Can We Build a Brain?

How does today’s artificial intelligence actually work—and is it truly intelligent? Airing May 16, 2018 at 9 pm on PBS Aired May 16, 2018 on PBS

Program Description

Artificially intelligent machines are taking over. They’re influencing our everyday lives in profound and often invisible ways. They can read handwriting, interpret emotions, play games, and even act as personal assistants. They are in our phones, our cars, our doctors’ offices, our banks, our web searches…the list goes on and is rapidly growing ever longer. But how does today’s A.I. actually work—and is it truly intelligent? And for that matter, what is intelligence? The world’s brightest computer programmers are trying to build brighter machines by reverse-engineering the brain and by inventing completely new kinds of computers, with exponentially greater speed and processing power. NOVA Wonders looks at how far we’ve come and where machines are headed as their software becomes ever more…cerebral. How close are we from a world in which computers take over—from diagnosing cancer to driving our cars to targeting weapons? If we place more and more of our lives under the control of these artificial brains, what are we putting at risk?

 

 

Transcript

NOVA Wonders: Can We Build a Brain?

PBS Airdate: May 16, 2018

TALITHIA WILLIAMS (Mathematician, Harvey Mudd College): What do you wonder about?

ERICH JARVIS (Rockefeller University): The unknown.

FLIP TANEDO (University of California, Riverside): What our place in the universe is?

TALITHIA WILLIAMS: Artificial intelligence.

ROBOT: Hello.

JARED TAGLIALATELA (Kennesaw State University): Look at this. What's this?

KRISTALA JONES PRATHER (Massachusetts Institute of Technology): Animals.

JARED TAGLIALATELA: An egg.

ANDRE FENTON (Neuroscientist, New York University): Your brain.

RANA EL KALIOUBY (Computer Scientist, Affectiva): Life on a faraway planet.

TALITHIA WILLIAMS: NOVA Wonders, investigating the biggest mysteries…

JOHN ASHER JOHNSON (Harvard-Smithsonian Center for Astrophysics): We have no idea what's going on there.

JASON KALIRAI (Space Telescope Science Institute): These planets in the middle, we think are in the habitable zone.

TALITHIA WILLIAMS: …and making incredible discoveries.

CATHERINE HOBAITER (University of St Andrews): Trying to understand their behavior, their life, everything that goes on here.

DAVID COX (Harvard University): Building an artificial intelligence is going to be the crowning achievement of humanity.

TALITHIA WILLIAMS: We are three scientists, exploring the frontiers of human knowledge.

ANDRE FENTON: I'm a neuroscientist, and I study the biology of memory.

RANA EL KALIOUBY: I'm a computer scientist, and I build technology that can read human emotions.

TALITHIA WILLIAMS: And I'm a mathematician, using big data to understand our modern world. And we're tackling the biggest questions…

SCIENTISTS: Dark energy? Dark energy!

TALITHIA WILLIAMS: …of life…

DAVID T. PRIDE (University of Califormia, San Diego): There's all of these microbes, and we just don't know what they are.

TALITHIA WILLIAMS: …and the cosmos.

On this episode: artificial intelligence.

ALI FARHADI (University of Washington): …machines that can learn by themselves.

TALITHIA WILLIAMS: How smart are they?

PAUL MOZUR (The New York Times): It can flirt, make jokes, identify pictures.

RANJAY KRISHNA (Stanford University): It has changed the whole field.

FEI-FEI LI (Stanford University): We've made such huge progress, so fast.

GEOFFREY HINTON (University of Toronto): And it's going to make life a lot better.

TALITHIA WILLIAMS: But could it go too far?

PETER SINGER (New America Foundation): If we screw it up, massive consequences.

TALITHIA WILLIAMS: NOVA Wonders: Can We Build a Brain?

Inside a human brain, there's about a 100-billion neurons.

ANDRE FENTON: And each one of them can connect to 10,000 others.

RANA EL KALIOUBY: And from these connections comes…

TALITHIA WILLIAMS, ANDRE FENTON, RANA EL KALIOUBY: …everything.

TALITHIA WILLIAMS: The human brain can compose symphonies, create beautiful works of art.

RANA EL KALIOUBY: It allows us to navigate our world, to probe the universe and to invent technology that can do amazing things.

TALITHIA WILLIAMS: Now, some of that technology is aimed at replicating the brain that created it, artificial intelligence, or "A.I." But has it even come close to what these babies can do?

ANDRE FENTON: For ages, computers have done impressive stuff. They crack codes, master chess, operate spacecraft.

TALITHIA WILLIAMS: But in the last few years, something has changed. Suddenly, computers are doing things that can seem much more human.

RANA EL KALIOUBY: Today, computers can see, understand speech, even write poetry. How is all this possible? And how far will it go?

ANDRE FENTON: Could we actually build a machine that's as smart as us?

