[SnazzyJeff]

IMO a chatbot is sufficient to demonstrate AGI. I think the chinese room problem is as good as anything.

Of course, there isn't much money in teaching a bot that only knows english chinese.

EDIT, Wikipedia page for context: https://en.wikipedia.org/wiki/Chinese_room

[ActorNightly]

We can probably all agree than an AGI should be able to form questions, or more generally seek out information that it needs to figure out the answer in some form and way.

Not only are there no LLMs in existence today can do this without explicit action mapping, but the mechanism for storing that piece of information would rely on doing a large number of training runs for transfer learning to retain that information, and we humans don't actually work like that.

[adrianN]

> we humans don't actually work like that

That is probably not a good criterion to decide whether something is intelligent or not.

[CuriouslyC]

People like to shit on the Turing test, but if you step back from the subjective judgement angle, and instead imagine that the person performing the Turing test is a scientist trying to collect evidence that the agent that it is communicating with is _NOT_ intelligent/human, it is actually still very relevant. Tools like statistical analysis of output and responses to jailbreak prompts and recursive/self referential prompts designed to confuse machines and generate emotional responses from humans could be used to generate probability of human/not human in a much more rigorous way.

[skybrian]

The actual Turing Test is a party game like Werewolf. If the humans are skilled then they should be able to authenticate by picking a subject that the AI can’t compete on. This would be a very difficult game to build a computer opponent for and nobody really tries.

[krisoft]

> picking a subject that the AI can’t compete on

Isn’t that the point? If there are no more such subjects then the AI reached humans level cognition.

[skybrian]

Yes, but people underestimate what that involves. Serious players would study previous games for weaknesses, looking for subject areas that include unpublished knowledge that isn’t in the training set.

Casual talk about “passing the Turing Test” sets a much lower bar.

[kylebenzle]

Try, "ChatGPT, what do you think about this song"...

LLMs do not constitute "AI" let alone the more rigorous AGI. They are a GREAT statistical parlor trick for people that don't understand statistics though.

[JoshCole]

> LLMs do not constitute "AI" let alone the more rigorous AGI.

I have a textbook, "Artificial Intelligence: A Modern Approach," which covers Language Models in Chapter 23 (page 824) and the Transformer architecture in the following chapter. In any field technical terms emerge to avoid ambiguity. Laymen often adopt less accurate definitions from popular culture. LLMs do qualify as AI, even if not according to the oversimplified "AI" some laymen refer to.

It has been argued for the last several decades that every advance which was an AI advance according to AI researchers and AI textbooks was not in fact AI. This is because the laymen have a stupid definition of what constitutes an AI. It isn't because the field hasn't made any progress, but instead because people outside the field lack the sophistication to make coherent statements when discussing the field because their definitions are incoherent nonsense derived from fiction.

> They are a GREAT statistical parlor trick for people that don't understand statistics though.

The people who believe that LLMs constitute AI in a formal sense of the word aren't statistically illiterate. AIMA covers statistics extensively: chapter 12 is on Quantifying Uncertainty, 13 on Probabilistic Reasoning, 14 on Probabilistic Reasoning Over Time, 15 on Probabilistic Programming, and 20 on Learning Probabilistic Models.

Notably, in some of these chapters probability is proven to be optimal and sensible; far from being a parlor trick it can be shown with mathematical rigor that failing to abide by its strictures is not optimal. The ontological commitments of probability theory are quite reasonable; they're the same commitments logic makes. That we model accordingly isn't a parlor trick, but a reasonable and rational choice with ledger arguments proving that failing to do so would lead to regret.

[fragmede]

You're going to have to spell it out for me. I asked ChatGPT 4 about a random song and it gave me a decent description.

https://chat.openai.com/share/71d438d7-d1f5-4f0f-9b63-8b5dd6...

[notahacker]

I suspect that part of the OP's point was that ChatGPT happily parrots the aggregate critical opinion as its "thoughts" despite never having heard the song (or parsed its MP3 file or stems or sheet music)

If you ask me what I think of a song I've never heard, I'm general enough to want to listen to it...

[jpnc]

Giving a description of a song is not the same as saying what you think about it.

[krisoft]

So? Do you think we can’t make an LLM which picks a favourite song and writes about it with the gusto a person does? What is this example supposed to illustrate?

[jpnc]

We clearly can, but not with gusto. LLM can't feel gusto about anything. If the point is that it doesn't matter as long as a person reading it is convinced there is gusto, then my point is that the 'opinion' of such a thing is irrelevant.

[famouswaffles]

This is circular reasoning. LLMs can't feel gusto because they can't feel gusto. You have any way to measure "gusto" that we're all aware of ?

If other humans ascribing the quality of something you can't properly define isn't enough then you clearly don't care about what a LLM does or does not have, only what you are convinced it doesn't have.

[yladiz]

Maybe, but 1. the point isn’t to describe, but to explain an abstract and potentially novel idea (thought) on the song, and 2. we can’t train this kind of thing in a generic way right now.

[SnazzyJeff]

LLMs are not capable of passing the chinese room test, fairly trivially.