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Does anybody else find it peculiar that the majority of these articles about AI say things like "of course I don't doubt that AI will lead to major discoveries"

Does anybody else find it peculiar that the majority of these articles about AI say things like "of course I don't doubt that AI will lead to major discoveries", and then go on to explain how they aren't useful in any field whatsoever? Where are the AI-driven breakthroughs? Or even the AI-driven incremental improvements? Do they exist anywhere? Or are we just using AI to remix existing general knowledge, while making no progress of any sort in any field using it?

moffkalast 1y ago
> AlphaEvolve’s procedure found an algorithm to multiply 4x4 complex-valued matrices using 48 scalar multiplications, improving upon Strassen’s 1969 algorithm that was previously known as the best in this setting. This finding demonstrates a significant advance over our previous work, AlphaTensor, which specialized in matrix multiplication algorithms, and for 4x4 matrices, only found improvements for binary arithmetic. > To investigate AlphaEvolve’s breadth, we applied the system to over 50 open problems in mathematical analysis, geometry, combinatorics and number theory. The system’s flexibility enabled us to set up most experiments in a matter of hours. In roughly 75% of cases, it rediscovered state-of-the-art solutions, to the best of our knowledge. > And in 20% of cases, AlphaEvolve improved the previously best known solutions, making progress on the corresponding open problems. For example, it advanced the kissing number problem. This geometric challenge has fascinated mathematicians for over 300 years and concerns the maximum number of non-overlapping spheres that touch a common unit sphere. AlphaEvolve discovered a configuration of 593 outer spheres and established a new lower bound in 11 dimensions. https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/AlphaEvolve.pdf https://storage.googleapis.com/deepmind-media/DeepMind.com/B... (this is an LLM driven pipeline)
- Ygg2 1y ago
  That's less LLM and more three projects by DeepMind team. And it's far from commercial availability.
  - moffkalast 1y ago
    Well to me personally it at least proves something that's long been touted as impossible, that the current architecture can in fact do better than all humans at novel tasks, even if it needs a crutch at the moment. An LLM-based system now holds the SOTA approach on several math problems, how crazy is that? I wasn't convinced before, but now I guess it won't be many decades before we view making new scientific advancements as viable as winning against stockfish.
    - Ygg2 1y ago
      Yeah, but those rely on setting up the evolve function. And they aren't well guaranteed to be better than humans. They might find you an improvement, but aren't guaranteed to do as shown here[1] (green means better, red means worse, gray means same). [1] https://youtu.be/sGCmu7YKgPA?t=480 https://youtu.be/sGCmu7YKgPA?t=480
      - moffkalast 1y ago
        Last I checked, humans weren't guaranteed to do anything either.
        Ygg2 1y ago
        Sure, but they have one thing that makes them better than CPUs, they consume more ;)
simianparrot 1y ago
It’s why it keeps looking exactly like NFT’s and crypto hype cycles to me: Yes the technology has legitimate uses, but the promises of groundbreaking use cases that will change the world are obviously not materialising and to anyone that understands the tech it can’t. It’s people making money off hype until it dies and move on to the next scam-with-some-use.
- guardian5x 1y ago
  AI looks exactly like NFTs to you? I don't understand what you mean by that. AI already has tons more uses.
  - waldrews 1y ago
    One is a technical advance as important as anything in human history, realizing a dream most informed thinkers thought would remain science fiction long past our lifetimes, upending our understanding of intelligence, computation, language, knowledge, evolution, prediction, psychology... before we even mention practical applications. The other is worse than nothing.
- StopDisinfo910 1y ago
  I don’t remember when NFTs and cryptos helped me draft an email, wrote my meetings minutes for me or allowed me to easily search information previously locked in various documents. I think there is this weird take amongst some on HN where LLMs are either completely revolutionary and making break through or utterly useless. The truth is that they are useful already as a productivity tool.
  - bgnn 1y ago
    Exactly this. What we expect from them is our speculation. In reality nobody knows the future and there's no way to know the future.
    - bgnn 1y ago
      Wow I didn't expect this to be down voted. I guess there are people who knows the future.
deleted 1y ago
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eschaton 1y ago
If they didn’t say that the rah-rah-AI crowd would come for them with torches and pitchforks. It’s a ward against that, nothing more.
- Sharlin 1y ago
  Similar to the way many Trump supporters, when daring to criticize him, feel the need to assert that they still love him and would vote for him again. (See, eg. r/LeopardsAteMyFace for examples. It’s fascinating.)
  - NotCamelCase 1y ago
    Or any time one dares to criticize Israel for their recent contributions to peace on Earth (wink wink) -- it has to be prefaced with "Let me say that I'm the biggest defender of the Jews and fight against anti-Semitism". It's moot.
apples_oranges 1y ago
"AI is a competent specialist in all fields except in mine."
perlgeek 1y ago
> Where are the AI-driven breakthroughs? The only thing that seems to live up to the hype is AlphaFold, which predicts protein folding based on amino acid sequences, and of which people say that it actually makes their work significantly easier. But, disclaimer, this is only from second-hand knowledge, I'm not working in the field.
