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?
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), 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")
The goal of science has always been to discover underlying principles and not merely to predict the outcome of experiments. I don't see any way to classify an opaque ML model as a scientific artifact since by definition it can't reveal the underlying principles. Maybe one could claim the ML model itself is the scientist and everyone else is just feeding it data. I doubt human scientists would be comfortable with that, but if they aren't trying to explain anything, what are they even doing?
That's the aspirational goal. And I would say that it's a bit of an inflexible one- for example, if we had an ML that could generate molecules that cure diseases that would pass FDA approval, I wouldn't really care if scientists couldn't explain the underlying principles. But I'm an ex-scientist who is now an engineer, because I care more about tools that produce useful predictions than understanding underlying principles. I used to think that in principle we could identify all the laws of the universe, and in theory, simulate that would enough accuracy, and inspect the results, and gain enlightenment, but over time, I've concluded that's a really bad way to waste lots of time, money, and resources.
It's not either-or, it's yes-and. We don't have to abandon one for the other.
AlphaFold 3 can rapidly reduce a vast search space in a way physically-based methods alone cannot. This narrowly focused search space allows scientists to apply their rigorous, explainable, physical methods, which are slow and expensive, to a small set of promising alternatives. This accelerates drug discovery and uncovers insights that would otherwise be too costly or time-consuming.
The future of science isn't about AI versus traditional methods, but about their intelligent integration.
Or you can treat AlphaFold as a black box / oracle and work at systems biology level, i.e. at pathway and cellular level. Protein structures and interactions are always going to be hard to predict with interpretable models, which I also prefer.
My only worry is that AlphaFold and others, e.g. ESM, seem to be bit fragile for out-of-distribution sequences. They are not doing a great job with unusual sequences, at least in my experience. But hopefully they will improve and provide better uncertainty measures.
> if we had an ML that could generate molecules that cure diseases that would pass FDA approval, I wouldn't really care if scientists couldn't explain the underlying principles
It’s actually required as part of the submission for FDA approval that you posit a specific Mechanism of Action for why your drug works the way it does. You can’t get approval without it
A substantial proportion of FDA-approved drugs have an unknown mechanism of action - we can handwave about protein interactions, but we have no useful insight into how they actually work. Drug discovery is bureaucratically rigid, but scientifically haphazard.
How much do you believe that the MoA actually matches what is happening in the underlying reality of a disease and its treatment?
Vioxx is a nice example of a molecule that got all the way to large-scale deployment before being taken off the market for side effects that were known. Only a decade before that, I saw a very proud pharma scientist explaining their "mechanism of action" for vioxx, which was completely wrong.
Underlying principles are nice for science, whatever works is nice for engineering. There is plenty of historical precedent where we build stuff that works without knowing exactly why it works.
Discovering underlying principles and predicting outcomes is two sides of the same coin in that there is no way to confirm you have discovered underlying principles unless they have some predictive power.
Some had tried to come up with other criteria to confirm you have discovered an underlying principle without predictive power, such as on aesthetics - but this is seen by the majority of scientists as basically a cop out. See debate around string theory.
Note that this comment is summarizing a massive debate in the philosophy of science.
If all you can do is predict an outcome without being able to explain how then what have you really discovered? Asking someone to just believe you can predict outcomes without any reasoning as to how, even if you're always right, sounds like the concept of faith in religion.
The how is actually just further hypotheses. It's turtles all the way down:
There is a car. We think it drives by burning petrol somehow.
How do we test this? We take petrol away and it stops driving.
Ok, so we know it has something to do with petrol. How does it burning the petrol make it drive?
We think it is caused by the burned petrol pushing the cylinders, which are attached to the wheels through some gearing. How do we test it? Take away the gearing and see if it drives.
Anyway, this never ends. You can keep asking questions, and as long as the hypothesis is something you can test, you are doing science.
In the vein of "can a biologist fix a radio" and "can a neuroscientist understand a microprocessor", see https://review.ucsc.edu/spring04/bio-debate.html which is an absolutely wonderful explanation of how geneticists and biochemists would go about reverse-engineering cars.
The best part is where the geneticist ties the arms of all the suit-wearing employees and it has no functional effect on the car.
When you read about a wizard using magic to lay waste to invading armies, how much value would you guess the armies place in whether or not the wizard truly understands the magic being used against them?
Probably none. Because the fact that the wizard doesn’t fully understand why magic works does not prevent the wizard from using it to hand invaders their asses. Science is very much the same - our own wizards used medicine that they did not understand to destroy invading hordes of bacteria.
