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AI in physics: are we facing a scientific revolution?

196 points by ezrakewa 6y ago

jhrmnn 6y ago
IMO not a revolution, but I can see a solid evolution. My reading of the work on embedding ML into physical models so far is that the best strategy is to take it as far as possible with the standard physics approach of abstraction and reduction, and once you exhaust that, apply ML to solve the remaining (often crucial) complex behavior.
LatteLazy 6y ago
I am not working in AI so I only know what I read here or on other sites etc. There seems to be a lot of buzz for AI and ML. But where actually are these techs succeeding currently? I feel like there is supposed to be a revolution going on everywhere but anywhere I look, it's just plans and press releases...
- brootstrap 6y ago
  just being jaded , all the money is in shareholders and investors pockets. Because once you have a disruptive AI-based startup, you have become englightened and now are on the course to change the future of human race with your amazing AI. (AI? i meant series of if statements and linear regression).
  - WrtCdEvrydy 6y ago
    AI means you have managed to throw a shitton of processing power at a problem and P-hack the shit out your results so it shows you made a significant improvement.
- yummypaint 6y ago
  In physics it just recently became mainstream to try experimenting with/incorporating ML into thesis projects. Most stuff ive seen it used for is signal processing related. An example might be particle track reconstruction in a time projection chamber with ML instead of a hough transform. I think it's inevitable that these methods will grow in application, but the two biggest problems right now in my opinion are reproducability and quantification of uncertainties. It's much easier to believe someone's stated uncertainties when you can see the analytic functions they were propagated through. There are ways to kindof work around this, but in my mind those two points are the main things holding back ML from broader applications in science. The article talks about ML tools closer to proof assistants / tools for experimentally driven mathematics. Less of a problem in that domain since the ML model only need make an interesting conjecture which can then be examined the traditional way.
  - ylem 6y ago
    I agree with you on UQ. For example, I have seen a couple of talks in my field of neutron scattering, where people are using denoising autoencooders to remove artifacts and fit data. It's also clear that no one has any idea how this effects the uncertainties on parameters for models that are fit on the denoised data, much less what happens if the models are not appropriate for the data. I think reproducibility can be tackled--at least some journals (shameless plug--I'm a lowly associate editor on science advance) are strongly encouraging people include data/code with publications. I have reviewed papers in Nature Comput. Materials where people have included data/jupyter notebooks (not perfect, but a very good start). It would be great if funding agencies started adding more teeth to requirements on data sharing. However, many more groups are putting their code on Github.
- 256lie 6y ago
  Any type of preditive analysis in high dimensional data (medical imaging, surveillance, remote sensing, machine translation, speech recognition/synthesis, music information retrieval). Other important work being done on causality, AI ethics/safety, and explainability (XAI) but little industry impact yet.
- suifbwish 6y ago
  Well.. for starters with machine learning, we can automatically make anyone naked by just applying a few algorithms and an ML model to a picture of a clothed person. Depending on who you are talking to that’s quite a break through for some people.
- comicjk 6y ago
  Predicting quantum mechanics energies of molecules using neural networks actually works, and can be used to speed up geometry optimization during drug discovery. See: https://arxiv.org/abs/1912.05079 https://arxiv.org/abs/1912.05079 https://chemrxiv.org/articles/Extending_the_Applicability_of_the_ANI_Deep_Learning_Molecular_Potential_to_Sulfur_and_Halogens/11819268/1 https://chemrxiv.org/articles/Extending_the_Applicability_of...
  - fock 6y ago
    well, this is not predicting "quantum mechanics energies", it's just parametrizing the molecular bond interaction potential with a neural network instead of an analytic function (such as e.g. a Lennard-Jones potential). It's nice, but not really quantum-mechanics level (which is maybe HF, DFT or coupled cluster), which takes a lot more cycles (but also allows to optimize geometries without knowing wether a bond exists)
    - comicjk 6y ago
      These neural network models do not need to know whether a bond exists - in fact, they have no concept of bond topology. They are designed to be a drop-in replacement for DFT in terms of energies and forces. The only inputs are XYZ coordinates and chemical element labels for the nuclei (and, in the near future, net charge of the system).
      - fock 6y ago
        with the non-bonded interactions parametrized with dimers it might work... sometimes (might be good enough for a lot of things though)
- bcrosby95 6y ago
  I always assumed most audio agents (e.g. Siri) use some form of AI and/or ML. And that Google search results probably has some somewhere in their pipeline. But don't know for sure.
- curiousllama 6y ago
  One interesting facets of the hype cycle is that it never focuses on what IS being done, because it's boring. Plus, tech improvements like this tend to be pretty operational in nature, so you wouldn't notice it without looking. Nonetheless, "AI" applications are pervasive: Auto - improved robotics, adaptive cruise control Finance - High Frequency Trading, Credit Risk Modeling (i.e. your Credit Score) Health Care - Health insurance risk estimation, Predictive staffing Government - Predictive policing, recidivism risk, benefits decisions Retail - improved customer targeting, inventory management etc etc etc - name an industry, I'll give you 3 examples. The issue isn't that it's not there; the issue is that it's BORING. And nobody gives a press release saying "we saved 0.4% of COGS from improved inventory demand forecasts," even if that represents $10M, because nobody cares. But boring doesn't mean it's not a bazillion dollar opportunity for a lot of companies.
- Veedrac 6y ago
  Siri, Google Assistant, speech detection, speech generation, textual photo library search, similar data augmentations for web search, Google Translate, recommendation algorithms, phone cameras, server cooling optimization, phone touch screens touch detection, video game upscaling, noise reduction in web calls, file prefetching, Google Maps, OCR, etc. AI has already won, most people just don't realize it.
  - dougmwne 6y ago
    Yes, thank you. There has already been a revolution over the past 5 years or so and many things that had been too audacious for science fiction became every day products. I think the ML revolution hit me personally about 5 years ago as I was able to get perfect speech recognition from my phone on a loud, crowded subway platform as a train was pulling in. I would have never thought that possible. I would have been skeptical if star trek had shown it.
  - the_af 6y ago
    I know there is this moving target where once a given piece of (allegedly) AI becomes mainstream, people claim "it's not AI". That said, what definition of AI are you using? It seems to me you're stretching it a bit...
    - Veedrac 6y ago
