My implied point is that the line between hard math and easy math for humans is rather arbitrary, drawn mostly by evolution. AI is designed, not evolved, so the line between hard and easy for AI is based on the algorithm complexity and processing power, not on millions of years of trying to catch a prey or reach a fruit.
I’m not sure I agree with that. Currently most progress in AI is with neural networks, which are very similar to human brains. Not exactly the same, but they have very similar strengths and weaknesses.
We may not be bad at things because we didn’t evolve to do them. They might just be limits of our type of intelligence. NNs are good at big messy analog pattern matching, and bad at other things like doing lots of addition or solving chess boards.
They might just be limits of our type of intelligence. NNs are good at big messy analog pattern matching, and bad at other things like doing lots of addition or solving chess boards.
That could be true, we don’t know enough about the issue. But interfacing a regular computer with a NN should be a… how should I put it… no-brainer?
My implied point is that the line between hard math and easy math for humans is rather arbitrary, drawn mostly by evolution. AI is designed, not evolved, so the line between hard and easy for AI is based on the algorithm complexity and processing power, not on millions of years of trying to catch a prey or reach a fruit.
I’m not sure I agree with that. Currently most progress in AI is with neural networks, which are very similar to human brains. Not exactly the same, but they have very similar strengths and weaknesses.
We may not be bad at things because we didn’t evolve to do them. They might just be limits of our type of intelligence. NNs are good at big messy analog pattern matching, and bad at other things like doing lots of addition or solving chess boards.
That could be true, we don’t know enough about the issue. But interfacing a regular computer with a NN should be a… how should I put it… no-brainer?