- rafaelmn 1y ago
  This is another dimension of the problem - what's even considered AI ? AlphaFold is a very specialized model - and I feel the AI boom is driven by hypothesis that general models eventually outperform specialized ones given enough size/data/whatever.
  - _0ffh 1y ago
    While I hate the apparent renaming of everything ML to "AI", things like AlphaFold would be "narrow AI". As to the common idea of having to wait for general AI (AGI) to bring the gains, I have been quite sure since the start of the recent AI hype cycle that narrow AI will have silently transformed much of the world before AGI even hits the town.
  - perlgeek 1y ago
    In my head, I just substitute "AI" with "machine learning" or "statistics". > and I feel the AI boom is driven by hypothesis that general models eventually outperform specialized ones given enough size/data/whatever. I think in the sciences, I'd generally put my money on the specialized models. I hope that the hype around AI makes it easier (by providing tooling, platforms, better algorithms, educational materials etc.) to train specialized models. Kind of a trickle-down of hype money :-)
    - rafaelmn 1y ago
      I expect the opposite - hardware/compute going to be locked up in AGI quests unless the bubble pops and then it gets discounted
yfontana 1y ago
From the article: > Besides protein folding, the canonical example of a scientific breakthrough from AI, a few examples of scientific progress from AI include:1 > Weather forecasting, where AI forecasts have had up to 20% higher accuracy (though still lower resolution) compared to traditional physics-based forecasts. > Drug discovery, where preliminary data suggests that AI-discovered drugs have been more successful in Phase I (but not Phase II) clinical trials. If the trend holds, this would imply a nearly twofold increase in end-to-end drug approval rates.
strogonoff 1y ago
There is rarely a constructive discussion around the term “AI”. You can’t say anything useful about what it might lead to or how useful it might be, because it is purely a marketing term that does not have a specific meaning (neither do both of the words in its abbreviation). Interesting discussions tend to avoid “AI” in favour of specific terms such as “ML”, “LLM”, “GAN”, “stable diffusion”, “chatbot”, “image generation”. These terms refer to specific tech and applications of that tech, and allow to argue about specific consequences for sciences or society (use of ML in biotech vs. proliferation of chatbots). However, certain sub-industries prefer “AI” precisely because it’s so vague, offers seemingly unlimited potential (please give us more investment money/stonks go up), and creates a certain vibe of a conscious being useful when pretending not to be working around IP laws and creating tools based on data obtained without relevant licensing agreements (cf. the countless “if humans have the freedom to read, therefore it’s unfair to restrict the uses of a software tool” fallacies, often perpetuated even by seemingly technically literate people, in pretty much every relevant forum thread).
- mnky9800n 1y ago
  This article is all about PINNs being overblown. I think it’s a reasonable take. I’ve seen way too many people dump all their eggs in the PINNs basket when there are plenty of options out there. Those options just don’t include a ticket to the hype train.
- Closi 1y ago
  I think AI is a useful term which usually means a neural network architecture but without specifying the exact architecture. I think Machine Learning doesn't mean this as a word, as it can also refer to linear regression, non-linear optimisation, decision trees, bayesian networks etc. That's not saying that AI isn't abused as a term - but I do think a more general term to describe the latest 5 years advancements in neural networks to solve problems is useful. Particularly as it's not obvious which model architectures would apply to which fields without more work (or even if novel architectures will be required for frontier science applications).
  - GrantMoyer 1y ago
fabian2k 1y ago
I suspect that people saying this are avoiding to make broad conclusions based only on the AI tools that exist right now at this moment. So they leave a lot of room for the next versions to improve. Maybe too much room, but it's hard to predict if AI tools will overcome their limitations in the near future.
swyx 1y ago
> Where are the AI-driven breakthroughs? Or even the AI-driven incremental improvements? literally last week https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/ https://deepmind.google/discover/blog/alphaevolve-a-gemini-p...
- dwroberts 1y ago
  But it only seems to be labs and companies that also have a vested interest in selling it as a product that are able to achieve these breakthroughs. Which is a little suspect, right?
  - swyx 1y ago
    too tinfoil hat. google is perfectly happy to spend billions dogfooding their own TPUs and not give the leading edge to the public.
    - dwroberts 1y ago
      I’m not saying they’re phoney - just we need to take this stuff with a big pinch of salt. The Microsoft paper around the quantum “ breakthrough ” is in a different field, but maybe a good example of why we need to be a little more cautious of research-as-marketing
- boxed 1y ago
  Yea, except that we see "breakthrough" stuff like this all the time, and it almost always is quickly found out that it's fraudulent in some way. How many times are we to be fooled before we catch on and don't believe press releases with massive selection bias?
Wilsoniumite 1y ago
Some new ish maths has been discovered. It's up to you if this is valid or impressive enough, but I think it's significant for things to come: https://youtu.be/sGCmu7YKgPA?si=EG9i0xGHhDu1Tb0O https://youtu.be/sGCmu7YKgPA?si=EG9i0xGHhDu1Tb0O
- snodnipper 1y ago
