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  • JEFF DEAN: I'm really excited to be here.

  • I think it was almost four years ago to the day

  • that we were about 20 people sitting in a small conference

  • room in one of the Google buildings.

  • We've woken up early because we wanted to kind of time

  • this for an early East Coast launch where

  • we were turning on the TensorFlow.org website

  • and releasing the first version of TensorFlow

  • as an open source project.

  • And I'm really, really excited to see what it's become.

  • It's just remarkable to see the growth and all

  • the different kinds of ways in which people have used

  • this system for all kinds of interesting things

  • around the world.

  • So one thing that's interesting is the growth

  • in the use of TensorFlow also kind

  • of mirrors the growth in interest in machine learning

  • and machine learning research generally around the world.

  • So this is a graph showing the number of machine

  • learning archive papers that have been posted

  • over the last 10 years or so.

  • And you can see it's growing quite, quite rapidly, much more

  • quickly than you might expect.

  • And that lower red line is kind of the nice doubling

  • every couple of years growth rate, exponential growth

  • rate we got used to in computing power, due to Moore's law

  • for so many years.

  • That's now kind of slowed down.

  • But you can see that the machine learning research community

  • is generating research ideas at faster than that rate, which

  • is pretty remarkable.

  • We've replaced computational growth with growth of ideas,

  • and we'll see those both together will be important.

  • And really, the excitement about machine learning

  • is because we can now do things we couldn't do before, right?

  • As little as five or six years ago, computers really

  • couldn't see that well.

  • And starting in about 2012, 2013,

  • we started to have people use deep neural networks to try

  • to tackle computer vision problems, image

  • classification, object detection, things like that.

  • And so now, using deep learning and deep neural networks,

  • you can feed in the raw pixels of an image

  • and fairly reliably get a prediction of what kind

  • of object is in that image.

  • Feed in the pixels there.

  • Red, green, and blue values in a bunch of different

  • coordinates, and you get out the prediction leopard.

  • This works for speech as well.

  • You can feed an audio wave forms,

  • and by training on lots of audio wave forms and transcripts

  • of what's being said in those wave forms,

  • we can actually take a completely new recording

  • and tell you what is being said amid a transcript.

  • Bonjour, comment allez-vous?

  • You can even combine these ideas and have

  • models that take in pixels, and instead of just predicting

  • classifications of what are in the object,

  • it can actually write a short sentence, a short caption,

  • that a human might write about the image--

  • a cheetah lying on top of a car.

  • That's one of my vacation photos, which was kind of cool.

  • And so just to show the progress in computer vision, in 2011,

  • Stanford hosts an ImageNet contest every year

  • to see how well computer vision systems can

  • predict one of 1,000 categories in a full color image.

  • And you get about a million images to train on,

  • and then you get a bunch of test images

  • your model has never seen before.

  • And you need to make a prediction.

  • In 2011, the winning entrant got 26% error, right?

  • So you can kind of make out what that is.

  • But it's pretty hard to tell.

  • We know from human experiment that human error

  • of a well-trained human, someone who's

  • practiced at this particular task

  • and really understands 1,000 categories,

  • gets about 5% error.

  • So this is not a trivial task.

  • And in 2016, the winning entrant got 3% error.

  • So just look at that tremendous progress

  • in the ability of computers to resolve and understand

  • computer imagery and have computer vision

  • that actually works.

  • This is remarkably important in the world,

  • because now we have systems that can perceive

  • the world around us and we can do all kinds of really

  • interesting things about.

  • We've seen similar progress in speech recognition and language

  • translation and things like that.

  • So for the rest of the talk, I'd like to kind of structure it

  • around this nice list of 14 challenges

  • that the US National Academy of Engineering

  • put out and felt like these were important things

  • for the science and engineering communities

  • to work on for the next 100 years.

  • They put this out in 2008 and came up

  • with this list of 14 things after some deliberation.

  • And I think you'll agree that these

  • are sort of pretty good large challenging problems,

  • that if we actually make progress

  • on them, that we'll actually have

  • a lot of progress in the world.

  • We'll be healthier.

  • We'll be able to learn things better.

  • We'll be able to develop better medicines.

  • We'll have all kinds of interesting energy solutions.

  • So I'm going to talk about a few of these.

  • And the first one I'll talk about

  • is restoring and improving urban infrastructure.

  • So we're on the cusp of the sort of widespread commercialization

  • of a really interesting new technology that's

  • going to really change how we think about transportation.

  • And that is autonomous vehicles.

  • And this is a problem that has been worked on

  • for quite a while, but it's now starting

  • to look like it's actually completely

  • possible and commercially viable to produce these things.

  • And a lot of the reason is that we now

  • have computer vision and machine learning techniques

  • that can take in sort of raw forms of data

  • that the sensors on these cars collect.

  • So they have the spinning LIDARs on the top that

  • give them 3D point cloud data.

  • They have cameras in lots of different directions.

  • They have radar in the front bumper and the rear bumper.

  • And they can really take all this raw information in,

  • and with a deep neural network, fuse

  • it all together to build a high level understanding of what

  • is going on around the car.

