[Jun8]

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.

[tetha]

I 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.

[the8472]

That 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.

[IshKebab]

> 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]

Everyone 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).

[Scarblac]

At 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.

[contextfree]

Yes, 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]

It'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.

[thechao]

I'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]

> consciousness may be just another emotion: happiness, sadness, egoness.

It's clearly much more than that.

[nozzlegear]

That'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]

I'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]

I 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.

[slidehero]

> 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]

> 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.

[2snakes]

Yeah. Artificial knowledge not artificial intelligence.

[superloika]

There 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]

If 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]

The 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]

It'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]

The idea that grammar is all it takes to process natural language is absolute beans.

[AgentME]

That's just defining intelligence or consciousness as whatever we specifically can't put in a machine. It's not a very useful definition. (And it's one that completely breaks down to nothing if we do manage to fully put these things into machines or somehow prove it's possible.)

[School-Cotton]

What 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]

Yeah 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--effic...

[School-Cotton]

> 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.

[My_Name]

It'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.

[efebarlas]

i 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