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Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. What happens when the best meth

Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-interpretable theories and mathematical models / explanations? Is that even iteratively sustainable in the way that scientific progress has proven to be? Interesting times ahead.

jpadkins 2y ago
Hook the protein model up to an LLM model, have the LLM interpret the results. Problem solved :-) Then we just have to trust the LLM is giving us correct interpretations.
cgearhart 2y ago
This is a neat observation. Slightly terrifying, but still interesting. Seems like there will also be cases where we discover new theories through the uninterpretable models—much easier and faster to experiment endlessly with a computer.
fnikacevic 2y ago
I can only hope the models will be sophisticated enough and willing to explain their reasoning to us.
thomasahle 2y ago
> Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. I mean, it's just faster, no? I don't think anyone is claiming it's a more _accurate_ model of the universe.
- Jerrrry 2y ago
  Collision libraries and fluid libraries have had baked-in memorized look-up tables that were generated with ML methods nearly a decade ago. World is still here, although the Matrix/metaverse is becoming more attractive daily.
  - hahaurfunny 2y ago
    [dead]
xanderlewis 2y ago
It depends whether the value of science is human understanding or pure prediction. In some realms (for drug discovery, and other situations where we just need an answer and know what works and what doesn’t), pure prediction is all we really need. But if we could build an uninterpretable machine learning model that beats any hand-built traditional ‘physics’ model, would it really be physics? Maybe there’ll be an intermediate era for a while where ML models outperform traditional analytical science, but then eventually we’ll still be able to find the (hopefully limited in number) principles from which it can all be derived. I don’t think we’ll ever find that Occam’s razor is no use to us.
- failTide 2y ago
  > But if we could build an uninterpretable machine learning model that beats any hand-built traditional ‘physics’ model, would it really be physics? At that point I wonder if it would be possible to feed that uninterpretable model back into another model that makes sense of it all and outputs sets of equations that humans could understand.
- gmarx 2y ago
  The success of these ML models has me wondering if this is what Quantum Mechanics is. QM is notoriously difficult to interpret yet makes amazing predictions. Maybe wave functions are just really good at predicting system behavior but don't reflect the underlying way things work. OTOH, Newtonian mechanics is great at predicting things under certain circumstances yet, in the same way, doesn't necessarily reflect the underlying mechanism of the system. So maybe philosophers will eventually tell us the distinction we are trying to draw, although intuitive, isn't real
  - kolinko 2y ago
    That’s what thermodynamics is - we initially only had laws about energy/heat flow, and only later we figured out how statistical particle movements cause these effects.
- RandomLensman 2y ago
  Pure prediction is only all we need if the total end-to-end process is predicted correctly - otherwise there could be pretty nasty traps (e.g., drug works perfectly for the target disease but does something unexpected elsewhere etc.).
ozten 2y ago
Science has always given us better, but error prone tooling to see further and make better guesses. There is still a scientific test. In a clinical trial, is this new drug safe and effective.
nexuist 2y ago
As a steelman, wouldn't the abundance of infinitely generate-able situations make it _easier_ for us to develop strong theories and models? The bottleneck has always been data. You have to do expensive work in the real world and accurately measure it before you can start fitting lines to it. If we were to birth an e.g. atomically accurate ML model of quantum physics, I bet it wouldn't take long until we have mathematical theories that explain why it works. Our current problem is that this stuff is super hard to manipulate and measure.
- moconnor 2y ago
  Maybe; AI chess engines have improved human understanding of the game very rapidly, even though humans cannot beat engines.
  - whymauri 2y ago
    I've seen generative models for molecular structures produce results that looked non-sensical at first glance; however, when passed along to more experienced medicinal chemists they identified a bit 'creativity' that only a very advanced practitioner would understand or appreciate. Those hypotheses, which would not be produced by most experts, served as an anchor for further exploration of novel structures and ideas. So in a way, what you say is already possible. Just how GMs in chess specialize in certain openings or play styles, master chemists have pre-existing biases that can affect their designs; algorithms can have different biases which push exploration to interesting places. Once you have a good latent representation of relevant chemical space, so you can optimize for this sort of creativity (a practical but boring example is to push generation outside of patent space).
- alfalfasprout 2y ago
  This is an important aspect that's being ignored IMO. For a lot of problems, currently you either don't have an an analytical solution and the alternative is a brute force-ish numerical approach. As a result the computational cost of simulating things enough times to be able to detect behavior that can inform theories/models (potentially yielding a good analytical result) is not viable. In this regard, ML models are promising.
CapeTheory 2y ago
Many of our existing physical models can be decomposed into "high-confidence, well tested bit" plus "hand-wavy empirically fitted bit". I'd like to see progress via ML replacing the empirical part - the real scientific advancement then becomes steadily reducing that contribution to the whole by improving the robust physical model incrementally. Computational performance is another big influence though. Replacing the whole of a simulation with an ML model might still make sense if the model training is transferrable and we can take advantage of the GPU speed-ups, which might not be so easy to apply to the foundational physical model solution. Whether your model needs to be verified against real physical models depends on the seriousness of your use-case; for nuclear weapons and aerospace weather forecasts I imagine it will remain essential, while for a lot of consumer-facing things the ML will be good enough.
- jononor 2y ago
  Physics-informed machine learning is a whole (nascent) subfield that is very much in line with this thinking. Steve Brunton has some good stuff about this on YouTube.
jncfhnb 2y ago
These processes are both beyond human comprehension because they contain vast layers of tiny interactions and also not practical to simulate. This tech will allow for exploration for accurate simulations to better understand new ideas if needed.
tomrod 2y ago
A few things: 1. Research can then focus on where things go wrong 2. ML models, despite being "black boxes," can still have brute-force assessment performed of the parameter space over covered and uncovered areas by input information 3. We tend to assume parsimony (i.e Occam's razor) to give preference to simpler models when all else is equal. More complex black-box models exceeding in prediction let us know the actual causal pathway may be more complex than simple models allow. This is okay too. We'll get it figured out. Not everything is closed-form, especially considering quantum effects may cause statistical/expected outcomes instead of deterministic outcomes.
kylebenzle 2y ago
That is not a real concern, just a confusion on how statistics works :(
timschmidt 2y ago
There will be an iterative process built around curated training datasets - continually improved, top tier models, teams reverse engineering the model's understanding and reasoning, and applying that to improve datasets and training.
adw 2y ago
> What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? A better analogy is "weather forecasting".
- wayeq 2y ago
  interesting choice considering the role chaos theory plays in forever rendering long term weather predictions impossible, by humans or LLMs.
jeffreyrogers 2y ago
I asked a friend of mine who is chemistry professor at a large research university something along these lines a while ago. He said that so far these models don't work well in regions where either theory or data is scarce, which is where most progress happens. So he felt that until they can start making progress in those areas it won't change things much.
- mensetmanusman 2y ago
  Major breakthroughs happen when clear connections can be made and engineered between the many bits of solved but obscured solutions.
bbor 2y ago
This is exactly how the physicists felt at the dawn of quantum physics - the loss of meaningful human inquiry to blindly effective statistics. Sobering stuff… Personally, I’m convinced that human reason is less pure than we think it to be, and that the move to large mathematical models might just be formalizing a lack-of-control that was always there. But that’s less of a philosophy of science discussion and more of a cognitive science one
krzat 2y ago
We will get better with understanding black boxes, if a model can be compressed into simple math formula then it's both easier to understand and to compute.
ldoughty 2y ago
My argument is: weather. I think it is fine & better for society to have applications and models for things we don't fully understand... We can model lots of small aspects of weather, and we have a lot of factors nailed down, but not necessarily all the interactions.. and not all of the factors. (Additional example for the same reason: Gravity) Used responsibly. Of course. I wouldn't think an AI model designing an airplane that no engineers understand how it works is a good idea :-) And presumably all of this is followed by people trying to understand the results (expanding potential research areas)
- GaggiX 2y ago
  It would be cool to see an airplane made using generative design.
  - tech_buddha 2y ago
    How about spaceship parts ? https://www.nasa.gov/technology/goddard-tech/nasa-turns-to-ai-to-design-mission-hardware/ https://www.nasa.gov/technology/goddard-tech/nasa-turns-to-a...
t14n 2y ago
A new-ish field of "mechanistic interpretability" is trying to poke at weights and activations and find human-interpretable ideas w/in them. Making lots of progress lately, and there are some folks trying to apply ideas from the field to Alphafold 2. There are hopes of learning the ideas about biology/molecular interactions that the model has "discovered". Perhaps we're in an early stage of Ted Chiang's story "The Evolution of Human Science", where AIs have largely taken over scientific research and a field of "meta-science" developed where humans translate AI research into more human-interpretable artifacts.
philip1209 2y ago
It makes me think about how Einstein was famous for making falsifiable real-world predictions to accompany his theoretical work. And, sometimes it took years for proper experiments to be run (such as measuring a solar eclipse during the breakout of a world war). Perhaps the opportunity here is to provide a quicker feedback loop for theory about predictions in the real world. Almost like unit tests.