  Personally, I have been very pleased with the results despite the limitations. Like many (I suspect), I have had several users provide comments that the AI processes I have defined have made meaningful impacts on their daily lives - often saving them double digit hours of effort per week. Progress.
shusaku 1y ago
> Or even the AI-driven incremental improvements? You have no idea what you are talking about. Every day there is plenty of research published that used AI to help achieve scientific goals. Now LLMs are another matter, and probably a ways off before we reap the benefit beyond day to day programming / writing productivity.
montebicyclelo 1y ago
> then go on to explain how they aren't useful in any field whatsoever > Where are the AI-driven breakthroughs > are we just using AI to remix existing general knowledge, while making no progress of any sort in any field using it? The obvious example of a highly significant AI-driven breakthrough is Alphafold [1]. It has already had a large impact on biotech, helping with drug discovery, computational biology, protein engineering... [1] https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/ https://blog.google/technology/ai/google-deepmind-isomorphic...
- boxed 1y ago
  I'm personally waiting for the other shoe to drop here. I suspect that, since nature begins with an existing protein and modifies it slightly, AlphaFold is crazy overfitted to the training data. Furthermore, the enormous success of AlphaFold means that the number of people doing protein structure solving has likely crashed. So not only are we using an overfitting model that probably can't handle truly novel proteins, we have stopped actually doing the research to notice when this happens. Pretty bad.
  - pixl97 1y ago
    Why do you expect this, or is this just a "I need to find a reason to hate AI" thing?
    - boxed 1y ago
      It's like every time AI "can code" and then falls on its face when presented with super basic problems that are outside its training data. Why would protein folding be different?
  - flobosg 1y ago
    > that probably can't handle truly novel proteins AlphaFold is able to predict novel folds, see https://www.nature.com/articles/s42003-022-03357-1 https://www.nature.com/articles/s42003-022-03357-1
isaacfrond 1y ago
The article itself lists as successful, even breakthrough, applications of AI: protein folding, weather forecasting, and drug discovery.
rsynnott 1y ago
They do mention that it has been somewhat useful in protein folding. > Or are we just using AI to remix existing general knowledge, while making no progress of any sort in any field using it? AIUI they are generally not talking about LLMs here.
phpnode 1y ago
it's the new "I love my tesla but here are 15 reasons why it's broken" - if you don't provide platitudes the mob provides pitchforks
nerdponx 1y ago
New numerical computing algorithms are being developed with AI assistance, which probably would not have been discovered otherwise. There was an article here a few days ago about one of those. It's incremental but it's not nothing.
PurpleRamen 1y ago
> Where are the AI-driven breakthroughs? Define breakthrough. When is the improvement big enough to count as one? Define AI. Are you talking about modern LLM, or is old school ML also in that question? I mean Googles AI-company had with AlphaFold and other project quite the impact. > Or are we just using AI to remix existing general knowledge Is remixing bad? Isn't many science today "just" remixing with slight improvements? I mean, there is a reason why we have theoretical and practical scientists. Doing boring Lab-work and accidentally discovering something exciting is not the only way science is happening. Analysing data and remixing information, building new theories, is also important. And don't forget, we don't have AGI yet. Whatever AI is doing today, is limited by what humans are using it for. Another question is, whether LLM is not normalized enough already that we do not see it as very special any more, if it's used somewhere. So we might not even see it if AI has significant impact on any breakthrough.
biophysboy 1y ago
An example of an "AI" incremental improvement would be Oxford Nanopore sequencing. They extrude DNA through a nanopore, measure the current, and decode the bases using recurrent neural networks. They exist all over science, but they are just one method among many, and they do not really drive hypotheses or interpretations (even now)
currymj 1y ago
AlphaFold is real. To the extent you care about chess and Go as human activities, progress there is real. there are some other scientific computing problems where AI or neural-network-based methods do appear to be at least part of the actual state-of-the-art (weather forecasting, certain single-molecule quantum chemistry simulations). i would like the hype of the kind described in the article to be punctured, but this is hard to do if critics make strong absolute claims ("aren't useful in any field whatsoever") which are easily disproven. it hurts credibility.
- literalAardvark 1y ago
  Even more relvant, AlphaEvolve is real. Could easily be brick 1 of self-improvement and the start of the banana zone.
  - gthompson512 1y ago
    > "the start of the banana zone" What does this mean? Is it some slang for exponential growth, or is it a reference to something like the "paperclip maximizer"?
    - literalAardvark 1y ago
      It's slang for the J part of the exponential curve. Didn't expect that to be a problem here, sorry.
      - gthompson512 1y ago
        There is a bunch of new phrases people have been using around AI topics, so it can be hard to tell what exactly is being talked about, like "Roko's Basilisk", the "lottery card hypothesis?"(not quite remembering the phrasing on this one), "the bitter lesson", "paperclip maximizing", "stochastic parrot", etc.. Thank you for clarifying. I was kind of hoping there was a fun blog or story with a "banana zone".
  - dirtyhippiefree 1y ago
ergsef 1y ago