      From my position this narrowing of the term AI to refer only to ‘real intelligence’ has always seemed like little more than an attempt to control the narrative against an astonishingly successful trend of connectionist architectures doing incredible things. Nobody complained when Pac-Man's ghosts got called AI, but now it's political. All of what I mentioned are neural networks.
      - the_af 6y ago
        You got a point there with the neural networks. Though I never considered the ghosts from Pac-Man to have an AI. And why are you framing this as a "control the narrative"/political argument?
- calebkaiser 6y ago
  I think this is down to the loudest, most ambitious projects ("AGI! Fully autonomous vehicles!") getting a lot of press. The reality is, production ML is basically everywhere already: - Basically every piece of software that makes recommendations (Netflix, Google, Facebook, YouTube, Instagram, TikTok, etc.) uses machine learning. - Anything that makes time series forecasts (Uber/Maps ETA prediction, Walmart's 2 hour delivery, etc.) uses machine learning. - All the most popular speech-to-text assistants (Alexa, Google Assistant, Siri) use machine learning. - Smartphone cameras use machine learning to enhance picture quality. - A lot of very highly-used security monitoring solutions (Stripe's fraud detection, CloudFlare's bot detection, etc.) rely on machine learning. - A surprising number of physical commerce-type situations rely on machine learning (autonomous filling stations, for example, are pretty common in the trucking industry). - A lot of smart image manipulation tools (Instagram/SnapChat filters, etc.) rely on deep learning. - Email clients, particularly Gmail, use machine learning for spam filtering and for things like Smart Compose. - Some infrastructure products use machine learning, as in the case of EC2's predictive autoscaling. And those are just hyper-scale examples. There's a ton earlier-stage-but-still-in-production projects doing awesome things with ML: - Wildlife Protection Solutions legitimately doubled their detection rate of poachers in nature preserves with ML. - PostEra, Benevolent AI, and a bunch of other ML-based medicine platforms (medicinal chemistry, drug discovery, etc.) have already had exciting results. - There are a bunch of startups building industry-specific APIs out of models, like Glisten.ai, that are already profitable. - A number of computer vision products have been brought to market in the healthcare space—Ezra.ai screens full-body MRIs for cancers, SkinVision detects melanomas. - ML-powered chatbots are a pretty huge market. Olivia (a financial assistant) has something like 500k users. AdmitHub has successfully lowered summer melt (the attrition of college-intending students between spring and fall) at a bunch of colleges. Rasa is an entire platform that helps startups build NLP-powered bots. Sorry that went a bit long, but basically, the production ML space is incredibly deep, and spans most industries/company sizes. Unfortunately, press coverage of ML tends to treat it as if it's this mystic, sci-fi future technology, and as a result, this "Show me AGI or it's snake oil" mindset naturally emerges.
- reportgunner 6y ago
  > But where actually are these techs succeeding currently? Marketing.
- socialdemocrat 6y ago
  Speech recognition, google search engine, autonomous driving are just some of the areas we are seeing major advances in thanks to ML methods. Expert system approach to search and speech reckognition never worked well. Digital assistance predicting that it needs to remind you about an upcoming flight, going to work etc are other examples.
- mellosouls 6y ago
  It becomes easier to take the progress seriously and understand it when you drop the "intelligence"-style labels which misleads people into thinking something is there that isn't. Machine "learning" isn't ideal either, but is at least a bit more limited in the scope of what it conveys. Once you leave the hype baggage behind, it's more easy to see the significant progress that these tools - in concert with increased power and data resources - have made in many different areas over the last few years, some of them listed elsewhere in the answers to your question.
norcon4 6y ago
The site is throwing a security error for me: PR_CONNECT_RESET_ERROR Anybody else have the same issue? Or is the site just being hugged to death.
- LatteLazy 6y ago
  I'm OK (on mobile, android and chrome).
- pmontra 6y ago
  Do you use (willingly or not) any proxy, including an antivirus? The problem might be there.
timwaagh 6y ago
I think this is pretty significant. I would have guessed this to be among the very last things to be automated.
- ben_w 6y ago
  I was expecting AI to become an indispensable part of science well before it was able to turn natural language descriptions into functional code, but: https://mobile.twitter.com/sharifshameem/status/1284103765218299904 https://mobile.twitter.com/sharifshameem/status/128410376521...
cameronperot 6y ago
I'm studying in the intersection of physics and data science, and I think there's a number of places where physics can benefit from ML. From my current point of view though, most of these applications lie more on the experimental/computational sides of physics rather than the theoretical side. One of the current use cases is using ML to aid in the processing and analysis of data obtained from experiments. I would like to see more truly innovative work done on the theoretical side, but I don't think we'll see "AI" bridge the gap between QFT and GR any time soon. I think in order for something like that to happen we need a new approach, as the current approach of throwing deep learning models at it doesn't feel like the right answer. On a more general note, the SciML organization [1] has been quite successful in helping incorporating more ML into science. [1] https://sciml.ai/ https://sciml.ai/
- md2020 6y ago
  I agree that the potential impact of ML on the theoretical side is very exciting. I think there’s a lot of bridging to be done between the most advanced mathematics and the most advanced physics that could lead to new insight, but it’s a hard problem for humans to tackle since we have very few people who are deeply proficient in both—although it is becoming more common. I’m thinking something like GPT-3 trained on literature in both fields could be the kind of thing we want, but like you I still doubt that a DL system is likely to come up with any real insight. I’d like to be proven wrong, though.
  - spyder 6y ago
    GPT-3 is already not too bad with basic physics: https://www.lesswrong.com/posts/L5JSMZQvkBAx9MD5A/is-gpt-3-capable-of-reasoning https://www.lesswrong.com/posts/L5JSMZQvkBAx9MD5A/is-gpt-3-c... And this is without training on the specific task. It's getting scary...
- ylem 6y ago
  Really cool! What problem are you working on? I live on the experimental side. At least in condensed matter, there are people having fun on the theory side as well.
wenc 6y ago
