[HN Gopher] AI Is Ushering in a New Scientific Revolution
___________________________________________________________________
AI Is Ushering in a New Scientific Revolution
Author : spansoa
Score : 83 points
Date : 2022-06-06 16:50 UTC (6 hours ago)
(HTM) web link (thegradient.pub)
(TXT) w3m dump (thegradient.pub)
| frakt0x90 wrote:
| I agree with the skepticism in the comments but one area where I
| think ML does have a lot of potential in scientific discovery is
| the field of SciML and Symbolic Regression.
|
| The first uses physics-informed neural networks in systems of
| ODEs to solve complex problems faster and even helps derive DEs
| that fit some data.
|
| The second uses a fixed set of symbols to fit a tree of them to
| data. This gives a closed-form solution that best fits the data.
| Both are pretty hot in the Julia community and have results
| published
| stevenjgarner wrote:
| Would it behoove the standardization of some kind of rubric to
| codify scientific papers on publication where they clearly xml-
| style state dependencies, conclusions, conditionalities, need for
| further research, keywords etc? Or is the state of natural
| language processing adequate to parse that information out of the
| publications the way they are?
| TaupeRanger wrote:
| Whenever you see the words "AI" and "ushering" in a headline, you
| can be sure it's mostly BS. All you have to do to write one of
| these articles is wildly extrapolate from very modest current
| systems to completely unfounded speculative miracles in the
| future. Could Alpha Fold _eventually_ lead to some drugs that
| help _real_ patients live _longer_ or _better_ lives?
| Sure...maybe...or maybe not. Maybe literally nothing will come
| from this technology, except some great headlines for companies
| claiming to have created a "wholly-AI-designed drug", whatever
| the hell that actually means.
| andreyk wrote:
| AlphaFold already has 3000 citations... For instance "A
| structural biology community assessment of AlphaFold 2
| applications" which states "In summary, we find that these
| advances are likely to have a transformative impact in
| structural biology and broader life science research."
| https://www.biorxiv.org/content/10.1101/2021.09.26.461876v1....
|
| Is the title hyperbolic wrt this being a revolution? Maybe. Is
| it fair to say AI is 'ushering' a lot of new science research
| into existence? Absolutely.
| mensetmanusman wrote:
| This is actually an appropriate use of the word ushering.
|
| We finally have great uses for all these transistors:)
| concinds wrote:
| I don't understand the comments here.
|
| AI means we can literally brute-force any scientific mystery by
| trying out millions of algorithms until we find one that fits
| (like Wolfram's New Kind of Science). General relativity was a
| brilliant theory, but we don't have billions of Einsteins to keep
| coming up with new theories. Soon, we will. And obviously, brute
| forcing is only a last resort, AIs will be smarter than that for
| most problem-sets.
|
| Who cares that it's a black box? Humans still need to verify a
| new model or theory (the AI will help with that too) but it'll
| shave decades off of discoveries.
|
| Why would humans lose domain knowledge? (There's plenty of non-
| economists writing economics papers already, and they seem
| human!) AI might dramatically increase the rate of scientific
| discoveries, and make a lot more people want to become
| scientists. It'll also help reclaim science from big institutions
| and academia, and help small independent groups make significant
| discoveries again without gatekeepers. Patent clerks might be
| able to make scientific discoveries again!
|
| And why would AI necessarily be purely imitative (i.e. only be
| capable of building off past knowledge, rather than generating
| new knowledge or at least using abstract reasoning)? See this
| tweet from a DeepMind researcher, plenty of people are working on
| this: https://twitter.com/Inoryy/status/1522621712382234624
| mdp2021 wrote:
| > _Who cares that it 's a black box_
|
| To solve a specific problem, you just need a "mysterious
| solver". To increase human knowledge, you instead need a
| formally expressed insight.
|
| In the article I read, <<In 2020, Google's AI team DeepMind
| announced that its algorithm, AlphaFold, had solved the
| protein-folding problem>>. Now, I understand that AlphaFold
| "solves protein-folding problem_s_", while "the protein-folding
| problem" would be a theoretical problem which calls for a
| theory to be solved. This is the difference. They are two
| different matters, having things done and having us more
| knowledgeable.
| randcraw wrote:
| "AI means we can literally brute-force any scientific mystery
| by trying out millions of algorithms..."
|
| This is exactly wrong. If true, then AI could readily solve any
| human-level problem we could throw at it. But AI still can't
| master _any_ of the basic skills inherent in human children,
| much less in adults working in construction crews, or health
| care aides, or janitors, or even just driving a car.
