Top 40 Jeff Dean Quotes

I think robotics is a really hard problem – to make robots that operate in sort of arbitrary environments, like a big conference room with chairs and stuff.

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.

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.

We have way more unsupervised data in the world than supervised data.

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.

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.

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

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.

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.

I think multimodal kinds of models are pretty interesting – like can you combine text with imagery or audio or video in interesting ways?

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

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

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.

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.

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

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

Computers can see, and understand what people say via speech recognition.

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.

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.

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.

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 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.

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.

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.

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

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.

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.

In Google data centers, our energy usage throughout the year for all our computing needs is 100 percent renewable.

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

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

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.

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

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

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.

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.

There’s a lot of work in machine learning systems that is not actually machine learning.

The healthcare space is a very complicated one for a variety of reasons: It’s much more regulated than some other kinds of industries, for good reason.

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

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