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Ie, the skills aren't particularly complicated in principle, but the conditions needed to acquire them aren't widely available, so the pool of people with the skills is limited.
I wonder if the comparison is actually original.
The sorts of useful analogies I was mostly talking about are those that appear in scientific research involving actionable technical details. Eg, diffusion models came about when folks with a background in statistical physics saw some connections between the math for variational autoencoders and the math for non-equilibrium thermodynamics. Guided by this connection, they decided to train models to generate data by learning to invert a diffusion process that gradually transforms complexly structured data into a much simpler distribution -- in this case, a basic multidimensional Gaussian.
I feel like these sorts of technical analogies are harder to stumble on than more common "linguistic" analogies. The latter can be useful tools for thinking, but tend to require some post-hoc interpretation and hand waving before they produce any actionable insight. The former are more direct bridges between domains that allow direct transfer of knowledge about one class of problems to another.
I have to disagree because the distinction between "superficial similarities" and genuinely "useful" analogies is pretty clearly one of degree. Spend enough time and effort asking even a low-intelligence AI about "dumb" similarities, and it'll eventually hit a new and perhaps "useful" analogy simply as a matter of luck. This becomes even easier if you can provide the AI with a lot of "context" input, which is something that models have been improving at. But either way it's not superintelligent or superhuman, just part of the general 'wild' weirdness of AI's as a whole.
I think you're basically agreeing with me. Ie, current models are not superintelligent. Even though they can "think" super fast, they don't pass a minimum bar of producing novel and useful connections between domains without significant human intervention. And, our evaluation of their abilities is clouded by the way in which their intelligence differs from our own.
Similarly, "deeper insight" may be surfaced occasionally simply by making a low-intelligence AI 'think' for longer, but this is not something you can count on under any circumstances, which is what you may well expect from something that's claimed to be "super intelligent".
In general, I agree that these models are in some sense extremely knowledgeable, which suggests they are ripe for producing productive analogies if only we can figure out what they're missing compared to human-style thinking. Part of what makes it difficult to evaluate the abilities of these models is that they are wildly superhuman in some ways and quite dumb in others.
The most successful folks tend to mix talent and hard work with a bit of luck in terms of early gold striking to gain a quick boost of credibility that helps them draw other people into their fold (eg, grad students in a big lab) who can handle a lot of the metric maxxing to free up some (still not enough) time for more ambitious thinking.
Why wouldn't it be? If the world is ingressed via video sensors and lidar sensor, what's the hangup in recording such input and then replaying it faster?