- HanClinto 2y ago
  > Perhaps the opportunity here is to provide a quicker feedback loop for theory about predictions in the real world. Almost like unit tests. Or jumping the gap entirely to move towards more self-driven reinforcement learning. Could one structure the training setup to be able to design its own experiments, make predictions, collect data, compare results, and adjust weights...? If that loop could be closed, then it feels like that would be a very powerful jump indeed. In the area of LLMs, the SPAG paper from last week was very interesting on this topic, and I'm very interested in seeing how this can be expanded to other areas: https://github.com/Linear95/SPAG https://github.com/Linear95/SPAG
- goggy_googy 2y ago
  Agreed. At the very least, models of this nature let us iterate/filter our theories a little bit more quickly.
  - jprete 2y ago
    The model isn't reality. A theory that disagrees with the model but agrees with reality shouldn't be filtered, but in this process it will be.
mnky9800n 2y ago
I believe it simply tells us that our understanding of mechanical systems, especially chaotic ones, is not as well defined as we thought. https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.5.043252 https://journals.aps.org/prresearch/abstract/10.1103/PhysRev...
thelastparadise 2y ago
The ML models will help us understand that :)
jes5199 2y ago
every time the two systems disagree, it's an opportunity to learn something. both kinds of models can be improved with new information, done through real-world experiments
ogogmad 2y ago
Some machine learning models might be more interpretable than others. I think the recent "KAN" model might be a step forward.
dekhn 2y ago
If you're a scientist who works in protein folding (or one of those other areas) and strongly believe that science's goal is to produce falsifiable hypotheses, these new approaches will be extremely depressing, especially if you aren't proficient enough with ML to reproduce this work in your own hands. If you're a scientist who accepts that probabilist models beat interpretable ones (articulated well here: https://norvig.com/chomsky.html https://norvig.com/chomsky.html), then you'll be quite happy because this is yet another validation of the value of statistical approaches in moving our ability to predict the universe forward. If you're the sort of person who believes that human brains are capable of understanding the "why" of how things work in all its true detail, you'll find this an interesting challenge- can we actually interpret these models, or are human brains too feeble to understand complex systems without sophisticated models? If you're the sort of person who likes simple models with as few parameters as possible, you're probably excited because developing more comprehensible or interpretable models that have equivalent predictive ability is a very attractive research subject. (FWIW, I'm in the camp of "we should simultaneously seek simpler, more interpretable models, while also seeking to improve native human intelligence using computational augmentation")
- narrator 2y ago
  What if our understanding of the laws of the natural sciences are subtly flawed and AI just corrects perfectly for our flawed understanding without telling us what the error in our theory was? Forget trying to understand dark matter. Just use this model to correct for how the universe works. What is actually wrong with our current model and if dark matter exists or not or something else is causing things doesn't matter. "Shut up and calculate" becomes "Shut up and do inference."
  - dekhn 2y ago
    All models are wrong, but some models are useful.
    - narrator 2y ago
      The black box AI models could calculate epicycles perfectly so the middle ages Catholic Church could say just use those instead of being a geocentrrism denier.
tobrien6 2y ago
I suspect that ML will be state-of-the-art at generating human-interpretable theories as well. Just a matter of time.
sdwr 2y ago
> Does this decouple progress from our current understanding of the scientific process? Thank God! As a person who uses my brain, I think I can say, pretty definitively, that people are bad at understanding things. If this actually pans out, it means we will have harnessed knowledge/truth as a fundamental force, like fire or electricity. The "black box" as a building block.
- tantalor 2y ago
  This type of thing is called an "oracle". We've had stuff like this for a long time. Notable examples: - Temple priestesses - Tea-leaf reading - Water scrying - Palmistry - Clairvoyance - Feng shui - Astrology The only difference is, the ML model is really quite good at it.
  - unsupp0rted 2y ago
    > The only difference is, the ML model is really quite good at it. That's the crux of it: we've had theories of physics and chemistry since before writing was invented. None of that mattered until we came upon the ones that actually work.
insane_dreamer 2y ago
For me the big question is how do we confidently validate the output of this/these model(s).
- topaz0 2y ago
  It's the right question to ask, and the answer is that we will still have to confirm them by experimental structure determination.
tambourine_man 2y ago
Our metaphors and intuitions were crumbling already and stagnating. See quantum physics: sometimes a particle, sometimes a wave, and what constitute a measurement anyway? I’ll take prediction over understanding if that’s the best our brains can do. We’ve evolved to deal with a few orders of magnitude around a meter and a second. Maybe dealing with light-years and femtometer/seconds is too much to ask.
dyauspitr 2y ago
Whatever it is if we needed to we could follow each instruction through the black box. It’s never going to be as opaque as something organic.
wslh 2y ago
This is the topic of epistemology of the sciences in books such as "New Direction in the Philosophy of Mathematics" [1] and happened before with problems such as the four color theorem [2] where AI was not involved. Going back to the uninterpretable ML models in the context of AlphaFold 3, I think one method for trying to explain the findings is similar to the experimental methods of physics with reality: you perform experiments with the reality (in this case AlphaFold 3) to came up with sound conclusions. AI/ML is an interesting black-box system. There are other open discussions on this topic. For example, can our human brain absorbe that knowledge or it is limited somehow with the scientific language that we have now? [1] https://www.google.com.ar/books/edition/New_Directions_in_the_Philosophy_of_Math/HFa03eq-9LQC?hl=en&gbpv=1&printsec=frontcover https://www.google.com.ar/books/edition/New_Directions_in_th... [2] https://en.wikipedia.org/wiki/Four_color_theorem https://en.wikipedia.org/wiki/Four_color_theorem
torrefatto 2y ago
You are conflating the whole scientific endeavor to a very specific problem to which this specific approach is effective at producing results that fit with the observable world. This has nothing to do with science as a whole.
scotty79 2y ago
We should be thankful that we live in the universe that obeys math simple enough to comprehend that we were able to reach that level. Imagine if optis was complex enough that it would require ML model to predict anything. We'd be in permanent stone age without a way out.
- lupire 2y ago
  What would a universe look like that lacked simple things, and somehow only complex things existed? It makes me think of how Gaussian integers have irreducibles but not prime numbers, where some large things cannot be uniquely expressed as combination of smaller things.
mberning 2y ago
I would assume that given enough hints from AI and if it is deemed important enough humans will come in to figure out the “first principles” required to arrive at the conclusion.
- RobCat27 2y ago
  I believe this is the case also. With a well enough performing AI/ML/probabilistic model where you can change the model's input parameters and get a highly accurate prediction basically instantly, we can test theories approximately and extremely fast rather than running completely new experiments, which will always come with it's own set of errors and problems.
danielmarkbruce 2y ago
"better and better models of the world" does not always mean "more accurate" and never has. We already know how to model the vast majority of things, just not at a speed and cost which makes it worthwhile. There are dimensions of value - one is accuracy, another speed, another cost, and in different domains additional dimensions. There are all kinds of models used in different disciplines which are empirical and not completely understood. Reducing things to the lowest level of physics and building up models from there has never been the only approach. Biology, geology, weather, materials all have models which have hacks in them, known simplifications, statistical approximations, so the result can be calculated. It's just about choosing the best hacks to get the best trade off of time/money/accuracy.
Gimpei 2y ago
Might be easier to come up with new models with analytic solutions if you have a probabilistic model at hand. A lot easier to evaluate against data and iterate. Also, I wouldn't be surprised if we develop better tools for introspecting these models over time.
_yb2s 2y ago
It means we now have an accurate surrogate model or "digital twin" that can be experimented on almost instantaneously. So we can massively accelerate the traditional process of developing mechanistic understanding through experiment, while also immediately be able to benefit from the ability to make accurate predictions, even without needing understanding. In reality, science has already pretty much gone this way long ago, even if people don't like to admit it. Simple, reductionist explanations for complex phenomena in living systems don't really exist. Virtually all of medicine nowadays is empirical: try something, and if you can prove its safe and effective, you keep doing it. We almost never have a meaningful explanation for how it really works, and when we think we do, it gets proven wrong repeatedly, while the treatment keeps working as always.
- mathgradthrow 2y ago
  instead of "in mice", we'll be able to say "in the cloud"
  - unsupp0rted 2y ago
    In vivo in humans in the cloud
    - dekhn 2y ago
      one of the companies I worked for, "insitro", is specificallyt named that to mean the combination of "in vivo, in vitro, in silicon".
  - topaz0 2y ago
    "In nimbo" (though what people actually say is "in silico").
  - d_silin 2y ago
    "in silico"
- imchillyb 2y ago
