16 ms·

The Einstein AI Model

261 points by 9woc 2y ago

jrimbault 2y ago
What about the not-LLMs works? I know barely anything about it but it seems some people are interested and excited about protein engineering powered by neural networks.
- tim333 2y ago
  Deepmind are working on simulating a whole cell which will be interesting and potentially useful.
Agingcoder 2y ago
The author seems to assume that conjuring up a conjecture is the hard part - yet it will be filled with the same standard mathematics ( granted, sometimes wrapped as new tools, and the proof ends up being as important as the result), often at great cost. Having powerful assistants that allow people to try out crazy mathematical ideas without fear of risking their careers or just having fun with ideas is likely to have an outsized impact anyway I think.
- timewizard 2y ago
  New things AI will magically fix by existing: The completely broken university career publishing pipeline. *fingers crossed*
- kristianc 2y ago
  As Isaac Newton himself put it, “if I have seen further it is by standing on the shoulders of Giants.” It was ever thus.
- aleksiy123 2y ago
  The Bitter Lesson seems relevant here again. http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html I think I read somewhere about Erdős having this somewhat brute force approach. Whenever fresh techniques were developed (by himself or others), he would go back to see if they could be used on one of his long-standing open questions.
  - HappMacDonald 2y ago
    I think this is the second time I've read this blog post, but it increasingly strikes me as parenting advice. Translated to that domain, it reads "teach your kids how to think, not what to think".
    - robwwilliams 2y ago
      Which is great advice that almost no parents follow.
    - causal 2y ago
      Paradoxically, as a parent I find the notion that humans are blank slates completely false. Babies come with a tremendous amount of pre-programmed behaviors and interests.
- tensor 2y ago
  Even worse, people seem to forget that “science” is not math. You need to test hypotheses with physical (including biological) experiments. The vast majority of the time spent doing “science” is running these experiments. An LLM like AI won’t help with that. It would still be a huge help in finding and correlating data and information though.
neilv 2y ago
A nice post (that should be somewhere smarter than contemporary Twitter/X). > PS: You might be wondering what such a benchmark could look like. Evaluating it could involve testing a model on some recent discovery it should not know yet (a modern equivalent of special relativity) and explore how the model might start asking the right questions on a topic it has no exposure to the answers or conceptual framework of. This is challenging because most models are trained on virtually all human knowledge available today but it seems essential if we want to benchmark these behaviors. Overall this is really an open question and I’ll be happy to hear your insightful thoughts. Why benchmarks? A genius (human or AI) could produce novel insights, some of which could practically be tested in the real world. "We can gene-edit using such-and-such approach" => Go try it. No sales brochure claims, research paper comparison charts to show incremental improvement, individual KPIs/OKRs to hit, nor promotion packets required.
- vessenes 2y ago
  The reason you'd have a benchmark is that you want to be able to check in on your model programmatically. DNA wetwork is slow and expensive. While you're absolutely right that benchmarks aren't the best thing ever and that they are used for marketing and sales purposes, they also do seem to generally create capacity momentum in the market. For instance, nobody running local LLMs right now would prefer a 12 month-old model to one of the top models today at the same size - they are significantly more capable, and many researchers believe that training on new and harder benchmarks has been a way to increase that capacity.
internet_points 2y ago
If an llm is trained on knowledge up until say September 2023, could you use a corpus of interesting/insightful scientific discoveries and new methods developed after that date to evaluate/tune it? (Though I fear it would be a small corpus.)
- kingkongjaffa 2y ago
  Get a research paper, look at the references. Give an llm all of the references but not the current paper. See if it can conclude something like the current paper? Or at least design the same experiment as detailed in the paper?
  - pishpash 2y ago
    The fact that the references are what they are, out of all possible sets of references, is a significant part of the research. It's not about reading, it's about aiming in a direction without knowing where it ends up.
- Yizahi 2y ago
  One of the problems would be acquiring said corpus. NN corporations got away with scraping all human made content for free (arguably stealing it all), but no one can really prove that their specific content was taken without asking, so no lawsuits. NYT tried but that was workaround and I don't know the status of that case. But if NN corpo will come out with explicitly saying that "here, we are using a Nature journal dump from 2024" then Nature journal will come to them and say "oh, really?".
wewewedxfgdf 2y ago
I'm still waiting for the end of the world caused by AI as predicted by a very large number of prominent figures such as Sam Altman, Hinton, Musk, signers of the Center for AI Safety statement, Shane Legg, Martin Minsky, Eliezer Yudkowsky. No sign yet. On the other hand, LLMs are writing code which I can debug and eventually get to work in a real code base - and script writers everywhere are writing scripts more quickly, marketing people are writing better ad copy, employers are writing better job ads and real estate agents writing better ads for houses.
- netdevphoenix 2y ago
  Fact is even if the world was to end, finding the causes would be extremely difficult because...well the world would have ended.
- lionkor 2y ago
  Prominent figure says baseless thing to boost stock prices, more news at 6
  - godelski 2y ago
    The oddity isn't that people lie, the oddity is that people continue to believe those who lie. They even give more trust to those who constantly lie. This is certainly odd
  - TeMPOraL 2y ago
    Yes, except half of the list isn't made of prominent people. Whose stock price was Eliezer boosting when he was talking about these things 15 years ago? Nah, it's more that the masses got exposed to those ideas recently - ideas which existed long ago, in obscurity - and of course now everyone is a fucking expert in this New Thing No One Talked About Before ChatGPT. Even the list GP gave, the specific names on it - the only thing that this particular grouping communicates is one having no first clue what they're talking about.
- matusp 2y ago
  I am still waiting to see the impact on GDP or any other economic measure.
berkes 2y ago
I've had some luck instructing AI to "Don't make up anything. If there's no answer, say I don't know". Which made me think that AI would be far more useful (for me?) if it was tuned to "Dutchness" rather than "Americanness". "Dutch" famously known for being brutally blunt, rude, honest, and pushing back. Yet we seem to have "American" AI, tuned to "the customer is always right", inventing stuff just to not let you down, always willing to help even if that makes things worse. Not "critical thinking" or "revolutionary" yet. Just less polite and less willing to always please you. In human interaction, the Dutch bluntness and honesty can be very off-putting, but It is quite efficient and effective. Two traits I very much prefer my software to have. I don't need my software to be polite or to not hurt my feelings. It's just a tool!
- XCabbage 2y ago
  Obvious thought that I haven't tested: can you literally achieve this by getting it to answer in Dutch, or training an AI on Dutch text? Plausibly* Dutch-language training data will reflect this cultural difference by virtue of being written primarily by Dutch people. * (though not necessarily, since the Internet is its own country with its own culture, and much training data comes from the Internet)
  - zoover2020 2y ago
    I've tried Dutch answers and it is more than happy to hallucinate and give me answers that are very "American". Doesn't help that our culture is very inspired by the US pop culture as well since the internet. Haven't tried prompt engineering with the Dutch stereotype, though.
  - berkes 2y ago
    That hardly works. Though from my limited experiments, claude's models are better at this than OpenAIs. OpenAI will, quite often, come with suggestions that are literal translations of "anglicist" phrases. Such as "Ik hoop dat deze email u gezond vindt" (I hope this email finds you well), which is so wrong that not even "simple" translation tools would suggest this. Seeing that OpenAIs models can (could? This is from a large test we did months ago) not even use proper localized phrases but uses American ones, I highly doubt it can or will respond by refusing answers when it has none based on the training data.
OtherShrezzing 2y ago
>We're currently building very obedient students, not revolutionaries. This is perfect for today’s main goal in the field of creating great assistants and overly compliant helpers. But until we find a way to incentivize them to question their knowledge and propose ideas that potentially go against past training data, they won't give us scientific revolutions yet. This would definitely be an interesting future. I wonder what it'd do to all of the work in alignment & safety if we started encouraging AIs to go a bit rogue in some domains.
TeMPOraL 2y ago
> If something was not written in a book I could not invent it unless it was a rather useless variation of a known theory. __More annoyingly, I found it very hard to challenge the status-quo__, to question what I had learned. (__emphasis__ mine) As if "challenging the status-quo" was the goal in the first place. You ain't gonna get any Einstein by asking people to think inside the "outside the box" box. "Status quo" isn't the enemy, and defying it isn't the path to genius; if you're measuring your own intellectual capacity by proxy of how much you question, you ain't gonna get anywhere useful. After all, questioning everything is easy, and doesn't require any particular skill. The hard thing is to be right, despite both the status-quo and the "question the status-quo" memes. (It also helps being in the right time and place, to have access to the results of previous work that is required to make that next increment - that's another, oft forgotten factor.)
mentalgear 2y ago