|
| AI's ability to build robots capable of even basic tasks like
| giving a haircut or climbing over broken objects littering a
| stair -- they're nonexistent, and even after a decade of deep
| learning, they're not getting appreciably better.
|
| Deep learning has shown us two things: 1) deep NNs can learn
| how to solve approximation problems astonishingly efficiently
| and capably. That's AlphaGo and AlphaFold. And 2) a lot of what
| humans assumed to be signs of intelligence, like playing go and
| chess, understanding voice and speaking, recognizing objects in
| images -- these are just "monkey see, monkey do" skills based
| in probabilistic pattern matching, which deep NNs can do very,
| very well.
|
| But cognitive skills that require reasoning -- deduction,
| induction, and the recognition and rejection of contradiction
| -- these must identify relevant domain knowledge (semantics)
| and the ability to reason about it, not just the efficient
| matching of a pattern in a matrix using a GPU. Alas, the
| effective and scaleable means of solving these problems still
| remain a total mystery.
|
| So if you can generate a _zillion_ clueless hypotheses per
| second using the very best deep NN, it will still drive over a
| cop dressed like a clown who 's waving his arms the path of
| your smartcar, nor will it ever invent general relativity.
| mateo1 wrote:
| >First, AI can quickly read through the scientific literature,
| allowing it to learn the fundamental rules, facts, and equations
| of science, and help scientists manage the flood of papers and
| data that is drowning every field.
|
| That's the first in a series of bs claims. AI tools can, in time,
| bring an engineering revolution but a new scientific dark age
| whereby idiots with little to no domain knowledge use "ai" tools
| such as the super resolution ones mentioned, draw wrong
| conclusions and then publish them so other idiots can do flawed
| ai-powered (or not) meta-analysis and further damage the state of
| concensus and integrity in their field.
| andreyk wrote:
| How is this BS? "How AI technology can tame the scientific
| literature" https://www.nature.com/articles/d41586-018-06617-5
|
| "SCIBERT: A Pretrained Language Model for Scientific Text"
| https://arxiv.org/pdf/1903.10676.pdf
|
| "Domain-specific language model pretraining for biomedical
| natural language processing"
| https://dl.acm.org/doi/pdf/10.1145/3458754
|
| These are just examples I pulled up in a couple minutes of
| Googling. Yeah, the wording here may be a little too grandiose,
| but nevertheless the fact that AI can read through vast sets of
| papers and data quickly and help humans sort through it is
| fundamentally true.
| visarga wrote:
| Isn't that how we use all technology? As soon as we abstract
| something away we are susceptible to modelling problems. Even
| speech is a poor approximation of what we sometimes want to
| express - another imperfect model shoehorned on reality.
| andrewmutz wrote:
| I agree that this line is BS. I disagree that it discredits the
| whole article.
|
| In particular, the protein folding example is extremely
| important and validates that AI is indeed having a big and real
| impact on science.
| visarga wrote:
| Why is it BS. AI allows scientists to zoom into information
| they seek. For example by training a language model on the
| latest papers, a scientist could use question & answering to
| find answers to specific questions. It's similar to using a
| search engine.
| andrewmutz wrote:
| I've never seen anything in the AI space that can "quickly
| read through the scientific literature, allowing it to
| learn the fundamental rules, facts, and equations of
| science".
| randcraw wrote:
| Yeah, that's exactly what IBM claimed Watson would do --
| learn biology, chemistry, and medicine on its own and
| then outperform human physicians. Instead, Watson
| Medicine was a catastrophe and revealed their AI
| marketeers to be hopelessly clueless. That's a mistake
| all AI boosters should take to heart. Don't overstate
| your accomplishment. To wit, claims that the protein
| folding problem has been entirely solved by AlphaFold
| _is_ such an overstatement.
|
| For a more measured view on the use of AF in drug
| development (especially), check out med chemist Derek
| Lowe's blog, especially his articles: "Fooling the
| Protein Folding Software"
| https://www.science.org/content/blog-post/fooling-
| protein-fo... , and "AlphaFold Excitement"
| https://www.science.org/content/blog-post/alphafold-
| exciteme...
| Swizec wrote:
| Sounds to me like what we call "A Search Engine" in
| software engineering.
|
| I sure as heck can't keep track of all the blogposts and
| articles that come out every day about software
| development. But I can expertly wield "an AI" to answer my
| questions and do just-in-time research when it's needed to
| solve problems.
