> Looking at it solely on rate of learning has LLMs way smarter than humans already which doesn't seem right to say
Sure, but "rate" also has two meanings, both useful, but importantly different: per unit of wall-clock time, and per example.
Transistors are just so much faster than synapses, that computers can (somewhat) compensate for being absolutely terrible by the latter meaning — at least, in cases where there's enough examples for them to learn from.
In cases where the supply of examples is too small (and cannot be enhanced with synthetic data, simulations and so on), state of the art AI models still suck. In cases where there is sufficient data, for example self-play in games of chess and go, the AI can be super-human by a substantial margin.
Sure, but "rate" also has two meanings, both useful, but importantly different: per unit of wall-clock time, and per example.
Transistors are just so much faster than synapses, that computers can (somewhat) compensate for being absolutely terrible by the latter meaning — at least, in cases where there's enough examples for them to learn from.
In cases where the supply of examples is too small (and cannot be enhanced with synthetic data, simulations and so on), state of the art AI models still suck. In cases where there is sufficient data, for example self-play in games of chess and go, the AI can be super-human by a substantial margin.