BlueSky version: https://bsky.app/profile/thomwolf.bsky.social/post/3ljpkl6c6ss24 https://bsky.app/profile/thomwolf.bsky.social/post/3ljpkl6c6... --- Quite interesting post that asks the right question about "asking the right questions". Yet one aspect I felt missing (which might automatically solve this) is first-principles-based causal reasoning. A truly intelligent system — one that reasons from first principles by running its own simulations and physical experiments — would notice if something doesn't align with the "textbook version". It would recognize when reality deviates from expectations and ask follow-up questions, naturally leading to deeper insights and the right questions - and answers. Fascinating in this space is the new "Reasoning-Prior" approach (MIT Lab & Harvard), which trains reasoning capabilities learned from the physical world as a foundation for new models (before evening learning about text). Relevant paper: "General Reasoning Requires Learning to Reason from the Get-go."
- mentalgear 2y ago
  PS: great explainer video https://www.youtube.com/watch?v=seTdudcs-ws&t=180s https://www.youtube.com/watch?v=seTdudcs-ws&t=180s
- zombot 2y ago
  "Reality" is an alien concept to an LLM. All they have is sequences of words that they can complete.
  - reverius42 2y ago
    "Reality" is an alien concept to a Human. All they have is sequences of motions and noises they can complete.
    - neom 2y ago
      I explained to someone mum is mum because of lips, air and sounds and they didn't believe me, so that's what we're contending with....
  - herculity275 2y ago
moralestapia 2y ago
>Just consider the crazy paradigm shift of special relativity and the guts it took to formulate a first axiom like “let’s assume the speed of light is constant in all frames of reference” defying the common sense of these days (and even of today…) I'm not an expert on this. Wasn't this an observed phenomenon before Albert put together his theory?
- tim333 2y ago
  It was an observed phenomenon - https://en.wikipedia.org/wiki/Michelson%E2%80%93Morley_experiment https://en.wikipedia.org/wiki/Michelson%E2%80%93Morley_exper... Einsteins more impressive stuff was explaining that by time passing at different rates for different observers
- zesterer 2y ago
  Weird problems with physics were everywhere before Einstein. Maxwell comes painfully close to discovering GR in some of his musings on black body radiation. Noticing that there was a problem was not the breakthrough: trying something bizarre and counter-cultural - like assuming light speed is invariant over the observer - just to see if anything interesting drops out was the breakthrough.
tim333 2y ago
>I’m afraid AI won't give us a "compressed 21st century". There's no mention of exponential growth which seems a major omission when you are talking about centuries. Computers have kept improving in a Moore's law like way in terms of compute per dollar and no doubt will keep on like that for a while yet. Give it a few years and AI tech will be way better than what we have now. I don't know about exact timings like 5-10 years but in a while.
- dimitri-vs 2y ago
  What exponential growth? By all accounts things are slowing down: sonnet3.7 is not exponentially better, neither is gpt4.5, grok3 is just catching up. I'm still using sonnet3.5 for a lot of coding because IMO it's better than 3.7.
  - tim333 2y ago
    Exponential growth of computing power which will lead to a gradual increase in AI performance. I think the oldest LLM you mention there is nine months old which is not very long in the scheme of things but give it a couple of years and you'll probably see a good improvement.
- zesterer 2y ago
  The whole point of this post is that the things AI isn't good at and has never been good at will be the limit to otherwise-exponential growth.
  - tim333 2y ago
    Well, yeah the post kind of tries to argue that but it is also talking about how we don't have an Einstein or Newton like AI. Those two are outliers thought of as some of the smartest scientists ever to have lived and so are a bit of an unrealistic target just now. As to whether AI can go beyond doing what it's told and make new discoveries, we've sort of seen that a bit with for example the AlphaGo type programs coming up with modes of play humans hadn't thought of. I guess I don't buy the hypothesis that if you had an AI smarter than Einstein it wouldn't be able to make Einstein like discoveries due to not being a rebel.
rcarmo 2y ago
He means YMaaS, no? Might as well coin the acronym early.
dang 2y ago
(Most comments here were posted to https://news.ycombinator.com/item?id=43317269 https://news.ycombinator.com/item?id=43317269 and then moved hither.)
ilaksh 2y ago
I think it's more of a social phenomenon than an intellectual characteristic. I guess these days people would just assume that outlier ideas come from autism, but I think that isn't necessarily true. But maybe it helps to be socially isolated or just stubborn. People do not want to accept new approaches. Clearly they do eventually, but there is always some friction. But I think that it's been shown that through promoting and various types of training or tuning, LLMs can be configured to be non- sycophantic. It's just that humans don't want to be contradicted so that can be trained out of them during reinforcement. Along with the training process just generally being aimed at producing expected rather than unexpected answers.
deleted 2y ago
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randomNumber7 2y ago
Thing about the Einstein example is, that it was already known the speed of light is constant. The question he asked was just that this fact was not compatible with the Maxwell equations.
systemstops 2y ago
Wouldn't the ability to "ask the right questions" require that AI could update its own weights, as those weights determine which questions can be asked?
- esafak 2y ago
  If the existing weights don't let it ask the right questions, assuming it is properly fit, why would retraining it fix the problem?
ypeterholmes 2y ago
Hey look, the goalposts are being moved again. This time it's from top end researcher to generational genius. Question: what evidence is there that this benchmark will not be reached also? Time and again these essays make the mistake of assuming AI is a static thing, and refuse to acknowledge the inexorable march forward we are witnessing. As humans, we cling to our own fragile superiority. Even on this thread- I thought Hinton said the world would be transformed by now. That's NOT what was claimed. We are like three years in! Posts like this will be laughable in 10 years.
- nl 2y ago
  > Hey look, the goalposts are being moved again. Typically the "moving goalpost" posts are "we don't have AI because ....". That's not what this post is doing - it's pointing out a genuine weakness and a way forward.
  - ypeterholmes 2y ago
    As I noted, this post is saying AI can't achieve "genius" level creativity. Just a year ago the criticisms were that it couldn't match a human. How is that not moving the goalposts?
    - pishpash 2y ago
      It doesn't say genius-level creativity, just any novel research-like creativity. I don't agree but that's a strawman.
    - nl 2y ago
      The "moving goalposts" thing is typically "When AI can do this we will have AI" then AI does the thing and people say "no it's not AI because it can't do this other thing" I agree entirely this is annoying. This case is different because there is no claim that we don't have AI, nor a claim that once we get that we will have AI. Instead it's a very specific discussion of a particular weakness of current AI systems (that few would disagree with) and some thoughts about a roadmap for progress.
janalsncm 2y ago
> Many have been proposing "move 37" as evidence that AI has already reached Einstein-level intelligence I don’t think this example applies in the ways we care about. Sure, in the domain of go we have incredibly powerful engines. Poker too, which is an imperfect information game which you could argue is more similar to life in that regard. But life has far more degrees of freedom than go or poker, and the “value” of any one action is impossible to calculate due to imperfect information. And unlike in poker, where probabilities can be calculated, we don’t even have the probability distribution for most events, even if we could enumerate them.
- haswell 2y ago
  I didn't interpret the mention of move 37 in the way I think you are here. The author brought it up specifically to highlight that they don't believe move 37 signifies what many people think it does, and that while impressive, it's not general enough to indicate what some people seem to believe it indicates. In essence, I think they said the same thing you are using different words.
  - janalsncm 2y ago
    I don’t disagree with the author, I just think their argument isn’t as strong as it could be. Excelling in a constrained decision space like go is fundamentally less difficult than doing the same in the real world. It’s a categorical difference that the author didn’t mention. I’m also not even convinced move 37 was properly explained as a “straight A student” behavior. AlphaGo did bootstrap by studying human games but it also learned more fundamental value functions via self play.
phillipcarter 2y ago
A way I've been thinking about this today is: We can't distinguish between a truly novel response from an LLM or a hallucination. We can get some of the way there, such as if we know what the outcome to a problem should look like, and are seeking a better function to achieve that outcome. Certainly at small scales and in environments where there are minimal consequences for failure, this could work. But this breaks down as things get more complicated. We won't be able to test the effectiveness of 100 million potential solutions to eradicating brain tumors at once. Even if we somehow arrive at guaranteeing that every unforeseen consequence is also accounted for in our exercise in specifying the goals and constraints of the problem. We just simply don't have the logistics to run 100 million clinical trials where we also know how to account for countless confounding effects (let alone consent!)
tyronehed 2y ago
The first thing you need to understand is that no current llm based, transformer architected AI is going to get to agi. The design in essence is not capable of that kind of creativity. In fact no AI that has at its root a statistical analysis or probabilistic correlation will get us past the glorified Google parlor trick that is the modern llm in every form. A great leap in IP but unfortunately is too important to blab about widely, is the solution to this problem and the architecture that will be contained in the ultimate AGI solution that emerges.
hackerknew 2y ago
Could we train an AI model on the corpus of physics knowledge up to the year 1905 and then see if we can adjust the prompt to get it to output the theory of relativity? This would be an interesting experiment for other historical discoveries too. I'm now curious if anybody has created a model with "old data" like documents and books from hundreds of years ago, and see if comes up with the same conclusions as researchers and scientists of the past. Would AI have been able to predict the effectiveness of vaccines, insulin, other medical discoveries?
