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I'm not a scientist but I like to LARP as one in my free time, and I have found ChatGPT/Claude extremely useful for research, and I'd go as far as to say it supercharged it for me.

When I'm learning about a new subject, I'll ask Claude to give me five papers that are relevant to what I'm learning about. Often three of the papers are either irrelevant or kind of shit, but that leaves 2/5 of them that are actually useful. Then from those papers, I'll ask Claude to give me a "dependency graph" by recursing on the citations, and then I start bottom-up.

This was game-changing for me. Reading advanced papers can be really hard for a variety of reasons, but one big one can simply be because you don't know the terminology and vernacular that the paper writers are using. Sometimes you can reasonably infer it from context, but sometimes I infer incorrectly, or simply have to skip over a section because I don't understand it. By working from the "lowest common denominator" of papers first, it generally makes the entire process easier.

I was already doing this to some extent prior to LLMs, as in I would get to a spot I didn't really understand, jump to a relevant citation, and recurse until I got to an understanding, but that was kind of a pain in the ass, so having a nice pretty graph for me makes it considerably easier for me to read and understand more papers.

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One heuristic I used during my masters degree research thesis was to look for the seminal people or papers in a field by using google scholar to find the most cited research papers and then reading everything else by that author / looking at the paper's references for others. You often only need to go back 3-4 papers to find some really seminal/foundational stuff.
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Yeah, that's actually how I discovered Leslie Lamport like ten years ago. I was looking for papers on distributed consensus, and it's hard not to come across Paxos when doing that. It turns out that he has oodles of really great papers across a lot of different cool things in computer science and I feel like I understand a lot more about this space because of it.

It doesn't hurt that Lamport is exceptionally good at explaining things in plain language compared to a lot of other computer scientists.

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I absolutely believe that AI will supercharge science.

I do not believe it will replace humans.

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I absolutely believe that AI will supercharge science and replace humans.

Why shouldn't it? Humans are poorly optimized for almost anything, and built on a substrate that's barely hanging together

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I’d give humans some credit, they’re an adaptable bunch. AI won’t replace humans in the same way humans did not replace cockroaches. It’s a non-sequitur.
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We generally don’t allow cockroaches to thrive in the spaces we claim for ourselves. Question is how much space (economic or otherwise) will AI claim for itself and whether there will be any left for us.
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> Humans are poorly optimized for almost anything, and built on a substrate that's barely hanging together

Goodness gracious!

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Well, for starters AI doesn't have goals. If there was a super intelligence with goals, why would they work for us?
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Fwiw if you trained an LLM in an RL sandbox that would require it to have goals, the output llm probably would "have goals"
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Not like large language models, which only required tens of megawatts of power and use highly efficient monte carlo methods, eh
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Individual humans are processing nodes on human culture as a whole, which runs on rather more than tens of megawatts.
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Also it costs a lot to train and run individual humans, and they can only be run for brief periods per day before they crash, hallucinate and possibly get irretrievably broken.
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replace, no. obsolete, yes
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lol

(That's the first time I used that expression on HN.)

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It's a very complicated matter honestly. This is a new height that AI has reached, even though it follows the usual methods of success that it has had.

What strikes me as unusual though is that they do make a point of saying things like "this is a general purpose model that wasn't trained on the problem" among a few other things as if that's new. The last bountied problem they accomplished used a public model that ALSO didn't rely on specialized training. And that didn't make their blog.

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Not only it supercharged science it supercharges scientist. Research on any narrow topic is a different world now. Agents can read 50 papers for you and tell you what's where. This was impossible with pure text search. Looking up non-trivial stuff and having complex things explained to you is also amazing. I mean they don't even have to be complex, but can be for adjacent field where these are basics from the other field but happen to be useful in yours. The list goes on. It's a hammer you need to watch your fingers, it's not good at cutting wood, but it's definitely worth having.
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It's a very heavy hammer. I used it in the way you describe and after double-checking noticed some crucial details were missed and certain facts were subtly misrepresented.

But I agree with you, especially in areas where they have a lot of training data, they can be very useful and save tons of time.

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I don't think there's a substitute for reading the source material. You have to read the actual paper that's cited. You have to read the code that's being sourced/generated. But used as a reasoning search engine, it's a huge enabler. I mean so much of research literally is reasoning through piles of existing research. There's probably a large amount of good research (especially the kind that don't easily get grant funding) that can "easily" shake out through existing literature that humans just haven't been able to synthesize correctly.
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Isn’t that a joke? It already has supercharged science
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Since "supercharged science" is as ill-defined as AGI, ASI, etc., people will be able to debate it endlessly for no reason.
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Where are the second order effects of this supercharging of science? Or has it not been enough time for those to propagate?
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It will notice things that humans may have missed. That said - it can only work off the body of work SOMEONE did in the past.
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> it can only work off the body of work SOMEONE did in the past.

And so do humans. Gotta stand on these shoulders of giants.

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Can't the previous body of work be from AI too?
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Of course it can be, but it's overeager. No matter what your context window is, we will use AI collectively to flood the zone with shit.
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To be strict, Math is not Science.

But AI is supercharging Math like there is no tomorrow.

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LLM's? I doubt it. Systems with Prolog, Common Lisp and the like with proof solvers? For sure.

LLM's are doomed to fail. By design. You can't fix them. It's how do they work.

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You can have a word with Terrence Tao, he had different opinions here
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