  Medicine can be explained fairly simply, and the why of how it works as it does is also explained by this: Imagine a very large room that has every surface covered by on-off switches. We cannot see inside of this room. We cannot see the switches. We cannot fit inside of this room, but a toddler fits through the tiny opening leading into the room. The toddler cannot reach the switches, so we equip the toddler with a pole that can flip the switches. We train the toddler, as much as possible, to flip a switch using the pole. Then, we send the toddler into the room and ask the toddler to flip the switch or switches we desire to be flipped, and then do tests on the wires coming out of the room to see if the switches were flipped correctly. We also devise some tests for other wires to see if that naughty toddler flipped other switches on or off. We cannot see inside the room. We cannot monitor the toddler. We can't know what _exactly_ the toddler did inside the room. That room is the human body. The toddler with a pole is a medication. We can't see or know enough to determine what was activated or deactivated. We can invent tests to narrow the scope of what was done, but the tests can never be 100% accurate because we can't test for every effect possible. We introduce chemicals then we hope-&-pray that the chemicals only turned on or off the things we wanted turned on or off. Craft some qualifications testing for proofs, and do a 'long-term' study to determine if there were other things turned on or off, or a short circuit occurred, or we broke something. I sincerely hope that even without human understanding, our AI models can determine what switches are present, which ones are on and off, and how best to go about selecting for the correct result. Right now, modern medicine is almost a complete crap-shoot. Hopefully modern AI utilities can remedy the gambling aspect of medicine discovery and use.
tnias23 2y ago
I wonder if ML can someday be employed in deciphering such black box problems; a second model that can look under the hood at all the number crunching performed by the predictive model, identify the pattern that resulted in a prediction, and present it in a way we can understand. That said, I don’t even know if ML is good at finding patterns in data.
- lupire 2y ago
  > That said, I don’t even know if ML is good at finding patterns in data. That's the only thing ML does.
burny_tech 2y ago
We need to advance mechanistic interpretability (field reverse engineering neural networks) https://www.youtube.com/watch?v=P7sjVMtb5Sg https://www.youtube.com/watch?v=P7sjVMtb5Sg https://www.youtube.com/watch?v=7t9umZ1tFso https://www.youtube.com/watch?v=7t9umZ1tFso https://www.youtube.com/watch?v=2Rdp9GvcYOE https://www.youtube.com/watch?v=2Rdp9GvcYOE
goggy_googy 2y ago
I think at some point, we will be able to produce models that are able to pass data into a target model and observe its activations and outputs and put together some interpretable pattern or loose set of rules that govern the input-output relationship in the target model. Using this on a model like AlphaFold might enable us to translate inferred chemical laws into natural language.
pen2l 2y ago
The most moneyed and well-coordinated organizations have honed a large hammer, and they are going to use it for everything, and so almost certainly future big findings in the areas you mention, probabilistically inclined models coming from ML will be the new gold standard. But yet the only thing that can save us from ML will be ML itself because it is ML that has the best chance to be able to extrapolate patterns from these blackbox models to develop human interpretable models. I hope we do dedicate explicit effort to this endeavor, and so continue the human advances and expanse of human knowledge in tandem with human ingenuity with computers at our assistance.
- optimalsolver 2y ago
  Spoiler: "Interpretable ML" will optimize for output that either looks plausible to humans, reinforces our preconceptions, or appeals to our aesthetic instincts. It will not converge with reality.
  - kolinko 2y ago
    That is not considered interpretable then, and I think most people working in the field are aware of this gotcha. Iirc when EU required banks to have interpretable rules for loans, a plain explanation was not considered enough. What was required was a clear process that was used from the beginning - i.e. you can use an AI to develop an algorightm to make a decision, but you can’t use AI to make a decision and explains reasons afterwards.
  - DoctorOetker 2y ago
    Spoiler: basic / hard sciences describe nature mathematically. Open a random physics book, and you will find lots and lots of derivations (using more or less acceptable assumptions depending on circumstance under consideration). Derivations and assumptions can be formally verified, see for example https://us.metamath.org https://us.metamath.org Ever more intelligent machine learning algorithms and data structures replacing human heuristic labor, will simply shift the expected minimum deliverable from associations to ever more rigorous proofs in terms of less and less assumptions. Machine learning will ultimately be used as automated theorem provers, and their output will eventually be explainable by definition. When do we classify an explanation as explanatory? When it succeeds in deriving a conclusion from acceptable assumptions without hand waving. Any hand waving would result in the "proof" not having passed formal verification.
thegrim33 2y ago
Reminds me of the novel Blindsight - in it there's special individuals who work as synthesists, whos job it is to observe and understand and then somehow translate back to "lay person" the seemingly undecipherable actions/decisions of advanced computers and augmented humans.
6gvONxR4sf7o 2y ago
"Best methods" is doing a lot of heavy lifting here. "Best" is a very multidimensional thing, with different priorities leading to different "bests." Someone will inevitably prioritize reliability/accuracy/fidelity/interpretability, and that's probably going to be a significant segment of the sciences. Maybe it's like how engineers just need an approximation that's predictive enough to build with, but scientists still want to understand the underlying phenomena. There will be an analogy to how some people just want an opaque model that works on a restricted domain for their purposes, but others will be interested in clearer models or unrestricted/less restricted domain models. It could lead to a very interesting ecosystem of roles. Even if you just limit the discussion to using the best model of X to design a better Y, limited to the model's domain of validity, that might translate the usage problem to finding argmax_X of valueFunction of modelPrediction of design of X. In some sense a good predictive model is enough to solve this with brute force, but this still leaves room for tons of fascinating foundational work. Maybe you start to find that the (wow so small) errors in modelPrediction are correlated with valueFunction, so the most accurate predictions don't make it the best for argmax (aka optimization might exploit model errors rather than optimizing the real thing). Or maybe brute force just isn't computationally feasible, so you need to understand something deeper about the problem to simplify the optimization to make it cheap.
RandomLensman 2y ago
We could be entering a new age of epicycles - high accuracy but very flawed understanding.
advisedwang 2y ago
In physics, we already deal with the fact that many of the core equations cannot be analytically solved for more than the most basic scenarios. We've had to adapt to using approximation methods and numerical methods. This will have to be another place where we adapt to a practical way of getting results.
topaz0 2y ago
In case it's not clear, this does not "beat" experimental structure determination. The matches to experiment are pretty close, but they will be closer in some cases than others and may or may not be close enough to answer a given question about the biochemistry. It certainly doesn't give much information about the dynamics or chemical perturbations that might be relevant in biological context. That's not to pooh-pooh alphafold's utility, just that it's a long way from making experimental structure determination unnecessary, and much much further away from replacing a carefully chosen scientific question and careful experimental design.
bluerooibos 2y ago
> What happens when... I can only assume that existing methods would still be used for verification. At least we understand the logic used behind these methods. The ML models might become more accurate on average but they could still throw out results that are way off occasionally, so their error rate would have to become equal to the existing methods.
GistNoesis 2y ago
The frontier in model space is kind of fluid. It's all about solving differential equations. In theoretical physics, you know the equations, you solve equations analytically, but you can only do that when the model is simple. In numerical physics, you know the equations, you discretize the problem on a grid, and you solve the constraint defined by the equations with various numerical integration schemes like RK4, but you can only do that when the model is small and you know the equations, and you find a single solution. Then you want the result faster, so you use mesh-free methods and adaptive grids. It works on bigger models but you have to know the equations, finding a single solution to the differential equations. Then you compress this adaptive grid with a neural network, while still knowing the governing equations, and you have things like Physics Informed Neural Networks ( https://arxiv.org/pdf/1711.10561 https://arxiv.org/pdf/1711.10561 and following papers) where you can bound the approximation error. This method allows solve all solutions to the differential equations simultaneously, sharing the computations. Then when knowing explicitly your governing equations is too complex, so you assume that there are some governing stochastic equations implicitly, which you learn the end-result of the dynamic with a diffusion model, that's what this alpha-fold is doing. ML is kind of a memoization technique, analog to hashlife in the game of life, that allows you reuse your past computational efforts. You are free to choose on this ladder which memory-compute trade-off you want to use to model the world.
visarga 2y ago
No, science doesn't work that way. You can just calculate your way to scientific discoveries, you got to test them in the real world. Learning, both in humans and AI, is based on the signals provided by the environment. There are plenty of things not written anywhere, so the models can't simply train on human text to discover new things. They learn directly from the environment to do that, like AlphaZero did when it beat humans at Go.
slibhb 2y ago
It's interesting to compare this situation to earlier eras in science. Newton, for example, gave us equations that were very accurate but left us with no understanding at all of why they were accurate. It seems like we're repeating that here, albeit with wildly different methods. We're getting better models but by giving up on the possibility of actually understanding things from first principles.
- slashdave 2y ago
  Not comparable. Our current knowledge of the physics involved in these systems is complete. It is just impossibly difficult to calculate from first principles.
ChuckMcM 2y ago
Interesting times indeed. I think the early history of medicines takes away from your observation though. In the 19th and early 20th century people didn't know why medicines worked, they just did. The whole "try a bunch of things on mice, pick the best ones and try them on pigs, and then the best of those and try a few on people" kind of thing. In many ways the mice were a stand in for these models, at the time scientists didn't understand nearly as much about how mice worked (early mice models were pretty crude by today's standards) but they knew they were a close enough analog to the "real thing" that the information provided by mouse studies was usefully translated into things that might help/harm humans. So when you're tools can produce outputs that you find useful, you can then use those tools to develop your understanding and insights. As a tool, this is quite good.
