6y agoI think it means - just as closing your eyes doesn't mean the room becomes empty, wiring the learning network randomly doesn't mean you'll end up with no pre-conceptions (e.g. the rule system at least will need to be programmed in).
- blamestross 6y agoIt also doesn't avoid preconceptions directly. It just initializes random ones.
- thesz 6y agoWhy not shamelessly plug my work here? I see no reason not to. So, here it is: https://github.com/thesz/nn/tree/master/series https://github.com/thesz/nn/tree/master/series A proof of concept implementation of training neural networks process where loss function is a potential energy in Lagrangian function and I even incorporated "speed of light" - the "mass" of particle gets corrected using Lorenz multiplier m=m0/sqrt(1-v^2/c^2). Everything is done using ideas from quite interesting paper about power of lazy semantics: https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.4535 https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.... PS Proof-of-concept here means it is grossly inefficient, mainly due to amount of symbolic computation. Yet it works. In some cases. ;)