- knowaveragejoe 2y ago
  Now that would be interesting!
- Garlef 2y ago
  Great idea! But there might not be enough text. And: There's a similar situation to why double blind studies are necessary - The questions we pose to such a system would be contaminated by our cultural background; We'd might be leading the system. And if the system is autonomous and we wait for something true to appear how would we know that the final system, trained on current data produced something worthwhile? Take maths: Producing new proofs and new theorems might not be the issue. Rather: Why should we care about these result? Thousands of PhD students produce new mathematics all the time. And most of it is irrelevant.
- ilamparithi 2y ago
  Had the same thought sometime back about AI discovering theory of relativity with only the data before 1905. It would give a definite answer about whether any reasoning involved in the LLM output.
- esafak 2y ago
  That's the ideal, but I think today's models are too crude for that. Relativity is built on differential geometry, which was new at the time. I think inventing or even building that is beyond today's models; there's an infinitely large space of mathematics that can be invented, and barely a gradient to guide the search. Humans don't coin mathematics by gradient descent. The most I've seen is fitting observations using existing mathematics; a technique known as symbolic regression. The E=mc^2 equation could be curve fitted like this, but it would afford no insight. https://en.wikipedia.org/wiki/Symbolic_regression https://en.wikipedia.org/wiki/Symbolic_regression
nahuel0x 2y ago
We saw algorithms designing circuits that no human engineer would design, even before the LLM (using genetic algorithms). So out-the-box thinking can be also more reachable than this author thinks.
- niccl 2y ago
  including, IIRC, at least one FPGA-based circuit that had a blob of logic not connected to anything else (ie could not possibly be involved in the logical functioning of the circuit), but when removed the implementation stopped working. So the actual circuit wasn't a sensible design option, just a very implementation-specific local minimum. I think the original design challenge was something like a tone discriminator circuit. I can't recall the details
  - robwwilliams 2y ago
    Yes, another case like this in which stray capacitance/inductances between traces was optimized in making an effective FPGA. Initial the developers had no idea why it worked so well. They found it to be exceedingly temperature sensitive. That clue gave them the answer.
- audunw 2y ago
  But there's a reason we don't use those algorithms. We don't need out-of-the-box thinking that's so far outside the box that it's useless. With these kinds of circuits, they were so sensitive to the specific conditions that the circuit was tested in (temperature, process variation, ..) that the solution couldn't be generalized to be used outside of that specific experiment. We need the kind of intelligence that can question what assumptions can be challenged, and which we need to keep to have a viable (eventually commercially viable) solution.
  - torginus 2y ago
    If that was the case, then the algorithm was useless or flawed. IRL autorouters must take into account real physical constraints, lie wire length, signal integrity and tolerances to produce valid designs. A circuit that doesn't perform well under IRL conditions violates those constraints.
aaurelions 2y ago
Einstein Mode LLM: Temperature - 2 Then it's just a matter of checking all the “nonsense” that's been generated.
pama 2y ago
The reality is much simpler than what is often presented about science geniuses. Lorentz and Poincare had the math down to explain early experiments and the Maxwell’s equations predictions of the constancy of the speed of light, and Einstein indeed provided a neat shift in the interpretation by taking a different perspective. (His photoelectric effect experiment and interpretation was a more genuine original contribution that came from experimental data and got him a Nobel prize.) The ideas behind gene editing existed since forever, but the observation that certain bacteria use a more accurate and selective gene editing system than viruses led to CRISPR. I have trouble with arguments where many examples in a row are not based on popular, often slightly mistaken beliefs, especially when it comes to discussions related to using such examples as analogies and arguments for predicting the future. I have talked to and worked with many different Nobel prize winners across different fields of science in my life, and although they were all extraordinary bright and focused individuals, the introductory part of this article misses the point. I agree that there is no linear extrapolation from being a good student, but I dont think the additional abilities are beyond the reach of machines. Focus, knowledge, perserverence, and the ability to analyze data very carefully are strict and challenging requirements. Asking the right questions is very important as well, but much easier than people assume.
chr15m 2y ago
If this take is correct and we need creative B students, we might still get a compressed 21st century with human creative B students working together with AI A students who support the human with research, validation, workshopping ideas, etc.
- downboots 2y ago
  "Alpha children wear grey. They work much harder than we do, because they're so frightfully clever. I'm awfully glad I'm a Beta, because I don't work so hard." "The lower the caste, the shorter the oxygen."
knowitnone 2y ago
Yes, you may want this but all I want are straight facts, not inituition. I don't want a conscience AI.
adamtaylor_13 2y ago
I have yet to find a model that will stick, strictly, to factfulness. So I’m not entirely sure we don’t already have models that can question “facts” and invent novel things. He said it himself, it’s just finding new/interesting gaps between existing knowledge.
robwwilliams 2y ago
Read Feyerabend’s Against Method Thom. You have rephrased (very well) the necessity of counter-inductive thinking.
engfan 2y ago
I have never heard anyone think this way: “The main mistake people usually make is thinking Newton or Einstein were just scaled-up good students, that a genius comes to life when you linearly extrapolate a top-10% student. The reason such people are widely lauded as geniuses is precisely because people can’t envision smart students producing paradigm-shifting work as they did. Yes, people may be talking about AI performance as genius-level but any comparison to these minds is just for marketing purposes.
- 8note 2y ago
  we kinda think too much of them though. each is also a product of their surroundings, and had contemporaries who could or did come to the same revelations.
  - downboots 2y ago
    If the universe is not intelligent, how can a subset of it be intelligent? If it is all computation, what is the purpose?
    - sebastiennight 2y ago
      The first question is weird. Many subsets of X can have property Y without X having it, wouldn't you say? "If the desert is not covered in palm trees, how can a subset of it be covered in palm trees?" "If the neural network is not activating, how can a node of the network be activating?"
      - downboots 2y ago
        Good note. Your examples suggest thinking of 'property' as a sort of discontinuous indicator function on the subsets. I'm thinking about the interdependence between the function values or across subsets, regardless of continuity, in the context of universal computation. How to localize or define intelligence? Take the example of IQ, as a platonic ideal for measuring intelligence, vs all possible groups you could make with those people. Hard to define intelligence https://news.ycombinator.com/item?id=39977664 https://news.ycombinator.com/item?id=39977664
sinuhe69 2y ago
I agree. But in response, I'd also point out that AI, even in its current form, can help speed up our tasks: collecting data, verifying/cleaning, recognizing primary patterns, writing simple code to automate, conducting a critical self-conversation, drafting and refining our writing, etc.... Science requires a lot of mundane, tedious work, and AI can undoubtedly help us in this aspect. The idea that we are developing AI to replace our brains to make scientific progress for us is misguided, to say the least .
- captainclam 2y ago
  Exactly. If the whole "deep research" thing pans out, and we have models that can reliably produce proper literature reviews in 10 minutes...that alone will be an enormous boon to research. Then add all the practical/mundane tasks that you mentioned, and you've got quite the multiplier.
Scrapemist 2y ago
It’s not man vs machine but man plus machine: human ask the right question, machine gives plausible answers. Human processes this and comes up with a new question. Without a human in the loop a breakthrough isn’t even registered.
ANarrativeApe 2y ago
That this accurate article is considered noteworthy is scary. ChatGPT will tell you the same - if you ask it.
6stringmerc 2y ago
Creativity is inherently disobedient. That’s why it’s such an enigma.
jillesvangurp 2y ago
The reality with people is that most of them don't come close to Einstein level intelligence. A lot of the stuff I ask perplexity or chatgpt is way beyond what I could reasonably ask from the vast majority of people I know. I love my relatives. But they are kind of useless for the vast majority of stuff that bounces around in my head. AIs are at this point a useful tool for knowledge workers. They don't replace them but enhance their productivity. For scientific work, having an LLM that is trained on essentially all of the scientific work published, ever (until the cutoff date) is probably useful. You can now have conversations with an AI about cross referencing your ideas with existing work. You might analyze a paper you are writing and ask it to summarize key claims, criticize those, and your methodology, cross reference claims with literature, etc. Find counter points to your claims, etc. And you could probably use it to come up with interesting follow up questions, let it formulate hypotheses and ways to verify those, etc. Most scientific work isn't Archimedes going Eureka while taking a bath but undergraduates, post docs, and other under paid research stuff grinding through piles and piles of existing work and filling their heads with enough information until finally something new and original pops out. I got my Ph. D. in 2003. I'm part of the first generation of researchers that was able to use Google. At the time that was a huge enabler for tracking down obscure references and authors. Getting a paper published involves an enormous amount of what I just outlined. And LLMs can assist you with that. Will it hallucinate. Absolutely. But it will also dig out valid points, references, etc. Sorting that out is still work that you need to do. But it probably saves a lot of time. Will it propose original new theories. Maybe, maybe not. But it will speed up the process of zooming in on unanswered ones. Science isn't necessarily about coming up with answers but coming up with interesting questions. That's what Einstein did: ask interesting questions. Researchers are still trying to answer some of them and verifying some of the answers he predicted.
