Comment by thereitgoes456
10 hours ago
AI cannot explain chess moves it comes up with in an elegant way. What makes you think it will be able to do so for math?
10 hours ago
AI cannot explain chess moves it comes up with in an elegant way. What makes you think it will be able to do so for math?
It's kind of astonishing that after all we have seen in the last years people still find the position that AI will not be able to do an obviously valuable thing likely and it requiring an explanation (instead of the other way around).
No, this is different, and this is coming from someone who has been studying deep learning for the last decade. We are talking about the difference between RLHF and RLVR strategies. The former benefits clarity and explanation, while the latter concerns only correctness. AI was moving in a particularly damaging direction by pushing on the first path, so it was natural to move to the second. But the second will come at the cost of clarity of explanation. It will likely get better at its explanations, but not fast enough to render its most advanced accomplishments readily understandable to the user. The chess example is a pretty good one (that is an RLVR approach).
The problem is that people strongly believe that this is an insurmountable problem that will persist indefinitely (or for a long time) and plan accordingly, while this, most likely, will be fixed soon by adding RLCAF (RL on conversational agent feedback) or something like that.
tbf GM explaining their 2700 elo moves are only understandable when vague, as elo goes up explanation becomes closer to "in this specific position there's these dpecific lines", why should 3500 elo moves have simple reasoning?
Maybe if we start with giving simple AI generated analysis of those clumsy humans with their measly 2700 elo moves
Some of us still remember 2016, when we had a couple of cars sorta half-driving themselves, and Tesla, Uber and others promised we were only one year or two away from three million people in the US working as drivers being out of a job. And here we are, a decade later. AI is pretty amazing, but companies have a tendency to severely and comically overestimate and oversell it's capabilities, and underestimate the challenges.
> And here we are, a decade later.
With Waymo and Tesla increasingly doing what they said they would do, and a small number of early adopters happily paying money for their services, that do work.
So what's the critique? That the timelines are not correct? Sure. And how about the timeline of the people who said "research level math, never in my lifetime" and the people inside the ai companies who are apparently increasingly spooked by how quick the progress is? How about the various levels of code/programming jobs that AI was supposedly never going to be able to do, but, in reality, now just does?
We are engaging in some very one-sided discrediting, and I am not sure, why.
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I remember a biologist wryly commenting that it took a lot longer to evolve good senses and appendages in nature than to evolve human intelligence, compared to the ape niveau.
Maybe the really hard thing isn't abstract cognitive capability, but perception+movement.
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Driving a car is unimaginably more difficult than proving the Riemann hypothesis.
You just don’t notice that because evolution has given you 99% of what is needed to drive a car before you were even born.
Interesting claim. That's true for old models that simply have no way to explain, LLMs however can. [1]
[1] https://dev.to/natcher/researchers-develop-method-to-train-l...