aaroninsf 2y ago
The top HN response to this should be, what happens is an opportunity has entered the chat. There is a wave coming—I won't try to predict if it's the next one—where the hot thing in AI/ML is going to be profoundly powerful tools for analyze other such tools and render them intelligible to us, which will I imagine mean providing something like a zoomable explainer. At every level there are footnotes; if you want to understand why the simplified model is a simplification, you look at the fine print. Which has fine print. Which has... Which doesn't mean there is not a stable level at which some formal notion of "accurate" cannot be said to exist, which is the minimum viable level of simplification. Etc. This sort of thing will of course will the input to many other things.
signal_space 2y ago
Is alphafold doing model generation or is it just reducing a massive state space? The current computational and systems biochemistry approaches struggle to model large biomolecules and their interactions due to the large degrees of freedom of the models. I think it is reasonable to rely on statistical methods to lead researchers down paths that have a high likelihood of being correct versus brute forcing the chemical kinetics. After all chemistry is inherently stochastic…
jononor 2y ago
I think it likely that instead of replacing existing methods, we will see a fusion. Or rather, many different kinds of fusions - depending on the exact needs of the problems at hand (or in science, the current boundary of knowledge). If nothing else then to provide appropriate/desirable level of explainability, correctness etc. Hypothetically the combination will also have better predictive performance and be more data efficient - but it remains to be seen how well this plays out in practice. The field of "physics informed machine learning" is all about this.
Grieverheart 2y ago
Perhaps for understanding the structure itself, but having the structure available allows us to focus on a coarser level. We also don't want to use quantum mechanics to understand the everyday world, and that's why we have classic mechanics etc.
nico 2y ago
Even if we don’t understand the models themselves, you can still use them as a basis for understanding For example, I have no idea how a computer works in every minute detail (ie, exactly the physics and chemistry of every process that happens in real time), but I have enough of an understanding of what to do with it, that I can use it as an incredibly useful tool for many things Definitely interesting times!
- tecleandor 2y ago
  Not the same. There is a difference between "I cannot understand the deeper details of certain model but some others can and there's the possibility of explaining it in detail" and "Nobody can understand it and there's not a clear cause-effect that we know" . Except for weird cases, computers (or cars, or cameras, or lots of other man made devices) are clearly known and you (or another specialist) can clearly show why a device does X when you input Y on it.
phn 2y ago
I'm not a scientist by any means, but I imagine even accurate opaque models can be useful in moving the knowledge forward. For example, they can allow you to accurately simulate reality, making experiments faster and cheaper to execute.
GuB-42 2y ago
We already have the absolute best method for accurately predicting the world, and it is by experimentation. In the protein folding case, it works by actually making the protein and analyzing it. For designing airplanes, computer models are no match for building the thing, or even using physical models and wind tunnels. And despite having these "best method", it didn't prevent progress in theoretical physics, theory and experimentation complement each other. ML models are just another kind of model that can help both engineering and fundamental research. Their working is close to the old guy in the shop who knows intuitively what is good design, because he has seen it all. That old guys in shops are sometimes better than modeling using physics equations help scientific progress, as scientists can work together with the old guy, combining the strength of intuition and experience with that of scientific reasoning.
flawsofar 2y ago
How do they compare on accuracy per watt?
theGnuMe 2y ago
The models are learning an encoding based on evolutionary related and known structures. We should be able to derive fundamental properties from those encodings eventually. Or at least our biophysical programmed models should map into that encoding. That might be a reasonable approach to look at the folding energy landscape.
MobiusHorizons 2y ago
Is it capable of predictions though? Ie can it accurately predict the folding of new molecules? Otherwise how do you distinguish accuracy from overfitting.
slashdave 2y ago
In terms of docking, you can call the conventional approaches "physically-based", however, they are rather poor physical models. Namely, they lack proper electrostatics, and, most importantly, basically ignore entropic contributions. There is no reason for concern.
trueismywork 2y ago
To paraphrase Kahan, it's not interesting to me whether a method is accurate enough or not, but whether you can predict how accurate you can be. So, if ML methods can predict that they're right 98% of times then we can build this in our systems, even if we don't understand how they work. Deterministic methods can predict result with a single run, ML methods will need ensemble of results to show the same confidence. It is possible at the end of day that the difference in cost might not he that high over time.
abledon 2y ago
Next decade we will focus on building out debugging and visualization tools for deep learning , to glance inside the current black box
hyperthesis 2y ago
Engineering often precedes Science. It's just more data.
salty_biscuits 2y ago
I'd say it's not new. Take fluid dynamics as an example, the navier stokes equations predict the motion of fluids very well but you need to approximately solve them on a computer in order to get useful predictions for most setups. I guess the difference is the equation is compact and the derivation from continuum mechanics is easy enough to follow. People still rely on heuristics to answer "how does a wing produce lift?". These heuristic models are completely useless at "how much lift will this particular wing produce under these conditions?". Seems like the same kind of situation. Maybe progress forward will look like producing compact models or tooling to reason about why a particular thing happened.
Brian_K_White 2y ago
Perhaps an ai can be made to produce the work as well as a final answer, even if it has to reconstruct or invent the work backwards rather than explain it's own internal inscrutable process. "produce a process that arrives at this result" should be just another answer it can spit out. We don't necessarily care if the answer it produces is actually the same as what originally happened inside itself. All we need is that the answer checks out when we try it.
JacobThreeThree 2y ago
As a tool people will use it as any other tool, by experimenting, testing, tweaking and iterating. As a scientific theory for fundamentally explaining the nature of the universe, maybe it won't be as useful.
robwwilliams 2y ago
This is a key but secondary concern to many of us working in molecular geneticist who will use AlphaFold 3 to evaluate pair-wise interactions. We often have genetic support for an interaction between proteins A and B. For example, in a study of genetic variation in responses of mice to morphine I currently have two candidate proteins that interact epistatically, suggesting a possible “lock and key” model—-the mu opiate receptor (MOR) and FGF12. I can now evaluate the likelihood of a direct molecular interaction between these proteins and possible amino acids substitutions that account for individuals difference. In other words I bring a hypothesis to AF3 and ask for it to refute or affirm.
Jupe 2y ago
> Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-interpretable theories and mathematical models / explanations? Replace "human-interpretable theories" with "every man interpretable theories", and you'll have a pretty good idea of how > 90% of the world feels about modern science. It is indistinguishable from magic, by the common measure. Obtuse example: My parents were alive when the first nuclear weapon was detonated. They didn't know that they didn't know this weapon was being built, let alone that it might have ignited the atmosphere. With sophisticated enough ML, that 90% will become 99.9% - save the few who have access to (and can trust) ML tools that can decipher the "logic" from the original ML tools. Yes, interesting times ahead... indeed.
TheBicPen 2y ago
Perhaps related, the first computer-assisted mathematics proof: https://en.wikipedia.org/wiki/Four_color_theorem https://en.wikipedia.org/wiki/Four_color_theorem I'm sure that similar arguments for and against the proof apply here as well.
andy_ppp 2y ago
What happens if we get to the stage of being able to simulate every chemical and electrical reaction in a human brain, is doing this torture or wrong?
- jasondigitized 2y ago
  So the Matrix?
  - andy_ppp 2y ago
    The brains were in “the real” in the Matrix or did I not watch it closely enough :-)
jasondigitized 2y ago
All I can see anymore is that March of Progress illustration [1] with a GPU being added to the far right. Interesting times indeed. [1] https://en.m.wikipedia.org/wiki/March_of_Progress https://en.m.wikipedia.org/wiki/March_of_Progress
andrewla 2y ago
Physicists like to retroactively believe that our understanding of physical phenomena preceded the implementation of uses of those phenomena, when the reality is that physics has always come in to clean up after the engineers. There are some rare exceptions, but usually the reason that scientific progress can be made in an area is that the equipment to perform experiments has been commoditized sufficiently by engineering demand for it. We had semiconductors and superconductors before we understood how they worked -- on both cases arguably we still don't completely understand the phenomena. Things like the dynamo and the electric motor were invented by practice and later explained by scientists, not derived from first principles. Steam engines and pumps were invented before we had the physics to describe how they worked.
mycall 2y ago
A New Kind Of Science?
kajic 2y ago
It’s much easier to reverse engineer a solution that you don’t understand (and discover important underlying theories on that journey), than it is to arrive at that same solution and the underlying theories without knowing in advance where you are going. For this reason, discoveries made by AI will be immensely useful for accelerating scientific progress, even if those discoveries are opaque at first.
yieldcrv 2y ago
I think it creates new studies, such as diagnosing these models behaviors without the doctor having an intricate understanding of all of the model's processes/states just like with natural organisms
goodmachine 2y ago
In order for that not to happen (uninterpretable ML models) some research on symbolic distillation, aka symbolic regression https://arxiv.org/abs/2006.11287 https://arxiv.org/abs/2006.11287 https://www.science.org/doi/10.1126/sciadv.aay2631 https://www.science.org/doi/10.1126/sciadv.aay2631
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jpadkins 2y ago