| version_five wrote:
| I agree with your comment, though I've found it's hard to
| defend statements like that, people can always pull out some
| headlines (that are easy to find, like ths guy telling you that
| all he had to do was google) and claim that makes you wrong and
| AI really is revolutionary.
|
| It's been overhyped so much that it's very hard to convince non
| specialists that basically all the framing of AI they can find
| is hype. The best I can say is, show me a company that's
| actually making money off of a product that's based around one
| of these claims, that actually has the ML as a core
| differentiator.
|
| No doubt people will still find headlines, but they'll be more
| scarce, and if you look into the commercial success of such
| companies, you'll find almost nothing.
|
| Disclaimer, I run an AI company and am an AI bull. Misplaced
| hype helps charlatans and scammers
| narrator wrote:
| Right now we can look at DALL-E and say that it did a good job
| or it didn't because it is producing familiar objects like
| teddy bears or whatever. If the science equivalent of DALL-E
| said this is a process to extract an enantiomer with right
| chirality and it's actually is for left chirality who is going
| to know it made a mistake except a trained chemist? Thus,
| double checking the work of the AI is going to be a lot of the
| work of scientists in the future.
| pessimizer wrote:
| > Thus, double checking the work of the AI is going to be a
| lot of the work of scientists in the future.
|
| That seems similar to the idea of self-driving cars that
| screw up frequently and have to suddenly turn over control to
| the driver being safer than just paying attention to driving.
|
| Peer review sucks now. Reviewing something that makes errors
| that no human would make seems like it would be a lot worse.
| walnutclosefarm wrote:
| I'm missing how you own unsupported claim in any way refutes
| the claim in the article that you assert to be bs.
|
| I have in fact been involved in projects that did exactly what
| you claim is BS - read a large corpus of scientific papers (and
| written medical records), and discerned connections worth
| further scientific investigation that had not been detected by
| the body of scientists following the field, ultimately leading
| to new drug discovery science.
| AnimalMuppet wrote:
| "Worth further scientific investigation" is one thing. But
| mateo1 called BS on the claim that AI could "learn the
| fundamental rules, facts, and equations of science" by
| reading the literature, which is a different claim. You claim
| that the AI can find patterns and correlations; mateo1
| disputes that the AI can actually _learn the rules_ of the
| domain from the papers.
|
| So while you are right that mateo1 also did not prove his
| claim, your experience doesn't disprove his claim.
| walnutclosefarm wrote:
| I don't agree. The specific techniques we used (a variant
| of the transformer architecture behind most large language
| models) means, among other things, that you can ask the
| model questions, and get answers about what the model
| "knows" in return. So, if you train a model on thousands of
| scientific papers that contain information about drug
| effects on biomarkers, side effects, and the like, the
| model will learn, e.g., that drug A is typically used to
| treat cancer of type beta, and that it is frequently
| associated with lowering of biomarker X. It can then answer
| questions such as "what drugs are useful for lowering
| biomarker X in patients?" It can further answer a question
| "what mechanisms could be associated with this effect?"
|
| Of course, it can only answer with mechanisms that are
| identified in the literature it analyzed - it's not a PhD
| biochemist / M.D. able to infer or speculate on mechanism
| from first biochemical and medical principles. But there is
| no way to interpret what it can do, in my mind, other than
| to say that the model has learned drug biochemistry at some
| significant level, and can represent that learning to a
| user.
|
| In the case of what Deep Mind has done with protein
| folding, I don't know how you interpret its capabilities
| other than to say their model has learned much of the
| fundamental body of rules and facts that govern how amino
| acid sequences role up into proteins. There is a very real
| sense in which it embodies a theory of protein folding,
| much as say, Maxwell's equations and a knowledge of
| calculus embodies a theory of electromagnetism. That it
| cannot as yet articulate the rules of that theory is
| clearly a profound limitation, but it's a limitation of
| conveyance, not of utility.
| visarga wrote:
| Depends on what you mean by "learn the rules, facts, and
| equations of science". I mean, a language model could
| memorise it and retell it in poetry if you wanted. On the
| other hand it's not just a parrot, as some have claimed. AI
| is useful in accelerating progress in hard scientific
| problems. For one - a neural network is a universal
| function approximator. That's got to be really useful in
| many places where an approximation is good enough.
| mdp2021 wrote:
| I used in this pages the expression "parroting" for some
| AI implementations. Let us disambiguate.
