| ▲ | user_7832 13 hours ago |
| > I love that we're still learning the emergent properties of LLMs! TBH, this is (very much my opinion btw) the least surprising thing. LLMs (and especially their emergent properties) are still black boxes. Humans have been studying the human brain for millenia, and we are barely better at predicting how humans work (or for eg to what extent free will is a thing). Hell, emergent properties of traffic was not understood or properly given attention to, even when a researcher, as a driver, knows what a driver does. Right now, on the front page, is this post: > 14. Claude Code Found a Linux Vulnerability Hidden for 23 Years (mtlynch.io) So it's pretty cool we're learning new things about LLMs, sure, but it's barely surprising that we're still learning it. (Sorry, mini grumpy man rant over. I just wish we knew more of the world but I know that's not realistic.) |
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| ▲ | AlphaAndOmega0 12 hours ago | parent | next [-] |
| I'm a psychiatry resident who finds LLM research fascinating because of how strongly it reminds me of our efforts to understand the human brain/mind. I dare say that in some ways, we understand LLMs better than humans, or at least the interpretability tools are now superior. Awkward place to be, but an interesting one. |
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| ▲ | p1esk 11 hours ago | parent | next [-] | | LLMs are orders of magnitude simpler than brains, and we literally designed them from scratch. Also, we have full control over their operation and we can trace every signal. Are you surprised we understand them better than brains? | | |
| ▲ | jeremyjh 9 hours ago | parent | next [-] | | We've been studying brains a lot longer. LLMs are grown, not built. The part that is designed are the low-level architecture - but what it builds from that is incomprehensible and unplanned. | |
| ▲ | danielmarkbruce 9 hours ago | parent | prev | next [-] | | "Designed" is a bit strong. We "literally" couldn't design programs to do the interesting things LLMs can do. So we gave a giant for loop a bunch of data and a bunch of parameterized math functions and just kept updating the parameters until we got something we liked.... even on the architecture (ie, what math functions) people are just trying stuff and seeing if it works. | | |
| ▲ | batshit_beaver 9 hours ago | parent [-] | | > We "literally" couldn't design programs to do the interesting things LLMs can do. That's a bit of an overstatement. The entire field of ML is aimed at problems where deterministic code would work just fine, but the amount of cases it would need to cover is too large to be practical (note, this has nothing to do with the impossibility of its design) AND there's a sufficient corpus of data that allows plausible enough models to be trained. So we accept the occasionally questionable precision of ML models over the huge time and money costs of engineering these kinds of systems the traditional way. LLMs are no different. | | |
| ▲ | danielmarkbruce 7 hours ago | parent | next [-] | | Saying ML is a field where deterministic code would work just fine conveniently leaves out the difficult part - writing the actual code.... Which we haven't been able to do for most of the tasks at hand. What you are saying is fantasy nonsense. | | |
| ▲ | astrange 5 hours ago | parent [-] | | They did not leave it out. > but the amount of cases it would need to cover is too large to be practical (note, this has nothing to do with the impossibility of its design) |
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| ▲ | yunnpp 7 hours ago | parent | prev | next [-] | | > would work just fine, but the amount of cases it would need to cover is too large to be practical So it doesn't work. | |
| ▲ | idiotsecant 6 hours ago | parent | prev | next [-] | | And all you have to do is write an infinite amount of code to cover all possible permutations of reality! No big deal, really. | |
| ▲ | growpdifjkl 6 hours ago | parent | prev [-] | | > That's a bit of an overstatement. The GP said, "I'm a psychiatry resident". The entire industry is propped up by misinformed people burping up the CEO farts they are sucking. | | |
| ▲ | AlphaAndOmega0 5 hours ago | parent [-] | | I'm a psychiatry resident who has been into ML since... at least 2017. I even contemplated leaving medicine for it in 2022 and studied for that, before realizing that I'd never become employable (because I could already tell the models were getting faster than I am). You would be sorely mistaken to think I'm utterly uninformed about LLM-research, even if I would never dare to claim to be a domain expert. |
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| ▲ | ctoth 7 hours ago | parent | prev [-] | | > Also, we have full control over their operation and we can trace every signal. Are you surprised we understand them better than brains? Very, monsieur Laplace. |
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| ▲ | 6 hours ago | parent | prev | next [-] | | [deleted] | |
| ▲ | evilduck 8 hours ago | parent | prev [-] | | To be fair to your field, that advancement seems expected, no? We can do things to LLMs that we can't ethically or practically do to humans. | | |
| ▲ | AlphaAndOmega0 5 hours ago | parent [-] | | I'm still impressed by the progress in interpretability, I remember being quite pessimistic that we'd achieve even what we have today (and I recall that being the consensus in ML researchers at the time). In other words, while capabilities have advanced at about the pace I expected from the GPT-2/3 days, mechanistic interpretability has advanced even faster than I'd hoped for (in some ways, we are very far from completely understanding the ways LLMs work). |
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| ▲ | bensyverson 13 hours ago | parent | prev | next [-] |
| Learning about the emergent properties of these black boxes is not surprising, but it's also not daily. I think every new insight is worth celebrating. |
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| ▲ | user_7832 11 hours ago | parent | next [-] | | Oh I very much agree that it's great to see more research and findings and improvements in this field. I'm just a little puzzled by GP's tone (which suggested that it isn't completely expected to find new things about LLMs, a few years in). | | |