- zombot 2y ago
  > At the time that was a huge enabler for tracking down obscure references and authors. Would that still work today, in the highly commercialized and highly sanitized/censored internet? Where Google wouldn't show you those search results because they aren't profitable enough? And how do you even train an LLM on a fair representation of human knowledge when you only find stuff that is mainstream and commercially viable?
zombot 2y ago
Articles like this are worth the author's weight in gold. But as evidenced by the comments here, only few people understand the argument. The rest just "know better".
DeathArrow 2y ago
I don't know about you, but I am always nice and friendly with my AI, I always say please and thank you. In the event AI will take over the world.
hoseja 2y ago
>we don't just need a system that knows all the answers, but rather one that can ask questions nobody else has thought of or dared to ask. That's called hate speech and every AI has been aggressively lobotomized to never do it by an army of RLHFers.
Geee 2y ago
Exactly. The hallmark of intelligence is the ability to disagree with everyone else and be right.
msvana 2y ago
I have a few thoughts after reading this: - I started to see LLMs as a kind of search engines. I cannot say they are better than traditional search engines. On one hand, they are better at personalizing the answer, on the other hand, they hallucinate a lot. - There is a different view on how new scientific knowledge is made. It's all about connecting existing dots. Maybe LLMs can assist with this task by helping scientists discover relevant dots to connect. But as the author suggests, this is only part of the job. To find the correct ways to connect the dots, you need to ask the right questions, examine the space of counterfactuals, etc. LLMs can be useful tool, but they are not autonomous scientists (yet). - As someone developing software on top of LLMs, I am slowly coming to a conclusion that human-in-the-loop approaches seem to work better than fully autonomous agents.
- downboots 2y ago
  Instead of connecting language with physical existence, or entities, it's connecting tokens. An LLM may be able to describe scenes in a video, but a model would tell you that said video is a deep fake because of some principle like conservation of energy and mass informed by experience, assumptions, inference rules, etc.
eterevsky 2y ago
This article seems to argues from the way scientific discoveries are made by humans. It seems to me that its gist is similar to some article from the 80s that claims that computers will never play good chess, or an article from the 2000s that claims the same for go. The general shape of these arguments is: "Playing chess/go well, or making scientific discoveries requires specific way of strategic thinking or the ability to form the right hypotheses. Computers don't do this, ergo they won't be able to play chess or make scientific discoveries". I don't think this is a very good frame of reasoning. A scientific question can take one of the following shapes: - (Mathematical) Here's a mathematical statement. Prove either it or its negation. - (Fundamental natural science) Here're the results of the observations. What are the simplest possible model that explains all of them? - (Engineering) We need to do X. What's an efficient way of doing it? All of these questions could be solved in a "human" way, but it also possible to train AIs to approach them without going through the same process as the human scientists.
- deleted 2y ago
  [deleted]
- sweezyjeezy 2y ago
  > but it also possible to train AIs to approach them without going through the same process as the human scientists With chess the answer was more or less completely brute force the problem space, but will that work with math / science? Is there a way to widely explore the problem space with AI, especially in a way that goes above or even against the contents of it's training data? I don't know the answer, but that seems to be the crucial question here.
smougel 2y ago
The current culture about AI & LLMs is that we are "only" memorizing the Web into model parameters and that a LLM is unable to "invent" new paradigms. Maybe we are under estimating what Unsupervised Learning & RL could provide. Re-inforcement learning is about exploring and finding new ways to accomplish a task and I see no limit here (except the computational resources needed).
jaxr 2y ago
The most useful way to leverage LLMs for me has been as "content fillers". I'm a software engineer, and work with a rather large code base. Some parts are rarely touched, and loading the context into my brain whenever I need to go back to them requires quite a bit of effort. I found that asking cursor/Claude to suggest how to make the required chande rarely comes up with the right solution but usually points me in the right direction and is enough to help me load the context up. Similarly with my side projects, which typically involves knowledge that I don't use in my day to day.
ongytenes 2y ago
I think discoveries by AI would be due to pattern matching. Like finding overlooked cancer markers that can be used for an earlier prognosis. The genius of Einstein and his thought experiments may elude an AI. It may take an AI designed on some other future model other than an LLM to "compress a century" He called it wishful thinking. I believe the hype over AI is due to attempts to justify the enormous investments going into AI development has created an echo chamber.
- seanhunter 2y ago
  For one thing, how do we know that all discoveries are not pattern matching? In "The Act of Creation" for example, Arthur Koestler proposes the idea that all creativity is essentially the act of finding connections between diverse frames of reference and that extraordinary/genius-level creativity might just be that you can spot connections between even weirder and more diverse frames of reference than the regular creative person might be able to. I am certain there is a self-reinforcing hype cycle around LLMs specifically at the moment, but AI progress is definitely gathering pace and starting to get to the point where it is impacting normal people to the extent that hasn't been seen since the dot com boom. So the people making investments are for sure stampeding to pour in capital so as not to miss out on the big winners from this change.
msabalau 2y ago
It doesn't seem correct to dismiss the creativity of Move 37 because real originality is "something more fundamental, like inventing the rules of Go itself" It would seem more fruitful to simply point out that LLMs aren't all of AI, and that excelling at mimicking human-like text production isn't really doing the work that AlphaGo was attempting. Just because both things might be given as (different) examples of deep reinforcement learning in an AI survey course doesn't mean that we have much reason to believe that the vast investments in LLMs result in AlphaGo like achievements.
- Nesco 2y ago
  Modern LRMs do have some tiny degrees of intelligence
wegfawefgawefg 2y ago
creativity is possibly just random noise in feature space. at best it could be random noise in a feature space of a thing modeling its own thought trajectory.
- captainclam 2y ago
  Crucially, this doesn't just require noise but it requires "taste." I tend to fall back on music creation as an example of this notion. Lots of innovation in music is experimentation/exploration of "noise," (not necessarily literal white noise) but requires the ear of a discerning musician who ultimately goes "Ooh! I liked that" or passes a "generated sample" by. This is where I wonder if LLMs can ever innovate. I'm not sure they can develop "taste" for things outside of their distribution. However, I could just as easily be convinced that humans can't either, and sophisticated "taste" is just the exploration of obscure regions of the combinatorial space generated from previously observed samples!
EigenLord 2y ago
I think the author has a point. LLMs struggle with what you might call epistemically constructive novelty. It's the ability not just to synthesize existing knowledge, but to identify what's missing and conjecture something to fill the gap and demonstrate it to satisfaction. Out-of-distribution knowledge gaps are typically where LLMs "hallucinate." Unlike highly skilled human researchers, they don't pause and construct the bridge that will get them from known to unknown, they just immediately rush to fill in the blank with whatever sounds most plausible. They need to ask questions that haven't been asked before, or answer ones that haven't been answered. Is this just some missing subroutine that we'll eventually figure out? Or is this conjecture-proving process much more elaborate than whatever existing models, no matter how scaled, can manage? I'm not sure. But the answer starts with a question.
omnee 2y ago
I agree that any system claiming general intelligence must be able to form and modify a model of the world. A fundamental part of this is being able to ask counterfactual questions on its own understanding or knowledge of the world. The history of science is full of countless such examples. As of right now I'm not aware of any LLMs or indeed any AI system being able to do so.
_cs2017_ 2y ago
I wonder if people could just write their blogs posts in a short form: claim, argument in favor, counter-argument; consequences (this is optional). Like this whole blog post could be: Claim: Current AI is unlikely to usher in an era of dramatically accelerated scientific discovery. Argument in favor: A genius does not come to life when you linearly extrapolate a top-10% student. Newton or Einstein is not just scaled-up good students. To create an Einstein, we need a system can ask questions nobody else has thought of or dared to ask. One that writes 'What if everyone is wrong about this?' when all textbooks, experts, and common knowledge suggest otherwise. Existing benchmarks don't test such skills. And existing systems are likely hopelessly far from this capability (based on the author's personal feelings). Counter-argument: none. Consequences: obvious.
nahanniSpirit 2y ago
Can someone use AI to model einsteins (Ulam's) spiral. I believe its related to emergent properties from seemingly random data. It even 3 dimensionally models our galaxy...