|
| It is called an Artificial Intelligence that engineering
| product that can replace a Natural Intelligence in
| finding solutions - such as, through the automated
| development of sophisticated function approximators. This
| is one thing, and akin to the content of the article. And
| it is one meaning of the term "intelligence"
| ("intelligence2").
|
| On the other hand, I have seen algorithms that appear to
| collect associations by frequency being called
| Intelligence, in a very different sense of the term, and
| to that I note:
|
| -- that Intelligence1 is an ontology developer - in my
| terms; it "investigates what things are". In the very
| recent terms of James Fodor, which I present here because
| it is a different formulation by coincidence just
| published today, <<humans learn by making structured
| mental concepts, in which many different properties and
| associations are linked together>> [actually the crucial
| part is in /how/ the concepts are refined];
|
| -- and that without operation of "critical reflection"
| crucial in the ontology development, and instead with a
| "tendency" to just trust the input and solve conflict
| without foundational considerations, an inputs-imitating
| system, which may be said "based on parroting", is the
| _opposite_ of Intelligence1 .
| raincom wrote:
| Classification, at best, helps in developing heuristics. Such a
| classifying activity can never become a scientific theory. There
| are two things. (1) what makes some phenomenon, any phenomenon,
| into X? This is the identity question. (2) Given a group, how to
| classify some phenomenon as an instance of X or an instance of Y,
| etc,. This is the individuation question. When we do "folk"
| science, we conflate the question of individuation with that of
| identity.
|
| It is like asking what makes something a cat, then coming up with
| a classificatory answer: oh, this image is a cat, that image,
| another cat, etc.
| nathias wrote:
| if scientific discovery could be automated, that would be the
| most significant event in human history
| andreyk wrote:
| This article has some great examples, but there are MANY it does
| not mention. Here are just a few others I think are super cool
| (links to media articles, papers are typically linked to within
| article):
|
| * AI model's insight helps astronomers propose new theory for
| observing far-off worlds https://techcrunch.com/2022/06/03/ai-
| models-insight-helps-as...
|
| * Scientists Discover Method to Break Down Plastic in Days, Not
| Centuries https://www.vice.com/en/article/akvm5b/scientists-
| discover-m...
|
| * Machine Learning Helps See into a Volcano's Depths
| https://eos.org/editor-highlights/machine-learning-helps-see...
|
| * Stanford University use AI computing to cut DNA sequencing down
| to five hours https://www.zdnet.com/article/stanford-uni-nvidia-
| use-ai-com...
|
| AI is just a tool, and is increasingly becoming a big part of
| scientists' toolsets. I don't know if it's fair to say it will
| lead to a scientific revolution, but it's impacts will most
| certainly be vast.
|
| Self-plug warning: I know of all of these articles because I run
| the newsletter Last Week in AI (lastweekin.ai) and co-host its
| related podcast (https://www.lastweekinai.com). We've covered a
| dozen or more of these types of stories, and new ones keep
| showing up. So it's definitely a big trend IMHO.
| Ericson2314 wrote:
| Artificial Intelligence is Human Ignorance. Being able to do
| things without insteranding why can only be temporary stop-gap in
| the knowledge-gaining process. It cannot revolutionize science by
| definition.
|
| The biggest effect it could have is _ending_ science, convincing
| the powers that be that understanding is no longer a prerequisite
| of capability, and thus science can once again be relegated as a
| nerdy hobby that is of no interest to the state or corporation.
| alar44 wrote:
| Agreed. This is why I do long division by hand.
| Ericson2314 wrote:
| What the hell. The ALU of the machine does something not to
| dissimilar to long division. I would be fine with saying no
| getting to program with division until you learn how to do
| the long division algorithm by hand, however. But once you
| have _learned_ to do the algorithm it 's fine to let the
| computer do it.
|
| Anti-ignorance != anti-automation
| visarga wrote:
| When you do a Covid test do you understand why the colour
| changes? Some people do, but not most. We can use black box
| systems and we understand how they make false positives and
| negatives. In fact most complex systems are impossible to
| "understand". We still have to act with imperfect
| understanding.
| mdp2021 wrote:
| > _Some people do, but not most_
|
| Knowledge (of something) is something one could achieve, not
| something everyone must achieve. The relevant purpose is that
| those who should become competent and knowledgeable do so -
| for judgement and for production of further knowledge.
| Ericson2314 wrote:
| That is not a good comparison. People taking the test are not
| in general scientists. There was never any expectation they
| would understand how the test works.