Speaking out against the hype is frowned upon. I'm sure even this very measured article about "I tried it and it didn't work for me" will draw negative attention from people who think AI is the Second Coming. It's also very hard to prove a negative. If you predict "AI will never do anything of value" people will point to literally any result to prove you wrong. TFA does a good job debunking some recent hype, but the author cannot possibly wade through every hyperbolic paper in every field to demonstrate the claims are overblown.
dfxm12 1y ago
There's hype, then there's the understanding that you never use version 1.0 of a product. In this sense, AI as we know is barely in an alpha version. I think the authors you're referring to understand the marketing around AI overstates its usefulness, but they are hopeful for a future where AI can be helpful.
chermi 1y ago
Protein structure prediction is a pretty useful tool. There are various 'foundation' models in biology now that are quite useful. I don't know if you want to count those as AI or ML. If you're looking for breakthroughs due to AI, they're not going to be obviously attributable to AI I think. Focusing on the biology related foundation models ... The ability to more quickly search through the space of sequences->structure, drugs, and predicted cell states (1) like with the biology based foundation models will certainly lead to some things being discovered/rejected/validated faster. I heard about this vevo company recently so it's on my mind. Biology experiments are hard and time consuming, and often hard to exactly replicate across labs. A lot of the data is of the form a) start in cell state X (usual 'healthy' normal state) under conditions Y (environment variables like temperature, concentrations, etc.) b) introduce some environmental pertubation P like some drug/chemical at some concentration, or maybe multiple pertubations at once c) record trajectory or steady/final state of cell. This data is largely hidden within published papers in non-standardized format. Vevo is attempting to collate all of those results with considerations for reproducibility into a standard easy-to-use format. The idea being that you can gradually build up a virtual sort of input-output (causal!) model that you can throw ideas for interventions against and see what it thinks would happen. Cells and biology are obviously enormously complicated so it's certainly not going to be 100% accurate/predictive, but my experience with network models in quantitative biology plus their proclaimed results make me pretty confident it's a sound approach. Thus approach is clearly "AI" driven (maybe I would call this ML) and if their claims are anything close to reality, this is an incredibly powerful tool for all of academia and industry. You can reduce the search space enormously and target your experiments to cover the areas the virtual model doesn't seem so good, continuously improving it in a sort of crowd-sourced "active learning" manner. A continuously improving experimentally backed causal (w.r.t. perturbations) model of cells has so many applications. Again, i don't think this will directly lead to a breakthrough, but it can certainly make the breakthrough more likely and come faster. There are many other examples like this that are some combination of 1) collating+filtering+refining existing data into an accessible easily query-able format 2) combining data + some physically motivated modeling to yield predictions where there is no data 3) targeted, informed feedback loop via experiments, simulations, or modeling to improve the whole system where it's known to be weak or where more accuracy is desired. Assuming it all stays relatively open, that's undeniably a very powerful model for more effective science. And that's just one approach. In physics ML can be use for finding and characterizing phase transitions, as one example. In the world of soft matter/biophysics simulation here's a few ways ML is used: a) more efficient generation of configurations (Noe generative models). This is a big one, albeit still kind of early stages. Historically(simplifying), to generate independent samples in the right regions of phase means you need to integrate the system for long enough to hit that region multiple times. So regions of the space separated by rare transitions will take a loooong time to hit multiple times. The solution was simply longer simulations. Now, under some restrictions, you can leverage and augment existing data (including simulation data) to directly generate independent samples in the regions of interest. This is a really big deal. b) more efficient, complex and accurate NN force-fields. Better incorporation of many-body and even quantum effects. c) more complex simulation approaches via improved pipelines like automated parameterization and discovery of collective variables to more efficiently explore relevant configuration space. Again, this is tooling that improves the process of discovery & investigation and thus directly contributes to science. Maybe not in the way you're picturing, but it is happening right now. 1) vevo https://www.tahoebio.ai/ https://www.tahoebio.ai/
nyarlathotep_ 1y ago
> say things like "of course I don't doubt that AI will lead to major discoveries", and then go on to explain how they aren't useful in any field whatsoever? This is "paying the toll", otherwise one will be accused of being a "luddite."
Konnstann 1y ago
Depending on your definition of AI, a pipeline for drug repurposing my team used was able to identify a therapeutic for a rare disease that was beating the state of the art in every test they threw at it, eventually being given orphan drug designation by the FDA. I doubt this would have happened without machine learning or AI or whatever you want to call it. I'm also against "agents as scientists" as a concept for numerous reasons, but deep learning etc has or is leading to breakthroughs.