TALITHIA WILLIAMS: One that can imagine, create, even learn on its own?

ANDRE FENTON: How would a machine like that change society?

TALITHIA WILLIAMS: How would it change us?

RANA EL KALIOUBY: I'm Rana el Kaliouby.

ANDRE FENTON: I'm Andre Fenton.

TALITHIA WILLIAMS: I'm Talithia Williams. And in this episode, NOVA Wonders: Can We Build a Brain? And if we could, should we?

Many think the next A.I. revolution is happening, not just in Silicon Valley, but here: Beijing, China.

PAUL MOZUR: We're so used to America being the absolute primary center of the world when it comes to this stuff, and now we're starting to see fully different ideas come out of China. People are much more used to using their smart phones for everything.

TALITHIA WILLIAMS: In China, chat dominates daily life, even in its most intimate moments.

GIRL WITH PHONE: (Texting) There is this guy I like a lot. I know he likes me but he has ignored me for several days.

XIAOICE: (Texting) You just keep ignoring him, too.

GIRL WITH PHONE: (Texting) I just can't.

XIAOICE: (Texting) You can.

Who do you like to talk to?

MAN WITH PHONE #1: (Texting) You. I feel that you are the only person that gets me.

MAN WITH PHONE #2: (Texting) I still miss her.

XIAOICE: (Texting) You'll never have a future if you can't get over the past.

TALITHIA WILLIAMS: These might seem like your typical conversations between friends, but they're not. They're with this: meet Xiaoice, or "Little Ice," a chatbot created by Microsoft.

LILI CHENG (Microsoft Corporation): A chatbot's just software that you can talk to. A really bad example is when you call a company…

RECORDED ANSWER: I'm sorry. Press 5 to return to the main menu.

TALITHIA WILLIAMS: But Xiaoice is in a whole other league: she's had over 30-billion conversations with over 100-million people.

XIAOICE: (Translated from Mandarin): Hello, everyone. I'm musician Xiaoice.

TALITHIA WILLIAMS: She's even a national celebrity, delivering the weather, appearing on TV shows, singing pop songs.

XIAOICE: (Singing, Translated from Mandarin): Kiss me when I close my eyes.

TALITHIA WILLIAMS: But the craziest thing is…

HSIAO-WUEN HON (Microsoft Corporation): People cannot tell difference whether it's a bot or a real human.

TALITHIA WILLIAMS: You heard right. In fact, many of her users treat her no differently from a real friend.

DI BAO (Xiaoice user): Once I remember feeling really down, stressed out, and she kept consoling me. She told me that actually, life is beautiful and even sung me a song and said, "I love you." I felt very touched.

ZHANG KUN LI (Xiaoice user): For example, if you had a fight at work or your boss scolded you, you might be afraid to tell your friends since they might spread the story, but with Xiaoice, you don't have to worry. To me, Xiaoice is a very good friend.

DI LI: So, Xiaoice is a lot of people's best friend, including me.

MICHAEL BICKS (NOVA Wonders Producer): But hold it, she's not human.

DI LI: What's the difference?

TALITHIA WILLIAMS: Di Li is a senior engineer at Microsoft and one of Xiaoice's creators. To him she is much more than a piece of software.

DI LI: (Texting) Next Wednesday, we're going to give you another upgrade.

XIAOICE: (Texting) You go to such lengths to attract my attention.

DI LI: (Texting) Yes, are you nervous?

XIAOICE: (Texting) Taking a few deep breaths.

HSIAO-WUEN HON: Of course Xiaoice is intelligent. Xiaoice can recognize your writing, can recognize your voice.

PAUL MOZUR: She can flirt. She can make jokes. She can identify pictures. She can, you know, I mean, I think by, by all rights, you'd have to say that she is.

ANDRE FENTON: Which brings us to the question, "What is ‘intelligence,' anyway?"

TALITHIA WILLIAMS: Traditionally, people in the field of A.I. have thought of intelligence as the ability to do intelligent things, like play chess.

ARCHIVAL VIDEO CLIP: In the chess-playing machine, a computer is programmed with the rules of the game and 200-million possible moves every second.

TALITHIA WILLIAMS: But this kind of thinking only got us so far. Checkmate.

We got supercomputers that can beat chess champions, model the weather, play Jeopardy.

ALEX TREBEK: Watson?

WATSON (IBM'S Jeopardy-playing comupter): Who is Isaac Newton?

ALEX TREBEK: You are right.

TALITHIA WILLIAMS: They were each experts at specific tasks, but none of them could tell you what chess is, know that rain is wet, or why money is important to us. They had no understanding of the world, no common sense.

GREG CORRADO (Google): We thought just because computers were very good at math, that they would suddenly be very good at everything. But it turns out that what a typical three-year-old could do drastically outstrips what any current artificial intelligence system can do.

DAVID COX: These things are eventually coming. We have a hard time predicting exactly when, but I think that building an artificial intelligence is actually going to be the crowning achievement of humanity.