Hook the protein model up to an LLM model, have the LLM interpret the results. Problem solved :-) Then we just have to trust the LLM is giving us correct interpretations.

cgearhart 2y ago

This is a neat observation. Slightly terrifying, but still interesting. Seems like there will also be cases where we discover new theories through the uninterpretable models—much easier and faster to experiment endlessly with a computer.

fnikacevic 2y ago

I can only hope the models will be sophisticated enough and willing to explain their reasoning to us.

thomasahle 2y ago

> Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. I mean, it's just faster, no? I don't think anyone is claiming it's a more _accurate_ model of the universe.

Jerrrry 2y ago
Collision libraries and fluid libraries have had baked-in memorized look-up tables that were generated with ML methods nearly a decade ago. World is still here, although the Matrix/metaverse is becoming more attractive daily.
- hahaurfunny 2y ago
  [dead]

xanderlewis 2y ago

It depends whether the value of science is human understanding or pure prediction. In some realms (for drug discovery, and other situations where we just need an answer and know what works and what doesn’t), pure prediction is all we really need. But if we could build an uninterpretable machine learning model that beats any hand-built traditional ‘physics’ model, would it really be physics? Maybe there’ll be an intermediate era for a while where ML models outperform traditional analytical science, but then eventually we’ll still be able to find the (hopefully limited in number) principles from which it can all be derived. I don’t think we’ll ever find that Occam’s razor is no use to us.

failTide 2y ago
> But if we could build an uninterpretable machine learning model that beats any hand-built traditional ‘physics’ model, would it really be physics? At that point I wonder if it would be possible to feed that uninterpretable model back into another model that makes sense of it all and outputs sets of equations that humans could understand.
gmarx 2y ago
The success of these ML models has me wondering if this is what Quantum Mechanics is. QM is notoriously difficult to interpret yet makes amazing predictions. Maybe wave functions are just really good at predicting system behavior but don't reflect the underlying way things work. OTOH, Newtonian mechanics is great at predicting things under certain circumstances yet, in the same way, doesn't necessarily reflect the underlying mechanism of the system. So maybe philosophers will eventually tell us the distinction we are trying to draw, although intuitive, isn't real
- kolinko 2y ago
  That’s what thermodynamics is - we initially only had laws about energy/heat flow, and only later we figured out how statistical particle movements cause these effects.
RandomLensman 2y ago
Pure prediction is only all we need if the total end-to-end process is predicted correctly - otherwise there could be pretty nasty traps (e.g., drug works perfectly for the target disease but does something unexpected elsewhere etc.).

ozten 2y ago

Science has always given us better, but error prone tooling to see further and make better guesses. There is still a scientific test. In a clinical trial, is this new drug safe and effective.

nexuist 2y ago

As a steelman, wouldn't the abundance of infinitely generate-able situations make it _easier_ for us to develop strong theories and models? The bottleneck has always been data. You have to do expensive work in the real world and accurately measure it before you can start fitting lines to it. If we were to birth an e.g. atomically accurate ML model of quantum physics, I bet it wouldn't take long until we have mathematical theories that explain why it works. Our current problem is that this stuff is super hard to manipulate and measure.

moconnor 2y ago
Maybe; AI chess engines have improved human understanding of the game very rapidly, even though humans cannot beat engines.
- whymauri 2y ago
  I've seen generative models for molecular structures produce results that looked non-sensical at first glance; however, when passed along to more experienced medicinal chemists they identified a bit 'creativity' that only a very advanced practitioner would understand or appreciate. Those hypotheses, which would not be produced by most experts, served as an anchor for further exploration of novel structures and ideas. So in a way, what you say is already possible. Just how GMs in chess specialize in certain openings or play styles, master chemists have pre-existing biases that can affect their designs; algorithms can have different biases which push exploration to interesting places. Once you have a good latent representation of relevant chemical space, so you can optimize for this sort of creativity (a practical but boring example is to push generation outside of patent space).
alfalfasprout 2y ago
This is an important aspect that's being ignored IMO. For a lot of problems, currently you either don't have an an analytical solution and the alternative is a brute force-ish numerical approach. As a result the computational cost of simulating things enough times to be able to detect behavior that can inform theories/models (potentially yielding a good analytical result) is not viable. In this regard, ML models are promising.

CapeTheory 2y ago

Many of our existing physical models can be decomposed into "high-confidence, well tested bit" plus "hand-wavy empirically fitted bit". I'd like to see progress via ML replacing the empirical part - the real scientific advancement then becomes steadily reducing that contribution to the whole by improving the robust physical model incrementally. Computational performance is another big influence though. Replacing the whole of a simulation with an ML model might still make sense if the model training is transferrable and we can take advantage of the GPU speed-ups, which might not be so easy to apply to the foundational physical model solution. Whether your model needs to be verified against real physical models depends on the seriousness of your use-case; for nuclear weapons and aerospace weather forecasts I imagine it will remain essential, while for a lot of consumer-facing things the ML will be good enough.

jononor 2y ago
Physics-informed machine learning is a whole (nascent) subfield that is very much in line with this thinking. Steve Brunton has some good stuff about this on YouTube.

jncfhnb 2y ago

These processes are both beyond human comprehension because they contain vast layers of tiny interactions and also not practical to simulate. This tech will allow for exploration for accurate simulations to better understand new ideas if needed.

tomrod 2y ago

A few things: 1. Research can then focus on where things go wrong 2. ML models, despite being "black boxes," can still have brute-force assessment performed of the parameter space over covered and uncovered areas by input information 3. We tend to assume parsimony (i.e Occam's razor) to give preference to simpler models when all else is equal. More complex black-box models exceeding in prediction let us know the actual causal pathway may be more complex than simple models allow. This is okay too. We'll get it figured out. Not everything is closed-form, especially considering quantum effects may cause statistical/expected outcomes instead of deterministic outcomes.

kylebenzle 2y ago

That is not a real concern, just a confusion on how statistics works :(

timschmidt 2y ago

There will be an iterative process built around curated training datasets - continually improved, top tier models, teams reverse engineering the model's understanding and reasoning, and applying that to improve datasets and training.

adw 2y ago

> What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? A better analogy is "weather forecasting".

wayeq 2y ago
interesting choice considering the role chaos theory plays in forever rendering long term weather predictions impossible, by humans or LLMs.

jeffreyrogers 2y ago

I asked a friend of mine who is chemistry professor at a large research university something along these lines a while ago. He said that so far these models don't work well in regions where either theory or data is scarce, which is where most progress happens. So he felt that until they can start making progress in those areas it won't change things much.

mensetmanusman 2y ago
Major breakthroughs happen when clear connections can be made and engineered between the many bits of solved but obscured solutions.

bbor 2y ago

This is exactly how the physicists felt at the dawn of quantum physics - the loss of meaningful human inquiry to blindly effective statistics. Sobering stuff… Personally, I’m convinced that human reason is less pure than we think it to be, and that the move to large mathematical models might just be formalizing a lack-of-control that was always there. But that’s less of a philosophy of science discussion and more of a cognitive science one

krzat 2y ago

We will get better with understanding black boxes, if a model can be compressed into simple math formula then it's both easier to understand and to compute.

ldoughty 2y ago

My argument is: weather. I think it is fine & better for society to have applications and models for things we don't fully understand... We can model lots of small aspects of weather, and we have a lot of factors nailed down, but not necessarily all the interactions.. and not all of the factors. (Additional example for the same reason: Gravity) Used responsibly. Of course. I wouldn't think an AI model designing an airplane that no engineers understand how it works is a good idea :-) And presumably all of this is followed by people trying to understand the results (expanding potential research areas)

GaggiX 2y ago
It would be cool to see an airplane made using generative design.
- tech_buddha 2y ago
  How about spaceship parts ? https://www.nasa.gov/technology/goddard-tech/nasa-turns-to-ai-to-design-mission-hardware/ https://www.nasa.gov/technology/goddard-tech/nasa-turns-to-a...

t14n 2y ago

A new-ish field of "mechanistic interpretability" is trying to poke at weights and activations and find human-interpretable ideas w/in them. Making lots of progress lately, and there are some folks trying to apply ideas from the field to Alphafold 2. There are hopes of learning the ideas about biology/molecular interactions that the model has "discovered". Perhaps we're in an early stage of Ted Chiang's story "The Evolution of Human Science", where AIs have largely taken over scientific research and a field of "meta-science" developed where humans translate AI research into more human-interpretable artifacts.

philip1209 2y ago

It makes me think about how Einstein was famous for making falsifiable real-world predictions to accompany his theoretical work. And, sometimes it took years for proper experiments to be run (such as measuring a solar eclipse during the breakout of a world war). Perhaps the opportunity here is to provide a quicker feedback loop for theory about predictions in the real world. Almost like unit tests.

