Quotes from Jeff Dean

It's nice to have short-term to medium-term things that we can apply and see real change in our products, but also have longer-term, five to 10 year goals that we're working toward.

It's pretty clear that machine learning is going to a big part of science and engineering.

Health care - the ability of neural networks to ingest lots of data and make predictions is very well suited to this area, and potentially will have a huge societal impact.

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.

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.

I am concerned in general about carbon emissions and machine learning.

I've always liked code that runs fast.

You need to find someone that you're gonna pair-program with who's compatible with your way of thinking, so that the two of you together are a complementary force.

As a society I think we are going to be much better off by having machines that can work in conjunction with humans to do things more efficiently and even better in some cases. That will 'enable humans to do things that they do better than machines.

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.

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.

Supervised learning works so well when you have the right data set, but ultimately unsupervised learning is going to be a really important component in building really intelligent systems - if you look at how humans learn, it's almost entirely unsupervised.

Previously, we might use machine learning in a few sub-components of a system. Now we actually use machine learning to replace entire sets of systems, rather than trying to make a better machine learning model for each of the pieces.

The idea behind reinforcement learning is you don't necessarily know the actions you might take, so you explore the sequence of actions you should take by taking one that you think is a good idea and then observing how the world reacts. Like in a board game where you can react to how your opponent plays.

We want to build systems that can generalize to a new task. Being able to do things with much less data and with much less computation is going to be interesting and important.

Health care has a lot of interesting machine-learning problems - outpatient outcomes, or when you have x-ray images and you want to predict things.

I do kind of think there's a bit of an overemphasis on - in the community - on sort of achieving ever-so-slightly better state-of-the-art results on particular problems, and a little underappreciation of completely different approaches to problems that maybe don't get state of the art because it's actually super hard and a pretty explored area.

If you only have 10 examples of something, it's going to be hard to make deep learning work. If you have 100,000 things you care about, records or whatever, that's the kind of scale where you should really start thinking about these kinds of techniques.

Very simple techniques, when you have a lot of data, work incredibly well.

I tend to be very impatient, thinking about all the ways we can do something, my mind and hands spinning at a very fast rate.

I think one of the things about reinforcement learning is that it tends to require exploration. So using it in the context of physical systems is somewhat hard.

I think that is one of the main goals of pushing forward in machine learning: having computers provide the wisdom that a human companion would be able to provide in offering advice, looking up more information when necessary and those kinds of things.

We're training Google Street View to recognize street numbers.

Understanding language is core to a lot of Google products such as Gmail.