64 Quotes by Jeff Dean

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.

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

I've always liked code that runs fast.

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 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.

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.

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

Vision I think is going to be an important input. Like, if you're using Google Glass, it's going to be able to look around and read all the text on signs and do background lookups on additional information and serve that. That will be pretty exciting.

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.