There's a ML group at Fermilab just outside Chicago working on ML applications in high energy physics and astrophysics. https://computing.fnal.gov/machine-learning/ https://computing.fnal.gov/machine-learning/ One of the "AI" applications I remember seeing -- potentially applicable outside physics -- involved using CNNs to read a 2D graph (as in graphical plot, not G = (V,E)) in order to visually detect certain patterns/aberration. (probably many physics groups around the world are doing the same) At first glance this sounds kind of silly and trivial -- one might say, why not just detect those patterns from the data arrays directly? Instead of from a bitmap image of a plot of the data? Unfortunately some patterns are contextual. A trained human eye can detect them easily, while writing a foolproof mathematical algorithm is difficult: e.g. it has to pick out the pattern, apply a bunch of exclusion rules etc. (One instance of this, for example, is an old mechanic telling you what's going on under the hood just from listening the vibrations of a car, while a traditional DSP algorithm might not be able to do it as reliably because it hasn't seen all the patterns and contexts in which those sounds arise.) This is a domain where neural networks/transfer learning really shines. It can capture "intuition" by learning the surrounding context, rather than relying on handcrafted features. So Fermilab has an AI algorithm that looks at millions of graphs via a CNN, which replicates the work of thousands of human physicists looking for patterns. We've already seen examples of this in radiology.
- sjg007 6y ago
  Makes sense. A graph can be represented by a matrix which is what an image is.
  - oivey 6y ago
    Images and matrices are 2D data structures of numbers, but that is where the similarities end. An image is more like a vector, which matrices can be applied to. You would never matrix multiply an image onto another vector. Still, it isn’t uncommon to visualize matrices as images.
    - sjg007 6y ago
      Well a matrix is a collection of vectors so... I guess I somewhat agree.. You can certainly apply projections to images, I mean this is what photoshop does.
jshaqaw 6y ago
The article confuses me. I was doing symbolic genetic algorithms to derive formulas back in the mid-90s so that's not new. But this seems to suggest a combined genetic algorithm/NN approach is being used. Curious to see the underlying paper.
- jackcosgrove 6y ago
  I'm also interested to see how this is different from generalized additive models (GAMs - not GANs). It seems to be the same principle except with a genetic mutation and selection aspect.
ylem 6y ago
Shameless plug--The American Physical Society has a topical group on Data Science. Since our annual meeting was cancelled due to Covid, we've been running a free series of webinars on data science and physics: https://www.youtube.com/channel/UCfPG-nSsgnFeWuzgPcbKlCw/videos https://www.youtube.com/channel/UCfPG-nSsgnFeWuzgPcbKlCw/vid... If anyone is interested, we have one on data science in industry coming up: https://attendee.gotowebinar.com/register/6044839360356437776 https://attendee.gotowebinar.com/register/604483936035643777...
fmakunbound 6y ago
> If AI is like Columbus, computing power is Santa Maria Does that mean when AI finally arrives, it slaughters all of us?
- SiempreViernes 6y ago
  A lot of the death was due to the introduction of new diseases, so maybe AI is to blame for Covid-19? Also, in the similie I think humanity is supposed to be the Old world, so I'm really wondering who we're supposed to find and enslave ...
  - jessaustin 6y ago
    Everybody thinks they're the center of the universe. Sometimes they're right, for a time. When they decide they were wrong, they tear down the old statues, if only to make the new overlords feel welcome...
currymj 6y ago
Some applications in computational physics involve solving a "variational" problem, where you have some parameterized function and try to numerically find the parameters that minimize energy or error. This does not necessarily involve supervised learning from outside data as in this article -- it can be purely an optimization problem. But neural networks are very good parametric function approximators, generally better than what traditionally gets used in physics (b-splines or whatever). So people have started to design neural networks that are well-suited as function approximators for specific physical systems. It's fairly straightforward -- it's not an "AI" that has "knowledge" of "physics" -- just using modern techniques and hardware to solve a numerical minimization problem. I think this will probably become pretty widespread. It won't be flashy or exciting though -- it will be boring to anyone but specialists, as the rest of machine learning ought to be.
- wenc 6y ago
  So the idea of surrogate models (for parameter estimation) has been around for some time, where f(x, θ) is some (computationally) simplified model of a complex model/simulation (x = factors, θ = parameters). f can be any arbitrary choice that works. Not sure if the choice of f being a NN is necessarily related to AI, where some cognitive function is being replicated. It is a good function approximator though.
- mumbisChungo 6y ago
  Why ought machine learning be boring to anyone but specialists? Does this imply that specialists ought to be born, rather than become specialists out of interest?
  - catalogia 6y ago
    I don't think they mean ML in general is boring, just that this particular application of it isn't particularly flashy.
    - mumbisChungo 6y ago
      Maybe I misinterpreted this: >it will be boring to anyone but specialists, as the rest of machine learning ought to be.
ylem 6y ago
There are a lot of cool advances in AI and physics. In my particular field of condensed matter physics, a number come to mind. One is trying to automatically extract synthesis recipes from the literature. Imagine that you want to see how people have synthesized a given solid state compound. Then searching through the literature can be painful. A great collaboration from MIT/Berkeley did this using NLP. I don't know what blood oaths they signed, but they were able to obtain a huge corpus of articles. But, how to know if an article contains a synthesis recipe? They set up their internal version of Mechanical Turk and had their students label a number of articles. Then they had to find the recipes, represent them as a DAG, etc. They have now incorporated the result with the Materials project (https://materialsproject.org/apps/synthesis/# https://materialsproject.org/apps/synthesis/#). There are groups that are using graph neural networks to understand statistical mechanics and microscopy. There are also a number of groups working on trying to automate synthesis (most of it is Gaussian process based, a handful of us are trying reinforcement learning--it's painful). On the theory side, there is work speeding up simulation efforts (ex. DFT functionals) as well as determining if models and experiment agree (Eun Ah Kim rocks!). Outside of my field, there has been a push with Lagrangian/Hamiltonian NNs that is really cool in that you get interpretability for "free" when you encode physics into the structure of the network. Back to my field, Patrick Riley (Google) has played with this in the context of encoding symmetries in a material into the structure of NNs. There are of course challenges. In some fields, there is a huge amount of data--in others, we have relatively small data, but rich models. There are questions on what are the correct representations to use. Not to mention the usual issues of trust/interpretability. There's also a question of talent given opportunities in industry.
pjc50 6y ago
GPT3 + replication crisis = huge volume of scientific papers produced, but nobody can know if they're accurate or not. Landmark to watch for will be when the first GPT-generated paper gets a citation in a human-authored paper without the human realising.
tabtab 6y ago
Re: "scientific progress could be bound by Moore's law and increase so much." Moore's law appears to be slumping lately. Re: "This coincides with our previous experience in physics, says Cranmer: "The language of simple symbolic models describes the universe correctly." As an approximation, yes, but that doesn't mean a "true" formula has necessarily been found.
