[HN Gopher] From models of galaxies to atoms, simple AI shortcut...
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From models of galaxies to atoms, simple AI shortcuts speed up
simulations
Author : DarkContinent
Score : 35 points
Date : 2020-02-15 14:02 UTC (8 hours ago)
(HTM) web link (www.sciencemag.org)
(TXT) w3m dump (www.sciencemag.org)
| [deleted]
| Fomite wrote:
| Interestingly, my lab has been working in emulators for one of
| our simulation models, and we're _really_ struggling to make
| meaningful improvements.
|
| It's faster, but we're not there yet on accuracy.
| willis936 wrote:
| I was at a talk last week where the speaker spent a little bit of
| time on using machine learning on a regression matrix that is
| trained by the results of a simulation. The simulation and
| variables in the regression matrix were chosen such that the AI
| could recreate an approximation of a known physical law. This is
| fairly exciting to me because if used to recreate a lot of laws
| in this field, it could then be used on experimental data to
| untangle some of the mess and identify the relationships for us.
| I could see this speeding along development of science.
| fxtentacle wrote:
| "When they were turbocharged with specialized graphical
| processing chips, they were between about 100,000 and 2 billion
| times faster than their simulations."
|
| Now the critical question is: How much faster is it without AI,
| just because of the specialized dedicated processing chips?
|
| Otherwise, they might be comparing a single virtualized CPU core
| against a high-end GPU for things like matrix multiplication ...
| and then the result that GPU > slow CPU isn't really that
| impressive.
| rrss wrote:
| An alternative question is: how much faster is it with the
| neural network-based emulation ("AI"), without the used of the
| specialized dedicated processing chips? I think the answer to
| this gives the information you are looking for.
|
| The paper answers this question:
|
| > While the simulations presented typically run in minutes to
| days, the DENSE emulators can process multiple sets of input
| parameters in milliseconds to a few seconds with one CPU core,
| or even faster when using a Titan X GPU card. For the GCM
| simulation which takes about 1150 CPU-hours to run, the
| emulator speedup is a factor of 110 million on a like-for-like
| basis, and over 2 billion with a GPU card. The speed up
| achieved by DENSE emulators for each test case is shown in
| Figure 2(h)
| allovernow wrote:
| >Now the critical question is: How much faster is it without
| AI, just because of the specialized dedicated processing chips?
|
| Based on similar work we are doing at the startup I work for,
| this isn't just GPU magic. ML is a heuristic alternative to
| simulations which already operate on specialized GPUs and TPUs.
| This modeling acceleration is one of the many ways in which ML
| is poised to change everything.
|
| The same way that a human can, for instance, approximately draw
| iso-temperature lines around a candle flame, without having to
| perform simulations...except the neural net is some 99%+ as
| accurate and detailed as a full simulation. That's exactly why
| neural nets excel - they learn complex heuristics much like
| humans do, but with the added power of digitized computation
| and memory.
| aimoderate wrote:
| > It randomly inserts layers of computation between the networks'
| input and output, and tests and trains the resulting wiring with
| the limited data. If an added layer enhances performance, it's
| more likely to be included in future variations.
|
| Sounds a lot like genetic algorithms but with neural networks. I
| suspect we'll see more of this as people figure out how to run
| the search over neural network architectures that fit their own
| domains. Convolutions and transformers are great and all but we
| might as well let the computers do the search and optimization as
| well instead of waiting on human insights for stacking functions.
| dukoid wrote:
| For some reason this reminds me of the famous xerox copier where
| the compression algorithm would swap out digits:
| https://news.ycombinator.com/item?id=6156238
| chewxy wrote:
| Who'd think compression works so well?
|
| (yes, neural networks are compression engines)
| agumonkey wrote:
| I always thought programming and even theory were knowledge
| compression
| andbberger wrote:
| Not necessarily
| tanilama wrote:
| Compression in your context is as meaningless as
| Generalization.
|
| Yes, you can say generalization is compression.
| fxtentacle wrote:
| Except that "generalization" implies that it works for
| previously unseen problems, which is usually not the case for
| AI.
|
| Compression, on the other hand, nicely captures the "learn
| and reproduce" approach that using AI entails.
| tanilama wrote:
| Unseen problems is a ill defined term. There is a
| distinction between in domain and out of domain, both can
| be unseen by the model before.
|
| Even human as agent requires training before being deployed
| to unseen problems. Generalization is conditioned on
| experience, after all.
|
| AI generalizes to unseen in domain data given a specific
| task. That is why it is useful in the first place.
| nabla9 wrote:
| This is not meaningful analogy due to being too generic. Using
| it does not add anything to discussion.
|
| Any mathematical model is 'compressed' form of reality and
| that's why they works well. Instead of compressed, simplified
| or abstracted, is better term. Machine Learning adds heuristic
| data driven model to scientific model.
| FartyMcFarter wrote:
| Do you mean in the same way that any mathematical function is a
| compression engine? That is, you implement something that can
| handle many cases (1+1, 2+3, 5+6) in a concise form?
|
| It seems to me like the real magic of neural networks is that
| they make it easier to search for a function that solves (to
| some extent) a particular problem.
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