Exactly! The magic to lay waste to invading armies is packaged into a large flask and magical metal birds are flown to above the army. There the flask is released from the birds bellies and gently glides down. When the flask is at optimum height it releases the power of the sun and all that are beneath it get vaporized. A newer version of this magic is attached to a gigantic fireworks rocket that can fly over whole mountain ranges and seas.
it's still an extremely valuable tool. just as we see in mathematics, closed forms (and short and elegant proofs) are much coveted luxury items.
for many basic/fundamental mathematical objects we don't (yet) have simple mechanistic ways to compute them.
so if a probabilistic model spits out something very useful, we can slap a nice label on it and call it a day. that's how engineering works anyway. and then hopefully someday someone will be able to derive that result from "first principles" .. maybe it'll be even more funky/crazy/interesting ... just like mathematics arguably became more exciting by the fact that someone noticed that many things are not provable/constructable without an explicit Axiom of Choice.
but in usual natural sciences we don't have proofs, only data and models, and then we do model selection (and through careful experiments we end up with confidence intervals)
and it seems with these molecular biology problems we constantly have the problem of specificity (model prediction quality) vs sensitivity (model applicability), right? but due to information theory constraints there's also a dimension along model size/complexity.
so if a ML model can push the ROC curve toward the magic left-up corner then likely it's getting more and more complex.
and at one point we simply are left with models that are completely parametrized by data and there's virtually zero (direct) influence of the first principles. (I mean that at one point as we get more data even to do model selection we can't use "first principles" because what we know through that is already incorporated into previous versions of the models. Ie. the information we gained from those principles we already used to make decisions in earlier iterations.)
Of course then in theory we can do model distillation, and if there's some hidden small/elegant theory we can probably find it. (Which would be like a proof through contradiction, because it would mean that we found model with the same predictive power but with smaller complexity than expected.)
// NB: it's 01:30 here, but independent of ignorance-o-clock ... it's quite possible I'm totally wrong about this, happy to read any criticism/replies
Isn’t that basically true of most of the fundamental laws of physics? There’s a lot we don’t understand about gravity, space, time, energy, etc., and yet we compose our observations of how they behave into very useful tools.
And much of the 20th century was characterized by a very similar progression - we had no clue what the actual mechanism of action was for hundreds of life saving drugs until relatively recently, and we still only have best guesses for many.
That doesn’t diminish the value that patients received in any way even though it would be more satisfying to make predictions and design something to interact in a way that exactly matches your theory.
We were using the compass for navigation for thousands of years, without any clue about what it was doing or why. Ofcourse lot of people got lost cause compasses are not perfect. And the same will happen here. Theory of Bounded Rationality applies.
That ship sailed with Quantum physics. Nearly perfect at prediction, very poor at giving us a concrete understanding of what it all means.
This has happened before. Newtonian mechanics was incomprehensible spooky action at a distance, but Einstein clarified gravity as the bending of spacetime.
I think this relies on either the word “concrete” or a particular choice of sense for “concrete understanding”.
Like, quantum mechanics doesn’t seem, to me, to just be a way of describing how to predict things. I view it as saying substantial things about how things are.
Sure, there are different interpretations of it, which make the same predictions, but, these different interpretations have a lot in common in terms of what they say about “how the world really is” - specifically, they have in common the parts that are just part of quantum mechanics.
The qau that can be spoken in plain language without getting into the mathematics, is not the eternal qau, or whatever.
The goal of science has always been to predict the outcome of experiments, because that's what distinguishes science from philosophy or alchemy or faith. Anyone who believes that they've discovered an underlying principle is almost certainly mistaken; with time, "underlying principles" usually become discredited theories or, sometimes, useful but crude approximations that we teach to high schoolers and undergrads.
Prediction is understanding. What we call "understanding" is a cognitive illusion, generated by plausible but brittle abstractions. A statistically robust prediction is an explanation in itself; an explanation without predictive power explains nothing at all. Feeling like something makes sense is immeasurably inferior to being able to make accurate predictions.
Scientists are at the dawn of what chess players experienced in the 90s. Humans are just too stupid to say anything meaningful about chess. All of the grand theories we developed over centuries are just dumb heuristics that are grossly outmatched by an old smartphone running Stockfish. Maybe the computer understands chess, maybe it doesn't, but we humans certainly don't and we've made our peace with the fact that we never will. Moore's law does not apply to thinking meat.
Kepler famously compiled troves of data on the night sky, and just fitted some functions to them. He could not explain why but he could say what. Was he not a scientist?