moffkalast 1y ago

> AlphaEvolve’s procedure found an algorithm to multiply 4x4 complex-valued matrices using 48 scalar multiplications, improving upon Strassen’s 1969 algorithm that was previously known as the best in this setting. This finding demonstrates a significant advance over our previous work, AlphaTensor, which specialized in matrix multiplication algorithms, and for 4x4 matrices, only found improvements for binary arithmetic. > To investigate AlphaEvolve’s breadth, we applied the system to over 50 open problems in mathematical analysis, geometry, combinatorics and number theory. The system’s flexibility enabled us to set up most experiments in a matter of hours. In roughly 75% of cases, it rediscovered state-of-the-art solutions, to the best of our knowledge. > And in 20% of cases, AlphaEvolve improved the previously best known solutions, making progress on the corresponding open problems. For example, it advanced the kissing number problem. This geometric challenge has fascinated mathematicians for over 300 years and concerns the maximum number of non-overlapping spheres that touch a common unit sphere. AlphaEvolve discovered a configuration of 593 outer spheres and established a new lower bound in 11 dimensions. https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/AlphaEvolve.pdf https://storage.googleapis.com/deepmind-media/DeepMind.com/B... (this is an LLM driven pipeline)

Ygg2 1y ago
That's less LLM and more three projects by DeepMind team. And it's far from commercial availability.
- moffkalast 1y ago
  Well to me personally it at least proves something that's long been touted as impossible, that the current architecture can in fact do better than all humans at novel tasks, even if it needs a crutch at the moment. An LLM-based system now holds the SOTA approach on several math problems, how crazy is that? I wasn't convinced before, but now I guess it won't be many decades before we view making new scientific advancements as viable as winning against stockfish.
  - Ygg2 1y ago
    Yeah, but those rely on setting up the evolve function. And they aren't well guaranteed to be better than humans. They might find you an improvement, but aren't guaranteed to do as shown here[1] (green means better, red means worse, gray means same). [1] https://youtu.be/sGCmu7YKgPA?t=480 https://youtu.be/sGCmu7YKgPA?t=480
    - moffkalast 1y ago
      Last I checked, humans weren't guaranteed to do anything either.
      - Ygg2 1y ago
        Sure, but they have one thing that makes them better than CPUs, they consume more ;)

simianparrot 1y ago

It’s why it keeps looking exactly like NFT’s and crypto hype cycles to me: Yes the technology has legitimate uses, but the promises of groundbreaking use cases that will change the world are obviously not materialising and to anyone that understands the tech it can’t. It’s people making money off hype until it dies and move on to the next scam-with-some-use.

guardian5x 1y ago
AI looks exactly like NFTs to you? I don't understand what you mean by that. AI already has tons more uses.
- waldrews 1y ago
  One is a technical advance as important as anything in human history, realizing a dream most informed thinkers thought would remain science fiction long past our lifetimes, upending our understanding of intelligence, computation, language, knowledge, evolution, prediction, psychology... before we even mention practical applications. The other is worse than nothing.
StopDisinfo910 1y ago
I don’t remember when NFTs and cryptos helped me draft an email, wrote my meetings minutes for me or allowed me to easily search information previously locked in various documents. I think there is this weird take amongst some on HN where LLMs are either completely revolutionary and making break through or utterly useless. The truth is that they are useful already as a productivity tool.
- bgnn 1y ago
  Exactly this. What we expect from them is our speculation. In reality nobody knows the future and there's no way to know the future.
  - bgnn 1y ago
    Wow I didn't expect this to be down voted. I guess there are people who knows the future.

deleted 1y ago

[deleted]

eschaton 1y ago

If they didn’t say that the rah-rah-AI crowd would come for them with torches and pitchforks. It’s a ward against that, nothing more.

Sharlin 1y ago
Similar to the way many Trump supporters, when daring to criticize him, feel the need to assert that they still love him and would vote for him again. (See, eg. r/LeopardsAteMyFace for examples. It’s fascinating.)
- NotCamelCase 1y ago
  Or any time one dares to criticize Israel for their recent contributions to peace on Earth (wink wink) -- it has to be prefaced with "Let me say that I'm the biggest defender of the Jews and fight against anti-Semitism". It's moot.

apples_oranges 1y ago

"AI is a competent specialist in all fields except in mine."

perlgeek 1y ago

> Where are the AI-driven breakthroughs? The only thing that seems to live up to the hype is AlphaFold, which predicts protein folding based on amino acid sequences, and of which people say that it actually makes their work significantly easier. But, disclaimer, this is only from second-hand knowledge, I'm not working in the field.