261 points by 9woc 2y ago

What about the not-LLMs works? I know barely anything about it but it seems some people are interested and excited about protein engineering powered by neural networks.

tim333 2y ago
Deepmind are working on simulating a whole cell which will be interesting and potentially useful.

Agingcoder 2y ago

The author seems to assume that conjuring up a conjecture is the hard part - yet it will be filled with the same standard mathematics ( granted, sometimes wrapped as new tools, and the proof ends up being as important as the result), often at great cost. Having powerful assistants that allow people to try out crazy mathematical ideas without fear of risking their careers or just having fun with ideas is likely to have an outsized impact anyway I think.

timewizard 2y ago
New things AI will magically fix by existing: The completely broken university career publishing pipeline. *fingers crossed*
kristianc 2y ago
As Isaac Newton himself put it, “if I have seen further it is by standing on the shoulders of Giants.” It was ever thus.
aleksiy123 2y ago
The Bitter Lesson seems relevant here again. http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html I think I read somewhere about Erdős having this somewhat brute force approach. Whenever fresh techniques were developed (by himself or others), he would go back to see if they could be used on one of his long-standing open questions.
- HappMacDonald 2y ago
  I think this is the second time I've read this blog post, but it increasingly strikes me as parenting advice. Translated to that domain, it reads "teach your kids how to think, not what to think".
  - robwwilliams 2y ago
    Which is great advice that almost no parents follow.
  - causal 2y ago
    Paradoxically, as a parent I find the notion that humans are blank slates completely false. Babies come with a tremendous amount of pre-programmed behaviors and interests.
tensor 2y ago
Even worse, people seem to forget that “science” is not math. You need to test hypotheses with physical (including biological) experiments. The vast majority of the time spent doing “science” is running these experiments. An LLM like AI won’t help with that. It would still be a huge help in finding and correlating data and information though.

neilv 2y ago

A nice post (that should be somewhere smarter than contemporary Twitter/X). > PS: You might be wondering what such a benchmark could look like. Evaluating it could involve testing a model on some recent discovery it should not know yet (a modern equivalent of special relativity) and explore how the model might start asking the right questions on a topic it has no exposure to the answers or conceptual framework of. This is challenging because most models are trained on virtually all human knowledge available today but it seems essential if we want to benchmark these behaviors. Overall this is really an open question and I’ll be happy to hear your insightful thoughts. Why benchmarks? A genius (human or AI) could produce novel insights, some of which could practically be tested in the real world. "We can gene-edit using such-and-such approach" => Go try it. No sales brochure claims, research paper comparison charts to show incremental improvement, individual KPIs/OKRs to hit, nor promotion packets required.

vessenes 2y ago
The reason you'd have a benchmark is that you want to be able to check in on your model programmatically. DNA wetwork is slow and expensive. While you're absolutely right that benchmarks aren't the best thing ever and that they are used for marketing and sales purposes, they also do seem to generally create capacity momentum in the market. For instance, nobody running local LLMs right now would prefer a 12 month-old model to one of the top models today at the same size - they are significantly more capable, and many researchers believe that training on new and harder benchmarks has been a way to increase that capacity.

internet_points 2y ago

If an llm is trained on knowledge up until say September 2023, could you use a corpus of interesting/insightful scientific discoveries and new methods developed after that date to evaluate/tune it? (Though I fear it would be a small corpus.)

kingkongjaffa 2y ago
Get a research paper, look at the references. Give an llm all of the references but not the current paper. See if it can conclude something like the current paper? Or at least design the same experiment as detailed in the paper?
- pishpash 2y ago
  The fact that the references are what they are, out of all possible sets of references, is a significant part of the research. It's not about reading, it's about aiming in a direction without knowing where it ends up.
Yizahi 2y ago
One of the problems would be acquiring said corpus. NN corporations got away with scraping all human made content for free (arguably stealing it all), but no one can really prove that their specific content was taken without asking, so no lawsuits. NYT tried but that was workaround and I don't know the status of that case. But if NN corpo will come out with explicitly saying that "here, we are using a Nature journal dump from 2024" then Nature journal will come to them and say "oh, really?".

wewewedxfgdf 2y ago

I'm still waiting for the end of the world caused by AI as predicted by a very large number of prominent figures such as Sam Altman, Hinton, Musk, signers of the Center for AI Safety statement, Shane Legg, Martin Minsky, Eliezer Yudkowsky. No sign yet. On the other hand, LLMs are writing code which I can debug and eventually get to work in a real code base - and script writers everywhere are writing scripts more quickly, marketing people are writing better ad copy, employers are writing better job ads and real estate agents writing better ads for houses.

netdevphoenix 2y ago
Fact is even if the world was to end, finding the causes would be extremely difficult because...well the world would have ended.
lionkor 2y ago
Prominent figure says baseless thing to boost stock prices, more news at 6
- godelski 2y ago
  The oddity isn't that people lie, the oddity is that people continue to believe those who lie. They even give more trust to those who constantly lie. This is certainly odd
- TeMPOraL 2y ago
  Yes, except half of the list isn't made of prominent people. Whose stock price was Eliezer boosting when he was talking about these things 15 years ago? Nah, it's more that the masses got exposed to those ideas recently - ideas which existed long ago, in obscurity - and of course now everyone is a fucking expert in this New Thing No One Talked About Before ChatGPT. Even the list GP gave, the specific names on it - the only thing that this particular grouping communicates is one having no first clue what they're talking about.
matusp 2y ago
I am still waiting to see the impact on GDP or any other economic measure.

berkes 2y ago

I've had some luck instructing AI to "Don't make up anything. If there's no answer, say I don't know". Which made me think that AI would be far more useful (for me?) if it was tuned to "Dutchness" rather than "Americanness". "Dutch" famously known for being brutally blunt, rude, honest, and pushing back. Yet we seem to have "American" AI, tuned to "the customer is always right", inventing stuff just to not let you down, always willing to help even if that makes things worse. Not "critical thinking" or "revolutionary" yet. Just less polite and less willing to always please you. In human interaction, the Dutch bluntness and honesty can be very off-putting, but It is quite efficient and effective. Two traits I very much prefer my software to have. I don't need my software to be polite or to not hurt my feelings. It's just a tool!

XCabbage 2y ago
Obvious thought that I haven't tested: can you literally achieve this by getting it to answer in Dutch, or training an AI on Dutch text? Plausibly* Dutch-language training data will reflect this cultural difference by virtue of being written primarily by Dutch people. * (though not necessarily, since the Internet is its own country with its own culture, and much training data comes from the Internet)
- zoover2020 2y ago
  I've tried Dutch answers and it is more than happy to hallucinate and give me answers that are very "American". Doesn't help that our culture is very inspired by the US pop culture as well since the internet. Haven't tried prompt engineering with the Dutch stereotype, though.
- berkes 2y ago
  That hardly works. Though from my limited experiments, claude's models are better at this than OpenAIs. OpenAI will, quite often, come with suggestions that are literal translations of "anglicist" phrases. Such as "Ik hoop dat deze email u gezond vindt" (I hope this email finds you well), which is so wrong that not even "simple" translation tools would suggest this. Seeing that OpenAIs models can (could? This is from a large test we did months ago) not even use proper localized phrases but uses American ones, I highly doubt it can or will respond by refusing answers when it has none based on the training data.

OtherShrezzing 2y ago

>We're currently building very obedient students, not revolutionaries. This is perfect for today’s main goal in the field of creating great assistants and overly compliant helpers. But until we find a way to incentivize them to question their knowledge and propose ideas that potentially go against past training data, they won't give us scientific revolutions yet. This would definitely be an interesting future. I wonder what it'd do to all of the work in alignment & safety if we started encouraging AIs to go a bit rogue in some domains.