  • Or is it another car to my side, there's a pedestrian

  • up here to the left, there's a light post over there.

  • I don't really need to worry about that moving.

  • And really help to understand the environment in which

  • they're operating and then what actions can

  • they take in the world that are both legal, safe,

  • obey all the traffic laws, and get them from A to B.

  • And this is not some distant far-off dream.

  • Alphabet's Waymo subsidiary has actually

  • been running tests in Phoenix, Arizona.

  • Normally when they run tests, they

  • have a safety driver in the front seat,

  • ready to take over if the car does

  • something kind of unexpected.

  • But for the last year or so, they've

  • been running tests in Phoenix with real passengers

  • in the backseat and no safety drivers in the front seat,

  • running around suburban Phoenix.

  • So suburban Phoenix is a slightly easier training ground

  • than, say, downtown Manhattan or San Francisco.

  • But it's still something that is like not really far off.

  • It's something that's actually happening.

  • And this is really possible because

  • of things like machine learning and the use

  • of TensorFlow in these systems.

  • Another one that I'm really, really excited

  • about is advance health informatics.

  • This is a really broad area, and I

  • think there's lots and lots of ways

  • that machine learning and the use of health data

  • can be used to make better health care

  • decisions for people.

  • So I'll talk about one of them.

  • And really, I think the potential here

  • is that we can use machine learning

  • to bring the wisdom of experts through a machine learning

  • model anywhere in the world.

  • And that's really a huge, huge opportunity.

  • So let's look at this through one problem

  • we've been working on for a while, which

  • is diabetic retinopathy.

  • So diabetic retinopathy is the fastest growing cause

  • of preventable blindness in the world.

  • And screening every year, if you're at risk for this,

  • and if you have diabetes or early sort of symptoms that

  • make it likely you might develop diabetes, you should really get

  • screened every year.

  • So there's 400 million people around the world that

  • should be screened every year.

  • But the screening is really specialized.

  • Doctors can't do it.

  • You really need ophthalmologist level of training

  • in order to do this effectively.

  • And the impact of the shortage is significant.

  • So in India, for example, there's

  • a shortage of 127,000 eye doctors

  • to do this sort of screening.

  • And as a result, 45% of patients who

  • are diagnosed to this disease actually

  • have suffered either full or partial vision loss

  • before they're actually diagnosed and then treated.

  • And this is completely tragic because this disease,

  • if you catch it in time, is completely treatable.

  • There's a very simple 99% effective treatment

  • that we just need to make sure that the right people get

  • treated at the right time.

  • So what can you do?

  • So, it turns out diabetic retinopathy screening is also

  • a computer vision problem, and the progress

  • we've made on general computer vision problems

  • where you want to take a picture and tell if that's

  • a leopard or an aircraft carrier or a car

  • actually also works for diabetic retinopathy.

  • So you can take a retinal image, which

  • is what the screening camera, sort of the raw data that

  • comes off the screening camera, and try

  • to feed that into a model that predicts 1, 2, 3, 4, or 5.

  • That's how these things are graded,

  • 1 being no diabetic retinopathy, 5 being proliferative,

  • and the other numbers being in between.

  • So it turns out you can get a collection of data

  • of retinal images and have ophthalmologists label them.

  • Turns out if you ask two ophthalmologists

  • to label the same image, they agree

  • with each other 60% of the time on the number 1, 2, 3, 4, or 5.

  • But perhaps slightly scarier if you

  • ask the same ophthalmologist to grade the same image

  • a few hours apart, they agree with themselves 65%

  • of the time.

  • But you can fix this by actually getting each image labeled

  • by a lot of ophthalmologists, so you'll

  • get it labeled by seven ophthalmologists.

  • If five of them say it's a 2, and two of them say it's a 3,

  • it's probably more like a 2 than a 3.

  • Eventually, you have a nice, high quality

  • data set you can train on.

  • Like many machine learning problems,

  • high quality data is the right raw ingredient.

  • But then you can apply, basically,

  • an off-the-shelf computer vision model trained on this data set.

  • And now you can get a model that is

  • on par or perhaps slightly better than the average board

  • certified ophthalmologist in the US, which is pretty amazing.

  • It turns out you can actually do better than that.

  • And if you get the data labeled by retinal specialists, people

  • who have more training in retinal disease

  • and change the protocol by which you label things,

  • you get three retinal specialists

  • to look at an image, discuss it amongst themselves,

  • and come up with what's called a sort of coordinated assessment

  • and one number.

  • Then you can train a model and now

  • be on par with retinal specialists, which

  • is kind of the gold standard of care in this area.

  • And that's something you can now take and distribute widely

  • around the world.

  • So one issue particularly with health care kinds of problems

  • is you want explainable models.

  • You want to be able to explain to a clinician

  • why is this person, why do we think this person has

  • moderate diabetic retinopathy.

  • So you can take a retinal image like this,

  • and one of the things that really helps

  • is if you can show in the model's assessment

  • why this is a 2 and not a 3.