He did attempt to explain why. Wikipedia:
"On 4 February 1600, Kepler met Tycho Brahe....Tycho guarded his data closely, but was impressed by Kepler's theoretical ideas and soon allowed him more access. Kepler planned to test his theory from Mysterium Cosmographicum based on the Mars data, but he estimated that the work would take up to two years (since he was not allowed to simply copy the data for his own use)."
Can underlying principles be discovered using the framework of scientific method? The primary goal of models and theories it develops is to support more experiments and eventually be disproven. If no model can be correct, complete and provable in finite time, then a theory about underlying principles that claims completeness would have to be unfalsifiable. This is reasonable in context of philosophy, but not in natural sciences.
Scientific method can help us rule out what underlying principles are definitely not. Any such principles are not actually up to be “discovered”.
If probabilistic ML comes along and does a decent job at predicting things, we should keep in mind that those predictions are made not in context of absolute truth, but in context of theories and models we have previously developed. I.e., it’s not just that it can predict how molecules interact, but that the entire concept of molecules is an artifact of just some model we (humans) came up with previously—a model which, per above, is probably incomplete/incorrect. (We could or should use this prediction to improve our model or come up with a better one, though.)
Even if a future ML product could be creative enough to actually come up with and iterate on models all on its own from first principles, it would not be able to give us the answer to the question of underlying principles for the above-mentioned reasons. It could merely suggest us another incomplete/incorrect model; to believe otherwise would be to ascribe it qualities more fit for religion than science.
People clearly have been able to discover many underlying principles using the scientific method. Then they have been able to explain and predict many complex phenomena using the discovered principles, and create even more complex phenomena based on that. Complex phenomena such as the technology we are using for this discussion.
Words dont have any inherent meaning, just the meaning they gain from usage. The entire concept of truth is an artifact of just some model (language) we came up with previously—a model which, per above, is probably incomplete/incorrect. The kind of absolute truth you are talking about may make sense when discussing philosophy or religion. Then there is another idea of truth more appropriate for talking about the empirical world. Less absolute, less immutable, less certain, but more practical.
> The kind of absolute truth you are talking about may make sense when discussing philosophy or religion.
Exactly—except you are talking about it, too. When you say “discovering underlying principles”, you are implying the idea of absolute truth where there is none—the principles are not discovered, they are modeled, and that model is our fallible human construct. It’s a similar mistake as where you wrote “explain”: every model (there should always be more than one) provides a metaphor that 1) first and foremost, jives with our preexisting understanding of the world, and 2) offers a lossy map of some part of [directly inaccessible] reality from a particular angle—but not any sort of explanation with absolute truth in mind. Unless you treat scientific method as something akin to religion, which is a common fallacy and philosophical laziness, it does not possess any explanatory powers—and that is very much by design.
Now we come back to words gaining their meaning from usage.
You are assigning meanings to words like "discovering", "principles", and "explain" that other people don't share. Particularly people doing science. Because these absolute philosophical meanings are impossible in the real world, they are also useless when discussing the reality. Reserving common words for impossible concepts would not make sense. It would only hinder communication.
I can see what you mean. Then perhaps you could give a non-circular definition of what you mean by “underlying principles” and how that is different from any other prediction or model to deserve this distinct and quite strong-sounding term? or what you mean by “explain” that is different from “predict” or “model” to warrant such a distinctive term, and where exactly such explanatory activity fits within scientific method?
Communication is inherently circular, and words don't have definitions. But people are often capable of discovering what a particular word means in a particular context. And other people can sometimes help that by giving useful explanations.
Science is pretty much the same. We can often discover how the reality works, and use the discoveries to explain many things. Somehow that keeps happening all the time, even if we can never be fully sure about anything.
Any word can be given a definition, that’s how we communicate. A non-circular definition is a way to define what you mean by a term to another person.
Again: scientific method does not explain. Religion or philosophy are about explaining. Scientific method is about experimentation and making testable predictions. What experiments we perform is determined by how we understand the world, and if there is any subsequent explanation about “how things really are” (a.k.a. “the underlying principles”) then it has nothing to do with scientific method which does not make such claims by design; that is untestable/unfalsifiable beliefs and a product of either philosophical or religious thinking.
Since you insist on using specific words “explain” and “discover”, rather than more conventionally used in science “predict” or “model”, it implies they mean something different to you. I have provided the meanings of “explain” and “discover” I am familiar with, as it applies to the discussion at hand (which is about the philosophy of scientific process, underlying principles and truths about objectively existing reality). If you refuse to identify the meanings you are using those words in, I take it that you concede whatever point you had.
