Quotes of All Topics . Occasions . Authors
I've always liked code that runs fast.
Machine learning is a new way of creating problem solving.
Microsoft is in a lot of the same businesses that Google is in.
I am concerned in general about carbon emissions and machine learning.
Computers can see, and understand what people say via speech recognition.
Deep learning is a really powerful metaphor for learning about the world.
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.
There's a lot of work in machine learning systems that is not actually machine learning.
Transportation, with self-driving vehicles, is going to be a big use of machine learning.
It's pretty clear that machine learning is going to a big part of science and engineering.
Deep neural networks are responsible for some of the greatest advances in modern computer science.
It would be great to have every engineer have at least some amount of knowledge of machine learning.
It's important to engage with governments around the world in how they're thinking about AI - to help inform them.
In Google data centers, our energy usage throughout the year for all our computing needs is 100 percent renewable.
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.
Traditionally computers have not been that good at interacting with people in ways that people feel natural interacting with.
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.
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.
With TensorFlow, when we started to develop it, we kind of looked at ourselves and said: 'Hey, maybe we should open source this.'
I think really what cloud customers care about is, can they get their problem solved on any particular provider's cloud products?
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 think multimodal kinds of models are pretty interesting - like can you combine text with imagery or audio or video in interesting ways?
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.
The things that I really enjoy doing are finding interesting problems and working together with colleagues to figure out how we can solve them.
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.
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.
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.
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.
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.
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.
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 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.
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.
I think true artificial general intelligence would be a system that is able to perform human-level reasoning, understanding, and accomplishing of complicated tasks.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.