TeMPOraL 2y ago

> If something was not written in a book I could not invent it unless it was a rather useless variation of a known theory. __More annoyingly, I found it very hard to challenge the status-quo__, to question what I had learned. (__emphasis__ mine) As if "challenging the status-quo" was the goal in the first place. You ain't gonna get any Einstein by asking people to think inside the "outside the box" box. "Status quo" isn't the enemy, and defying it isn't the path to genius; if you're measuring your own intellectual capacity by proxy of how much you question, you ain't gonna get anywhere useful. After all, questioning everything is easy, and doesn't require any particular skill. The hard thing is to be right, despite both the status-quo and the "question the status-quo" memes. (It also helps being in the right time and place, to have access to the results of previous work that is required to make that next increment - that's another, oft forgotten factor.)

mentalgear 2y ago

BlueSky version: https://bsky.app/profile/thomwolf.bsky.social/post/3ljpkl6c6ss24 https://bsky.app/profile/thomwolf.bsky.social/post/3ljpkl6c6... --- Quite interesting post that asks the right question about "asking the right questions". Yet one aspect I felt missing (which might automatically solve this) is first-principles-based causal reasoning. A truly intelligent system — one that reasons from first principles by running its own simulations and physical experiments — would notice if something doesn't align with the "textbook version". It would recognize when reality deviates from expectations and ask follow-up questions, naturally leading to deeper insights and the right questions - and answers. Fascinating in this space is the new "Reasoning-Prior" approach (MIT Lab & Harvard), which trains reasoning capabilities learned from the physical world as a foundation for new models (before evening learning about text). Relevant paper: "General Reasoning Requires Learning to Reason from the Get-go."

mentalgear 2y ago
PS: great explainer video https://www.youtube.com/watch?v=seTdudcs-ws&t=180s https://www.youtube.com/watch?v=seTdudcs-ws&t=180s
zombot 2y ago
"Reality" is an alien concept to an LLM. All they have is sequences of words that they can complete.
- reverius42 2y ago
  "Reality" is an alien concept to a Human. All they have is sequences of motions and noises they can complete.
  - neom 2y ago
    I explained to someone mum is mum because of lips, air and sounds and they didn't believe me, so that's what we're contending with....
- herculity275 2y ago

moralestapia 2y ago

>Just consider the crazy paradigm shift of special relativity and the guts it took to formulate a first axiom like “let’s assume the speed of light is constant in all frames of reference” defying the common sense of these days (and even of today…) I'm not an expert on this. Wasn't this an observed phenomenon before Albert put together his theory?

tim333 2y ago
It was an observed phenomenon - https://en.wikipedia.org/wiki/Michelson%E2%80%93Morley_experiment https://en.wikipedia.org/wiki/Michelson%E2%80%93Morley_exper... Einsteins more impressive stuff was explaining that by time passing at different rates for different observers
zesterer 2y ago
Weird problems with physics were everywhere before Einstein. Maxwell comes painfully close to discovering GR in some of his musings on black body radiation. Noticing that there was a problem was not the breakthrough: trying something bizarre and counter-cultural - like assuming light speed is invariant over the observer - just to see if anything interesting drops out was the breakthrough.

tim333 2y ago

>I’m afraid AI won't give us a "compressed 21st century". There's no mention of exponential growth which seems a major omission when you are talking about centuries. Computers have kept improving in a Moore's law like way in terms of compute per dollar and no doubt will keep on like that for a while yet. Give it a few years and AI tech will be way better than what we have now. I don't know about exact timings like 5-10 years but in a while.

dimitri-vs 2y ago
What exponential growth? By all accounts things are slowing down: sonnet3.7 is not exponentially better, neither is gpt4.5, grok3 is just catching up. I'm still using sonnet3.5 for a lot of coding because IMO it's better than 3.7.
- tim333 2y ago
  Exponential growth of computing power which will lead to a gradual increase in AI performance. I think the oldest LLM you mention there is nine months old which is not very long in the scheme of things but give it a couple of years and you'll probably see a good improvement.
zesterer 2y ago
The whole point of this post is that the things AI isn't good at and has never been good at will be the limit to otherwise-exponential growth.
- tim333 2y ago
  Well, yeah the post kind of tries to argue that but it is also talking about how we don't have an Einstein or Newton like AI. Those two are outliers thought of as some of the smartest scientists ever to have lived and so are a bit of an unrealistic target just now. As to whether AI can go beyond doing what it's told and make new discoveries, we've sort of seen that a bit with for example the AlphaGo type programs coming up with modes of play humans hadn't thought of. I guess I don't buy the hypothesis that if you had an AI smarter than Einstein it wouldn't be able to make Einstein like discoveries due to not being a rebel.

rcarmo 2y ago

He means YMaaS, no? Might as well coin the acronym early.

dang 2y ago

(Most comments here were posted to https://news.ycombinator.com/item?id=43317269 https://news.ycombinator.com/item?id=43317269 and then moved hither.)

ilaksh 2y ago

I think it's more of a social phenomenon than an intellectual characteristic. I guess these days people would just assume that outlier ideas come from autism, but I think that isn't necessarily true. But maybe it helps to be socially isolated or just stubborn. People do not want to accept new approaches. Clearly they do eventually, but there is always some friction. But I think that it's been shown that through promoting and various types of training or tuning, LLMs can be configured to be non- sycophantic. It's just that humans don't want to be contradicted so that can be trained out of them during reinforcement. Along with the training process just generally being aimed at producing expected rather than unexpected answers.

deleted 2y ago

[deleted]

randomNumber7 2y ago

Thing about the Einstein example is, that it was already known the speed of light is constant. The question he asked was just that this fact was not compatible with the Maxwell equations.

systemstops 2y ago

Wouldn't the ability to "ask the right questions" require that AI could update its own weights, as those weights determine which questions can be asked?

esafak 2y ago
If the existing weights don't let it ask the right questions, assuming it is properly fit, why would retraining it fix the problem?

ypeterholmes 2y ago

Hey look, the goalposts are being moved again. This time it's from top end researcher to generational genius. Question: what evidence is there that this benchmark will not be reached also? Time and again these essays make the mistake of assuming AI is a static thing, and refuse to acknowledge the inexorable march forward we are witnessing. As humans, we cling to our own fragile superiority. Even on this thread- I thought Hinton said the world would be transformed by now. That's NOT what was claimed. We are like three years in! Posts like this will be laughable in 10 years.

nl 2y ago
> Hey look, the goalposts are being moved again. Typically the "moving goalpost" posts are "we don't have AI because ....". That's not what this post is doing - it's pointing out a genuine weakness and a way forward.
- ypeterholmes 2y ago
  As I noted, this post is saying AI can't achieve "genius" level creativity. Just a year ago the criticisms were that it couldn't match a human. How is that not moving the goalposts?
  - pishpash 2y ago
    It doesn't say genius-level creativity, just any novel research-like creativity. I don't agree but that's a strawman.
  - nl 2y ago
    The "moving goalposts" thing is typically "When AI can do this we will have AI" then AI does the thing and people say "no it's not AI because it can't do this other thing" I agree entirely this is annoying. This case is different because there is no claim that we don't have AI, nor a claim that once we get that we will have AI. Instead it's a very specific discussion of a particular weakness of current AI systems (that few would disagree with) and some thoughts about a roadmap for progress.

janalsncm 2y ago

> Many have been proposing "move 37" as evidence that AI has already reached Einstein-level intelligence I don’t think this example applies in the ways we care about. Sure, in the domain of go we have incredibly powerful engines. Poker too, which is an imperfect information game which you could argue is more similar to life in that regard. But life has far more degrees of freedom than go or poker, and the “value” of any one action is impossible to calculate due to imperfect information. And unlike in poker, where probabilities can be calculated, we don’t even have the probability distribution for most events, even if we could enumerate them.

haswell 2y ago
I didn't interpret the mention of move 37 in the way I think you are here. The author brought it up specifically to highlight that they don't believe move 37 signifies what many people think it does, and that while impressive, it's not general enough to indicate what some people seem to believe it indicates. In essence, I think they said the same thing you are using different words.
- janalsncm 2y ago
  I don’t disagree with the author, I just think their argument isn’t as strong as it could be. Excelling in a constrained decision space like go is fundamentally less difficult than doing the same in the real world. It’s a categorical difference that the author didn’t mention. I’m also not even convinced move 37 was properly explained as a “straight A student” behavior. AlphaGo did bootstrap by studying human games but it also learned more fundamental value functions via self play.

phillipcarter 2y ago

A way I've been thinking about this today is: We can't distinguish between a truly novel response from an LLM or a hallucination. We can get some of the way there, such as if we know what the outcome to a problem should look like, and are seeking a better function to achieve that outcome. Certainly at small scales and in environments where there are minimal consequences for failure, this could work. But this breaks down as things get more complicated. We won't be able to test the effectiveness of 100 million potential solutions to eradicating brain tumors at once. Even if we somehow arrive at guaranteeing that every unforeseen consequence is also accounted for in our exercise in specifying the goals and constraints of the problem. We just simply don't have the logistics to run 100 million clinical trials where we also know how to account for countless confounding effects (let alone consent!)

tyronehed 2y ago

The first thing you need to understand is that no current llm based, transformer architected AI is going to get to agi. The design in essence is not capable of that kind of creativity. In fact no AI that has at its root a statistical analysis or probabilistic correlation will get us past the glorified Google parlor trick that is the modern llm in every form. A great leap in IP but unfortunately is too important to blab about widely, is the solution to this problem and the architecture that will be contained in the ultimate AGI solution that emerges.

