Jeff Dean

“Computers don't usually have a sense of if you have a picture of something what is in that image. And if we can do a good job of understanding what is in an image, that can bring along a lot of new things you can do in applications.”

“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.”

“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.”

“There's a lot of potential for machine learning all around the world. We're seeing it in academia, at other companies, in government.”

“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.”

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

“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.”

“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 have a lot of work to do to get really important useful capabilities into people's hands - self-driving cars are going to save an enormous number of lives.”

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

“Deep neural networks are responsible for some of the greatest advances in modern computer science.”

“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.”

“A lot of human learning comes from unsupervised learning where you're just sort of observing the world around you and understanding how things behave.”

“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.”

“There's a lot of work in machine learning systems that is not actually machine learning.”

“Some people are happy to work in a particular domain or some field of computer science for years, and years. I personally like to kind of move around every few years, just to learn about new areas.”

“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.”

“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.”

“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.”