|
| The proper analogy would be scientists have some big machine
| which tries different chemical experiments until it has a
| Covid test that matches the training data. But how would we
| even trust the training data in that case?
|
| Having a good understanding of the basics of genetics and
| microbiology allows us both to understand how the tests work,
| and tests in the QAing of the testing procedure. There is no
| substitute.
| coliveira wrote:
| I agree that this has a potential to make people consider
| ignorance as a feature. AI is useful as a helper technology,
| the same way as telescopes or computers. But saying that it
| will discover new science is just a misleading idea.
|
| Even if AI is really capable of doing so (I don't think it is),
| it is similar to saying that a different species (not human)
| will now do science for us. How useful is that for humans? If
| that were even true, it would mean that humans will be left in
| ignorance and start to rely (without ways to confirm it) on the
| results provided by this new species. In other ways, the whole
| hoopla about AI doing science is nonsense on its core.
| Ericson2314 wrote:
| > Even if AI is really capable of doing so (I don't think it
| is), it is similar to saying that a different species (not
| human) will now do science for us. How useful is that for
| humans?
|
| Oh nice, thank you for that. Letting AIs doing science is
| domesticating ourselves. Shall the general AI feeds us
| kibbles too?
| gumby wrote:
| It's exciting that these computational tools have unlocked a new
| phase of "butterfly collecting". Can't wait for the theoretical
| developments people come up with on the back of it.
| lettergram wrote:
| Current AI methods are great for classification. That said, I'm
| not convinced AI is making discoveries. What I mean by that, is
| that AI is often non-explainable, can be non-deterministic and
| only as good as it's training data.
|
| For instance, asteroid discovery. Yes AI can be used to discover
| asteroids. BUT every asteroid needs to be checked by a human and
| some will undoubtably be false positives. We also miss some
| unknown true positives.
|
| We often (depending on method) can't explain why [in-fact] why
| something is a true positive vs false positive from the network
| itself.
|
| Which is why I say AI doesn't make discoveries. It's not
| identifying something knew, it's identifying something that looks
| like what it's seen before. It's error rates in the scientific
| frontier is also going to be unknown; as we often lack data.
| Using AI could potentially limit the scope of new discoveries -
| depending how heavily it's relied on by hyper optimizing on the
| wrong items.
|
| Anyway, sorry for the rambling, as a research in deep learning I
| find this trend something to monitor.
| albertzeyer wrote:
| Current humans are great for classification. A human is often
| non-explainable, can be non-deterministic and only as good as
| it's training data.
|
| Yes humans can be used to discover asteroids. BUT every
| asteroid needs to be checked by a human (or AI) and some will
| undoubtably be false positives.
|
| We often can't explain from the brain itself.
|
| Humans don't make discoveries.
|
| (I guess you get my point.)
| coding123 wrote:
| That's not a point, you just replaced words but his is true
| and yours is not. Unless you can explain why "Humans don't
| make discoveries" is true??? Also this is a VERY tired trope
| in Hacker News. Please stop doing this.
|
| Here's the gist: A human can show that 50 people are dying of
| cancer because they were recently exposed to toxic sludge.
|
| An AI can't say a bunch of people were exposed to toxic
| sludge - it can only raise your prediction of getting cancer
| because you live near such and such coordinates (the
| coordinates where toxic sludge exist - but that's not visible
| to the AI)
| BobbyJo wrote:
| He was making a counter argument. Parent used all the same
| logic to posit 'ai don't make discoveries'.
|
| If those are reasons ai don't make discoveries, and they
| all apply to humans as well, then it must be that humans
| also don't make discoveries.
|
| This is obviously false, which refutes the parent's point.
| coding123 wrote:
| Multiple places to refute the core logic:
|
| The asteroid has to be checked by a human (or AI) part is
| incorrect because a computer AI that's checking just
| images or data can't (yet) call a team of astronomers to
| focus telescopes somewhere and double check it. That part
| would almost nearly require general AI. Yes, someone
| could write a single-use software that tells a bunch of
| astronomers to check something that AI has found, but
| that's NOT "the AI" doing that.
|
| Humans don't have "training data" they have something
| else. I don't think "training data" and that "something
| else" is the same (yet).
| albertzeyer wrote:
| You would also not trust the result of a single human on
| the asteroid but it would be double checked by someone
| else. That's the point.
|
| What do humans get instead of training data? What do you
| mean by "something else", and why can't some artificial
| neural network (ANN) not have the same? Maybe you mean it
| does not get supervised labeled data? But unsupervised
| data is also just training data, and in fact ANNs are
| very successful in also using such unsupervised training
| data.