hackerknew 2y ago

Could we train an AI model on the corpus of physics knowledge up to the year 1905 and then see if we can adjust the prompt to get it to output the theory of relativity? This would be an interesting experiment for other historical discoveries too. I'm now curious if anybody has created a model with "old data" like documents and books from hundreds of years ago, and see if comes up with the same conclusions as researchers and scientists of the past. Would AI have been able to predict the effectiveness of vaccines, insulin, other medical discoveries?

knowaveragejoe 2y ago
Now that would be interesting!
Garlef 2y ago
Great idea! But there might not be enough text. And: There's a similar situation to why double blind studies are necessary - The questions we pose to such a system would be contaminated by our cultural background; We'd might be leading the system. And if the system is autonomous and we wait for something true to appear how would we know that the final system, trained on current data produced something worthwhile? Take maths: Producing new proofs and new theorems might not be the issue. Rather: Why should we care about these result? Thousands of PhD students produce new mathematics all the time. And most of it is irrelevant.
ilamparithi 2y ago
Had the same thought sometime back about AI discovering theory of relativity with only the data before 1905. It would give a definite answer about whether any reasoning involved in the LLM output.
esafak 2y ago
That's the ideal, but I think today's models are too crude for that. Relativity is built on differential geometry, which was new at the time. I think inventing or even building that is beyond today's models; there's an infinitely large space of mathematics that can be invented, and barely a gradient to guide the search. Humans don't coin mathematics by gradient descent. The most I've seen is fitting observations using existing mathematics; a technique known as symbolic regression. The E=mc^2 equation could be curve fitted like this, but it would afford no insight. https://en.wikipedia.org/wiki/Symbolic_regression https://en.wikipedia.org/wiki/Symbolic_regression

nahuel0x 2y ago

We saw algorithms designing circuits that no human engineer would design, even before the LLM (using genetic algorithms). So out-the-box thinking can be also more reachable than this author thinks.

niccl 2y ago
including, IIRC, at least one FPGA-based circuit that had a blob of logic not connected to anything else (ie could not possibly be involved in the logical functioning of the circuit), but when removed the implementation stopped working. So the actual circuit wasn't a sensible design option, just a very implementation-specific local minimum. I think the original design challenge was something like a tone discriminator circuit. I can't recall the details
- robwwilliams 2y ago
  Yes, another case like this in which stray capacitance/inductances between traces was optimized in making an effective FPGA. Initial the developers had no idea why it worked so well. They found it to be exceedingly temperature sensitive. That clue gave them the answer.
audunw 2y ago
But there's a reason we don't use those algorithms. We don't need out-of-the-box thinking that's so far outside the box that it's useless. With these kinds of circuits, they were so sensitive to the specific conditions that the circuit was tested in (temperature, process variation, ..) that the solution couldn't be generalized to be used outside of that specific experiment. We need the kind of intelligence that can question what assumptions can be challenged, and which we need to keep to have a viable (eventually commercially viable) solution.
- torginus 2y ago
  If that was the case, then the algorithm was useless or flawed. IRL autorouters must take into account real physical constraints, lie wire length, signal integrity and tolerances to produce valid designs. A circuit that doesn't perform well under IRL conditions violates those constraints.

aaurelions 2y ago

Einstein Mode LLM: Temperature - 2 Then it's just a matter of checking all the “nonsense” that's been generated.

pama 2y ago

The reality is much simpler than what is often presented about science geniuses. Lorentz and Poincare had the math down to explain early experiments and the Maxwell’s equations predictions of the constancy of the speed of light, and Einstein indeed provided a neat shift in the interpretation by taking a different perspective. (His photoelectric effect experiment and interpretation was a more genuine original contribution that came from experimental data and got him a Nobel prize.) The ideas behind gene editing existed since forever, but the observation that certain bacteria use a more accurate and selective gene editing system than viruses led to CRISPR. I have trouble with arguments where many examples in a row are not based on popular, often slightly mistaken beliefs, especially when it comes to discussions related to using such examples as analogies and arguments for predicting the future. I have talked to and worked with many different Nobel prize winners across different fields of science in my life, and although they were all extraordinary bright and focused individuals, the introductory part of this article misses the point. I agree that there is no linear extrapolation from being a good student, but I dont think the additional abilities are beyond the reach of machines. Focus, knowledge, perserverence, and the ability to analyze data very carefully are strict and challenging requirements. Asking the right questions is very important as well, but much easier than people assume.

chr15m 2y ago

If this take is correct and we need creative B students, we might still get a compressed 21st century with human creative B students working together with AI A students who support the human with research, validation, workshopping ideas, etc.

downboots 2y ago
"Alpha children wear grey. They work much harder than we do, because they're so frightfully clever. I'm awfully glad I'm a Beta, because I don't work so hard." "The lower the caste, the shorter the oxygen."

knowitnone 2y ago

Yes, you may want this but all I want are straight facts, not inituition. I don't want a conscience AI.

adamtaylor_13 2y ago

I have yet to find a model that will stick, strictly, to factfulness. So I’m not entirely sure we don’t already have models that can question “facts” and invent novel things. He said it himself, it’s just finding new/interesting gaps between existing knowledge.

robwwilliams 2y ago

Read Feyerabend’s Against Method Thom. You have rephrased (very well) the necessity of counter-inductive thinking.

engfan 2y ago

I have never heard anyone think this way: “The main mistake people usually make is thinking Newton or Einstein were just scaled-up good students, that a genius comes to life when you linearly extrapolate a top-10% student. The reason such people are widely lauded as geniuses is precisely because people can’t envision smart students producing paradigm-shifting work as they did. Yes, people may be talking about AI performance as genius-level but any comparison to these minds is just for marketing purposes.

8note 2y ago
we kinda think too much of them though. each is also a product of their surroundings, and had contemporaries who could or did come to the same revelations.
- downboots 2y ago
  If the universe is not intelligent, how can a subset of it be intelligent? If it is all computation, what is the purpose?
  - sebastiennight 2y ago
    The first question is weird. Many subsets of X can have property Y without X having it, wouldn't you say? "If the desert is not covered in palm trees, how can a subset of it be covered in palm trees?" "If the neural network is not activating, how can a node of the network be activating?"
    - downboots 2y ago
      Good note. Your examples suggest thinking of 'property' as a sort of discontinuous indicator function on the subsets. I'm thinking about the interdependence between the function values or across subsets, regardless of continuity, in the context of universal computation. How to localize or define intelligence? Take the example of IQ, as a platonic ideal for measuring intelligence, vs all possible groups you could make with those people. Hard to define intelligence https://news.ycombinator.com/item?id=39977664 https://news.ycombinator.com/item?id=39977664

sinuhe69 2y ago

I agree. But in response, I'd also point out that AI, even in its current form, can help speed up our tasks: collecting data, verifying/cleaning, recognizing primary patterns, writing simple code to automate, conducting a critical self-conversation, drafting and refining our writing, etc.... Science requires a lot of mundane, tedious work, and AI can undoubtedly help us in this aspect. The idea that we are developing AI to replace our brains to make scientific progress for us is misguided, to say the least .

captainclam 2y ago
Exactly. If the whole "deep research" thing pans out, and we have models that can reliably produce proper literature reviews in 10 minutes...that alone will be an enormous boon to research. Then add all the practical/mundane tasks that you mentioned, and you've got quite the multiplier.

Scrapemist 2y ago

It’s not man vs machine but man plus machine: human ask the right question, machine gives plausible answers. Human processes this and comes up with a new question. Without a human in the loop a breakthrough isn’t even registered.

ANarrativeApe 2y ago

That this accurate article is considered noteworthy is scary. ChatGPT will tell you the same - if you ask it.

6stringmerc 2y ago

Creativity is inherently disobedient. That’s why it’s such an enigma.

jillesvangurp 2y ago

The reality with people is that most of them don't come close to Einstein level intelligence. A lot of the stuff I ask perplexity or chatgpt is way beyond what I could reasonably ask from the vast majority of people I know. I love my relatives. But they are kind of useless for the vast majority of stuff that bounces around in my head. AIs are at this point a useful tool for knowledge workers. They don't replace them but enhance their productivity. For scientific work, having an LLM that is trained on essentially all of the scientific work published, ever (until the cutoff date) is probably useful. You can now have conversations with an AI about cross referencing your ideas with existing work. You might analyze a paper you are writing and ask it to summarize key claims, criticize those, and your methodology, cross reference claims with literature, etc. Find counter points to your claims, etc. And you could probably use it to come up with interesting follow up questions, let it formulate hypotheses and ways to verify those, etc. Most scientific work isn't Archimedes going Eureka while taking a bath but undergraduates, post docs, and other under paid research stuff grinding through piles and piles of existing work and filling their heads with enough information until finally something new and original pops out. I got my Ph. D. in 2003. I'm part of the first generation of researchers that was able to use Google. At the time that was a huge enabler for tracking down obscure references and authors. Getting a paper published involves an enormous amount of what I just outlined. And LLMs can assist you with that. Will it hallucinate. Absolutely. But it will also dig out valid points, references, etc. Sorting that out is still work that you need to do. But it probably saves a lot of time. Will it propose original new theories. Maybe, maybe not. But it will speed up the process of zooming in on unanswered ones. Science isn't necessarily about coming up with answers but coming up with interesting questions. That's what Einstein did: ask interesting questions. Researchers are still trying to answer some of them and verifying some of the answers he predicted.

zombot 2y ago
> At the time that was a huge enabler for tracking down obscure references and authors. Would that still work today, in the highly commercialized and highly sanitized/censored internet? Where Google wouldn't show you those search results because they aren't profitable enough? And how do you even train an LLM on a fair representation of human knowledge when you only find stuff that is mainstream and commercially viable?

zombot 2y ago

Articles like this are worth the author's weight in gold. But as evidenced by the comments here, only few people understand the argument. The rest just "know better".

