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Similar to how Cypher puts it: I know this is “just” next token inference, matrix mult and just software, ie there’s no “intelligence” there BUT, looking at thi
by Jun8 2mo ago
Similar to how Cypher puts it: I know this is “just” next token inference, matrix mult and just software, ie there’s no “intelligence” there BUT, looking at this convo … damn!
The fascinating this is that the LLM is not acting as a tool here AFAIk, but very much like a colleague.
I have no knowledge of the domain and have only PhD EE level math knowledge, so maybe my bar is too low.
- IshKebab 2mo ago> there’s no “intelligence” there BUT There is clearly intelligence there. We have no way to recognise intelligence other than the appearance of intelligence and this very clearly displays that. It's also quite clearly different to human intelligence in some notable ways, but not in any that preclude describing it as intelligent. At least for normal non-pedantic definitions of the word.
- majormajor 2mo agoEveryone uses "intelligence" to mean something slightly different, so for this to be a useful claim to make or refute we need to come up with new, intentionally-pedantic, terms (or new domain-specific definitions for vague existing ones).
- contextfree 2mo agoYes, trying to communicate (or watching others try to communicate) about these topics is incredibly frustrating because it's pretty much impossible to make any progress without interrogating people's different definitions, but nobody wants to do that because it would mean being pedantic, splitting hairs, etc.
- kadoban 2mo agoIt's not like this is a new problem. Turing had a definition most of a century ago, he wasn't the first and certainly wasn't the last. I don't think we need new terms necessarily, and I doubt we're all going to agree on a definition tomorrow.
- Scarblac 2mo agoAt any rate, if the AI's side in this conversation were a human, that would be an extremely intelligent human indeed. But there's no way the thinking times would have been that short, of course.
- nozzlegear 2mo agoThat's not clear at all. What's clear is that this is a very smart man who knows how to use this tool well.
- squidbeak 2mo agoI'd say an entity capable of instructing one of the leading mathematicians of his era is pretty clearly intelligent by any reasonable measure - however it might be arriving at its output.
- nozzlegear 2mo agoI think we have wildly different conclusions about what happened here. You see the machine as instructing Terrence Tao, as if it were Plato teaching Socrates about the theory of forms; I see Terrence Tao using the machine to teach himself, like an intelligent student uses a book. In this case, it's just a book that fools us into believing it can think and reason like we do, because it generates language in much the same way we do when we think and reason.
- 2snakes 2mo agoYeah. Artificial knowledge not artificial intelligence.
- slidehero 2mo ago> In this case, it's just a book that fools us into believing it can think and reason like we do that's some Harry Potter kind of "writes itself" book. at this point, for me, any comment about LLMs that begins with "it's just ..." is hard to take seriously. Terrence is impressed. Good enough for me.
- nozzlegear 2mo ago> that's some Harry Potter kind of "writes itself" book. I'm sure people thought calculators and, indeed, computers themselves were very Harry Potter as well when they first came out. But in the fullness of time the magic and mystique has drained away, and we're left with the understanding that they're just tools. > at this point, for me, any comment about LLMs that begins with "it's just ..." is hard to take seriously. Similarly I have a hard time taking seriously the people who make breathless claims of intelligence where there's only a text calculator with weights applied. It's like watching the devout cry "miracle!" at every strangely shaped piece of toast. Terrence is impressed, but he's not a believer.
- Diogenesian 2mo ago[flagged]
- anthonypasq 2mo ago> If AI researchers cared about scientific thinking, they would be intensely focused on the brains of bees. Basically every academic AI researcher in history was doing what you described. The AI industrialists stopped caring 6 years ago once they realized LLMs seem to have been the only thing in 80 years that actually seems to work at any useful level. There are plenty of pioneering scientists who are either returning to actual AI research (Yann Lecun, Ilya, etc), and plenty who never left (Richard Sutton) who are doing exactly what you are talking about.
- Diogenesian 2mo ago> Basically every academic AI researcher in history was doing what you described. That is not true. Alan Turing did not view things that way, his test would say that a dog has zero intelligence. Neither did any of the MIT Lispers. And neither do Lecun or Sutskever or Sutton! They are all focused on human intelligence. None of them are even slightly concerned about an AI which is intelligent before it learns any language. > the only thing in 80 years that actually seems to work at any useful level This isn't true either! Mathematica / Maple / etc are "old-fashioned AI" and they obviously work. The Lisp expert systems were also useful, though less so than an LLM.
