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Earlier quoted context omitted.
It's pretty easy, just follow the second example with the colors from the test input. (if it's the same puzzle 00576224 for you too)
https://arcprize.org/play?task=00576224 Yes the same puzzle. And I followed the second example. This was my solution: GRG OBO RGR B is the cyan like blue color. My solution looks right, but it says it’s wrong.
This is super cool. I share Francois' intuition that the presently data-hungry learning paradigm is not only not generalizable but unsustainable: humans do not need 10,000 examples to tell the difference between cats and dogs, and the main reason computers can today is because we have millions of examples. As a result, it may be hard to transfer knowledge to more esoteric domains where data is expensive, rare, and ha…
What about Theory of Mind which talks about the problem of multiple agents in the real world acting together? Like driving a car cannot be done right now without oodles of data or any robot - human problem that requires the robot to model human's goals and intentions.
I think the problem is definition of general intelligence: Intelligence in the context of what? How much effort(kwh, $$ etc) is the human willing to amortize over the learning cycle of a machine to teach it what it needs to do and how that relates to a personally needed outcome( like build me a sandwich or construct a house)? Hopefully this should decrease over time.
I believe the answer is that the only intelligence that really matters is Human-AI cooperative intelligence and our goals and whether a machine understands them. The problems then need to be framed as optimization of a multi attribute goal with the attribute weights adjusted as one learns from the human.
I know a few labs working on this, one is in ASU(Kambhampati, Rao et. al) and possibly Google and now maybe open ai.
Any details on how these tests were created? I.e. which kind of program was used for generation.
Recently Michael Hodel has reverse engineered 400 of the tasks, so more tasks can be generated. Interestingly it can generate python programs that solves the tasks too.
This is super cool. I share Francois' intuition that the presently data-hungry learning paradigm is not only not generalizable but unsustainable: humans do not need 10,000 examples to tell the difference between cats and dogs, and the main reason computers can today is because we have millions of examples. As a result, it may be hard to transfer knowledge to more esoteric domains where data is expensive, rare, and ha…
> humans do not need 10,000 examples to tell the difference between cats and dogs, I swear, not enough people have kids. Now, is it 10k examples? No, but I think it was on the order of hundreds, if not thousands. One thing kids do is they'll ask for confirmation of their guess. You'll be reading a book you've read 50 times before and the kid will stop you, point at a dog in the book, and ask "dog?" And there is a dev…
Of course for a human this can either mean "I have an idea about what a dog is, but I'm not sure whether this is one" or it can mean "Hey this is a... one of those, what's the word for it again?"
This is super cool. I share Francois' intuition that the presently data-hungry learning paradigm is not only not generalizable but unsustainable: humans do not need 10,000 examples to tell the difference between cats and dogs, and the main reason computers can today is because we have millions of examples. As a result, it may be hard to transfer knowledge to more esoteric domains where data is expensive, rare, and ha…
> humans do not need 10,000 examples to tell the difference between cats and dogs, I swear, not enough people have kids. Now, is it 10k examples? No, but I think it was on the order of hundreds, if not thousands. One thing kids do is they'll ask for confirmation of their guess. You'll be reading a book you've read 50 times before and the kid will stop you, point at a dog in the book, and ask "dog?" And there is a dev…
I have kids so I'm presuming I'm allowed to have an opinion here.
This is ignoring the fact that babies are not just learning labels, they're learning the whole of language, motion planning, sensory processing, etc.
Once they have the basics down concept acquisition time shrinks rapidly and kids can easily learn their new favorite animal in as little as a single example.
Compare this to LLMs which can one-shot certain tasks, but only if they have essentially already memorized enough information to know about that task. It gives the illusion that these models are learning like children do, when in reality they are not even entirely capable of learning novel concepts.
Beyond just learning a new animal, humans are able to learn entirely new systems of reasoning in surprisingly few examples (though it does take quite a bit of time to process them). How many homework questions did your entire calc 1 class have? I'm guessing less than 100 and (hopefully) you successfully learned differential calculus.
Earlier quoted context omitted.
>: humans do not need 10,000 examples to tell the difference between cats and dogs well, maybe. We view things in three dimensions at high fidelity: viewing a single dog or cat actually ends up being thousands of training samples, no?
Yes, but we do not call a couch in a leopard print a leopard. Because we understand that the print is secondary to the function.
Francois Chollet: OpenAI has set back the progress towards AGI by 5-10 years - https://news.ycombinator.com/item?id=40652818 - June 2024 (5 comments)
This is super cool. I share Francois' intuition that the presently data-hungry learning paradigm is not only not generalizable but unsustainable: humans do not need 10,000 examples to tell the difference between cats and dogs, and the main reason computers can today is because we have millions of examples. As a result, it may be hard to transfer knowledge to more esoteric domains where data is expensive, rare, and ha…
Humans, I would bet, could distinguish between two animals they've never seen based only on a loose or tangential description. I.e. "A dog hunts animals by tracking and chasing them long enough to exhaust their energy, but a cat is opportunistic and strikes using stealth and agility." A human that has never seen a dog or a cat could probably determine which is which based on looking at the two animals and their adapt…