I was thinking about what "actual" AI would be for me and it would be something that could answer questions like "tell me every time Nicolas Cage has blinked while on camera in one of his movies".
Sure, that is a contrived question, but I expect an "AI" to be capable pf obtaining every movie, watching them frame-by-frame, and getting an accurate count. All in a few seconds.
Current models (any LLM) cannot do that and I do not see a path for them to ever do that at a reasonable cost.
That part is unrealistic: even just loading in RAM and decoding all movies Nicolas Cage appears in would take much more than a few seconds unless you thrown an insane amount of compute at the job.
That being said, the current LLM tech is probably enough to help you implement a program that parses IMDB to get the list of all Nicolas Cage movie, then download it on thepiratebay and then implement the blink count you're looking for. And you'd likely get the result in just a couple hours.
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.
I agree. My example for something “AI” should be able to do is to create a CAD model for the Empire State Building or the Parthenon based on known facts and photos.
I don’t think these are “moving the goalposts” examples, they are things that an actual intelligence capable of passing a PhD physics exam should be able to do.
My point is that you could, given enough time and all the information available to you online about these well-documented buildings. You could learn CAD and figure out a reasonable way to output a 3D model, because you can think and reason spatially. The current batch of AI tools can regurgitate complex facts, but they can't actually think in 3D like an being that spends its life navigating physical spaces.
Maybe I'm wrong and we are well on our way to AI tools for this, but right now if I tell any of the current generation of image models to do something like "rotate object 70 degrees, tilt camera down 20 degrees and re-render" then what comes out is never even approximately close.
Sure, that is a contrived question, but I expect an "AI" to be capable pf obtaining every movie, watching them frame-by-frame, and getting an accurate count. All in a few seconds.
Current models (any LLM) cannot do that and I do not see a path for them to ever do that at a reasonable cost.