- anthonypasq 2mo ago> And neither do Lecun or Sutskever or Sutton! They are all focused on human intelligence. None of them are even slightly concerned about an AI which is intelligent before it learns any language. ??? https://www.youtube.com/watch?v=GvibIstOn_E https://www.youtube.com/watch?v=GvibIstOn_E his arguemtn here is clearly built around using some sort of sensory data to build a model of the world like humans (animals) do. also you clearly decline to mention Lecun who has made this point ad-infinitum > This isn't true either! Mathematica / Maple / etc are "old-fashioned AI" and they obviously work. The Lisp expert systems were also useful, though less so than an LLM. i personally find it very strange that non-deep learning AI approaches which essentially boiled down to a giant bundle of if statements, or some very simple statistical modeling were called AI in the first place.
- superloika 2mo agoThere is no intelligence. If anything, this just shows that natural language and mathematics are both fields which are structured in a logically computable way. And if you have a machine that can compute symbolic logic, you can process both natural language and mathematics. A second corollary is that rational consciousness and thought is less likely to be contained in language than previously thought, because if language is so simple that a machine can process it, it can't contain consciousness.
- ben_w 2mo agoIf natural language was structured in a logically computable way, we'd have had interesting chatbots by the late 80s, basically as soon as a dictionary fit in local RAM, and for the same reason we got compilers. Da hole raisin y nat-lang be v. hard is dat i kan rite lik dis an it be cool 4 native engrish speekrs 2 unerstand. LLMs are of course fine with this sentence in exactly the way that Zork's engine couldn't be.
- superloika 2mo agoThe underlying structure of language, which is grammar, is obviously logical. That the symbols used to represent this grammar can be sometimes fuzzy or ambiguous, is no problem for a machine that takes context and probability into account when translating words to the underlying grammar structure.
- ben_w 2mo agoIt's not "obviously logical", it's a pattern which we mimic to avoid mockery. example For, semi-randomise I word order can this like, Yoda worse than, and be understood. > is no problem for a machine that takes context and probability into account when translating words to the underlying grammar structure. We had to invent Transformers to be able to do that with reliability anything close to being worth caring about. Transformers have to learn from examples, not be pre-programmed.
- EnergyAmy 2mo agoThe idea that grammar is all it takes to process natural language is absolute beans.
- thechao 2mo agoI'm no intelligence researcher or philosopher; but, I think LLMs make us confront the (IMO, now clear) distinction between cleverness (intuition), reasoning (rational argument), and consciousness. I suspect that we think of "intelligence" as either of the first two welded to the latter. In that vein, I'd say that consciousness may be just another emotion: happiness, sadness, egoness.
- JCattheATM 2mo ago> consciousness may be just another emotion: happiness, sadness, egoness. It's clearly much more than that.
- the8472 2mo agoThat argument says very little, emergent behavior is a thing in complex systems with billions of parts. Humans can also be reduced to voltage potentials propagating along of tubes of fat and synapses getting rewired.
- tetha 2mo agoI think the "But this is not intelligence because it is known math" is not a correct argument. It is unknown how the overall higher intelligence of humans works. What I do notice however is that LLMs are becoming capable of doing an increasing part of the intellectual work I can do, and usually a lot faster. Just today I presented an agent framework that can take an informal incident statement and propose infrastructure changes to fix it, all evidence backed. This did nothing I could not to, but it did all 5 test cases in 6 - 12 minutes each. I would have found all of the monitoring indications it did, but it would have taken me a day per test case. The LLM also included sass to silly tickets. ("This is not even worth spending monitoring resources on. It's obviously a configuration problem.") That's how this is reading to me as well. It's just fast at slogging through a certain level of "simple" transformations.
- School-Cotton 2mo agoWhat does "predicting the next token" mean? I ask this every time people say "LLMs are just predicting the next token" and it's maddening that nobody can give a straight answer. Predicting it according to what probability distribution? Every process that produces a sequence of actions (including e.g. a human writing) can be modeled by some probability distribution and therefore their actions are indistinguishable from "predicting the next token" emitted by that distribution.
- the8472 2mo agoYeah that's pretty much what gwern argues here[0]. Or to adapt another proverb: to predict the next token you first need to model the universe. [0] https://gwern.net/scaling-hypothesis#gwern-difference--efficient-natural-languages https://gwern.net/scaling-hypothesis#gwern-difference--effic...
- School-Cotton 2mo ago> to predict the next token you first need to model the universe Exactly. The "most likely next" series of tokens, for example, when given the first half of a correct mathematical proof, is the correct rest of the proof. I have never seen anyone define "most likely next token" in such a way that this isn't true.
- efebarlas 2mo agoi think people say that thinking that only training to produce the next likely word would end up producing some local minimum word that generally fits but doesn't actually lead to intelligent thought. that feels like a misunderstanding of how the loss function behaves when used within a sequence
- My_Name 2mo agoIt's like saying that thinking cannot generate new knowledge because all thought is just rearranging the information we get from our senses, or memory of previous information from our senses, and we do nothing more than figure out the most likely word to say next in a conversation. Either humans are not capable of intelligence or computers are capable of becoming intelligent. Neither or both.
- peheje 2mo ago[flagged]