we need it to do things like reasoning, learning causality, and exploring the world in order to learn and acquire information…If you have a good causal model of the world you are dealing with, you can generalize even in unfamiliar situations. #ai https://t.co/TmPZeYKfZC
— Phil & Pam Lawson (@SocializingAI) November 17, 2018
You mention causality—in other words, grasping not just patterns in data but why something happens. Why is that important, and why is it so hard?
If you have a good causal model of the world you are dealing with, you can generalize even in unfamiliar situations. That’s crucial. We humans are able to project ourselves into situations that are very different from our day-to-day experience. Machines are not, because they don’t have these causal models.
We can hand-craft them, but that’s not enough. We need machines that can discover causal models. To some extent it’s never going to be perfect. We don’t have a perfect causal model of the reality; that’s why we make a lot of mistakes. But we are much better off at doing this than other animals.
Right now, we don’t really have good algorithms for this, but I think if enough people work at it and consider it important, we will make advances.