|
| So I really do not see the distinction between AI and
| human intelligence. And the argument why an AI can't make
| a discovery and a human can.
| coding123 wrote:
| If I were to call information we use (as humans) to
| become an adult "training data" it would be seeing the
| world, eating baby food, getting spanked, falling in
| love, etc... is, in my opinion, something else. It's not
| training data. It's a completely different kind of
| conditioning. I think for us to call "training data" and
| the complete social evolution of a single human mind the
| same thing is ridiculous - AT THIS TIME. I'm not saying
| NNs (or some future AI tech) is NOT capable of reaching
| this status - but that's General AI.
|
| I haven't been reached out by OpenAI 5 in Gmail saying
| hey do you want to play a game of Dota 2 with me? It
| hasn't called me on the phone wanting to hang out. It's
| completely different.
|
| When those things happen we can talk but the original
| poster and myself are predicating this on "CURRENT AI"
| not theoretical AI.
| JoshCole wrote:
| His point is built atop a tradition going back at least as
| far as ancient Greece to the formal discovery of what we
| call logic. In these times it was discovered that you could
| separate the contents of an argument from the argument
| itself and reason about the logical structure of the
| argument independent of what was being talked about. A
| technique called "refutation by substitution" came out of
| this thinking. If you take an argument structure, boil it
| down to its essential point, than rewrite the argument with
| different symbols you know more about you can very easily
| check whether the structure is valid.
|
| What you did, when you pointed out that his obviously wrong
| conclusion was wrong and asked him to defend it in order to
| be proved right is called a straw man. His point was that
| the conclusion was wrong and so insisting he defend it
| makes it very clear that you have poor comprehension of the
| logical tradition in which his comment is rooted.
| aaaaaaaaaaab wrote:
| Wow! Deep!
| visarga wrote:
| > Current humans are great for classification.
|
| More or less. Is Pluto still a planet?
| mensetmanusman wrote:
| If it is, then we would have to introduce ~hundred thousand
| additional planets to maintain our definitions.
| JoshCole wrote:
| > What I mean by that, is that AI is often non-explainable, can
| be non-deterministic and only as good as it's training data.
|
| By your logic, humans can't discover things, but AI can. You
| think you made the opposite claim - you didn't.
|
| 1. Consider that you can freeze a random seed, making an
| algorithm deterministic, but you can't do the same for a human.
|
| 2. Secondly, I dare you to explain to me exactly, down to the
| very activation of cells and synapses in your mind, how you
| came to understand these words as having meaning. You can't do
| it, but an AI that did this would do this _implicitly_. So it
| will do a much better job of explaining how this happens than
| you will.
|
| So you haven't ruled out AI being able to discover things.
| You've claimed that humans can't, but AI can. It directly
| contradicts your thesis.
|
| 3. We can point to specific examples of novel discoveries
| produced by modern ML that were made without the use of good
| training data provided by humans - indeed, without any training
| data provided by humans. Moreover, these techniques weren't
| limited by their training data being bad - they were built
| around the idea that the training data they would generate was
| bad.
| karpierz wrote:
| > 2. Secondly, I dare you to explain to me exactly, down to
| the very activation of cells and synapses in your mind, how
| you came to understand these words as having meaning. You
| can't do it, but an AI that did this would do this
| implicitly. So it will do a much better job of explaining how
| this happens than you will.
|
| I think you're working with a very different definition of
| "explainable" than is commonly used in the machine learning
| literature.
| JoshCole wrote:
| They are trying to create a distinction, not between the
| explainability of machine learning approaches, but between
| human approaches and machine learning approaches. Whatever
| paper you think I should be using for my definition, just
| go to there citations. You'll trace it back eventually to
| things like the Imitation Game paper by Alan Turing. You'll
| trace it back to the debate about whether machines can
| think, discover, learn, know. The papers you reference are
| built atop a mountain of papers that came before them which
| largely reject the claim that machines aren't capable of
| discovery. You'll also find my term usage early on in
| _most_ books about machine learning. Most authors like
| introducing machine learning by pointing out why we need
| machine learning. They point out that humans not being able
| to explain solutions to very hard problems means they can
| 't build programs that solve those problems. If they could
| explain their solution, they could code it up and they
| would have a program. Since they can't, they reach to
| learning tools to figure it out.