- Veedrac 6y ago
  > Moore's law appears to be slumping lately. Not so. https://docs.google.com/spreadsheets/d/1NNOqbJfcISFyMd0EsSrhppW7PT6GCfnrVGhxhLA5PVw https://docs.google.com/spreadsheets/d/1NNOqbJfcISFyMd0EsSrh...
Myrmornis 6y ago
> If you want to read a linear function from the data in a two-dimensional coordinate system in math lessons, you can do it in five minutes - or quickly watch a video on YouTube.The situation is different for more complex tasks: Physicists, for example, have been trying to combine quantum theory and relativity theory for almost a hundred years. And if this succeeds, it could take generations to clarify the effects, says physicist Lee Smolin . What on Earth does that paragraph mean? Parts of the article read to me like they were generated automatically, but other parts don't.
test6554 6y ago
Columbus is probably not the best character to use for analogies... "If AI is like Columbus, computing power is Santa Maria" and intractable physics problems are like... indigenous people?
BrandoElFollito 6y ago
I am actually surprised this is not more mainstream. 20 years ago I wrote my PhD thesis in physics, using genetic algorithms and neural networks to "guess" some basic physical behaviour in particle physics. It was difficult to find good reporters because the application was quite exotic but I felt that this is something which would be worth investigating. I quit academia afterwards and did not come back - but I am happy to see that this road is back on the radar.
- blablabla123 6y ago
  I wrote my diploma thesis 10 years ago and had to do a lot of pen and paper calculations. Actually it was kind of standard stuff (Lagrangians of Standard model, calculating parametrized decay widths) At that time I really hoped I could automatize the error-prone steps of plugging in and simplifying equations but I found nothing, except for isolated steps. Maybe this is also due to the fact that the most powerful tools for manipulating symbolic expressions are closed source. Not sure how it is now but as long as these tools are not expressive enough to work "end-to-end" with SM Lagrange densities, I doubt anything innovative could be done by automatizing that with AI.
  - physicsgraph 6y ago
    That problem of pen-and-paper calculations featuring unintended errors is what I try addressing in a project I work on [1]. My approach is to use Sympy (which has a lot of Physics support) to validate expressions entered by a human. Not quite the AI-focus of this thread, but still a machine augmenting the work of researchers. To your point about the complexity of the math, the Physics Derivation Graph is able to handle simple inference rules but there's nothing preventing more advanced use. [1] https://derivationmap.net/ https://derivationmap.net/
- stainforth 6y ago
  What field or occupation followed for you?
  - BrandoElFollito 6y ago
visarga 6y ago
> For this they use so called neural graph networks (GNN). These neural networks rely on graphs instead of layers arranged one after the other. This affirmation shows the author has little idea about GNNs. GNNs have layers, and each layer is a graph. In order to implement the graph GNNs use the adjacency matrix to propagate information along the edges. But there are multiple layers of GNN, without multiple layers they would not be able to do multi-hop inferences.
nestorD 6y ago
TLDR: Using neural network to model physical systems as black boxes and then, later, using symbolic regression (genetic algorithm to find a formula that fits a function) on the model to make it explainable and improve its generalization capacities. The system managed to reinvent Newton's second law and find a formula to predict the density of dark matter. (note that symbolic regression is often said to improve explainability but that, left unchecked, it tends to produce huge unwieldy formulas full of magical constants)
fxtentacle 6y ago
No, we have merely found a new and slightly better way of interpolating between (slow and properly calculated) known data points.
godelski 6y ago
I work in this space (intersection of science and ML) and I can say with high certainty that Betteridge's Law[0] is likely accurate. But then again, pretty much any article that uses AI instead of ML is hogwash too. Are we crediting someone with this one? [0] https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headlines https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headline...
ricksharp 6y ago
What is the purpose of the neural network and how does that help generate the symbolic regression using genetic algorithms? Are they somehow using the parameters of the ANN to seed the generic algorithms (and structure)?
dkural 6y ago
The author has no idea what he's writing about, calling it a "graphene" network, and several awkward phrasings about dark matter etc. Read the papers instead.
LoSboccacc 6y ago
curve fitting is no science, no matter how deep the net goes, it's great for calculus, and obtain numerical models of what we already can measure, but all the correlation would require an human to verify and a theory to be synthesize post fact, especially if there's a margin of error or confidence, as generating infinite correlation would only result in finding models that are not there this shows the effect of infinite dissecting data searching without a theory pretty well https://xkcd.com/882/ https://xkcd.com/882/
andrewon 6y ago
Not sure about if this is really science. Physical formula are derived from known physical laws in order to understand the original of the phenomenon. If theorists are allowed make up arbitrary formula of course it can fit the data with less error.
mola 6y ago
I think it's more of an engineering revolution. The opaqueness of (at least current) machine learning means we won't really enhance our understanding of the universe, just our ability to predict it. Some people would argue that these things are one, I think otherwise.
staycoolboy 6y ago
As someone who has worked on ADAS software and saw a simple un-optimized ML object detector beat a custom hardware solution at both speed and accuracy, I can honestly say machine learning is amazing. Just in this domain alone, excluding the 100 other applications of ML, and the fact that we haven't even begun optimization in earnest, I certainly believe ML will change the direction of computing. It already has: look at where investment and research dollars have gone. (not to say that trends don't happen, but when I saw the performance results I thought: sh*t, this is big.) Add to this the rise of the qubit, and the next 50 years are going to be even crazier than the last 50. Yes, I am a proselytizer of school of James Gleick. "Faster" was a prophecy[1]. [1] https://www.amazon.com/Faster-Acceleration-Just-About-Everything/dp/067977548X https://www.amazon.com/Faster-Acceleration-Just-About-Everyt...
tim333 6y ago
I've often thought that maybe the reason we can't get a quantized theory of gravity is that it's too complicated for human brains rather than we need a bigger accelerator. You might be able to get somewhere with a brute force type approach of almost randomly coming up with equations for a theory and then trying to see if they make any sense and predict anything interesting. I suspect a breakthrough may be like AlphaGo's move 37 where it leaves the humans saying wow what happened there? https://www.huffpost.com/entry/move-37-or-how-ai-can-change-the-world_b_58399703e4b0a79f7433b675 https://www.huffpost.com/entry/move-37-or-how-ai-can-change-...

