Computers have been far better than the best human at chess for well over ten years. It’s weird to see “Stockfish (2023)” cited here.

I need to read the full paper now, but going from just the table you posted, I see a problem with their "Narrow" classification, in that it doesn't correct for task characteristics and obscurity - which means the "Narrow" rating isn't useful without also giving the specific task to which it applies, and overall it feels off.

I mean, they put GPT-4 ("SOTA LLMs") for a subset of tasks at Level 2N, a spell checker at Level 3N, and chess and Go solvers at Levels 4N and 5N. It feels to me that any useful grading scheme would give the opposite ordering. Sure, the current classification meets their definitions - "outperforms X% humans" - but at least with chess and Go, the rating is dominated entirely by the fact that very few people play these games at non-joke level, and the activity itself is super specific, and super narrowly scoped, mathematically speaking. Feels almost like calling CPUs Level 5N at "adding numbers together", since they clearly outperform 100% of humans at that. Or, much less jokingly, one could convincingly argue a PID controller is Level 5 Narrow, because not only it's going to be outperforming 100% of humans at its task, it's also learning and adapting (within its operational envelope).

Computer Chess and Go is so far ahead of human ability now that it doesn't matter how many humans decide to take up the game seriously. I see your broader point but it doesn't really matter here.

Yes what is designated a task is a fair bit arbitrary but game playing AI's have been popularly designated narrow (well basically anything besides LLMs are narrow) for a long time now.

Sure. My point is that this classification is useful only within its category. Say I have a specific problem, like "play chess" or "play StarCraft" or "do maths" or "spellcheck essays". It would be great to have a task-specific list of algorithms ranked by their "Narrow" levels. It would help with use cases like: "oh, model X here spellchecks at level 5N, but is super complex, meanwhile algorithm Y is only Level 3N, but it fits on a napkin; Y is more than sufficient for my MVP". But this rating doesn't let us compare between tasks. That's fine by itself, but then it doesn't make sense to use the same rating for both specialized and general algorithms.

I don't even understand what the "AI" means in "narrow" AI.

Why is a compiler at Level 0 rather than Level 5?

Compilers aren't thought of as being intelligent. That's why they're level 0

What's intelligence though? GOFAI (level 1) is just logic and search, which are properties of modern optimizing compilers as well.

Even Deep Blue (level 4) is just logic and search.

Yes what's defined as intelligent is a fair bit arbitrary.

It's a start.

I think "general" needs to be more than a boolean though: The standard "wide range of non-physical tasks, including metacognitive abilities like learning new skills" is met by ChatGPT (as in the chart), which is much better in some languages than others, and at some tasks than others.

But how wide does it have to be to count as "wide"? A brain upload of any human ought to count, but most of us have much less breadth than any LLM.

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.

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.

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[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, and definitely not one that can chat with you about the news.

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

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.

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.

> to what percentage of skilled human workers it can replace

Any levels of insanity that makes people believe such statements?

Here’s an article by the Financial Times demonstrating its real-world impact today: https://archive.is/hlcL2

More capable systems will have a larger impact still.

Paid marketing content isn’t really relevant. Jobs are being lost left and right due to the overall economy. The only valid takeaway is that BCG consultants are such low quality that a chat bot can improve their productivity. I’d avoid such consultants, but that’s already known.

Additionally, chatgpt is the running joke - full of mistakes and misinformation. Probably appealing to that market segment that’s easily gullible and falls for whatever the running conspiracy theory is popular at the time.