So what you're saying is, LLMs are good enough to do something that humans are already capable of doing, in a timeframe that a human would be reasonably capable of doing it in, and its unrealistic to believe that LLMs will ever be able to do something truly superhuman. Got it :+1:

Being able to do “stuff a human is capable of doing” used to be the definition of “artificial intelligence” and until very recently it was seen as a dream that may never happen. And it hasn't completely happened yet BTW, there are still plenty of trivial stuff LLM can't do just because there's no available training data for that. Also their ability to do “reasoning” or few-shot-learning is overhyped (even if impressive).

If your definition of AI has become “superhuman intelligence” then it's definitely moving goalposts. And regaarding my initial remark, AI isn't going to do “faster than the speed of light” MPEG decoding ever, all physical limits apply to it.

> AI isn't going to do “faster than the speed of light” MPEG decoding ever, all physical limits apply to it.

This simply isn't a good faith take, because you're straw-manning the implementation of the query that the original poster put forward. They aren't asserting that the AI would need to do supernatural super-real time decoding of MPEG encoded files. What if the AI had already seen them? And was able to encode in the typically-compressed way LLMs do the information it needs to answer questions like that without re-decoding the original movies?

This raises many valid questions on topics like the structuring of data within an LLM, how large LLMs may eventually become, what systems should orbit around the LLM (does it make more sense for LLMs to watch YouTube videos, or have already watched YouTube videos?).

My definition of AI is the same definition that Nick Bostrom talks about in his 2014 book Superintelligence. There's no moving goalposts. Goal posts have been set in cement since 2014. Achieving human-level parity has obviously only been a "goal" insomuch as its a 10 millisecond stop on the gradient toward superintelligence. OpenAI is not worth $150 billion dollars because it purports to be building a human-and-nothing-more in a box.

> This simply isn't a good faith take, because you're straw-manning the implementation of the query that the original poster put forward. They aren't asserting that the AI would need to do supernatural super-real time decoding of MPEG encoded files.

No, they literally said the AI would watch every frame on demand:

> I expect an "AI" to be capable pf obtaining every movie, watching them frame-by-frame, and getting an accurate count.

Talk about bad faith.

> What if the AI had already seen them? And was able to encode in the typically-compressed way LLMs do the information it needs to answer questions like that

LLM are encoding (in a very lossy way) “important” details, that's what allow them to compress their knowledge in little amount of space with respect to the input. But if you're asking completely random questions like this there's no way an LLM will contain such an info, because storing all the random trivia like that is going to be wasting an enormous amount of space.

> There's no moving goalposts. Goal posts have been set in cement since 2014.

Wait until you realize that AI is something much older than 2014… Also, note how the book you're quoting isn't called “artificial intelligence”.

> OpenAI is not worth $150 billion dollars because it purports to be building a human-and-nothing-more in a box.

And yet there are many companies with much higher valuation with goals much more mundane than this. OpenAI has a hundred billion dollar valuation because investors believe it can make money, not matter what it technologically achieves in order to do so.