196 points by ezrakewa 6y ago

IMO not a revolution, but I can see a solid evolution. My reading of the work on embedding ML into physical models so far is that the best strategy is to take it as far as possible with the standard physics approach of abstraction and reduction, and once you exhaust that, apply ML to solve the remaining (often crucial) complex behavior.

LatteLazy 6y ago

I am not working in AI so I only know what I read here or on other sites etc. There seems to be a lot of buzz for AI and ML. But where actually are these techs succeeding currently? I feel like there is supposed to be a revolution going on everywhere but anywhere I look, it's just plans and press releases...

brootstrap 6y ago
just being jaded , all the money is in shareholders and investors pockets. Because once you have a disruptive AI-based startup, you have become englightened and now are on the course to change the future of human race with your amazing AI. (AI? i meant series of if statements and linear regression).
- WrtCdEvrydy 6y ago
  AI means you have managed to throw a shitton of processing power at a problem and P-hack the shit out your results so it shows you made a significant improvement.
yummypaint 6y ago
In physics it just recently became mainstream to try experimenting with/incorporating ML into thesis projects. Most stuff ive seen it used for is signal processing related. An example might be particle track reconstruction in a time projection chamber with ML instead of a hough transform. I think it's inevitable that these methods will grow in application, but the two biggest problems right now in my opinion are reproducability and quantification of uncertainties. It's much easier to believe someone's stated uncertainties when you can see the analytic functions they were propagated through. There are ways to kindof work around this, but in my mind those two points are the main things holding back ML from broader applications in science. The article talks about ML tools closer to proof assistants / tools for experimentally driven mathematics. Less of a problem in that domain since the ML model only need make an interesting conjecture which can then be examined the traditional way.
- ylem 6y ago
  I agree with you on UQ. For example, I have seen a couple of talks in my field of neutron scattering, where people are using denoising autoencooders to remove artifacts and fit data. It's also clear that no one has any idea how this effects the uncertainties on parameters for models that are fit on the denoised data, much less what happens if the models are not appropriate for the data. I think reproducibility can be tackled--at least some journals (shameless plug--I'm a lowly associate editor on science advance) are strongly encouraging people include data/code with publications. I have reviewed papers in Nature Comput. Materials where people have included data/jupyter notebooks (not perfect, but a very good start). It would be great if funding agencies started adding more teeth to requirements on data sharing. However, many more groups are putting their code on Github.
256lie 6y ago
Any type of preditive analysis in high dimensional data (medical imaging, surveillance, remote sensing, machine translation, speech recognition/synthesis, music information retrieval). Other important work being done on causality, AI ethics/safety, and explainability (XAI) but little industry impact yet.
suifbwish 6y ago
Well.. for starters with machine learning, we can automatically make anyone naked by just applying a few algorithms and an ML model to a picture of a clothed person. Depending on who you are talking to that’s quite a break through for some people.
comicjk 6y ago
Predicting quantum mechanics energies of molecules using neural networks actually works, and can be used to speed up geometry optimization during drug discovery. See: https://arxiv.org/abs/1912.05079 https://arxiv.org/abs/1912.05079 https://chemrxiv.org/articles/Extending_the_Applicability_of_the_ANI_Deep_Learning_Molecular_Potential_to_Sulfur_and_Halogens/11819268/1 https://chemrxiv.org/articles/Extending_the_Applicability_of...
- fock 6y ago
  well, this is not predicting "quantum mechanics energies", it's just parametrizing the molecular bond interaction potential with a neural network instead of an analytic function (such as e.g. a Lennard-Jones potential). It's nice, but not really quantum-mechanics level (which is maybe HF, DFT or coupled cluster), which takes a lot more cycles (but also allows to optimize geometries without knowing wether a bond exists)
  - comicjk 6y ago
    These neural network models do not need to know whether a bond exists - in fact, they have no concept of bond topology. They are designed to be a drop-in replacement for DFT in terms of energies and forces. The only inputs are XYZ coordinates and chemical element labels for the nuclei (and, in the near future, net charge of the system).
    - fock 6y ago
      with the non-bonded interactions parametrized with dimers it might work... sometimes (might be good enough for a lot of things though)
bcrosby95 6y ago
I always assumed most audio agents (e.g. Siri) use some form of AI and/or ML. And that Google search results probably has some somewhere in their pipeline. But don't know for sure.
curiousllama 6y ago
One interesting facets of the hype cycle is that it never focuses on what IS being done, because it's boring. Plus, tech improvements like this tend to be pretty operational in nature, so you wouldn't notice it without looking. Nonetheless, "AI" applications are pervasive: Auto - improved robotics, adaptive cruise control Finance - High Frequency Trading, Credit Risk Modeling (i.e. your Credit Score) Health Care - Health insurance risk estimation, Predictive staffing Government - Predictive policing, recidivism risk, benefits decisions Retail - improved customer targeting, inventory management etc etc etc - name an industry, I'll give you 3 examples. The issue isn't that it's not there; the issue is that it's BORING. And nobody gives a press release saying "we saved 0.4% of COGS from improved inventory demand forecasts," even if that represents $10M, because nobody cares. But boring doesn't mean it's not a bazillion dollar opportunity for a lot of companies.
Veedrac 6y ago
Siri, Google Assistant, speech detection, speech generation, textual photo library search, similar data augmentations for web search, Google Translate, recommendation algorithms, phone cameras, server cooling optimization, phone touch screens touch detection, video game upscaling, noise reduction in web calls, file prefetching, Google Maps, OCR, etc. AI has already won, most people just don't realize it.
- dougmwne 6y ago
  Yes, thank you. There has already been a revolution over the past 5 years or so and many things that had been too audacious for science fiction became every day products. I think the ML revolution hit me personally about 5 years ago as I was able to get perfect speech recognition from my phone on a loud, crowded subway platform as a train was pulling in. I would have never thought that possible. I would have been skeptical if star trek had shown it.
- the_af 6y ago
  I know there is this moving target where once a given piece of (allegedly) AI becomes mainstream, people claim "it's not AI". That said, what definition of AI are you using? It seems to me you're stretching it a bit...
  - Veedrac 6y ago
    From my position this narrowing of the term AI to refer only to ‘real intelligence’ has always seemed like little more than an attempt to control the narrative against an astonishingly successful trend of connectionist architectures doing incredible things. Nobody complained when Pac-Man's ghosts got called AI, but now it's political. All of what I mentioned are neural networks.
    - the_af 6y ago
      You got a point there with the neural networks. Though I never considered the ghosts from Pac-Man to have an AI. And why are you framing this as a "control the narrative"/political argument?
calebkaiser 6y ago
I think this is down to the loudest, most ambitious projects ("AGI! Fully autonomous vehicles!") getting a lot of press. The reality is, production ML is basically everywhere already: - Basically every piece of software that makes recommendations (Netflix, Google, Facebook, YouTube, Instagram, TikTok, etc.) uses machine learning. - Anything that makes time series forecasts (Uber/Maps ETA prediction, Walmart's 2 hour delivery, etc.) uses machine learning. - All the most popular speech-to-text assistants (Alexa, Google Assistant, Siri) use machine learning. - Smartphone cameras use machine learning to enhance picture quality. - A lot of very highly-used security monitoring solutions (Stripe's fraud detection, CloudFlare's bot detection, etc.) rely on machine learning. - A surprising number of physical commerce-type situations rely on machine learning (autonomous filling stations, for example, are pretty common in the trucking industry). - A lot of smart image manipulation tools (Instagram/SnapChat filters, etc.) rely on deep learning. - Email clients, particularly Gmail, use machine learning for spam filtering and for things like Smart Compose. - Some infrastructure products use machine learning, as in the case of EC2's predictive autoscaling. And those are just hyper-scale examples. There's a ton earlier-stage-but-still-in-production projects doing awesome things with ML: - Wildlife Protection Solutions legitimately doubled their detection rate of poachers in nature preserves with ML. - PostEra, Benevolent AI, and a bunch of other ML-based medicine platforms (medicinal chemistry, drug discovery, etc.) have already had exciting results. - There are a bunch of startups building industry-specific APIs out of models, like Glisten.ai, that are already profitable. - A number of computer vision products have been brought to market in the healthcare space—Ezra.ai screens full-body MRIs for cancers, SkinVision detects melanomas. - ML-powered chatbots are a pretty huge market. Olivia (a financial assistant) has something like 500k users. AdmitHub has successfully lowered summer melt (the attrition of college-intending students between spring and fall) at a bunch of colleges. Rasa is an entire platform that helps startups build NLP-powered bots. Sorry that went a bit long, but basically, the production ML space is incredibly deep, and spans most industries/company sizes. Unfortunately, press coverage of ML tends to treat it as if it's this mystic, sci-fi future technology, and as a result, this "Show me AGI or it's snake oil" mindset naturally emerges.
reportgunner 6y ago
> But where actually are these techs succeeding currently? Marketing.
socialdemocrat 6y ago
Speech recognition, google search engine, autonomous driving are just some of the areas we are seeing major advances in thanks to ML methods. Expert system approach to search and speech reckognition never worked well. Digital assistance predicting that it needs to remind you about an upcoming flight, going to work etc are other examples.
mellosouls 6y ago
It becomes easier to take the progress seriously and understand it when you drop the "intelligence"-style labels which misleads people into thinking something is there that isn't. Machine "learning" isn't ideal either, but is at least a bit more limited in the scope of what it conveys. Once you leave the hype baggage behind, it's more easy to see the significant progress that these tools - in concert with increased power and data resources - have made in many different areas over the last few years, some of them listed elsewhere in the answers to your question.