DeathArrow 2y ago

I don't know about you, but I am always nice and friendly with my AI, I always say please and thank you. In the event AI will take over the world.

hoseja 2y ago

>we don't just need a system that knows all the answers, but rather one that can ask questions nobody else has thought of or dared to ask. That's called hate speech and every AI has been aggressively lobotomized to never do it by an army of RLHFers.

Geee 2y ago

Exactly. The hallmark of intelligence is the ability to disagree with everyone else and be right.

msvana 2y ago

I have a few thoughts after reading this: - I started to see LLMs as a kind of search engines. I cannot say they are better than traditional search engines. On one hand, they are better at personalizing the answer, on the other hand, they hallucinate a lot. - There is a different view on how new scientific knowledge is made. It's all about connecting existing dots. Maybe LLMs can assist with this task by helping scientists discover relevant dots to connect. But as the author suggests, this is only part of the job. To find the correct ways to connect the dots, you need to ask the right questions, examine the space of counterfactuals, etc. LLMs can be useful tool, but they are not autonomous scientists (yet). - As someone developing software on top of LLMs, I am slowly coming to a conclusion that human-in-the-loop approaches seem to work better than fully autonomous agents.

downboots 2y ago
Instead of connecting language with physical existence, or entities, it's connecting tokens. An LLM may be able to describe scenes in a video, but a model would tell you that said video is a deep fake because of some principle like conservation of energy and mass informed by experience, assumptions, inference rules, etc.

eterevsky 2y ago

This article seems to argues from the way scientific discoveries are made by humans. It seems to me that its gist is similar to some article from the 80s that claims that computers will never play good chess, or an article from the 2000s that claims the same for go. The general shape of these arguments is: "Playing chess/go well, or making scientific discoveries requires specific way of strategic thinking or the ability to form the right hypotheses. Computers don't do this, ergo they won't be able to play chess or make scientific discoveries". I don't think this is a very good frame of reasoning. A scientific question can take one of the following shapes: - (Mathematical) Here's a mathematical statement. Prove either it or its negation. - (Fundamental natural science) Here're the results of the observations. What are the simplest possible model that explains all of them? - (Engineering) We need to do X. What's an efficient way of doing it? All of these questions could be solved in a "human" way, but it also possible to train AIs to approach them without going through the same process as the human scientists.

deleted 2y ago
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sweezyjeezy 2y ago
> but it also possible to train AIs to approach them without going through the same process as the human scientists With chess the answer was more or less completely brute force the problem space, but will that work with math / science? Is there a way to widely explore the problem space with AI, especially in a way that goes above or even against the contents of it's training data? I don't know the answer, but that seems to be the crucial question here.

smougel 2y ago

The current culture about AI & LLMs is that we are "only" memorizing the Web into model parameters and that a LLM is unable to "invent" new paradigms. Maybe we are under estimating what Unsupervised Learning & RL could provide. Re-inforcement learning is about exploring and finding new ways to accomplish a task and I see no limit here (except the computational resources needed).

jaxr 2y ago

The most useful way to leverage LLMs for me has been as "content fillers". I'm a software engineer, and work with a rather large code base. Some parts are rarely touched, and loading the context into my brain whenever I need to go back to them requires quite a bit of effort. I found that asking cursor/Claude to suggest how to make the required chande rarely comes up with the right solution but usually points me in the right direction and is enough to help me load the context up. Similarly with my side projects, which typically involves knowledge that I don't use in my day to day.

ongytenes 2y ago

I think discoveries by AI would be due to pattern matching. Like finding overlooked cancer markers that can be used for an earlier prognosis. The genius of Einstein and his thought experiments may elude an AI. It may take an AI designed on some other future model other than an LLM to "compress a century" He called it wishful thinking. I believe the hype over AI is due to attempts to justify the enormous investments going into AI development has created an echo chamber.

seanhunter 2y ago
For one thing, how do we know that all discoveries are not pattern matching? In "The Act of Creation" for example, Arthur Koestler proposes the idea that all creativity is essentially the act of finding connections between diverse frames of reference and that extraordinary/genius-level creativity might just be that you can spot connections between even weirder and more diverse frames of reference than the regular creative person might be able to. I am certain there is a self-reinforcing hype cycle around LLMs specifically at the moment, but AI progress is definitely gathering pace and starting to get to the point where it is impacting normal people to the extent that hasn't been seen since the dot com boom. So the people making investments are for sure stampeding to pour in capital so as not to miss out on the big winners from this change.

msabalau 2y ago

It doesn't seem correct to dismiss the creativity of Move 37 because real originality is "something more fundamental, like inventing the rules of Go itself" It would seem more fruitful to simply point out that LLMs aren't all of AI, and that excelling at mimicking human-like text production isn't really doing the work that AlphaGo was attempting. Just because both things might be given as (different) examples of deep reinforcement learning in an AI survey course doesn't mean that we have much reason to believe that the vast investments in LLMs result in AlphaGo like achievements.

Nesco 2y ago
Modern LRMs do have some tiny degrees of intelligence

wegfawefgawefg 2y ago

creativity is possibly just random noise in feature space. at best it could be random noise in a feature space of a thing modeling its own thought trajectory.

captainclam 2y ago
Crucially, this doesn't just require noise but it requires "taste." I tend to fall back on music creation as an example of this notion. Lots of innovation in music is experimentation/exploration of "noise," (not necessarily literal white noise) but requires the ear of a discerning musician who ultimately goes "Ooh! I liked that" or passes a "generated sample" by. This is where I wonder if LLMs can ever innovate. I'm not sure they can develop "taste" for things outside of their distribution. However, I could just as easily be convinced that humans can't either, and sophisticated "taste" is just the exploration of obscure regions of the combinatorial space generated from previously observed samples!

EigenLord 2y ago

I think the author has a point. LLMs struggle with what you might call epistemically constructive novelty. It's the ability not just to synthesize existing knowledge, but to identify what's missing and conjecture something to fill the gap and demonstrate it to satisfaction. Out-of-distribution knowledge gaps are typically where LLMs "hallucinate." Unlike highly skilled human researchers, they don't pause and construct the bridge that will get them from known to unknown, they just immediately rush to fill in the blank with whatever sounds most plausible. They need to ask questions that haven't been asked before, or answer ones that haven't been answered. Is this just some missing subroutine that we'll eventually figure out? Or is this conjecture-proving process much more elaborate than whatever existing models, no matter how scaled, can manage? I'm not sure. But the answer starts with a question.

omnee 2y ago

I agree that any system claiming general intelligence must be able to form and modify a model of the world. A fundamental part of this is being able to ask counterfactual questions on its own understanding or knowledge of the world. The history of science is full of countless such examples. As of right now I'm not aware of any LLMs or indeed any AI system being able to do so.

_cs2017_ 2y ago

I wonder if people could just write their blogs posts in a short form: claim, argument in favor, counter-argument; consequences (this is optional). Like this whole blog post could be: Claim: Current AI is unlikely to usher in an era of dramatically accelerated scientific discovery. Argument in favor: A genius does not come to life when you linearly extrapolate a top-10% student. Newton or Einstein is not just scaled-up good students. To create an Einstein, we need a system can ask questions nobody else has thought of or dared to ask. One that writes 'What if everyone is wrong about this?' when all textbooks, experts, and common knowledge suggest otherwise. Existing benchmarks don't test such skills. And existing systems are likely hopelessly far from this capability (based on the author's personal feelings). Counter-argument: none. Consequences: obvious.

nahanniSpirit 2y ago

Can someone use AI to model einsteins (Ulam's) spiral. I believe its related to emergent properties from seemingly random data. It even 3 dimensionally models our galaxy...