rafaelmn 1y ago
This is another dimension of the problem - what's even considered AI ? AlphaFold is a very specialized model - and I feel the AI boom is driven by hypothesis that general models eventually outperform specialized ones given enough size/data/whatever.
- _0ffh 1y ago
  While I hate the apparent renaming of everything ML to "AI", things like AlphaFold would be "narrow AI". As to the common idea of having to wait for general AI (AGI) to bring the gains, I have been quite sure since the start of the recent AI hype cycle that narrow AI will have silently transformed much of the world before AGI even hits the town.
- perlgeek 1y ago
  In my head, I just substitute "AI" with "machine learning" or "statistics". > and I feel the AI boom is driven by hypothesis that general models eventually outperform specialized ones given enough size/data/whatever. I think in the sciences, I'd generally put my money on the specialized models. I hope that the hype around AI makes it easier (by providing tooling, platforms, better algorithms, educational materials etc.) to train specialized models. Kind of a trickle-down of hype money :-)
  - rafaelmn 1y ago
    I expect the opposite - hardware/compute going to be locked up in AGI quests unless the bubble pops and then it gets discounted

yfontana 1y ago

From the article: > Besides protein folding, the canonical example of a scientific breakthrough from AI, a few examples of scientific progress from AI include:1 > Weather forecasting, where AI forecasts have had up to 20% higher accuracy (though still lower resolution) compared to traditional physics-based forecasts. > Drug discovery, where preliminary data suggests that AI-discovered drugs have been more successful in Phase I (but not Phase II) clinical trials. If the trend holds, this would imply a nearly twofold increase in end-to-end drug approval rates.

strogonoff 1y ago

There is rarely a constructive discussion around the term “AI”. You can’t say anything useful about what it might lead to or how useful it might be, because it is purely a marketing term that does not have a specific meaning (neither do both of the words in its abbreviation). Interesting discussions tend to avoid “AI” in favour of specific terms such as “ML”, “LLM”, “GAN”, “stable diffusion”, “chatbot”, “image generation”. These terms refer to specific tech and applications of that tech, and allow to argue about specific consequences for sciences or society (use of ML in biotech vs. proliferation of chatbots). However, certain sub-industries prefer “AI” precisely because it’s so vague, offers seemingly unlimited potential (please give us more investment money/stonks go up), and creates a certain vibe of a conscious being useful when pretending not to be working around IP laws and creating tools based on data obtained without relevant licensing agreements (cf. the countless “if humans have the freedom to read, therefore it’s unfair to restrict the uses of a software tool” fallacies, often perpetuated even by seemingly technically literate people, in pretty much every relevant forum thread).

mnky9800n 1y ago
This article is all about PINNs being overblown. I think it’s a reasonable take. I’ve seen way too many people dump all their eggs in the PINNs basket when there are plenty of options out there. Those options just don’t include a ticket to the hype train.
Closi 1y ago
I think AI is a useful term which usually means a neural network architecture but without specifying the exact architecture. I think Machine Learning doesn't mean this as a word, as it can also refer to linear regression, non-linear optimisation, decision trees, bayesian networks etc. That's not saying that AI isn't abused as a term - but I do think a more general term to describe the latest 5 years advancements in neural networks to solve problems is useful. Particularly as it's not obvious which model architectures would apply to which fields without more work (or even if novel architectures will be required for frontier science applications).
- GrantMoyer 1y ago

fabian2k 1y ago

I suspect that people saying this are avoiding to make broad conclusions based only on the AI tools that exist right now at this moment. So they leave a lot of room for the next versions to improve. Maybe too much room, but it's hard to predict if AI tools will overcome their limitations in the near future.

swyx 1y ago

> Where are the AI-driven breakthroughs? Or even the AI-driven incremental improvements? literally last week https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/ https://deepmind.google/discover/blog/alphaevolve-a-gemini-p...

dwroberts 1y ago
But it only seems to be labs and companies that also have a vested interest in selling it as a product that are able to achieve these breakthroughs. Which is a little suspect, right?
- swyx 1y ago
  too tinfoil hat. google is perfectly happy to spend billions dogfooding their own TPUs and not give the leading edge to the public.
  - dwroberts 1y ago
    I’m not saying they’re phoney - just we need to take this stuff with a big pinch of salt. The Microsoft paper around the quantum “ breakthrough ” is in a different field, but maybe a good example of why we need to be a little more cautious of research-as-marketing
boxed 1y ago
Yea, except that we see "breakthrough" stuff like this all the time, and it almost always is quickly found out that it's fraudulent in some way. How many times are we to be fooled before we catch on and don't believe press releases with massive selection bias?

