In my wayward youthformal education, I studied numerical optimization, controls systems, the science of decision-making, and related things, and so some part of me was always irked by the focus on utility functions and issues with them; take this early comment of mine and the resulting thread as an example. So I was very pleased to see a post that touches on the difference between the approaches and the resulting intuitions bringing it more into the thinking of the AIAF.
That said, I also think I’ve become more confused about what sorts of inferences we can draw from internal structure to external behavior, when there are Church-Turing-like reasons to think that a robot built with mental strategy X can emulate a robot built with mental strategy Y, and both psychology and practical machine learning systems look like complicated pyramids built out of simple nonlinearities that can approximate general functions (but with different simplicity priors, and thus efficiencies). This sort of distinction doesn’t seem particularly useful to me from the perspective of constraining our expectations, while it does seem useful for expanding them. [That is, the range of future possibilities seems broader than one would expect if they only thought in terms of selection, or only thought in terms of control.]
In my
wayward youthformal education, I studied numerical optimization, controls systems, the science of decision-making, and related things, and so some part of me was always irked by the focus on utility functions and issues with them; take this early comment of mine and the resulting thread as an example. So I was very pleased to see a post that touches on the difference between the approaches and the resulting intuitions bringing it more into the thinking of the AIAF.That said, I also think I’ve become more confused about what sorts of inferences we can draw from internal structure to external behavior, when there are Church-Turing-like reasons to think that a robot built with mental strategy X can emulate a robot built with mental strategy Y, and both psychology and practical machine learning systems look like complicated pyramids built out of simple nonlinearities that can approximate general functions (but with different simplicity priors, and thus efficiencies). This sort of distinction doesn’t seem particularly useful to me from the perspective of constraining our expectations, while it does seem useful for expanding them. [That is, the range of future possibilities seems broader than one would expect if they only thought in terms of selection, or only thought in terms of control.]