I've never met anyone capable of communicating with well-defined terms. Or giving definitions that actually match the real-word usage of the term. And all definitions are ultimately circular, because the number of words is finite. In any chain of definitions, you will eventually have to use a term you were trying to define.
What you call the scientific method is a philosophical construct that has little to do with actual science. And philosophers disagree on whether it's a good ideal for science. Given that it's neither a good description of science nor a universal ideal for science, I wouldn't focus too much on it when discussing science.
> And all definitions are ultimately circular, because the number of words is finite.
I can’t help thinking I’m talking to an LLM or a troll.
If you use a complex term that needs definition in a casual discussion, it’s most likely none of the words you use in the definition would themselves require definitions—and if this was to happen repeatedly, the conversation would halt long before we would be running out of words. It’s enough to avoid circularity within a couple of levels in good faith.
Anyway, I’m not sure whether we disagree or not or what exactly we are arguing about. My point is “ML making predictions is not a threat to us getting at underlying principles, because natural science (scientific method, predicting things) in general does lead us to any provable facts about those principles, and because ML would make predictions within an incorrect/incomplete model that we gave it.” In that, by “underlying principles” I mean some statements about objective reality. If we are on the same page here, we can continue discussion, otherwise let’s not.
It's an analogy. All communication is ultimately circular, and we can never be sure that we understand the terms the same way as the other party. Still, people often seem to be able to communicate.
Similarly, scientific method cannot discover the underlying principles or explain the nature. It can only rule out principles and explanations. Regardless, science seems to come up with principles and explanations all the time.
And that's because scientific method is not science. It's a theoretical model for a subset of (often ritualized) activities within science. Actual science is more than that. It can do things scientific method cannot, because it's less focused on philosophical ideas such as absolute truth or provable facts.
In my experience, scientific method is like a picture of an elephant drawn from a written description. Drawn by someone who has never seen the animal or a picture of it, and who has no idea what kind of an animal it is. There are some recognizable elements, but it definitely does not look like the real thing.
Then we should dedicate large fractions of human engineering towards finding ethical ways to improve human intelligence so that we can appreciate the underlying principles better.
I spend about 30 minutes reading this thread and links from it: I don't really follow your line of argument. I find it fascinating and well-communicated, the lack of understanding is on me: my attention flits around like a butterfly, in a way that makes it hard for me to follow people writing original content.
High level, I see a distinction between theory and practice, between an oracle predicting without explanation, and a well-thought out theory built on a partnership between theory and experiment over centuries, ex. gravity.
I have this feeling I can't shake that the knife you're using is too sharp, both in the specific example we're discussing, and in general.
In the specific example, folding, my understanding is we know how proteins fold & the mechanisms at work. It just takes an ungodly amount of time to compute and you'd still confirm with reality anyway. I might be completely wrong on that.
Given that, the proposal to "dedicate...engineer[s] towards finding ethical ways to improve...intelligence so that we can appreciate the underlying principles better" begs the question of if we're not appreciating the underlying principles.
It feels like a close cousin of physics theory/experimentalist debate pre-LHC, circa 2006: the experimentalists wanted more focus on building colliders or new experimental methods, and at the extremes, thought string theory was a complete was of time.
Which was working towards appreciating the underlying principles?
I don't really know. I'm not sure there's a strong divide between the work of recording reality and explaining it. I'll peer into a microscope in the afternoon, and take a shower in the evening, and all of a sudden, free associating gives me a more high-minded explanation for what I saw.
I'm not sure a distinction exists for protein folding, yes, I'm virtually certain this distinction does not exist in reality, only in extremely stilted examples (i.e. a very successful oracle at Delphi)
I read an article about the "unreasonable effectiveness of mathematics" that it was basically the result of a drunk looking for his keys under a lamp post because that's where the light is. We know how to use math to model parts of the world, and every where we look, there's _something_ we can model with math, but that doesn't mean that there's all there is to the universe. We could be understanding .0000001% of what's out there to understand, and it's the stuff that's amenable to mathematical analysis.
The ML model can also be an emulator of parts of the system that you don't want to personally understand, to help you get on with focusing on what you do want to figure out. Alternatively, the ML model can pretend to be the real world while you do experiments with it to figure out aspects of nature in minutes rather than hours-days of biological turnaround.