Wilsoniumite 1y ago

Some new ish maths has been discovered. It's up to you if this is valid or impressive enough, but I think it's significant for things to come: https://youtu.be/sGCmu7YKgPA?si=EG9i0xGHhDu1Tb0O https://youtu.be/sGCmu7YKgPA?si=EG9i0xGHhDu1Tb0O

snodnipper 1y ago
Personally, I have been very pleased with the results despite the limitations. Like many (I suspect), I have had several users provide comments that the AI processes I have defined have made meaningful impacts on their daily lives - often saving them double digit hours of effort per week. Progress.

shusaku 1y ago

> Or even the AI-driven incremental improvements? You have no idea what you are talking about. Every day there is plenty of research published that used AI to help achieve scientific goals. Now LLMs are another matter, and probably a ways off before we reap the benefit beyond day to day programming / writing productivity.

montebicyclelo 1y ago

> then go on to explain how they aren't useful in any field whatsoever > Where are the AI-driven breakthroughs > are we just using AI to remix existing general knowledge, while making no progress of any sort in any field using it? The obvious example of a highly significant AI-driven breakthrough is Alphafold [1]. It has already had a large impact on biotech, helping with drug discovery, computational biology, protein engineering... [1] https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/ https://blog.google/technology/ai/google-deepmind-isomorphic...

boxed 1y ago
I'm personally waiting for the other shoe to drop here. I suspect that, since nature begins with an existing protein and modifies it slightly, AlphaFold is crazy overfitted to the training data. Furthermore, the enormous success of AlphaFold means that the number of people doing protein structure solving has likely crashed. So not only are we using an overfitting model that probably can't handle truly novel proteins, we have stopped actually doing the research to notice when this happens. Pretty bad.
- pixl97 1y ago
  Why do you expect this, or is this just a "I need to find a reason to hate AI" thing?
  - boxed 1y ago
    It's like every time AI "can code" and then falls on its face when presented with super basic problems that are outside its training data. Why would protein folding be different?
- flobosg 1y ago
  > that probably can't handle truly novel proteins AlphaFold is able to predict novel folds, see https://www.nature.com/articles/s42003-022-03357-1 https://www.nature.com/articles/s42003-022-03357-1

isaacfrond 1y ago

The article itself lists as successful, even breakthrough, applications of AI: protein folding, weather forecasting, and drug discovery.

rsynnott 1y ago

They do mention that it has been somewhat useful in protein folding. > Or are we just using AI to remix existing general knowledge, while making no progress of any sort in any field using it? AIUI they are generally not talking about LLMs here.

phpnode 1y ago

it's the new "I love my tesla but here are 15 reasons why it's broken" - if you don't provide platitudes the mob provides pitchforks

nerdponx 1y ago

New numerical computing algorithms are being developed with AI assistance, which probably would not have been discovered otherwise. There was an article here a few days ago about one of those. It's incremental but it's not nothing.

PurpleRamen 1y ago

> Where are the AI-driven breakthroughs? Define breakthrough. When is the improvement big enough to count as one? Define AI. Are you talking about modern LLM, or is old school ML also in that question? I mean Googles AI-company had with AlphaFold and other project quite the impact. > Or are we just using AI to remix existing general knowledge Is remixing bad? Isn't many science today "just" remixing with slight improvements? I mean, there is a reason why we have theoretical and practical scientists. Doing boring Lab-work and accidentally discovering something exciting is not the only way science is happening. Analysing data and remixing information, building new theories, is also important. And don't forget, we don't have AGI yet. Whatever AI is doing today, is limited by what humans are using it for. Another question is, whether LLM is not normalized enough already that we do not see it as very special any more, if it's used somewhere. So we might not even see it if AI has significant impact on any breakthrough.

biophysboy 1y ago

An example of an "AI" incremental improvement would be Oxford Nanopore sequencing. They extrude DNA through a nanopore, measure the current, and decode the bases using recurrent neural networks. They exist all over science, but they are just one method among many, and they do not really drive hypotheses or interpretations (even now)

currymj 1y ago

AlphaFold is real. To the extent you care about chess and Go as human activities, progress there is real. there are some other scientific computing problems where AI or neural-network-based methods do appear to be at least part of the actual state-of-the-art (weather forecasting, certain single-molecule quantum chemistry simulations). i would like the hype of the kind described in the article to be punctured, but this is hard to do if critics make strong absolute claims ("aren't useful in any field whatsoever") which are easily disproven. it hurts credibility.

