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Comment by adampwells | Hacker News Reader

I think they make a category error by putting ChatGPT etc in the General column. As far as I can tell we only have narrow definitions of 'intelligence' and ChatGPT falls into one of those. I don't know of a general agreement on what 'General Intelligence' is in people, so how can we categorise anything is AGI? Knowing a bit about how ChatGPT works I feel it is a lot more like a chess program than a human.

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ChatGPT is the most general system ever created and packaged. You can throw arbitrary problems at it and get half-decent solutions for most of them. It can summarize and expand text, translate both explicitly and internally[0], play games, plan, code, transcode, draw, cook, rhyme, solve riddles and challenges, do basic reasoning, and many, many other things. Whether one is leaning more towards "stochastic parrot" or more towards "sparks of AGI" - it's undeniable that it's a general system.

[0] - The whole "fine-tune LLM on a super-specific task but only in language X (which is not English), its performance for that task improves in languages other than X" part, indicating it's not just learning tokens, but the meanings behind them too.

>it's undeniable that it's a general system.

it's very deniable, as Yann Lecun correctly pointed out, it can't even walk up a set of stairs (https://twitter.com/ylecun/status/1721648856260050970).

It can't cook, it can talk about cooking. It wouldn't be able to get a pan out of a drawer. I know all we do these days is produce text tokens on the internet, but it is in fact in itself a domain specific task. If you can talk about opening a can of beans you're an LLM. If you can do that and actually open the physical can we may be a little bit further towards general intelligence.

We don't even have a full self driving system, the limited systems we have are not LLMs, and there isn't even a system on the Horizon that can drive and talk to you about the news and cook you a dinner.

If that was a valid criticism of its intelligence, Stephen Hawking would have spent most of life categorised as a vegetable.

Also:

> We don't even have a full self driving system,

debatable given the accident rate of the systems we do have

> the limited systems we have are not LLMs,

they tautologically are LLMs

> and there isn't even a system on the Horizon that can drive and talk to you about the news and cook you a dinner

There's at least four cooking robots in use, and that's just narrow AI and used to show off. Here's one from 14 years back: https://youtu.be/nv7VUqPE8AE

And 5 years back: https://youtu.be/CAJJbMs0tos

And two years back: https://youtu.be/fNpBDwYLi-Q

And (uploaded) this year: https://youtu.be/r5GHWRhpzlw

There's also a few research models for general household robotics from Google and whichever of Musk's companies is doing that robot of his.

And here's another general learning-from-demonstrations system from Cambridge university from this year: https://youtu.be/EiIAN03MsRM

Stephen Hawking lost the capacity to move because his ALS paralyzed him, not because his brain lacked the capacity to do so, come on this has to be the worst analogy of the year. Also no, driving systems are not LLMs. LLMs are large language models, no existing self driving system runs on a language model. And also, that's not what the word "tautology" means. "All bachelors are unmarried" is a tautology.

https://wayve.ai/thinking/lingo-natural-language-autonomous-...

Ah, you wrote unclearly, it sounded like you were asserting that no system was an LLM rather than no driving system.

So while your claim is still false, I will accept that it isn't tautologically so.

Likewise, I am demonstrating that the actual definition you're using here is poor due to the consequence of it ruling out Stephen Hawking, and that goal means that the reason why he couldn't do things is unimportant: you still ruled him out with your standard.

Transformer models are surprisingly capable in multiple domains, so although ChatGPT hasn't got relatively many examples of labeled motor control input/output sequences and corresponding feedback values, this was my first search result for "llm robot control": https://github.com/GT-RIPL/Awesome-LLM-Robotics (note several are mentioning specifically ChatGPT).

There are millions of people who cannot do any of those things, never could and will never be able to.

At any rate, language models are in fact able to do those things.

https://tidybot.cs.princeton.edu/

https://deepmind.google/discover/blog/rt-2-new-model-transla...

https://wayve.ai/thinking/lingo-natural-language-autonomous-...

That's not a category error. GPT is general. It is able to perform many tasks (Creative Writing, playing Chess, Poker and other games, language translation, Code, robot piloting) etc

But not make toast. That's a general task that very nearly any human (intelligent or otherwise) can do.

If we define "general" as tasks with text input and output we are severely restricting the domain.

There are millions of humans who can't make toast and will never be able to.

At any rate, no it's not really limiting.

https://tidybot.cs.princeton.edu/

https://deepmind.google/discover/blog/rt-2-new-model-transla...

https://wayve.ai/thinking/lingo-natural-language-autonomous-...

Arguments like yours are why I regard "generality" (certainly in the context of AGI) as a continuum rather than a boolean. AlphaZero is more general than AlphaGo Zero, as the former can do three games and the latter only one. All LLMs are much more general than those game playing models, even if they aren't so wildly superhuman on any specific skill, and `gpt-4-vision-preview` is more general than any 3.5 model as 4 can take image inputs while the 3.5's can't.

Yes. If you read "Computing Machinery and Intelligence" this idea of generality being a continuum is a point that Turing makes actually (albeit in different words). What constitutes generality of an AI is really going to be very sensitive to your metric and the assessment is going to vary a lot from observer to observer.

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