Jeff Dean

"One of the things that I think is really important today in the field of machine learning research, that we’ll need to overcome, is…right now, when we want to build a machine learning system for a particular task we tend to have a human machine learning expert involved in that. So, we have some data, we have some computation capability, and then we have a human machine learning expert sit down and decide: Okay, we want to solve this problem, this is the way we’re going to go about it roughly. And then we have the system that can learn from observations that are provided to it, how to accomplish that task. That’s sort of what generally works, and that’s driving a huge number of really interesting things in the world today. And you know this is why computer vision has made such great strides in the last five years. This is why speech recognition works much better. This is why machine translation now works much, much better than it did a year or two ago. So that’s hugely important. But the problem with that is you’re building these narrowly defined systems that can do one thing and do it extremely well, or do a handful of things. And what we really want is a system that can do a hundred thousand things, and then when the hundred thousand-and-first thing comes along that it’s never seen before, we want it to learn from its experience to be able to apply the experience it’s gotten in solving the first hundred thousand things to be able to quickly learn how to do thing hundred thousand-and-one"

"If you can’t understand what’s in information then it’s going to be very difficult to organize it."

"The portion of evolution in which animals developed eyes was a big development. Now computers have eyes."

"I think there’s a lot of promise there, because I do see a path for agents with the right training process to eventually be able to do many, many things in the virtual computer environment that humans can do today. You know, right now they can sort of do some things, but not most things. But the path for increasing the capability there is reasonably clear. You get more reinforcement learning going, you have more agent experience that it can learn from. You have early nascent products that can do some things, but not most things, but are still incredibly useful for people."