Mastodon Feed
Boosted by cwebber@social.coop ("Christine Lemmer-Webber"):
cwebber@social.coop ("Christine Lemmer-Webber") wrote:
@mntmn I talk a bit about it in this talk https://www.youtube.com/watch?v=jI8gA68OXLM
But really it's using machine learning models PLUS symbolic reasoning systems (prolog, propagators, etc), which can help make them more reasonable for cheaper.
Think of the human brain as having two modes of thinking: a fast, gut thinking approach, and a slower reasoning approach. All the machine learning model research has been about beating the former into as powerful a shape as possible by throwing tons of compute at it. But we also have knowledge of how to do symbolic reasoning. And we can make things a lot better by combining them.
Leilani Gilpin's research is very interesting in this regard https://people.ucsc.edu/~lgilpin/
In particular, I recommend her dissertation which asks: if a car drives off the side of the road, how do we hold it accountable? https://people.ucsc.edu/~lgilpin/publication/dissertation/