HanClinto 2y ago
> Perhaps the opportunity here is to provide a quicker feedback loop for theory about predictions in the real world. Almost like unit tests. Or jumping the gap entirely to move towards more self-driven reinforcement learning. Could one structure the training setup to be able to design its own experiments, make predictions, collect data, compare results, and adjust weights...? If that loop could be closed, then it feels like that would be a very powerful jump indeed. In the area of LLMs, the SPAG paper from last week was very interesting on this topic, and I'm very interested in seeing how this can be expanded to other areas: https://github.com/Linear95/SPAG https://github.com/Linear95/SPAG
goggy_googy 2y ago
Agreed. At the very least, models of this nature let us iterate/filter our theories a little bit more quickly.
- jprete 2y ago
  The model isn't reality. A theory that disagrees with the model but agrees with reality shouldn't be filtered, but in this process it will be.

mnky9800n 2y ago

I believe it simply tells us that our understanding of mechanical systems, especially chaotic ones, is not as well defined as we thought. https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.5.043252 https://journals.aps.org/prresearch/abstract/10.1103/PhysRev...

thelastparadise 2y ago

The ML models will help us understand that :)

jes5199 2y ago

every time the two systems disagree, it's an opportunity to learn something. both kinds of models can be improved with new information, done through real-world experiments

ogogmad 2y ago

Some machine learning models might be more interpretable than others. I think the recent "KAN" model might be a step forward.

dekhn 2y ago

If you're a scientist who works in protein folding (or one of those other areas) and strongly believe that science's goal is to produce falsifiable hypotheses, these new approaches will be extremely depressing, especially if you aren't proficient enough with ML to reproduce this work in your own hands. If you're a scientist who accepts that probabilist models beat interpretable ones (articulated well here: https://norvig.com/chomsky.html https://norvig.com/chomsky.html), then you'll be quite happy because this is yet another validation of the value of statistical approaches in moving our ability to predict the universe forward. If you're the sort of person who believes that human brains are capable of understanding the "why" of how things work in all its true detail, you'll find this an interesting challenge- can we actually interpret these models, or are human brains too feeble to understand complex systems without sophisticated models? If you're the sort of person who likes simple models with as few parameters as possible, you're probably excited because developing more comprehensible or interpretable models that have equivalent predictive ability is a very attractive research subject. (FWIW, I'm in the camp of "we should simultaneously seek simpler, more interpretable models, while also seeking to improve native human intelligence using computational augmentation")

narrator 2y ago
What if our understanding of the laws of the natural sciences are subtly flawed and AI just corrects perfectly for our flawed understanding without telling us what the error in our theory was? Forget trying to understand dark matter. Just use this model to correct for how the universe works. What is actually wrong with our current model and if dark matter exists or not or something else is causing things doesn't matter. "Shut up and calculate" becomes "Shut up and do inference."
- dekhn 2y ago
  All models are wrong, but some models are useful.
  - narrator 2y ago
    The black box AI models could calculate epicycles perfectly so the middle ages Catholic Church could say just use those instead of being a geocentrrism denier.

tobrien6 2y ago

I suspect that ML will be state-of-the-art at generating human-interpretable theories as well. Just a matter of time.

sdwr 2y ago

> Does this decouple progress from our current understanding of the scientific process? Thank God! As a person who uses my brain, I think I can say, pretty definitively, that people are bad at understanding things. If this actually pans out, it means we will have harnessed knowledge/truth as a fundamental force, like fire or electricity. The "black box" as a building block.

tantalor 2y ago
This type of thing is called an "oracle". We've had stuff like this for a long time. Notable examples: - Temple priestesses - Tea-leaf reading - Water scrying - Palmistry - Clairvoyance - Feng shui - Astrology The only difference is, the ML model is really quite good at it.
- unsupp0rted 2y ago
  > The only difference is, the ML model is really quite good at it. That's the crux of it: we've had theories of physics and chemistry since before writing was invented. None of that mattered until we came upon the ones that actually work.

insane_dreamer 2y ago

For me the big question is how do we confidently validate the output of this/these model(s).

topaz0 2y ago
It's the right question to ask, and the answer is that we will still have to confirm them by experimental structure determination.

tambourine_man 2y ago

Our metaphors and intuitions were crumbling already and stagnating. See quantum physics: sometimes a particle, sometimes a wave, and what constitute a measurement anyway? I’ll take prediction over understanding if that’s the best our brains can do. We’ve evolved to deal with a few orders of magnitude around a meter and a second. Maybe dealing with light-years and femtometer/seconds is too much to ask.

dyauspitr 2y ago

Whatever it is if we needed to we could follow each instruction through the black box. It’s never going to be as opaque as something organic.

wslh 2y ago

This is the topic of epistemology of the sciences in books such as "New Direction in the Philosophy of Mathematics" [1] and happened before with problems such as the four color theorem [2] where AI was not involved. Going back to the uninterpretable ML models in the context of AlphaFold 3, I think one method for trying to explain the findings is similar to the experimental methods of physics with reality: you perform experiments with the reality (in this case AlphaFold 3) to came up with sound conclusions. AI/ML is an interesting black-box system. There are other open discussions on this topic. For example, can our human brain absorbe that knowledge or it is limited somehow with the scientific language that we have now? [1] https://www.google.com.ar/books/edition/New_Directions_in_the_Philosophy_of_Math/HFa03eq-9LQC?hl=en&gbpv=1&printsec=frontcover https://www.google.com.ar/books/edition/New_Directions_in_th... [2] https://en.wikipedia.org/wiki/Four_color_theorem https://en.wikipedia.org/wiki/Four_color_theorem

torrefatto 2y ago

You are conflating the whole scientific endeavor to a very specific problem to which this specific approach is effective at producing results that fit with the observable world. This has nothing to do with science as a whole.

scotty79 2y ago

We should be thankful that we live in the universe that obeys math simple enough to comprehend that we were able to reach that level. Imagine if optis was complex enough that it would require ML model to predict anything. We'd be in permanent stone age without a way out.

lupire 2y ago
What would a universe look like that lacked simple things, and somehow only complex things existed? It makes me think of how Gaussian integers have irreducibles but not prime numbers, where some large things cannot be uniquely expressed as combination of smaller things.

mberning 2y ago

I would assume that given enough hints from AI and if it is deemed important enough humans will come in to figure out the “first principles” required to arrive at the conclusion.

RobCat27 2y ago
I believe this is the case also. With a well enough performing AI/ML/probabilistic model where you can change the model's input parameters and get a highly accurate prediction basically instantly, we can test theories approximately and extremely fast rather than running completely new experiments, which will always come with it's own set of errors and problems.

danielmarkbruce 2y ago

"better and better models of the world" does not always mean "more accurate" and never has. We already know how to model the vast majority of things, just not at a speed and cost which makes it worthwhile. There are dimensions of value - one is accuracy, another speed, another cost, and in different domains additional dimensions. There are all kinds of models used in different disciplines which are empirical and not completely understood. Reducing things to the lowest level of physics and building up models from there has never been the only approach. Biology, geology, weather, materials all have models which have hacks in them, known simplifications, statistical approximations, so the result can be calculated. It's just about choosing the best hacks to get the best trade off of time/money/accuracy.

Gimpei 2y ago

Might be easier to come up with new models with analytic solutions if you have a probabilistic model at hand. A lot easier to evaluate against data and iterate. Also, I wouldn't be surprised if we develop better tools for introspecting these models over time.

_yb2s 2y ago

It means we now have an accurate surrogate model or "digital twin" that can be experimented on almost instantaneously. So we can massively accelerate the traditional process of developing mechanistic understanding through experiment, while also immediately be able to benefit from the ability to make accurate predictions, even without needing understanding. In reality, science has already pretty much gone this way long ago, even if people don't like to admit it. Simple, reductionist explanations for complex phenomena in living systems don't really exist. Virtually all of medicine nowadays is empirical: try something, and if you can prove its safe and effective, you keep doing it. We almost never have a meaningful explanation for how it really works, and when we think we do, it gets proven wrong repeatedly, while the treatment keeps working as always.

mathgradthrow 2y ago
instead of "in mice", we'll be able to say "in the cloud"
- unsupp0rted 2y ago
  In vivo in humans in the cloud
  - dekhn 2y ago
    one of the companies I worked for, "insitro", is specificallyt named that to mean the combination of "in vivo, in vitro, in silicon".
- topaz0 2y ago
  "In nimbo" (though what people actually say is "in silico").
- d_silin 2y ago
  "in silico"
imchillyb 2y ago
Medicine can be explained fairly simply, and the why of how it works as it does is also explained by this: Imagine a very large room that has every surface covered by on-off switches. We cannot see inside of this room. We cannot see the switches. We cannot fit inside of this room, but a toddler fits through the tiny opening leading into the room. The toddler cannot reach the switches, so we equip the toddler with a pole that can flip the switches. We train the toddler, as much as possible, to flip a switch using the pole. Then, we send the toddler into the room and ask the toddler to flip the switch or switches we desire to be flipped, and then do tests on the wires coming out of the room to see if the switches were flipped correctly. We also devise some tests for other wires to see if that naughty toddler flipped other switches on or off. We cannot see inside the room. We cannot monitor the toddler. We can't know what _exactly_ the toddler did inside the room. That room is the human body. The toddler with a pole is a medication. We can't see or know enough to determine what was activated or deactivated. We can invent tests to narrow the scope of what was done, but the tests can never be 100% accurate because we can't test for every effect possible. We introduce chemicals then we hope-&-pray that the chemicals only turned on or off the things we wanted turned on or off. Craft some qualifications testing for proofs, and do a 'long-term' study to determine if there were other things turned on or off, or a short circuit occurred, or we broke something. I sincerely hope that even without human understanding, our AI models can determine what switches are present, which ones are on and off, and how best to go about selecting for the correct result. Right now, modern medicine is almost a complete crap-shoot. Hopefully modern AI utilities can remedy the gambling aspect of medicine discovery and use.

