Humans seem to solve problems with a combination of learned stock knowledge, induction, and constrained improvisation.
Constrained improvisation is the most interesting part of that process, and the one we know least about.
It's one thing building a system that asymptotically improves over millions of trials, and then sending out a press release claiming your system is as smart as a human.
It's another building a system that learns a domain as efficiently as a human.
Compare the relatively small number of games played/analysed by a Go master on their way to master status, compared with the number of simulated games played/analysed by AlphaGo.
ML is still a rather naive form of constrained brute forcing. It's a long way short of efficient learning.
> ML is still a rather naive form of constrained brute forcing. It's a long way short of efficient learning.
Uh... Bayesian can do the above at in term of expert domain. They call it elicitation in the Bayesian world.
I think you're overall generalizing all learning techniques. And also it's not like we actually really know how the human brain learn. Psychology is a field with huge uncertainties and you can see that in their research papers with correlation values. So the concept of learning may be out dated and/or we are still learning about what makes us learn.
You're describing a technique for finding patterns. There are many possible techniques and machines can (probably) use any of them. The constraint would be if the technique involved kinetic or quantum mechanics.
Humans seem to solve problems with a combination of learned stock knowledge, induction, and constrained improvisation.
Constrained improvisation is the most interesting part of that process, and the one we know least about.
It's one thing building a system that asymptotically improves over millions of trials, and then sending out a press release claiming your system is as smart as a human.
It's another building a system that learns a domain as efficiently as a human.
Compare the relatively small number of games played/analysed by a Go master on their way to master status, compared with the number of simulated games played/analysed by AlphaGo.
ML is still a rather naive form of constrained brute forcing. It's a long way short of efficient learning.