Leaders in the Industry
Opinion by the leaders/ People to follow
Last updated
Opinion by the leaders/ People to follow
Last updated
(A **co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning)**
"I think we need to consider the hard challenges of AI and not be satisfied with short-term, incremental advances. I’m not saying I want to forget deep learning. On the contrary, I want to build on it. But we need to be able to extend it to do things like reasoning, learning causality, and exploring the world in order to learn and acquire information." -MIT Review
"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." - MIT Review
(Awarded with the Turing Award in 2011)
Judea Pearl says AI can’t be truly intelligent until it has a rich understanding of cause and effect, which would enable the introspection that is at the core of cognition.
(Director, Causal Artificial Intelligence Lab CausalAI Laboratory)
(Director of the Stanford Artificial Intelligence Lab)
I believe that today’s machine-learning and AI tools won’t be enough to bring about real AI. “It’s not just going to be data-rich deep learning,” she says. Li believes AI researchers will need to think about things like emotional and social intelligence. -
(Scientist, Author, Entrepreneur)
"In particular, we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets — often using an approach known as deep learning — and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space and causality"