AFAIK in wet science you need (or needed) to do tons of experimentations with liquids with specific molar compositions and temperatures splurging in and out of test tubes - basically just physically navigating a search space. I would view an AI model with super powerful guestimation capability as a much faster way of
A) cutting through search space
B) providing accidental discoveries while at it
Now, if we look at history of science and technology, there is a shit ton of practical stuff that was found only by pure accident - discoveries of which could not be predicted from any previous theory.
I would view both A) and B) as net positives. But our teaching of the next generation of scientists needs to adapt.
The worst case scenario is of course that the middle management driven enshittification of science will proceed to a point where there are only few people who actually are scientists and not glorified accountants. But I’m optimistic this will actually super charge science.
With good luck we will get rid of the both of the biggest pathologies in modern science - 1. number of papers published and referred as a KPI 2. Hype driven super politicized funding where you can focus only one topic “because that’s what’s hot” (i.e. string theory).
The best possible outcome is we get excitement and creativity back into science. Plus level up our tech level in this century to something totally unforeseen (singularity? That’s just a word for “we don’t know what’s gonna happen” - not a specific concrete forecasted scenario).
> singularity? That’s just a word for “we don’t know what’s gonna happen” - not a specific concrete forecasted scenario
It's more specific than you make it out. The singularity idea is that smart AIs working on improving AI will produce smarter AIs, leading to an ever increasing curve that at some point hits a mathematical singularity.
> 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?
I think chess engines, weirdly enough, have disabused me of this notion.
There are lots of factors a human considers when looking at a board. Piece activity. Bishop and knight imbalances. King safety. Open and semi-open file control. Tempo. And on and on.
But all of them are just convenient shortcuts that allow us to substitute reasonable guesses for what really matters: exhaustively calculating a winning line through to the end. “Positional play” is a model that only matters when you can’t calculate trillions of lines thirty moves deep, and it’s infinitely more important that a move survives your opponent’s best possible responses than it is to satisfy some cohesive higher level principle.
I don't understand why you would draw this conclusion. The deep search you describe is an algorithm that humans can understand perfectly fine. Humans just can't solve it in their heads and need to let a computer handle the number crunching. Just like a scientist may understand the differential equations to describe a system perfectly fine, but require a computer to approximate the solution for an initial value problem.
“Knowing” that some line works to some ridiculous depth is different than understanding how and why.
And at some level the answer is simply “because every possible refutation fails” and there is no simpler pattern to match against nor intuition to be had. That is the how and why of it.
The scientist can understand “how” the model works, how many layers there are, that each neuron has a weight, that some are connected… Parent comment and yours show that “understanding” is a fuzzy concept.
Chess engines actually do both now. They have ML models to evaluate positions, essentially a much more advanced version of your positional description, and deep calculations.
That might be the best we can practically achieve with technology, but the point stands. If positional evaluation says one thing but an exhaustive analysis of lines finds a solution 60 moves deep, that one is going to win.
Ignore the existence of engines for a moment. The reason a particular line works, at the end of the day, is simply because it does. Just because we have heuristics that help us skip a lot of evaluation doesn’t mean the heuristics have intrinsic meaning within the game. They don’t.
They’re shortcuts that let us skip having to do the impossible. The heuristics will always lose to concrete analysis to a deep enough depth.
And that’s my point. We come up with models that give us an intuition for “why” things are a certain way. Those models are inarguably helpful toward having a gut feeling. But models aren’t the thing itself, and every model we’ve found breaks down at some deeper point. And maybe at some level things simply “are” some way with no convenient shorthand explanation.
Because of our limitations, we have to compress reality more in order to reason about it. This means we're blind to some ideas that computers are not. Just like a depth 1 chess engine can't see what's happening at depth 3 but has to make an imperfect guess.
Fine tuned LLMs can play chess at grandmaster levels.
So its clear that there is in fact 'deeper patterns to chess' that allow one to play very well, without any search required (Since LLMs cannot search). Its just that those patterns are probably rather different to human understood ones.
I'm in the following camp:
It is wrong to think about the world or the models as "complex systems" that may or may not be understood by human intelligence. There is no meaning beyond that which is created by humans. There is no 'truth' that we can grasp in parts but not entirely.
Being unable to understand these complex systems means that we have framed them in such a way (f.e. millions of matrix operations) that does not allow for our symbol-based, causal reasoning mode. That is on us, not our capabilities or the universe.
All our theories are built on observation, so these empirical models yielding such useful results is a great thing - it satisfies the need for observing and acting. Missing explainability of the models merely means we have less ability to act more precisely - but it does not devalue our ability to act coarsely.