norcon4 6y ago

The site is throwing a security error for me: PR_CONNECT_RESET_ERROR Anybody else have the same issue? Or is the site just being hugged to death.

LatteLazy 6y ago
I'm OK (on mobile, android and chrome).
pmontra 6y ago
Do you use (willingly or not) any proxy, including an antivirus? The problem might be there.

timwaagh 6y ago

I think this is pretty significant. I would have guessed this to be among the very last things to be automated.

ben_w 6y ago
I was expecting AI to become an indispensable part of science well before it was able to turn natural language descriptions into functional code, but: https://mobile.twitter.com/sharifshameem/status/1284103765218299904 https://mobile.twitter.com/sharifshameem/status/128410376521...

cameronperot 6y ago

I'm studying in the intersection of physics and data science, and I think there's a number of places where physics can benefit from ML. From my current point of view though, most of these applications lie more on the experimental/computational sides of physics rather than the theoretical side. One of the current use cases is using ML to aid in the processing and analysis of data obtained from experiments. I would like to see more truly innovative work done on the theoretical side, but I don't think we'll see "AI" bridge the gap between QFT and GR any time soon. I think in order for something like that to happen we need a new approach, as the current approach of throwing deep learning models at it doesn't feel like the right answer. On a more general note, the SciML organization [1] has been quite successful in helping incorporating more ML into science. [1] https://sciml.ai/ https://sciml.ai/

md2020 6y ago
I agree that the potential impact of ML on the theoretical side is very exciting. I think there’s a lot of bridging to be done between the most advanced mathematics and the most advanced physics that could lead to new insight, but it’s a hard problem for humans to tackle since we have very few people who are deeply proficient in both—although it is becoming more common. I’m thinking something like GPT-3 trained on literature in both fields could be the kind of thing we want, but like you I still doubt that a DL system is likely to come up with any real insight. I’d like to be proven wrong, though.
- spyder 6y ago
  GPT-3 is already not too bad with basic physics: https://www.lesswrong.com/posts/L5JSMZQvkBAx9MD5A/is-gpt-3-capable-of-reasoning https://www.lesswrong.com/posts/L5JSMZQvkBAx9MD5A/is-gpt-3-c... And this is without training on the specific task. It's getting scary...
ylem 6y ago
Really cool! What problem are you working on? I live on the experimental side. At least in condensed matter, there are people having fun on the theory side as well.

