I remember reading ages ago in Scientific American about a much more interesting (and useful) AI application of this technique.
Genetic algorithms were used to evolve new, more efficient variants of existing electronic circuits. I dug it up - it was: https://www.scientificamerican.com/magazine/sa/2003/02-01/#a...
Article "Evolving inventions". I have no idea if there is an open-access version anywhere.
As far as I remember, that approach led to some patents, because some of the inventions were better than existing solutions. One of the examples in the article was a low-pass filter (I dont remember if AI version was actually better or worse than human-made).
The essential element of this approach was that in electronics (as in go) there exist a well defined set of rules, that allows researchers to build a simulation engine with optimization/evaluation function that the AI targets by itself, without supervision. It's great to see that this approach is still alive, although in my humble opinion, application in electronics is much more interesting than Go.
Genetic algorithms were used to evolve new, more efficient variants of existing electronic circuits. I dug it up - it was: https://www.scientificamerican.com/magazine/sa/2003/02-01/#a... Article "Evolving inventions". I have no idea if there is an open-access version anywhere.
As far as I remember, that approach led to some patents, because some of the inventions were better than existing solutions. One of the examples in the article was a low-pass filter (I dont remember if AI version was actually better or worse than human-made).
The essential element of this approach was that in electronics (as in go) there exist a well defined set of rules, that allows researchers to build a simulation engine with optimization/evaluation function that the AI targets by itself, without supervision. It's great to see that this approach is still alive, although in my humble opinion, application in electronics is much more interesting than Go.