Quotes from Jeff Dean

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.

The speech recognition is now good enough that I dictate emails on my phone rather than type them in. It's not perfect, but it's good enough that it changes how I interact with my phone.

The things that I really enjoy doing are finding interesting problems and working together with colleagues to figure out how we can solve them.

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.

I think there are a lot of industries that are collecting a lot of data and have not yet considered the implications of machine learning but will ultimately use it.

It's important to engage with governments around the world in how they're thinking about AI - to help inform them.

Traditionally computers have not been that good at interacting with people in ways that people feel natural interacting with.

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.

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.

It would be great to have every engineer have at least some amount of knowledge of machine learning.

As devices continue to shrink and voice recognition and other kinds of alternative user-interfaces become more practical, it is going to change how we interact with computing devices. They may fade into the background and just be around, allowing us to talk to them just as we would some other trusted companion.

AI can help solve some of the most difficult social and environmental challenges in areas like healthcare, disaster prediction, environmental conservation, agriculture, or cultural preservation.

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 think really what cloud customers care about is, can they get their problem solved on any particular provider's cloud products?

Machine learning is a new way of creating problem solving.

Some things are easier to parrellelize than others. It's pretty easy to train up 100 models and pick the best one. If you want to train one big model but do it on hundreds of machines, that's a lot harder to parallelize.

Deep learning is a really powerful metaphor for learning about the world.

Microsoft is in a lot of the same businesses that Google is in.

With TensorFlow, when we started to develop it, we kind of looked at ourselves and said: 'Hey, maybe we should open source this.'

I spend a fair amount of time dealing with email, mostly deleting them or skimming them to get a sense of what is going on.

I think true artificial general intelligence would be a system that is able to perform human-level reasoning, understanding, and accomplishing of complicated tasks.

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.

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

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.