|
| It really shouldn't surprise you that we have to go back to
| the early parts of the field; his argument restated is
| "machines can't make discoveries, only humans can" and this
| confused thinking is something that gets addressed in the
| early writings much moreso than the latter writings.
|
| Frankly, we've known algorithms as far back as Aristotle
| that were capable of discovery. Peter Norvig in AIMA cites
| Aristotle when talking about some search problems and path
| discovery is still discovery. Some of these algorithms
| literally include frontier in the algorithmic description;
| clearly a framing that implies thinking of them in terms of
| discovery. Honestly, what is really happening in my
| estimation is some people do this thing where they pretend
| AI is categorically reduced to the subset of things which
| they can criticize. It is a really weird tendency, but
| extremely common. You can see it in the persons post
| actually. Look at the contortions they go through to only
| focus on supervised learning - to only focus on a narrow
| subset of supervised learning restricted to deep learning -
| to only focus on a narrow problem domain of supervised
| learning - classification - and to only focus on a specific
| example of that problem domain (we can give good
| explanations for smaller models) - the ones that humans
| can't easily explain.
| dekhn wrote:
| I don't know that I agree.
|
| Historically, many good scientific models had generalizable
| predictive ability (relativity being a good example) that was
| used to make predcitions that led to discoveries). I would say
| that if a scientist can use a model (that's all an ML model is)
| to anticipate likely regions of phase space to explore (say, in
| making engineered glass or steel), it is in fact "discovering"
| things.
|
| Also, when you reach the point of needing a human to double
| check all your predictions and the humans experts disagree
| about the golden label as much as the model has uncertainty in
| the prediction, the model has reached human performance, and
| "human checking the results" doesn't even make sense since they
| would increase the false positive or negative rate of the
| system. Already we are seeing some image recognition systems
| whose classificaftion of hard examples exceed that of the best
| human experts (while being hundreds of times fasteR).
| ethanwillis wrote:
| It's not that it's only as good as it's training data. But
| depending on the underlying implementation it's also only as
| good as the quality of the random number it uses in its
| implementation.
|
| Let me give a somewhat broad example. In cases where a state
| space to be explored is absolutely massive and we have no
| currently good way to 1. prune it 2. traverse it to land at the
| states we want(that have certain properties), and so on. Then
| the best we have right now is "sample the state space." But
| "sampling" requires very good random number generation so that
| we're 1. not biasing ourselves (this is clear to everyone) but
| deeper than that 2. Good random number generation reduces the
| problem of us only grabbing states from the state space that
| confirm our existing ideas rather than the ones that might
| challenge our existing ideas.
|
| How many scientists are _developing_ their ideas on single
| workstations that are running out of entropy?
| mensetmanusman wrote:
| AI is analogous to a new type of lens.
|
| Lenses can be thought of thermodynamically as channeling the
| information included in rays of light into a different form
| more appreciable by humans.
|
| AI is that for rays of data :)
| sumy23 wrote:
| I disagree. Yes, results need to be validated, but that doesn't
| mean ML is not making discoveries. Even for basic
| classification tasks, discoveries are still being made. For
| instance, ML can reliably detect the race of a person from an
| x-ray. There is clearly some anatomical difference that we're
| not aware of that's detectable in an x-ray. Most of human
| inference, in general, is "connecting the dots" between related
| phenomena. ML is a great tool for doing this kind of
| correlative work at scale.
| idontpost wrote:
| > For instance, ML can reliably detect the race of a person
| from an x-ray. There is clearly some anatomical difference
| that we're not aware of that's detectable in an x-ray.
|
| I'm pretty sure forensic anthropologists have been doing that
| for decades.
| omgwtfbyobbq wrote:
| I think we need to be cautious about what ML is picking up.
| Finding patterns is great, but we need to be fairly sure
| about what those patterns are so we don't misapply them in
| some different context we don't understand.
|
| As for whether finding patterns is discovery, I feel very
| whatever about it.
| andi999 wrote:
| I remember the paper and the conclusion you say is doubtful.
| Even when the data was heavily distorted and resolution
| reduced to insanely big pixels it still could do this. So,
| since magic does not exist, there is probably a hidden
| cofactor (like xrays done on different machines, one in a
| hospital in a white neighbourhood, the other in a black one,
| and the machine giving a technical artifact like colorrange).
| This is just an example of course, I dont know the cofactor.
|
| Here is the study:https://www.thelancet.com/journals/landig/a
| rticle/PIIS2589-7... When i said insanely i didnt remember
| correctly, just saw even if reduce to 4x4 pixels they claim a
| statistical difference. So mind blowingly ludicrous
| resolution might have been a better term.
