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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 o

• fire_lake 2y ago
  What if the underlying principles of the universe are too complex for human understanding but we can train a model that very closely follows them?
  • dekhn 2y ago
    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.
    • refulgentis 2y ago
      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)
      • mistermann 2y ago
        There's a much easier route: consciousness is not included in the discussion...what a coincidence.
      • deleted 2y ago
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  • Wilduck 2y ago
    That sounds like useful engineering, but not useful science.
    • mrbungie 2y ago
      I think that a lot of scientific discoveries originate from initial observations made during engineering work or just out of curiosity without rigour. Not saying ML methods haven't shown important reproducibility challenges, but to just shut them down due to not being "useful science" is inflexible.
• dekhn 2y ago
  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.
  • deleted 2y ago
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  • panarky 2y ago
    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.
    • nextos 2y ago
      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.
  • gandalfthepink 2y ago
    Me like thee career path. Interesting.
• exe34 2y ago
  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.
• strogonoff 2y ago
  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.
  • jltsiren 2y ago
    I don't find that argument convincing. 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.
    • 2y ago
• ak_111 2y ago
  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.
  • thfuran 2y ago
    >there is no way to confirm you have discovered underlying principles unless they have some predictive power. Yes, but a perfect oracle has no explanatory power, only predictive.
    • nkingsy 2y ago
      increasing the volume of predictions produces patterns that often lead to underlying principles.
      • mikeyouse 2y ago
        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.
        • qp11 2y ago
          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.
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• toxik 2y ago
  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?
  • chemicalnovae 2y ago
    He might not have been able to explain why _but_ I'd bet anything he would have wanted to if he could.
  • jprete 2y ago
    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)."
    • toxik 2y ago
      Mixed it up! I meant Tycho Brahe actually.
  • YeGoblynQueenne 2y ago
    Sure he was. And then Newton came along and said it's all because of gravity and Kepler's laws were nothing but his laws of motion applied to planets. Newton was a bit of a brat but everybody accepted his explanation. Then the problem turned to trying to explain gravity. Thus science advances, one explanation at a time.
• Invictus0 2y ago
  Maybe the science of the past was studying things of lesser complexity than the things we are studying now.
• SJC_Hacker 2y ago
  What if it turns out that nature simply doesn't have nice, neat models that humans can comprehend for many observable phenomena?
  • empath-nirvana 2y ago
    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.
• gradus_ad 2y ago
  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.
  • drdeca 2y ago
    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.
• flawsofar 2y ago
  The machine understands, we do not, and so it is not science? Can we differentiate?
• empath-nirvana 2y ago
  If have an oracle that can predict the outcome of experiments does it _matter_ if you understand why?
• melagonster 2y ago
  they offered a good tool for science... so this is a part of science.
• jdietrich 2y ago
  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.
• fsloth 2y ago
  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).
  • tsimionescu 2y ago
    > 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.
    • fsloth 2y ago
      No it's not specific at all in predicting technological progress, which was the point of my comment. Nobody knows what singularity would actually mean from the point of view of specific technological development.