literalAardvark 1y ago
Even more relvant, AlphaEvolve is real. Could easily be brick 1 of self-improvement and the start of the banana zone.
- gthompson512 1y ago
  > "the start of the banana zone" What does this mean? Is it some slang for exponential growth, or is it a reference to something like the "paperclip maximizer"?
  - literalAardvark 1y ago
    It's slang for the J part of the exponential curve. Didn't expect that to be a problem here, sorry.
    - gthompson512 1y ago
      There is a bunch of new phrases people have been using around AI topics, so it can be hard to tell what exactly is being talked about, like "Roko's Basilisk", the "lottery card hypothesis?"(not quite remembering the phrasing on this one), "the bitter lesson", "paperclip maximizing", "stochastic parrot", etc.. Thank you for clarifying. I was kind of hoping there was a fun blog or story with a "banana zone".
- dirtyhippiefree 1y ago

ergsef 1y ago

Speaking out against the hype is frowned upon. I'm sure even this very measured article about "I tried it and it didn't work for me" will draw negative attention from people who think AI is the Second Coming. It's also very hard to prove a negative. If you predict "AI will never do anything of value" people will point to literally any result to prove you wrong. TFA does a good job debunking some recent hype, but the author cannot possibly wade through every hyperbolic paper in every field to demonstrate the claims are overblown.

dfxm12 1y ago

There's hype, then there's the understanding that you never use version 1.0 of a product. In this sense, AI as we know is barely in an alpha version. I think the authors you're referring to understand the marketing around AI overstates its usefulness, but they are hopeful for a future where AI can be helpful.

chermi 1y ago

Protein structure prediction is a pretty useful tool. There are various 'foundation' models in biology now that are quite useful. I don't know if you want to count those as AI or ML. If you're looking for breakthroughs due to AI, they're not going to be obviously attributable to AI I think. Focusing on the biology related foundation models ... The ability to more quickly search through the space of sequences->structure, drugs, and predicted cell states (1) like with the biology based foundation models will certainly lead to some things being discovered/rejected/validated faster. I heard about this vevo company recently so it's on my mind. Biology experiments are hard and time consuming, and often hard to exactly replicate across labs. A lot of the data is of the form a) start in cell state X (usual 'healthy' normal state) under conditions Y (environment variables like temperature, concentrations, etc.) b) introduce some environmental pertubation P like some drug/chemical at some concentration, or maybe multiple pertubations at once c) record trajectory or steady/final state of cell. This data is largely hidden within published papers in non-standardized format. Vevo is attempting to collate all of those results with considerations for reproducibility into a standard easy-to-use format. The idea being that you can gradually build up a virtual sort of input-output (causal!) model that you can throw ideas for interventions against and see what it thinks would happen. Cells and biology are obviously enormously complicated so it's certainly not going to be 100% accurate/predictive, but my experience with network models in quantitative biology plus their proclaimed results make me pretty confident it's a sound approach. Thus approach is clearly "AI" driven (maybe I would call this ML) and if their claims are anything close to reality, this is an incredibly powerful tool for all of academia and industry. You can reduce the search space enormously and target your experiments to cover the areas the virtual model doesn't seem so good, continuously improving it in a sort of crowd-sourced "active learning" manner. A continuously improving experimentally backed causal (w.r.t. perturbations) model of cells has so many applications. Again, i don't think this will directly lead to a breakthrough, but it can certainly make the breakthrough more likely and come faster. There are many other examples like this that are some combination of 1) collating+filtering+refining existing data into an accessible easily query-able format 2) combining data + some physically motivated modeling to yield predictions where there is no data 3) targeted, informed feedback loop via experiments, simulations, or modeling to improve the whole system where it's known to be weak or where more accuracy is desired. Assuming it all stays relatively open, that's undeniably a very powerful model for more effective science. And that's just one approach. In physics ML can be use for finding and characterizing phase transitions, as one example. In the world of soft matter/biophysics simulation here's a few ways ML is used: a) more efficient generation of configurations (Noe generative models). This is a big one, albeit still kind of early stages. Historically(simplifying), to generate independent samples in the right regions of phase means you need to integrate the system for long enough to hit that region multiple times. So regions of the space separated by rare transitions will take a loooong time to hit multiple times. The solution was simply longer simulations. Now, under some restrictions, you can leverage and augment existing data (including simulation data) to directly generate independent samples in the regions of interest. This is a really big deal. b) more efficient, complex and accurate NN force-fields. Better incorporation of many-body and even quantum effects. c) more complex simulation approaches via improved pipelines like automated parameterization and discovery of collective variables to more efficiently explore relevant configuration space. Again, this is tooling that improves the process of discovery & investigation and thus directly contributes to science. Maybe not in the way you're picturing, but it is happening right now. 1) vevo https://www.tahoebio.ai/ https://www.tahoebio.ai/

nyarlathotep_ 1y ago

> say things like "of course I don't doubt that AI will lead to major discoveries", and then go on to explain how they aren't useful in any field whatsoever? This is "paying the toll", otherwise one will be accused of being a "luddite."

Konnstann 1y ago

Depending on your definition of AI, a pipeline for drug repurposing my team used was able to identify a therapeutic for a rare disease that was beating the state of the art in every test they threw at it, eventually being given orphan drug designation by the FDA. I doubt this would have happened without machine learning or AI or whatever you want to call it. I'm also against "agents as scientists" as a concept for numerous reasons, but deep learning etc has or is leading to breakthroughs.