wenc 6y ago

There's a ML group at Fermilab just outside Chicago working on ML applications in high energy physics and astrophysics. https://computing.fnal.gov/machine-learning/ https://computing.fnal.gov/machine-learning/ One of the "AI" applications I remember seeing -- potentially applicable outside physics -- involved using CNNs to read a 2D graph (as in graphical plot, not G = (V,E)) in order to visually detect certain patterns/aberration. (probably many physics groups around the world are doing the same) At first glance this sounds kind of silly and trivial -- one might say, why not just detect those patterns from the data arrays directly? Instead of from a bitmap image of a plot of the data? Unfortunately some patterns are contextual. A trained human eye can detect them easily, while writing a foolproof mathematical algorithm is difficult: e.g. it has to pick out the pattern, apply a bunch of exclusion rules etc. (One instance of this, for example, is an old mechanic telling you what's going on under the hood just from listening the vibrations of a car, while a traditional DSP algorithm might not be able to do it as reliably because it hasn't seen all the patterns and contexts in which those sounds arise.) This is a domain where neural networks/transfer learning really shines. It can capture "intuition" by learning the surrounding context, rather than relying on handcrafted features. So Fermilab has an AI algorithm that looks at millions of graphs via a CNN, which replicates the work of thousands of human physicists looking for patterns. We've already seen examples of this in radiology.

sjg007 6y ago
Makes sense. A graph can be represented by a matrix which is what an image is.
- oivey 6y ago
  Images and matrices are 2D data structures of numbers, but that is where the similarities end. An image is more like a vector, which matrices can be applied to. You would never matrix multiply an image onto another vector. Still, it isn’t uncommon to visualize matrices as images.
  - sjg007 6y ago
    Well a matrix is a collection of vectors so... I guess I somewhat agree.. You can certainly apply projections to images, I mean this is what photoshop does.

jshaqaw 6y ago

The article confuses me. I was doing symbolic genetic algorithms to derive formulas back in the mid-90s so that's not new. But this seems to suggest a combined genetic algorithm/NN approach is being used. Curious to see the underlying paper.

jackcosgrove 6y ago
I'm also interested to see how this is different from generalized additive models (GAMs - not GANs). It seems to be the same principle except with a genetic mutation and selection aspect.

ylem 6y ago

Shameless plug--The American Physical Society has a topical group on Data Science. Since our annual meeting was cancelled due to Covid, we've been running a free series of webinars on data science and physics: https://www.youtube.com/channel/UCfPG-nSsgnFeWuzgPcbKlCw/videos https://www.youtube.com/channel/UCfPG-nSsgnFeWuzgPcbKlCw/vid... If anyone is interested, we have one on data science in industry coming up: https://attendee.gotowebinar.com/register/6044839360356437776 https://attendee.gotowebinar.com/register/604483936035643777...

fmakunbound 6y ago

> If AI is like Columbus, computing power is Santa Maria Does that mean when AI finally arrives, it slaughters all of us?

SiempreViernes 6y ago
A lot of the death was due to the introduction of new diseases, so maybe AI is to blame for Covid-19? Also, in the similie I think humanity is supposed to be the Old world, so I'm really wondering who we're supposed to find and enslave ...
- jessaustin 6y ago
  Everybody thinks they're the center of the universe. Sometimes they're right, for a time. When they decide they were wrong, they tear down the old statues, if only to make the new overlords feel welcome...

currymj 6y ago

Some applications in computational physics involve solving a "variational" problem, where you have some parameterized function and try to numerically find the parameters that minimize energy or error. This does not necessarily involve supervised learning from outside data as in this article -- it can be purely an optimization problem. But neural networks are very good parametric function approximators, generally better than what traditionally gets used in physics (b-splines or whatever). So people have started to design neural networks that are well-suited as function approximators for specific physical systems. It's fairly straightforward -- it's not an "AI" that has "knowledge" of "physics" -- just using modern techniques and hardware to solve a numerical minimization problem. I think this will probably become pretty widespread. It won't be flashy or exciting though -- it will be boring to anyone but specialists, as the rest of machine learning ought to be.

wenc 6y ago
So the idea of surrogate models (for parameter estimation) has been around for some time, where f(x, θ) is some (computationally) simplified model of a complex model/simulation (x = factors, θ = parameters). f can be any arbitrary choice that works. Not sure if the choice of f being a NN is necessarily related to AI, where some cognitive function is being replicated. It is a good function approximator though.
mumbisChungo 6y ago
Why ought machine learning be boring to anyone but specialists? Does this imply that specialists ought to be born, rather than become specialists out of interest?
- catalogia 6y ago
  I don't think they mean ML in general is boring, just that this particular application of it isn't particularly flashy.
  - mumbisChungo 6y ago
    Maybe I misinterpreted this: >it will be boring to anyone but specialists, as the rest of machine learning ought to be.

ylem 6y ago

There are a lot of cool advances in AI and physics. In my particular field of condensed matter physics, a number come to mind. One is trying to automatically extract synthesis recipes from the literature. Imagine that you want to see how people have synthesized a given solid state compound. Then searching through the literature can be painful. A great collaboration from MIT/Berkeley did this using NLP. I don't know what blood oaths they signed, but they were able to obtain a huge corpus of articles. But, how to know if an article contains a synthesis recipe? They set up their internal version of Mechanical Turk and had their students label a number of articles. Then they had to find the recipes, represent them as a DAG, etc. They have now incorporated the result with the Materials project (https://materialsproject.org/apps/synthesis/# https://materialsproject.org/apps/synthesis/#). There are groups that are using graph neural networks to understand statistical mechanics and microscopy. There are also a number of groups working on trying to automate synthesis (most of it is Gaussian process based, a handful of us are trying reinforcement learning--it's painful). On the theory side, there is work speeding up simulation efforts (ex. DFT functionals) as well as determining if models and experiment agree (Eun Ah Kim rocks!). Outside of my field, there has been a push with Lagrangian/Hamiltonian NNs that is really cool in that you get interpretability for "free" when you encode physics into the structure of the network. Back to my field, Patrick Riley (Google) has played with this in the context of encoding symmetries in a material into the structure of NNs. There are of course challenges. In some fields, there is a huge amount of data--in others, we have relatively small data, but rich models. There are questions on what are the correct representations to use. Not to mention the usual issues of trust/interpretability. There's also a question of talent given opportunities in industry.

pjc50 6y ago

GPT3 + replication crisis = huge volume of scientific papers produced, but nobody can know if they're accurate or not. Landmark to watch for will be when the first GPT-generated paper gets a citation in a human-authored paper without the human realising.

tabtab 6y ago

Re: "scientific progress could be bound by Moore's law and increase so much." Moore's law appears to be slumping lately. Re: "This coincides with our previous experience in physics, says Cranmer: "The language of simple symbolic models describes the universe correctly." As an approximation, yes, but that doesn't mean a "true" formula has necessarily been found.

