I definitely think that any physical process can be framed as optimization. Optimization not necessarily generates knowledge useful for other problems.
I like Schmidhuber's definition of intelligence as compression efficacy. In case you are not familiar, according to his theory of artificial curiosity the more one can compress data, the more one can understand it. Example of increase in compression efficacy: 1. raw observations of planetary orbits 2. geocentric Ptolemaic epicycles 3. heliocentric ellipses 4. Newtonian mechanics 5. general relativity 6.? Under this view, compression of data is understanding, improvement of compressor is learning, progress of improvement is intrinsic reward. To learn as fast as possible about a piece of data, one should decrease as rapidly as possible the number of bits one need to compress that data. If one can choose which data to observe or create, one should interact with environment in a way to obtain data that maximizes the decrease in bits — the compression progress — of everything already known. Here, understanding, learning and interacting with environment are framed as optimization problems.
In case of living beings intelligence might be less like optimization/search problems from CS and more like optimization of functionals from physics.
I definitely think that any physical process can be framed as optimization. Optimization not necessarily generates knowledge useful for other problems.
I like Schmidhuber's definition of intelligence as compression efficacy. In case you are not familiar, according to his theory of artificial curiosity the more one can compress data, the more one can understand it. Example of increase in compression efficacy: 1. raw observations of planetary orbits 2. geocentric Ptolemaic epicycles 3. heliocentric ellipses 4. Newtonian mechanics 5. general relativity 6.? Under this view, compression of data is understanding, improvement of compressor is learning, progress of improvement is intrinsic reward. To learn as fast as possible about a piece of data, one should decrease as rapidly as possible the number of bits one need to compress that data. If one can choose which data to observe or create, one should interact with environment in a way to obtain data that maximizes the decrease in bits — the compression progress — of everything already known. Here, understanding, learning and interacting with environment are framed as optimization problems.
In case of living beings intelligence might be less like optimization/search problems from CS and more like optimization of functionals from physics.