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  • [MUSIC PLAYING]

  • JEFF DEAN: I'm excited to be here today to tell you

  • about how I see deep learning and how

  • it can be used to solve some of the really challenging problems

  • that the world is facing.

  • And I should point out that I'm presenting

  • the work of many, many different people at Google.

  • So this is a broad perspective of a lot of the research

  • that we're doing.

  • It's not purely my work.

  • So first, I'm sure you may have all noticed,

  • but machine learning is growing in importance.

  • There's a lot more emphasis on machine learning research.

  • There's a lot more uses of machine learning.

  • This is a graph showing how many Arxiv papers--

  • Arxiv is a preprint hosting service

  • for all kinds of different research.

  • And this is the subcategories of it

  • that are related to machine learning.

  • And what you see is that, since 2009, we've actually

  • been growing the number of papers posted at a really

  • fast exponential rate, actually faster than the Moore's Law

  • growth rate of computational power that we got so nice

  • and used to for 40 years but it's now slowed down.

  • So we've replaced the nice growth in computing performance

  • with growth in people generating ideas, which is nice.

  • And deep learning is this particular form

  • of machine learning.

  • It's actually a rebranding in some sense

  • of a very old set of ideas around creating

  • artificial neural networks.

  • These are these collections of simple trainable mathematical

  • units organized in layers where the higher layers typically

  • build higher levels of abstraction

  • based on things that the lower layers are learning.

  • And you can train these things end to end.

  • And the algorithms that underlie a lot of the work

  • that we're doing today actually were

  • developed 35, 40 years ago.

  • In fact, my colleague Geoff Hinton

  • just won the Turing Award this year along with Yann LeCun

  • and Yoshua Bengio for a lot of the work

  • that they did over the past 30 or 40 years.

  • And really the ideas are not new.

  • But what's changed is we got amazing results 30 or 40 years

  • ago on toyish problems but didn't

  • have the computational resources to make these approaches work

  • on real large scale problems.

  • But starting about eight or nine years ago,

  • we started to have enough computation to really make

  • these approaches work well.

  • And so what are things-- think of a neural net as something

  • that can learn really complicated functions that

  • map from input to output.

  • Now that sounds kind of abstract.

  • You think of functions as like y equals x squared or something.

  • But really these functions can be very complicated

  • and can learn from very raw forms of data.

  • So you can take the pixels of an image

  • and train a neural net to predict

  • what is in the image as a categorical label like that's

  • a leopard.

  • That's one of my vacation photos.

  • From audio wave forms, you can learn

  • to predict a transcript of what is being said.

  • How cold is it outside?

  • You can learn to take input in one language-- hello,

  • how are you--

  • and predict the output being that sentence translated

  • into another language.

  • [SPEAKING FRENCH]

  • You can even do more complicated things

  • like take the pixels of an image and create a caption that

  • describes the image.

  • It's not just category.

  • It's like a simple sentence.

  • A cheetah lying on top of a car, which is kind of unusual

  • anyway.

  • Your priority for that should be pretty low.

  • And in the field of computer vision,

  • we've made great strides thanks to neural nets.

  • In 2011, the Stanford ImageNet contest,

  • which is a contest held every year,

  • the winning entry did not use neural nets.

  • That was the last year.

  • The winning entry did not use neural nets.

  • They got 26% error.

  • And that won the contest.

  • We know this task--

  • it's not a trivial task.

  • So humans themselves have about 5%

  • error, because you have to distinguish

  • among 1,000 different categories of things

  • including like a picture of a dog, you have to say which

  • of 40 breeds of dog is it.

  • So it's not a completely trivial thing.

  • And in 2016, for example, the winning entry got 3% error.

  • So this is just a huge fundamental leap

  • in computer vision.

  • You know, computers went from basically not

  • being able to see in 2011 to now we can see pretty darn well.

  • And that has huge ramifications for all kinds of things

  • in the world not just computer science

  • but like the application of machine learning and computing

  • to perceiving the world around us.

  • OK.

  • So the rest of this talk I'm going

  • to frame in a way of-- but in 2008, the US National

  • Academy of Engineering published this list of 14

  • grand engineering challenges for the 21st century.

  • And they got together a bunch of experts

  • across lots of different domains.

  • And they all collectively came up

  • with this list of 14 things, which

  • I think you can agree these are actually

  • pretty challenging problems.

  • And if we made progress on all of them,

  • the world would be a healthier place.

  • We'd have a safer place.

  • We'd have more scientific discovery.

  • All these things are important problems.

  • And so given the limited time, what I'm going to do

  • is talk about the ones in boldface.

  • And we have projects in Google Research that are focused

  • on all the ones listed in red.

  • But I'm not going to talk about the other ones.

  • And so that's kind of the tour of the rest of the talk.

  • We're just going to dive in and off we go.

  • I think we start with restoring and improving

  • urban infrastructure.

  • Right.

  • We know cities were designed-- the basic structure of cities

  • has been designed quite some time ago.

  • But there's some changes that we're

  • on the cusp of that are going to really dramatically change how

  • we might want to design cities.

  • And, in particular, autonomous vehicles

  • are on the verge of commercial practicality.

  • This is from our Waymo colleagues, part of Alphabet.

  • They've been doing work in this space for almost a decade.

  • And the basic problem of an autonomous vehicle

  • is you have to perceive the world around you

  • from raw sensory inputs, things like light [INAUDIBLE],,

  • and cameras, and radar, and other kinds of things.

  • And you want to build a model of the world and the objects

  • around you and understand what those objects are.

  • Is that a pedestrian or a light pole?

  • Is it a car that's moving?

  • What is it?

  • And then also be able to predict both a short time from now,

  • like where is that car going to be in one second,

  • and then make a set of decisions about what actions

  • you want to take to accomplish the goals,

  • get from A to B without having any trouble.

  • And it's really thanks to deep learning vision

  • based algorithms and fusing of all the sensor data

  • that we can actually build maps of the world

  • like this that are understandings

  • of the environment around us and actually

  • have these things operate in the real world.

  • This is not some distant far off dream.

  • Waymo is actually operating about 100 cars

  • with passengers in the back seat and no safety

  • drivers in the front seat in the Phoenix, Arizona area.

  • And so this is a pretty strong sense

  • that this is pretty close to reality.

  • Now Arizona is one of the easier self-driving car environments.

  • It's like it never rains.

  • It's too hot so there aren't that many pedestrians.

  • The streets are very wide.

  • The other drivers are very slow.