tnias23 2y ago

I wonder if ML can someday be employed in deciphering such black box problems; a second model that can look under the hood at all the number crunching performed by the predictive model, identify the pattern that resulted in a prediction, and present it in a way we can understand. That said, I don’t even know if ML is good at finding patterns in data.

lupire 2y ago
> That said, I don’t even know if ML is good at finding patterns in data. That's the only thing ML does.

burny_tech 2y ago

We need to advance mechanistic interpretability (field reverse engineering neural networks) https://www.youtube.com/watch?v=P7sjVMtb5Sg https://www.youtube.com/watch?v=P7sjVMtb5Sg https://www.youtube.com/watch?v=7t9umZ1tFso https://www.youtube.com/watch?v=7t9umZ1tFso https://www.youtube.com/watch?v=2Rdp9GvcYOE https://www.youtube.com/watch?v=2Rdp9GvcYOE

goggy_googy 2y ago

I think at some point, we will be able to produce models that are able to pass data into a target model and observe its activations and outputs and put together some interpretable pattern or loose set of rules that govern the input-output relationship in the target model. Using this on a model like AlphaFold might enable us to translate inferred chemical laws into natural language.

pen2l 2y ago

The most moneyed and well-coordinated organizations have honed a large hammer, and they are going to use it for everything, and so almost certainly future big findings in the areas you mention, probabilistically inclined models coming from ML will be the new gold standard. But yet the only thing that can save us from ML will be ML itself because it is ML that has the best chance to be able to extrapolate patterns from these blackbox models to develop human interpretable models. I hope we do dedicate explicit effort to this endeavor, and so continue the human advances and expanse of human knowledge in tandem with human ingenuity with computers at our assistance.

optimalsolver 2y ago
Spoiler: "Interpretable ML" will optimize for output that either looks plausible to humans, reinforces our preconceptions, or appeals to our aesthetic instincts. It will not converge with reality.
- kolinko 2y ago
  That is not considered interpretable then, and I think most people working in the field are aware of this gotcha. Iirc when EU required banks to have interpretable rules for loans, a plain explanation was not considered enough. What was required was a clear process that was used from the beginning - i.e. you can use an AI to develop an algorightm to make a decision, but you can’t use AI to make a decision and explains reasons afterwards.
- DoctorOetker 2y ago
  Spoiler: basic / hard sciences describe nature mathematically. Open a random physics book, and you will find lots and lots of derivations (using more or less acceptable assumptions depending on circumstance under consideration). Derivations and assumptions can be formally verified, see for example https://us.metamath.org https://us.metamath.org Ever more intelligent machine learning algorithms and data structures replacing human heuristic labor, will simply shift the expected minimum deliverable from associations to ever more rigorous proofs in terms of less and less assumptions. Machine learning will ultimately be used as automated theorem provers, and their output will eventually be explainable by definition. When do we classify an explanation as explanatory? When it succeeds in deriving a conclusion from acceptable assumptions without hand waving. Any hand waving would result in the "proof" not having passed formal verification.

thegrim33 2y ago

Reminds me of the novel Blindsight - in it there's special individuals who work as synthesists, whos job it is to observe and understand and then somehow translate back to "lay person" the seemingly undecipherable actions/decisions of advanced computers and augmented humans.

6gvONxR4sf7o 2y ago

"Best methods" is doing a lot of heavy lifting here. "Best" is a very multidimensional thing, with different priorities leading to different "bests." Someone will inevitably prioritize reliability/accuracy/fidelity/interpretability, and that's probably going to be a significant segment of the sciences. Maybe it's like how engineers just need an approximation that's predictive enough to build with, but scientists still want to understand the underlying phenomena. There will be an analogy to how some people just want an opaque model that works on a restricted domain for their purposes, but others will be interested in clearer models or unrestricted/less restricted domain models. It could lead to a very interesting ecosystem of roles. Even if you just limit the discussion to using the best model of X to design a better Y, limited to the model's domain of validity, that might translate the usage problem to finding argmax_X of valueFunction of modelPrediction of design of X. In some sense a good predictive model is enough to solve this with brute force, but this still leaves room for tons of fascinating foundational work. Maybe you start to find that the (wow so small) errors in modelPrediction are correlated with valueFunction, so the most accurate predictions don't make it the best for argmax (aka optimization might exploit model errors rather than optimizing the real thing). Or maybe brute force just isn't computationally feasible, so you need to understand something deeper about the problem to simplify the optimization to make it cheap.

RandomLensman 2y ago

We could be entering a new age of epicycles - high accuracy but very flawed understanding.

advisedwang 2y ago

In physics, we already deal with the fact that many of the core equations cannot be analytically solved for more than the most basic scenarios. We've had to adapt to using approximation methods and numerical methods. This will have to be another place where we adapt to a practical way of getting results.

topaz0 2y ago

In case it's not clear, this does not "beat" experimental structure determination. The matches to experiment are pretty close, but they will be closer in some cases than others and may or may not be close enough to answer a given question about the biochemistry. It certainly doesn't give much information about the dynamics or chemical perturbations that might be relevant in biological context. That's not to pooh-pooh alphafold's utility, just that it's a long way from making experimental structure determination unnecessary, and much much further away from replacing a carefully chosen scientific question and careful experimental design.

bluerooibos 2y ago

> What happens when... I can only assume that existing methods would still be used for verification. At least we understand the logic used behind these methods. The ML models might become more accurate on average but they could still throw out results that are way off occasionally, so their error rate would have to become equal to the existing methods.

GistNoesis 2y ago

The frontier in model space is kind of fluid. It's all about solving differential equations. In theoretical physics, you know the equations, you solve equations analytically, but you can only do that when the model is simple. In numerical physics, you know the equations, you discretize the problem on a grid, and you solve the constraint defined by the equations with various numerical integration schemes like RK4, but you can only do that when the model is small and you know the equations, and you find a single solution. Then you want the result faster, so you use mesh-free methods and adaptive grids. It works on bigger models but you have to know the equations, finding a single solution to the differential equations. Then you compress this adaptive grid with a neural network, while still knowing the governing equations, and you have things like Physics Informed Neural Networks ( https://arxiv.org/pdf/1711.10561 https://arxiv.org/pdf/1711.10561 and following papers) where you can bound the approximation error. This method allows solve all solutions to the differential equations simultaneously, sharing the computations. Then when knowing explicitly your governing equations is too complex, so you assume that there are some governing stochastic equations implicitly, which you learn the end-result of the dynamic with a diffusion model, that's what this alpha-fold is doing. ML is kind of a memoization technique, analog to hashlife in the game of life, that allows you reuse your past computational efforts. You are free to choose on this ladder which memory-compute trade-off you want to use to model the world.

visarga 2y ago

No, science doesn't work that way. You can just calculate your way to scientific discoveries, you got to test them in the real world. Learning, both in humans and AI, is based on the signals provided by the environment. There are plenty of things not written anywhere, so the models can't simply train on human text to discover new things. They learn directly from the environment to do that, like AlphaZero did when it beat humans at Go.

slibhb 2y ago

It's interesting to compare this situation to earlier eras in science. Newton, for example, gave us equations that were very accurate but left us with no understanding at all of why they were accurate. It seems like we're repeating that here, albeit with wildly different methods. We're getting better models but by giving up on the possibility of actually understanding things from first principles.

slashdave 2y ago
Not comparable. Our current knowledge of the physics involved in these systems is complete. It is just impossibly difficult to calculate from first principles.