| sumy23 wrote:
| If you look at their ROC-AUC, performance increases
| dramatically as resolution increases. Do you really think
| it's impossible to encode any information in 16 pixels?
| walnutclosefarm wrote:
| I agree that there is something odd going on in the race
| study. However, I've seen analogous "discovery" results
| that don't have this problem. Scientists at my former
| employer built a model, e.g., that diagnosed asymptomatic
| heart failure from ECGs. No electrophysiologist in that
| world-class cardiac medicine unit can do this, but the
| algorithm clearly perceives the signal. And this was in
| fact a true "discovery" in that they didn't set out to
| diagnose heart failure, but rather to "score" ventricular
| ejection fraction using ECGs. Training the algorithm
| revealed a signal (a so far unexplained cofactor to reduced
| ejection fraction) that predicts reduced ejection fraction,
| and thus heart failure, prior to grossly measurable reduced
| ejection fraction.
| andi999 wrote:
| Nice. I think this is very interesting, is it published?
| coliveira wrote:
| All these "discoveries" suffer from the problem of
| inconclusive data. As mentioned in another comment, this
| can be the result of data that is skewed somehow, for
| example by neighborhood, by the selection of patients, by
| the technology used, etc. Validating these results is not
| an easy task, especially when one talks about healthcare
| data.
| knicholes wrote:
| I think we've all seen what happens when we extrapolate.
| cameronh90 wrote:
| I think like anything it's a tool that can be used to great
| effect.
|
| However, I suppose there's a possibility we could become over-
| reliant on it and blind to the sorts of new ideas that you'd
| generate from manually exploring the data set yourself.
|
| For example, if the state-of-the-art AI asteroid discovering
| program has a blind spot where it never detects a certain type
| of asteroid, we might stop finding that type of asteroid
| altogether. Then when the training datasets themselves are
| mostly derived from asteroids that the AI had previously found,
| any blind spots and biases could end up self-reinforcing.
|
| Then again, is this really all that different to the many
| human-driven blind spots science has had in the past?
| visarga wrote:
| > is this really all that different to the many human-driven
| blind spots
|
| No. Humans are fallible like AI. Most humans can't perfectly
| apply logical reasoning to common day situations.
| mensetmanusman wrote:
| They are both fallible because humans supply the data.
| scroot wrote:
| Neil Postman used to say that we live in an era that is awash
| in information overabundance but simultaneously starving for
| wisdom. One of the immediate and great potential use for this
| kind of AI (ie, Skinner Boxes for computation) is to help us
| focus on what matters when faced with overwhelming piles of
| chaff.
| [deleted]
| schaefer wrote:
| Counterpoint: AlphaGo discovered new moves in the Game of Go,
| to the extent that books have been written about those new
| moves and how they change modern opening theory[1].
|
| In Go, there are hundreds of years of tradition studying the
| games of past master players, and analyzing how their
| innovations changed our cultural understanding of the game.
| Humanity can't have it both ways. If those contributions were
| valuable when made by a human, they should be equally valuable
| when made by an AI.
|
| [1] https://www.amazon.com/Using-AlphaGos-Enlarged-Corner-
| Enclos...
| JoshCole wrote:
| Another counterpoint is that the same technology applied to
| chess rediscovered human opening theory - then extended it in
| some openings. In chess, you don't have to write a research
| paper to make a notable contribution. If people find your
| move beautiful, they put a little exclamation point next to
| it in the notation. AlphaZero gets plenty of those - enough
| for a book about its play [1].
|
| This took multiple human generations for us to accomplish,
| with literal geniuses applied to the task and spending their
| whole lives on it. Rediscovery and expansion happened in a
| wall clock time that is better measured in minutes than
| generations.
|
| [1]: https://www.amazon.com/Game-Changer-AlphaZeros-
| Groundbreakin...
| visarga wrote:
| Another counterpoint - AlphaFold. It really discovers
| structure we could not previously discover.
| dekhn wrote:
| Not really- all of alphafold depends on having enough
| sequence alignment information to existing proteins with
| known structures. It really does not perform all that well
| on out-of-dataset prediction.
| fsloth wrote:
| Still having good combinatorical guestimates extrapolated
| from existing knowledge is not a useless trick in itself.
| visarga wrote:
| If you can simulate you can check and throw out the
| garbage, or get more training data for the next
| iteration.
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