AI in physics: are we facing a scientific revolution?

https://www.4alltech.com/2020/07/ai-in-physics-are-we-facing-science.html

by ezrakewa • 6 years ago

196 131 6 years ago

jhrmnn2• 6 years 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.

LatteLazy2• 6 years 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• 6 years 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• 6 years 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• 6 years 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.

ylem5• 6 years 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• 6 years 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• 6 years 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.

comicjk2• 6 years 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://chemrxiv.org/articles/Extending_the_Applicability_of...

fock2• 6 years 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)

bcrosby95• 6 years 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• 6 years 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.

Veedrac4• 6 years 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• 6 years 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_af2• 6 years 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...

goatlover• 6 years ago

Won what? Is there a competition? Humans still have jobs. Humans are still politicians, judges, CEOs, generals. They even still play chess!

All those successful forms of AI are narrow, not the AGI of science fiction (like Data, Skynet, HAL) or Ray Kurzweil predictions. AI is a tool humans use to extend human capabilities. It always has been. Maybe someday it will be something more.

calebkaiser• 6 years 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• 6 years ago

But where actually are these techs succeeding currently?

Marketing.

socialdemocrat• 6 years 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• 6 years 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• 6 years 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.

LatteLazy2• 6 years ago

I'm OK (on mobile, android and chrome).

pmontra• 6 years ago

Do you use (willingly or not) any proxy, including an antivirus? The problem might be there.

timwaagh• 6 years ago

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

ben_w• 6 years 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/128410376521...

cameronperot3• 6 years 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/

md2020• 6 years 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• 6 years ago

GPT-3 is already not too bad with basic physics:

https://www.lesswrong.com/posts/L5JSMZQvkBAx9MD5A/is-gpt-3-c...

And this is without training on the specific task. It's getting scary...

ylem5• 6 years 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.

cameronperot3• 6 years ago

I'm not working on any specific problem yet, but for my master's thesis I'm hoping to do something related to the use of neural networks in numerical solutions to differential equations. Along the lines of this sort of stuff [2].

[2] https://arxiv.org/pdf/2001.04385.pdf

p1esk• 6 years ago

On the theoretical side, ML can be used to find a conceptual pattern in the existing literature. E.g. here's a paragraph describing a novel idea, go read all physics (and beyond) papers and find those that describe similar ideas.

wenc5• 6 years 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/

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.

sjg0074• 6 years ago

Makes sense. A graph can be represented by a matrix which is what an image is.

oivey• 6 years 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.

gspr5• 6 years ago

An image "is" not a matrix. Yes, the values of each pixel can be considered an entry of a matrix, but that's where the similarity ends. Unless the graph is very dense, doing convolutions on the image representing the graph is a pretty arbitrary thing to do. Graph CNNs exist and make a lot more sense in general.

layoutIfNeeded• 6 years ago

using CNNs to read a 2D graph

Why don't they use CNNs on the data series itself?

wenc5• 6 years ago

I believe in effect that is what they do in execution.

The 2D plot is more for training -- a human physicist picks out visual artifacts of interest to bootstrap the training. Humans of course can see blips and weird curves better on a visualization than in a pure data series.

For instance, a human can say if this little tail off a contour bends this way, it's right; if it bends in a different way, it's wrong. Or if an contour is "prickly" or "blobby".

Whether something "looks right" or "wrong" is really hard to mathematically reduce to a parsimonious description, especially when there's variance in the samples -- after all, there could be multiple subtle descriptions of "looks right" or "looks wrong" -- but a CNN is perfect for generalizing based on labeled samples. Similar to a radiologist looking at a scan and toggling isTumor = (True, False).

jshaqaw• 6 years 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• 6 years 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.

ylem5• 6 years 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/vid...

If anyone is interested, we have one on data science in industry coming up: https://attendee.gotowebinar.com/register/604483936035643777...

fmakunbound• 6 years ago

If AI is like Columbus, computing power is Santa Maria

Does that mean when AI finally arrives, it slaughters all of us?

SiempreViernes• 6 years 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• 6 years 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...

currymj5• 6 years 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.

wenc5• 6 years 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.

mumbisChungo3• 6 years 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• 6 years ago

I don't think they mean ML in general is boring, just that this particular application of it isn't particularly flashy.

KineticLensman• 6 years ago

I interpreted this to mean something like 'how phones / car engines / etc work' is not of interest to most of their users as long as they get the job done. If they get 'interesting' it can means that something isn't working right. Where 'interesting' = 'suddenly noticeable'.

currymj5• 6 years ago

There are lots of things that I think are comparable to machine learning in the sense that they combine applied math and heavy computation and are very practically important, like simulating chemical reactions, solving operations research problems, or computational fluid dynamics. You cannot talk about these things at cocktail parties, though, because people will slowly shuffle away from you -- whereas you can talk about deep learning, which is odd.

Basically, I think if somebody wants to work in machine learning then they should be encouraged, and I think it's great that barriers to entry are lower than most fields, but the average person should not feel like they need to care about it, and if they do it might be because they have an inaccurate narrative.

