In a lot of these areas, from machine translation to search quality, you're always trying to balance what you can do computationally with each query.
Vision I think is going to be an important input. Like, if you're using Google Glass, it's going to be able to look around and read all the text on signs and do background lookups on additional information and serve that. That will be pretty exciting.
We're happy to work with military or other government agencies in ways that are consistent with our principles. So if we want to help improve the safety of Coast Guard personnel, that's the kind of thing we'd be happy to work on.
I am concerned in general about carbon emissions and machine learning.
In order to reason, you need a network to be able to bring in knowledge from several different areas, such as math, science, and philosophy, to reach reasonable conclusions on what it's been tasked with.
Reinforcement learning is the idea of being able to assign credit or blame to all the actions you took along the way while you were getting that reward signal.
If you pass a lot of data through a teeny network, like 20 neurons, it'll do what it can, but it's not going to be very good.
Traditionally computers have not been that good at interacting with people in ways that people feel natural interacting with.
People in my organization were very outspoken about what we should be doing with the Department of Defense. One of them is work on autonomous weapons. That, to me, is something I don't want to work on or have anything to do with.
One of the things that inspires me about working for Google is that when we solve a problem here, we can get that used by one million or even a billion people. That is very motivating as a computer scientist.
I worry policymakers are not putting enough attention on what we should be planning for 10 years down the road. In general, governments aren't necessarily that good at looking down the road when it is a difficult issue.
Deep learning is a really powerful metaphor for learning about the world.
We have way more unsupervised data in the world than supervised data.
With TensorFlow, when we started to develop it, we kind of looked at ourselves and said: 'Hey, maybe we should open source this.'
It's pretty clear that machine learning is going to a big part of science and engineering.
Definitely there's growing use of machine learning across Google products, both data-center-based services, but also much more of our stuff is running on device on the phone.
There's nothing like necessity of needing to do something to cause you to come up with abstractions that help you break through the forms.
One thing I think is true is that is you have someone who's really good in one or a few areas they can pick up something new pretty quickly and that's kind of a hallmark of someone you really want to hire because they can be very useful in a whole bunch of different areas.
I like working in small teams where people on the team have very different skills than what I have and that banter back and forth, and the ability to build something collectively that none of you could do individually is actually a really useful and valuable thing.
Machine learning is a new way of creating problem solving.