Veedrac 6y ago
> Moore's law appears to be slumping lately. Not so. https://docs.google.com/spreadsheets/d/1NNOqbJfcISFyMd0EsSrhppW7PT6GCfnrVGhxhLA5PVw https://docs.google.com/spreadsheets/d/1NNOqbJfcISFyMd0EsSrh...

Myrmornis 6y ago

> If you want to read a linear function from the data in a two-dimensional coordinate system in math lessons, you can do it in five minutes - or quickly watch a video on YouTube.The situation is different for more complex tasks: Physicists, for example, have been trying to combine quantum theory and relativity theory for almost a hundred years. And if this succeeds, it could take generations to clarify the effects, says physicist Lee Smolin . What on Earth does that paragraph mean? Parts of the article read to me like they were generated automatically, but other parts don't.

test6554 6y ago

Columbus is probably not the best character to use for analogies... "If AI is like Columbus, computing power is Santa Maria" and intractable physics problems are like... indigenous people?

BrandoElFollito 6y ago

I am actually surprised this is not more mainstream. 20 years ago I wrote my PhD thesis in physics, using genetic algorithms and neural networks to "guess" some basic physical behaviour in particle physics. It was difficult to find good reporters because the application was quite exotic but I felt that this is something which would be worth investigating. I quit academia afterwards and did not come back - but I am happy to see that this road is back on the radar.

blablabla123 6y ago
I wrote my diploma thesis 10 years ago and had to do a lot of pen and paper calculations. Actually it was kind of standard stuff (Lagrangians of Standard model, calculating parametrized decay widths) At that time I really hoped I could automatize the error-prone steps of plugging in and simplifying equations but I found nothing, except for isolated steps. Maybe this is also due to the fact that the most powerful tools for manipulating symbolic expressions are closed source. Not sure how it is now but as long as these tools are not expressive enough to work "end-to-end" with SM Lagrange densities, I doubt anything innovative could be done by automatizing that with AI.
- physicsgraph 6y ago
  That problem of pen-and-paper calculations featuring unintended errors is what I try addressing in a project I work on [1]. My approach is to use Sympy (which has a lot of Physics support) to validate expressions entered by a human. Not quite the AI-focus of this thread, but still a machine augmenting the work of researchers. To your point about the complexity of the math, the Physics Derivation Graph is able to handle simple inference rules but there's nothing preventing more advanced use. [1] https://derivationmap.net/ https://derivationmap.net/
stainforth 6y ago
What field or occupation followed for you?
- BrandoElFollito 6y ago

visarga 6y ago

> For this they use so called neural graph networks (GNN). These neural networks rely on graphs instead of layers arranged one after the other. This affirmation shows the author has little idea about GNNs. GNNs have layers, and each layer is a graph. In order to implement the graph GNNs use the adjacency matrix to propagate information along the edges. But there are multiple layers of GNN, without multiple layers they would not be able to do multi-hop inferences.

nestorD 6y ago

TLDR: Using neural network to model physical systems as black boxes and then, later, using symbolic regression (genetic algorithm to find a formula that fits a function) on the model to make it explainable and improve its generalization capacities. The system managed to reinvent Newton's second law and find a formula to predict the density of dark matter. (note that symbolic regression is often said to improve explainability but that, left unchecked, it tends to produce huge unwieldy formulas full of magical constants)

fxtentacle 6y ago

No, we have merely found a new and slightly better way of interpolating between (slow and properly calculated) known data points.

godelski 6y ago

I work in this space (intersection of science and ML) and I can say with high certainty that Betteridge's Law[0] is likely accurate. But then again, pretty much any article that uses AI instead of ML is hogwash too. Are we crediting someone with this one? [0] https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headlines https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headline...

ricksharp 6y ago

What is the purpose of the neural network and how does that help generate the symbolic regression using genetic algorithms? Are they somehow using the parameters of the ANN to seed the generic algorithms (and structure)?

dkural 6y ago

The author has no idea what he's writing about, calling it a "graphene" network, and several awkward phrasings about dark matter etc. Read the papers instead.

LoSboccacc 6y ago

curve fitting is no science, no matter how deep the net goes, it's great for calculus, and obtain numerical models of what we already can measure, but all the correlation would require an human to verify and a theory to be synthesize post fact, especially if there's a margin of error or confidence, as generating infinite correlation would only result in finding models that are not there this shows the effect of infinite dissecting data searching without a theory pretty well https://xkcd.com/882/ https://xkcd.com/882/

andrewon 6y ago

Not sure about if this is really science. Physical formula are derived from known physical laws in order to understand the original of the phenomenon. If theorists are allowed make up arbitrary formula of course it can fit the data with less error.

mola 6y ago

I think it's more of an engineering revolution. The opaqueness of (at least current) machine learning means we won't really enhance our understanding of the universe, just our ability to predict it. Some people would argue that these things are one, I think otherwise.

staycoolboy 6y ago

As someone who has worked on ADAS software and saw a simple un-optimized ML object detector beat a custom hardware solution at both speed and accuracy, I can honestly say machine learning is amazing. Just in this domain alone, excluding the 100 other applications of ML, and the fact that we haven't even begun optimization in earnest, I certainly believe ML will change the direction of computing. It already has: look at where investment and research dollars have gone. (not to say that trends don't happen, but when I saw the performance results I thought: sh*t, this is big.) Add to this the rise of the qubit, and the next 50 years are going to be even crazier than the last 50. Yes, I am a proselytizer of school of James Gleick. "Faster" was a prophecy[1]. [1] https://www.amazon.com/Faster-Acceleration-Just-About-Everything/dp/067977548X https://www.amazon.com/Faster-Acceleration-Just-About-Everyt...

tim333 6y ago

I've often thought that maybe the reason we can't get a quantized theory of gravity is that it's too complicated for human brains rather than we need a bigger accelerator. You might be able to get somewhere with a brute force type approach of almost randomly coming up with equations for a theory and then trying to see if they make any sense and predict anything interesting. I suspect a breakthrough may be like AlphaGo's move 37 where it leaves the humans saying wow what happened there? https://www.huffpost.com/entry/move-37-or-how-ai-can-change-the-world_b_58399703e4b0a79f7433b675 https://www.huffpost.com/entry/move-37-or-how-ai-can-change-...