But the human brain has limited working memory and experience. Even in software development we are often teetering at the edge of the mental power to grasp and relate ideas. We have tried so much to manage complexity, but real world complexity doesn't care about human capabilities. So there might be high dimensional problems where we simply can't use our brains directly.
A human mind is perfectly capable of following the same instructions as the computer did. Computers are stupidly simple and completely deterministic.
The concern is about "holding it all in your head", and depending on your preferred level of abstraction, "all" can perfectly reasonably be held in your head. For example: "This program generates the most likely outputs" makes perfect sense to me, even if I don't understand some of the code. I understand the system. Programmers went through this decades ago. Physicists had to do it too. Now, chemists I suppose.
Why do you think systems of partial differential equations (common in physics) are somehow provide more understanding than the corresponding ML math (at the end of the day both can produce results using a lots of matrix multiplications).
... because people understand things about what is described when dealing with such systems in physics, and people don't understand how the weights in ML learned NNs produce the overall behavior? (For one thing, the number of parameters is much greater with the NNs)
Stuff that we can't program directly, but can program using machine learning.
Speech recognition. OCR. Reccomendation engines.
You don't write OCR by going "if there's a line at this angle going for this long and it crosses another line at this angle then it's an A".
There's too many variables and influence of each of them is too small and too tightly coupled with others to be able to abstract it into something that is understandeable to a human brain.
> AI arguably accomplishes this using some form of abstraction though does it not?
It's unabstractable for people, because the most abstract model that works still has far too many variables for our puny brains.
> artists routinely engage in various forms of unusual abstraction
Abstraction in art is just another, unrelated meaning of the word. Like execution of a program vs execution of a person. You could argue executing the journalist for his opinions isn't bad, because execution of mspaint.exe is perfectly fine, but it won't get you far :)
While computer operations in solutions are computable by humans, the billions of rapid computations are unachievable by humans. In just a few seconds, a computer can perform more basic arithmetic operations than a human could in a lifetime.
I'm not saying it's achievable, I'm saying it's not magic. A chemist who wishes to understand what the model is doing can get as far as anyone else, and can reach a level of "this prediction machine works well and I understand how to use and change it". Even if it requires another PhD in CS.
That the tools became complex is not a reason to fret in science. No more than statistical physics or quantum mechanics or CNN for image processing - it's complex and opaque and hard to explain but perfectly reproduceable. "It works better than my intuition" is a level of sophistication that most methods are probably doomed to achieve.
> ... and strongly believe that science's goal is to produce falsifiable hypotheses, these new approaches will be extremely depressing
I don't quite understand this point — could you elaborate?
My understanding is that the ML model produces a hypothesis, which can then be tested via normal scientific method (perform experiment, observe results).
If we have a magic oracle that says "try this, it will work", and then we try it, and it works, we still got something falsifiable out of it.
Or is your point that we won't necessarily have a coherent/elegant explanation for why it works?
There is an issue scientifically. I think this point was expressed by Feynman: the goal of scientific theories isn’t just to make better predictions, it’s to inform us about how and why the world works. Many ancient civilizations could accurately predict the position of celestial bodies with calendars derived from observations of their period, but it wasn’t until Copernicus proposed the heliocentric model and Galileo provided supporting observations that we understood the why and how, and that really matters for future progress and understanding.
I agree the how/why is the main driving goal. That's kinda why I feel like this is not depressiong news — there's a new frontier to discover and attempt to explain. Scientists love that stuff (:
Knowing how to predict the motion of planets but without having an underlying explanation encourages scientists to develop their theories. Now, once more, we know how to predict something (protein folding) but without an underlying explanation. Hurray, something to investigate!
(Aside: I realize that there are also more human factors at play, and upsetting the status quo will always cause some grief. I just wanted to provide a counterpoint that there is some exciting progress represented here, too).
I was mainly responding to the claim that these black boxes produce a hypothesis that is useful as a basis for scientific theories. I don’t think it does, because it offers no explanation as to the how and why, which is as we agree the primary goal. It doesn’t provide a hypothesis per se, just a prediction, which is useful technologically and should indicate that there is more to be discovered (see my response to the sibling reply) scientifically but offers no motivating explanation.
But we do know why, it's just not simple. The atoms interact with one another because of a variety of fundamental forces, but since there can be hundreds of thousands of atoms in a single protein, it's plainly beyond human comprehension to explain why it folds the way it does, one fundamental force interaction at a time.