ChuckMcM 2y ago

Interesting times indeed. I think the early history of medicines takes away from your observation though. In the 19th and early 20th century people didn't know why medicines worked, they just did. The whole "try a bunch of things on mice, pick the best ones and try them on pigs, and then the best of those and try a few on people" kind of thing. In many ways the mice were a stand in for these models, at the time scientists didn't understand nearly as much about how mice worked (early mice models were pretty crude by today's standards) but they knew they were a close enough analog to the "real thing" that the information provided by mouse studies was usefully translated into things that might help/harm humans. So when you're tools can produce outputs that you find useful, you can then use those tools to develop your understanding and insights. As a tool, this is quite good.

aaroninsf 2y ago

The top HN response to this should be, what happens is an opportunity has entered the chat. There is a wave coming—I won't try to predict if it's the next one—where the hot thing in AI/ML is going to be profoundly powerful tools for analyze other such tools and render them intelligible to us, which will I imagine mean providing something like a zoomable explainer. At every level there are footnotes; if you want to understand why the simplified model is a simplification, you look at the fine print. Which has fine print. Which has... Which doesn't mean there is not a stable level at which some formal notion of "accurate" cannot be said to exist, which is the minimum viable level of simplification. Etc. This sort of thing will of course will the input to many other things.

signal_space 2y ago

Is alphafold doing model generation or is it just reducing a massive state space? The current computational and systems biochemistry approaches struggle to model large biomolecules and their interactions due to the large degrees of freedom of the models. I think it is reasonable to rely on statistical methods to lead researchers down paths that have a high likelihood of being correct versus brute forcing the chemical kinetics. After all chemistry is inherently stochastic…

jononor 2y ago

I think it likely that instead of replacing existing methods, we will see a fusion. Or rather, many different kinds of fusions - depending on the exact needs of the problems at hand (or in science, the current boundary of knowledge). If nothing else then to provide appropriate/desirable level of explainability, correctness etc. Hypothetically the combination will also have better predictive performance and be more data efficient - but it remains to be seen how well this plays out in practice. The field of "physics informed machine learning" is all about this.

Grieverheart 2y ago

Perhaps for understanding the structure itself, but having the structure available allows us to focus on a coarser level. We also don't want to use quantum mechanics to understand the everyday world, and that's why we have classic mechanics etc.

nico 2y ago

Even if we don’t understand the models themselves, you can still use them as a basis for understanding For example, I have no idea how a computer works in every minute detail (ie, exactly the physics and chemistry of every process that happens in real time), but I have enough of an understanding of what to do with it, that I can use it as an incredibly useful tool for many things Definitely interesting times!

tecleandor 2y ago
Not the same. There is a difference between "I cannot understand the deeper details of certain model but some others can and there's the possibility of explaining it in detail" and "Nobody can understand it and there's not a clear cause-effect that we know" . Except for weird cases, computers (or cars, or cameras, or lots of other man made devices) are clearly known and you (or another specialist) can clearly show why a device does X when you input Y on it.

phn 2y ago

I'm not a scientist by any means, but I imagine even accurate opaque models can be useful in moving the knowledge forward. For example, they can allow you to accurately simulate reality, making experiments faster and cheaper to execute.

GuB-42 2y ago

We already have the absolute best method for accurately predicting the world, and it is by experimentation. In the protein folding case, it works by actually making the protein and analyzing it. For designing airplanes, computer models are no match for building the thing, or even using physical models and wind tunnels. And despite having these "best method", it didn't prevent progress in theoretical physics, theory and experimentation complement each other. ML models are just another kind of model that can help both engineering and fundamental research. Their working is close to the old guy in the shop who knows intuitively what is good design, because he has seen it all. That old guys in shops are sometimes better than modeling using physics equations help scientific progress, as scientists can work together with the old guy, combining the strength of intuition and experience with that of scientific reasoning.

flawsofar 2y ago

How do they compare on accuracy per watt?

theGnuMe 2y ago

The models are learning an encoding based on evolutionary related and known structures. We should be able to derive fundamental properties from those encodings eventually. Or at least our biophysical programmed models should map into that encoding. That might be a reasonable approach to look at the folding energy landscape.

MobiusHorizons 2y ago

Is it capable of predictions though? Ie can it accurately predict the folding of new molecules? Otherwise how do you distinguish accuracy from overfitting.

slashdave 2y ago

In terms of docking, you can call the conventional approaches "physically-based", however, they are rather poor physical models. Namely, they lack proper electrostatics, and, most importantly, basically ignore entropic contributions. There is no reason for concern.

trueismywork 2y ago

To paraphrase Kahan, it's not interesting to me whether a method is accurate enough or not, but whether you can predict how accurate you can be. So, if ML methods can predict that they're right 98% of times then we can build this in our systems, even if we don't understand how they work. Deterministic methods can predict result with a single run, ML methods will need ensemble of results to show the same confidence. It is possible at the end of day that the difference in cost might not he that high over time.

abledon 2y ago

Next decade we will focus on building out debugging and visualization tools for deep learning , to glance inside the current black box

hyperthesis 2y ago

Engineering often precedes Science. It's just more data.

salty_biscuits 2y ago

I'd say it's not new. Take fluid dynamics as an example, the navier stokes equations predict the motion of fluids very well but you need to approximately solve them on a computer in order to get useful predictions for most setups. I guess the difference is the equation is compact and the derivation from continuum mechanics is easy enough to follow. People still rely on heuristics to answer "how does a wing produce lift?". These heuristic models are completely useless at "how much lift will this particular wing produce under these conditions?". Seems like the same kind of situation. Maybe progress forward will look like producing compact models or tooling to reason about why a particular thing happened.

Brian_K_White 2y ago

Perhaps an ai can be made to produce the work as well as a final answer, even if it has to reconstruct or invent the work backwards rather than explain it's own internal inscrutable process. "produce a process that arrives at this result" should be just another answer it can spit out. We don't necessarily care if the answer it produces is actually the same as what originally happened inside itself. All we need is that the answer checks out when we try it.

JacobThreeThree 2y ago

As a tool people will use it as any other tool, by experimenting, testing, tweaking and iterating. As a scientific theory for fundamentally explaining the nature of the universe, maybe it won't be as useful.

robwwilliams 2y ago

This is a key but secondary concern to many of us working in molecular geneticist who will use AlphaFold 3 to evaluate pair-wise interactions. We often have genetic support for an interaction between proteins A and B. For example, in a study of genetic variation in responses of mice to morphine I currently have two candidate proteins that interact epistatically, suggesting a possible “lock and key” model—-the mu opiate receptor (MOR) and FGF12. I can now evaluate the likelihood of a direct molecular interaction between these proteins and possible amino acids substitutions that account for individuals difference. In other words I bring a hypothesis to AF3 and ask for it to refute or affirm.

Jupe 2y ago

> Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-interpretable theories and mathematical models / explanations? Replace "human-interpretable theories" with "every man interpretable theories", and you'll have a pretty good idea of how > 90% of the world feels about modern science. It is indistinguishable from magic, by the common measure. Obtuse example: My parents were alive when the first nuclear weapon was detonated. They didn't know that they didn't know this weapon was being built, let alone that it might have ignited the atmosphere. With sophisticated enough ML, that 90% will become 99.9% - save the few who have access to (and can trust) ML tools that can decipher the "logic" from the original ML tools. Yes, interesting times ahead... indeed.

TheBicPen 2y ago

Perhaps related, the first computer-assisted mathematics proof: https://en.wikipedia.org/wiki/Four_color_theorem https://en.wikipedia.org/wiki/Four_color_theorem I'm sure that similar arguments for and against the proof apply here as well.

andy_ppp 2y ago

What happens if we get to the stage of being able to simulate every chemical and electrical reaction in a human brain, is doing this torture or wrong?

jasondigitized 2y ago
So the Matrix?
- andy_ppp 2y ago
  The brains were in “the real” in the Matrix or did I not watch it closely enough :-)

jasondigitized 2y ago

All I can see anymore is that March of Progress illustration [1] with a GPU being added to the far right. Interesting times indeed. [1] https://en.m.wikipedia.org/wiki/March_of_Progress https://en.m.wikipedia.org/wiki/March_of_Progress

andrewla 2y ago

Physicists like to retroactively believe that our understanding of physical phenomena preceded the implementation of uses of those phenomena, when the reality is that physics has always come in to clean up after the engineers. There are some rare exceptions, but usually the reason that scientific progress can be made in an area is that the equipment to perform experiments has been commoditized sufficiently by engineering demand for it. We had semiconductors and superconductors before we understood how they worked -- on both cases arguably we still don't completely understand the phenomena. Things like the dynamo and the electric motor were invented by practice and later explained by scientists, not derived from first principles. Steam engines and pumps were invented before we had the physics to describe how they worked.

mycall 2y ago

A New Kind Of Science?

kajic 2y ago

It’s much easier to reverse engineer a solution that you don’t understand (and discover important underlying theories on that journey), than it is to arrive at that same solution and the underlying theories without knowing in advance where you are going. For this reason, discoveries made by AI will be immensely useful for accelerating scientific progress, even if those discoveries are opaque at first.

yieldcrv 2y ago

I think it creates new studies, such as diagnosing these models behaviors without the doctor having an intricate understanding of all of the model's processes/states just like with natural organisms

goodmachine 2y ago

In order for that not to happen (uninterpretable ML models) some research on symbolic distillation, aka symbolic regression https://arxiv.org/abs/2006.11287 https://arxiv.org/abs/2006.11287 https://www.science.org/doi/10.1126/sciadv.aay2631 https://www.science.org/doi/10.1126/sciadv.aay2631

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