I still don't get how achieving 96% on some benchmark means it's a super genius but that last 4% is somehow still out of reach. The people who constantly compare robots to people should really ponder how a person who manages to achieve 90% on some advanced math benchmark still misses that last 10% somehow.
This feels like a maybe interesting position, but I don’t really follow what you mean. Is it possible to just state it directly? Asking us to ponder is sort of vague.
These math LLMs seem very different from humans. A person has a specialty. A LLM that was as skilled as, say, a middling PhD recipient (not superhuman), but also was that skilled in literally every field, maybe somebody could argue that’s superhuman (“smarter” than any one human). By this standard a room full of people or an academic journal could also be seen as superhuman. Which is not unreasonable, communication is our superpower.
Yeah - it's interesting where the edge is. In theory, an llm trained in everything should be more ready to make cross-field connections. But doing that well requires certain kind of translation and problem selection work which is hard even for humans. (I would even say, beyond PhD level - knowing which problem is with throwing PhD students at is the domain of professors... And many of them are bad at it, as well.)
On the human side, mathematical silos reduce our ability to notice opportunities for cross-silo applications. There should be lots of opportunity available.
do you think Terence Tao can solve any math problem in the world that is solvable by another matematician?
Humans have heuristic biases, and intuition often doesn't succeed with the unknown.
https://en.wikipedia.org/wiki/List_of_cognitive_biases
LLM are good at search, but plagiarism is not "AI".
Leonhard Euler discovered many things by simply trying proofs everyone knew was impossible at the time. Additionally, folks like Isaac Newton and Gottfried Leibniz simply invented new approaches to solve general problems.
The folks that assume LLM are "AI"... also are biased to turn a blind eye to clear isomorphic plagiarism in the models. Note too, LLM activation capping only reduces aberrant offshoots from the expected reasoning models behavioral vector (it can never be trusted.) Thus, will spew nonsense when faced with some unknown domain search space.
Most exams do not have ambiguous or unknown contexts in the answer key, and a machine should score 100% matching documented solutions without fail. However, LLM would also require >75% of our galaxy energy output to reach 1 human level intelligence error rates in general.
YC has too many true believers with "AI" hype, and it is really disturbing. =3
https://www.youtube.com/watch?v=X6WHBO_Qc-Q
> However, LLM would also require >75% of our galaxy energy output to reach 1 human level intelligence error rates in general.
citation needed
The activation capping effect on LLM behavior is available in this paper:
https://www.anthropic.com/research/assistant-axis
The estimated energy consumption versus error rate is likely projected from agent test and hidden-agent coverage.
You are correct, in that such a big number likely includes large errors itself given models change daily. =3
ok, your quote was over generalized, you meant "current LLM need..." and not "any conceivable LLM"
although the word "energy" does not appear on that page, not sure where you get the galaxy energy consumption from
In general, "any conceivable LLM" was the metric based on current energy usage trends within the known data-centers peak loads (likely much higher due to municipal NDA.) A straw-man argument on whether it is asymptotic or not is irrelevant with numbers that large. For example, 75% of a our galaxy energy output... now only needing 40% total output... does not correct a core model design problem.
LLM are not "AI", and unlikely ever will be due to that cost... but Neuromorphic computing is a more interesting area of study. =3
Humans also spew nonsense when faced with some unknown domain search space
Indeed, the list of human cognitive biases was posted above.
The activation capping effect on LLM behavior is available in this paper:
https://www.anthropic.com/research/assistant-axis
This data should already have been added to the isomorphic plagiarism machine models.
Some seem to want to bury this thread, but I think you are hilarious. =3
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