Fair. I guess the interesting thing for protein folding research then is that there appears to be a way to approximate/simplify the calculations required to predict folding patterns that doesn’t require the precision of existing folding models and software. In essence, AlphaFold is an existence proof that there should be a way to model protein folding more efficiently.
People will be depressed because they spent decades getting into professorship positions and publishing papers with ostensible comprehensible interpretations of the generative processes that produced their observations, only to be "beat" in the game by a system that processed a lot of observations and can make predicts in a way that no individual human could comprehend. And those professors will have a harder time publishing, and therefore getting promoted, in the future.
Whether ML models produce hypotheses is something of an epistemiological argument that I think muddies the waters without bringing any light. I would only use the term "ML models generate predictions". In a sense, the model itself is the hypothesis, not any individual prediction.
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."
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.
ML is accustomed with the idea that all models are bad, and there are ways to test how good or bad they are. It's all approximations and imperfect representations, but they can be good enough for some applications.
If you think carefully humans operate in the same regime. Our concepts are all like that - imperfect, approximative, glossing over some details. Our fundamental grounding and test is survival, an unforgiving filter, but lax enough to allow for anti-vaxxer movements during the pandemic - survival test is not testing for truth directly, only for ideas that fail to support life.
There have been times in the past when usable technology surpassed our scientific understanding, and instead of being depressing it provided a map for scientific exploration. For example, the steam engine was developed by engineers in the 1600s/1700s (Savery, Newcomen, and others) but thermodynamics wasn’t developed by scientists until the 1800s (Carnot, Rankine, and others).
I think the various contributors to the invention of the steam engine had a good idea of what they were trying to do and how their idea would physically work. Wikipedia lists the prerequisites as the concepts of a vacuum and pressure, methods for creating a vacuum and generating steam, and the piston and cylinder.
That's not too different from the alpha fold people knowing that there's a sequence to sequence translation, that an enormous number of cross-talk happens between the parts of the molecule, that if you get the potential fields just right, it'll fold in the way nature intended. They're not just blindly fiddling with a bunch of levers. What they don't know is the individual detailed interactions going on and how to approximate them with analytical equations.
What always struck me about Chomskyists is that they chose a notion of interpretable model that required unrealistic amounts of working interpretation. So Chomsky grammars have significant polynomial memory and computational costs for grammars as they approach something resembling human grammar. And you say, ok, the human brain can handle much more computation than that, and that's fine. But (for example) context-free grammars aren't just O(n^3) in computational cost; for a realistic description of human language they're O(n^3) in human-interpretable rules.
Other Chomsky-like models of human grammars have different asymptotic behavior and different choices of n, but the same fundamental problem; the big-O constant factor isn't neurons firing but rather human connections between the n inputs. How can you conceive of human minds being able to track O(n^3) (or whatever) cost where that n is everything being communicated -- words, concepts, symbols, representations, all that jazz and the polynomial relationships between them?
But I feel an apology is in order: I've had quite a few beers before coming home, and it's probably a mistake to try to express academically charged and difficult views on the Internet while in an inebriated state. Probably the alcohol has substantially decreased my mental computational power. However, it has only mildly impaired my ability to string together words and sentences in a grammatically complex fashion. In fact, I often feel that the more sober and clear-minded I am, the simpler my language is. Maybe human grammar is actually sub-polynomial. I have observed the same in ChatGPT; the more flowery and wordy it has become over time, the dumber its output.
As an aside but relevant to your point, my entire introduction to DNA and protein analysis was based on Chomsky grammars. My undergrad thesis advisor David Haussler handed me a copy of an article by David Searls "The Linguistics of DNA" (https://www.scribd.com/document/461974005/The-Linguistics-of...) . At the time, Haussler was in the middle of applying HMMs and other probabilistic graphical models to sequence analysis, and I knew all about DNA as a molecule, but not how to analyze it.
Searls paper basically walks through Chomsky's hierarchy, and how to apply it, using linguistic techniques to "parse" DNA. It was mind-bending and mind-expanding for me (it takes me a long time to read papers, for example I think I read this paper over several months, learning to deal with parsing along the way). To this day I am astounded at how much those approaches (linguistics, parsing, and grammars) have evolved- and yet not much has changed! People were talking about generative models in the 90s (and earlier) in much the same way we treat LLMs today. While much of Chomsky's thinking on how to make real-world language models isn't particuarly relevant, we still are very deeply dependent on his ideas for grammar...