| ▲ | bensyverson 10 hours ago | parent [-] | | I'm the GP! lol… Not sure how you got that from my tone, but I find these discoveries expected but not routine, and also interesting. |
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| ▲ | TeMPOraL 12 hours ago | parent | prev [-] | | Indeed. For me, it's also a good reminder that AI is here to stay as technology, that the hype and investment bubble don't actually matter (well, except to those that care about AI as investment vehicle, of which I'm not one). Even if all funding dried out today, even if all AI companies shut down tomorrow, and there are no more models being trained - we've barely begun exploring how to properly use the ones we have. We have tons of low-hanging fruits across all fields of science and engineering to be picked, in form of different ways to apply and chain the models we have, different ways to interact with them, etc. - enough to fuel a good decade of continued progress in everything. | | |
| ▲ | bathtub365 12 hours ago | parent [-] | | AI has been here to stay for decades | | |
| ▲ | TeMPOraL 12 hours ago | parent [-] | | Maybe, but you couldn't tell that these days, casually scrolling this or any other tech-oriented discussion board. | | |
| ▲ | ethin 11 hours ago | parent [-] | | I mean... You could? AI comes in all kinds of forms. It's been around practically since Eliza. What is (not) here to stay are the techbros who think every problem can be solved with LLMs. I imagine that once the bubble bursts and the LLM hype is gone, AI will go back to exactly what it was before ChatGPT came along. After all, IMO it's quite true that the AIs nobody talks about are the AIs that are actually doing good or interesting things. All of those AIs have been pushed to the backseat because LLMs have taken the driver and passenger seats, but the AIs working on cures for cancer (assuming we don't already have said cure and it just isn't profitable enough to talk about/market) for example are still being advanced. | | |
| ▲ | darkwater 9 hours ago | parent [-] | | Saying that LLMs will disappear once the financial hype desinflate is like saying that LLMs are the answer to everything. |
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| ▲ | amelius 12 hours ago | parent | prev | next [-] |
| Studies of LLMs belong in their own field of science, just like psychology is not being studied in the physics department. |
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| ▲ | guelo 6 hours ago | parent | next [-] | | ¸That field is called Machine Learning. | |
| ▲ | osigurdson 6 hours ago | parent | prev | next [-] | | That is a very interesting thought! | |
| ▲ | littlestymaar 10 hours ago | parent | prev | next [-] | | Interestingly enough, for a while physics used to be studied by philosophers (and used to be put in the natural philosophy basket, together with biology and most other hard sciences). | |
| ▲ | zer00eyz 12 hours ago | parent | prev [-] | | The intersection of physics isnt psychology it is philosophy, and the same is true (at present) with LLM's Much as Diogenes mocked Platos definition of a man with a plucked chicken, LLM's revealed what "real" ai would require: contigous learning. That isnt to diminish the power of LLM's (the are useful) but that limitation is a fairly hard one to over come if true AGI is your goal. | | |
| ▲ | andai 9 hours ago | parent | next [-] | | Is it because we haven't invented something better than backpropagation yet? From what I understand, a living neural network learns several orders of magnitude more efficiently than an artificial one. I'm not sure where that difference comes from. But my brain probably isn't doing back propagation, it's probably doing something very different. | | |
| ▲ | astrange 5 hours ago | parent [-] | | Your brain is doing several different things, because there are different parts of your brain. (eg different kinds of learning for long-term memory, short-term memory, languages, faces and reflexes.) |
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| ▲ | quantummagic 8 hours ago | parent | prev | next [-] | | What is "contigous" learning, and why is it a hard requirement of AGI? | |
| ▲ | amelius 12 hours ago | parent | prev [-] | | What do you mean by the intersection of physics? The intersection of what with physics? | | |
| ▲ | zer00eyz 10 hours ago | parent [-] | | The intersection of disciplines. Sir Roger Penrose, on quantum consciousness (and there is some regret on his part here) -- OR -- Jacob Barandes for a much more current thinking on this sort of intersectional exploratory thinking. |
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| ▲ | Invictus0 12 hours ago | parent | prev [-] |
| To say we've been studying the brain for millennia is an extreme exaggeration. Modern neuroscience is only about 50 years old. |
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| ▲ | user_7832 10 hours ago | parent | next [-] | | I hate to "umm, akshually" but apparently we have been studying the brain for thousands of years. I wasn't talking about purely modern neuroscience (which ironically for our topic of emergence, (often till recently/still in most places) treats the brain as the sum of its parts - be them neurons or neurotransmitters). > The earliest reference to the brain occurs in the Edwin Smith Surgical Papyrus, written in the 17th century BC. I was actually thinking of ancient greeks when writing my comment, but I suppose Egyptians have even older records than them. From https://en.wikipedia.org/wiki/History_of_neuroscience | | |
| ▲ | Invictus0 6 hours ago | parent [-] | | None of that counts as studying the brain. It's like saying rubbing sticks together to make fire counts as studying atomic energy. Those early "researchers" were hopelessly far away from even the most tangential understanding of the workings of the brain. |
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| ▲ | timcobb 12 hours ago | parent | prev [-] | | I came here to say this :) |
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