ChrisRackauckas7• 6 years ago

Yes, I think this is a great use for neural networks since they are effectively high dimensional function approximators, and something like Schrondinger's equation is a PDE where the number of dimensions is the number of observables so it can get very high dimensional very fast. Classical methods don't necessarily scale that well in high dimensions (curse of dimensionality: cost is exponential in dimensions), but using neural networks does very well. This gives rise to the physics-informed neural network and deep backwards stochastic differential equation approaches which will likely be driving a lot of future HPC applications in a way that blends physical equations with neural network approaches. We recently released a library, NeuralPDE [1], which utilizes a lot of these approaches to solve what were traditionally difficult equations in an automated form. I think the future is bright for scientific machine learning!

[1] https://neuralpde.sciml.ai/dev/

wenc5• 6 years ago

This is fascinating. ELI5: how does this work? (I'm couldn't find references on the linked site)

Let's say I supply a high-dimensional DAE, f(x', x, z) = 0, x(0) = x₀, where classical methods like quadrature are unwieldy. Does the algorithm generate n samples in the solution space by integrating n times and then fitting an NN? With different initial conditions? Or does it perform quadrature with NNs instead of polynomial basis functions?

currymj5• 6 years ago

this is very cool.

I was thinking specifically of this and related approaches https://arxiv.org/abs/1909.08423 where they search for the ground state by iteratively using an MCMC sampler and doing SGD. The innovation is a network architecture that takes classic approaches from physics and judiciously replaces parts with flexible NNs.

I had not even considered how things might work if you actually want to think about time.

Do you know if anybody has been running this NN+DiffEq solver stuff on big HPC systems that also have GPUs? If you know of any papers where they tried this, would be interesting to look at.

fluffything2• 6 years ago

I see a Poisson solver in the docs.

Is there a paper comparing the performance of this particular solver against the state of the art ?

(if you are using GPUs, the AmgX library has a finite-difference solver for Poisson in their examples - very far from the state of the art, but a comparison might put performance in perspective)

amelius• 6 years ago

Is this AI taking the place of preconditioners as used in iterative solvers?

Would an AI be able to learn how to apply multigrid methods?

ChrisRackauckas7• 6 years ago

We have prototypes in Julia for this. The answer is yes, there are tricks you can use to do this effectively.

whinvik5• 6 years ago

Almost every time a PDE is solved on a computer, it is a variational problem. Maybe neural networks are indeed good at this but I haven't seen any literature that shows that it is provably better. A reference would be good, especially to this point "But neural networks are very good parametric function approximators, generally better than what traditionally gets used in physics (b-splines or whatever)."

currymj5• 6 years ago

https://arxiv.org/abs/1909.08423 and https://arxiv.org/abs/1909.02487 are some examples I've been looking at recently.

btrettel2• 6 years ago

Almost every time a PDE is solved on a computer, it is a variational problem.

Not true. In computational fluid dynamics, variational methods are only one category out of many, and they aren't dominant.

noobermin• 6 years ago

Thank you. More importantly, this is not new. Sheesh, how much of AI is just hype. I am in favor in not using the term AI for such things.

baq• 6 years ago

John McCarthy, the Father of AI, famously said: "As soon as it works, no one calls it AI any more." Leading researcher Rodney Brooks says "Every time we figure out a piece of it, it stops being magical; we say, 'Oh, that's just a computation. '"

https://cacm.acm.org/blogs/blog-cacm/138907-john-mccarthy/fu...

bencw• 6 years ago

This is something I've wondered about (along with potential applications of autograd outside of deep learning). Do you have a recommended starting point for someone who wants to learn more about this?

exabyte• 6 years ago

The power that I see in machine learning is the techniques being developed to handle the unavoidable noise in empirical data. I think that poses a large obstacle for traditional techniques although I am not familiar enough to compare.

willis936• 6 years ago

To me the value is in matching relationships (equations) of curated parameters from empirical data and using simulated recreations of the experiment as the objective. As soon as you can recreate experimental results in a simulation then you’ve made a successful model for that domain. This is an incredibly important and difficult task for fluid dynamics and plasma physics.

ylem5• 6 years 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/#).

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• 6 years 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• 6 years 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.

Veedrac4• 6 years ago

Moore's law appears to be slumping lately.

Not so. https://docs.google.com/spreadsheets/d/1NNOqbJfcISFyMd0EsSrh...

Myrmornis• 6 years 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• 6 years 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?

BrandoElFollito2• 6 years 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• 6 years 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• 6 years 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/

stainforth2• 6 years ago

What field or occupation followed for you?

BrandoElFollito2• 6 years ago

During my time in CERN I discovered system administration and joined a large company to be initially in charge for the IT aspects of the R&D divisions in Europe.

This then extended to greenfield operations, and finally I moved to information security.

visarga• 6 years 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• 6 years 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• 6 years ago

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

godelski• 6 years 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_headline...

dkural• 6 years 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.

ricksharp• 6 years 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)?

LoSboccacc2• 6 years 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/

andrewon• 6 years 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• 6 years 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• 6 years 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-Everyt...

tim333• 6 years 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-...

AI in physics: are we facing a scientific revolution?

Related Stories