Anyway, back to your point. While CFGs may be O(n*3) I would say that there is a implicit, latent O(n) parseable grammar underlying human linguistics, and our brains can map that latent space to its own internal representation in O(1) time, where the n roughly correlates to the complexity of the idea being transferred. It does not seem even remotely surprising that we can make multi-language models that develop their own compact internal representation that is presumably equidistant from each source language.
> 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
This seems to me an empirical question about the world. It’s clear our minds are limited, and we understand complex phenomena through abstraction. So either we discover we can continue converting advanced models to simpler abstractions we can understand, or that’s impossible. Either way, it’s something we’ll find out and will have to live with in the coming decades. If it turns out further abstractions aren’t possible, well, enlightenment thought had lasted long enough. It’s exciting to live at a time in humanity’s history when we enter a totally uncharted new paradigm.
> can we actually interpret these models, or are human brains too feeble to understand complex systems without sophisticated models?
I think we will have to develop a methodology and supporting toolset to be able to derive the underlying patterns driving such ML models. It's just too much for a human to comb through by themselves and make sense of.
So the work to simplify ML models, reduce dimensions, etc. becomes the numeric way to seek simple actual scientific models. Scientific computing and science become one.
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 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.
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.
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).
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.
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.
> 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.
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
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.
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.).
> e.g., drug works perfectly for the target disease but does something unexpected elsewhere etc.
That's very common. It's the reason to test the new drug in petri dish, then rats, then dogs, then humans and if all test passed send it to the pharmacy.
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.
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.
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.
The more important point was that medications that do work still come in two forms: ones where we have a good idea of the mechanism of action that makes them work, and ones where we don't.
For example, we have a good idea of why certain antibiotics cure tuberculosis - we understand that tuberculosis is caused by certain bacteria, and we know how antibiotics affect the cellular chemistry of those bacteria to kill them. We also understand the dynamics of this, the fact that the body's immune system still has to be functioning well enough to kill many of the bacteria as well, etc. We don't fully understand all of the side-effects and possible interactions with other diseases or medications in every part of the body, but we understand the gist of it all.
Then there are drugs and diseases where we barely understand any of it. We don't have for example a clear understanding of what depression is, what the biochemistry of it is. We do know several classes of drugs that help with depression in certain individuals, but we know those drugs don't help with other individuals, and we have no way of predicting which is which. We know some of the biochemical effects of these drugs, but since we don't understand the underlying cause of depression, we don't actually know why the drugs help, or what's the difference in individuals where they don't help.
There are also widely used medications where we understand even less. Metamizole, a very widely used painkiller sold as Novalgin or Analgin and other names, discovered in 1922, has no firmly established mechanism of action.
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.
> 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:
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.
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.
"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.
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.
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.
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.
> One strong theme is the prevalence of context features (e.g. DNA, base64) and token-in-context features (e.g. the in mathematics – A/0/341, < in HTML – A/0/20). 29 These have been observed in prior work (context features e.g.
[38, 49, 45]
; token-in-context features e.g.
[38, 15]
; preceding observations
[50]
), but the sheer volume of token-in-context features has been striking to us. For example, in A/4, there are over a hundred features which primarily respond to the token "the" in different contexts. 30 Often these features are connected by feature splitting (discussed in the next section), presenting as pure context features or token features in dictionaries with few learned features, but then splitting into token-in-context features as more features are learned.
> [...]
> The general the in mathematical prose feature (A/0/341) has highly generic mathematical tokens for its top positive logits (e.g. supporting the denominator, the remainder, the theorem), whereas the more finely split machine learning version (A/2/15021) has much more specific topical predictions (e.g. the dataset, the classifier). Likewise, our abstract algebra and topology feature (A/2/4878) supports the quotient and the subgroup, and the gravitation and field theory feature (A/2/2609) supports the gauge, the Lagrangian, and the spacetime
I don't think "hundreds of different ways to represent the word 'the', depending on the context" is a-priori plausible, in line with our preconceptions, or aesthetically pleasing. But it is what falls out of ML interpretation techniques, and it does do a quantitatively good job (as measured by fraction of log-likelihood loss recovered) as an explanation of what the examined model is doing.
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.
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
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.
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.
Not comparable. Our current knowledge of the physics involved in these systems is complete. It is just impossibly difficult to calculate from first principles.
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.
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.
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.
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.
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?
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.
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.
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.
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.
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.
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.
> 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.
"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.
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.
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.
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)
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.
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.
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.
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.
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.
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
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.
> 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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
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.
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.
Is it capable of predictions though? Ie can it accurately predict the folding of new molecules? Otherwise how do you distinguish accuracy from overfitting.
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
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.
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.
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.
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.
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.