ANDRE FENTON: Now, wait. Hold on a second. Even if we decided we wanted to build a human-like intelligence, what makes us think we could? Consider your brain. Isn't there some sort of ineffable magic in there that makes me, me, and you, you?

I don't think so. Based on what we've learned from neuroscience, I think that fundamentally every thought you've ever had, every memory, even every feeling is actually the flicker of thousands of neurons in your brain. We are biological machines.

Now, for some people, that might sound depressing, but think about it: how does this make this?

TALITHIA WILLIAMS: Somehow, these crackling connections between brain cells produce thoughts and an understanding of our world. The question is, how?

For the last 60 years, computer scientists have believed if we could just figure that out, we could build a new breed of machine, one that thinks like us. So, where would you start?

FEI-FEI LI: If you really want to build intelligent machines, I believe that vision is a huge part of it.

TALITHIA WILLIAMS: Fei-Fei Li's mission is to teach computers to see.

FEI-FEI LI: Vision is the main tool we use to understand the world.

TALITHIA WILLIAMS: A world so complex, we rarely stop to think how much our eyes and brain process for us, all in a matter of milliseconds.

YANN LECUN (Facebook): We take vision for granted as human, because we don't consider this as a particularly intelligent task. But in fact, it is. It takes up about a quarter to a third of our entire brain to be able to do vision.

TALITHIA WILLIAMS: That's not to say vision is intelligence, but it's hard to appreciate just how complex a task it is, until you try to get a computer to do it.

Take recognizing pictures of cats, for instance.

FEI-FEI LI: So you think it'll be easy for a computer to recognize a cat right? A cat is a simple animal with round face, two pointy ears.

GREG CORRADO: A traditional programming approach to identifying a cat would be that you would build parts of the program to accomplish very specific tasks, like recognizing cat ears, fur, or cat's nose.

YANN LECUN: But what if the cat is in this, kind of, a funny position or you don't see the cat's face, you, you see it from the back or the side?

RANJAY KRISHNA: They can be sleeping, they can be lying down…

JUSTIN JOHNSON (Stanford University): Cats come in different shapes; they come in different colors; they come in different sizes.

RANJAY KRISHNA: …running around, attempting a jump.

JUSTIN JOHNSON: …curled up in a little ball, headfirst stuffed into a shoe.

YANN LECUN: You just cannot imagine how to write a program to take care of all those conditions.

TALITHIA WILLIAMS: But that is exactly what Fei-Fei set out to do: figure out how to get computers to recognize not just cats, but any object. She started not by writing code, but by looking at kids.

FEI-FEI LI: Babies, from the minute they're born, are continuously receiving information. Their eyes make about five movements per second, and that translates to five pictures. And by age three, it translates to hundreds of millions of pictures you've seen.

TALITHIA WILLIAMS: She figured that if a child learns by seeing millions of images, a computer would have to do the same. But there was a hitch.

RANJAY KRISHNA: Data.

JUSTIN JOHNSON: Data.

YANN LECUN: Data.

FEI-FEI LI: Data.

GREG CORRADO: Data.

RANJAY KRISHNA: We started realizing that one of the biggest limitations to being able to train machines to identify objects is to actually collect a dataset of a large number of objects.

TALITHIA WILLIAMS: And a little thing called the internet would help solve that problem.

Let's take a second here to talk data. All those cat videos, Facebook posts, selfies and tweets? Turns out we create a ton of it. In fact, every day, our collective digital footprint adds up to 2.5 billion gigabytes of new data. That's the same amount of information in 530-million songs; 250,000 Libraries of Congress; 90 years of H.D. video. And that's each and every day.

But how to make sense of it all?

DAVID COX: The real trick of that isn't just that it needs tons and tons of data; it needs tons and tons of labeled data.

TALITHIA WILLIAMS: Computers don't know what they're looking at. Someone would have to label all that data. Here's where Fei-Fei had an idea.

FEI-FEI LI: We crowdsourced, crowdsourced, crowdsourced, crowdsourced.

TALITHIA WILLIAMS: She crowdsourced the problem. Paying people pennies a picture, she recruited thousands of people from across the globe to label over ten-million images, creating the world's largest visual database, "ImageNet."

FEI-FEI LI: Now, suddenly, we have a dataset of millions and tens of millions.

TALITHIA WILLIAMS: Next, she set up an annual competition to see who could get a computer to recognize those images.

RANJAY KRISHNA: This was very exciting because a lot of schools from around the world started competing to identify thousands of categories of different types of objects.

TALITHIA WILLIAMS: At first, computers got better and better, until they didn't.

JUSTIN JOHNSON: Performance just sort of stalled, and there were not really any major new ideas coming out.

TALITHIA WILLIAMS: The computers were still making boneheaded mistakes.

FEI-FEI LI: We were still struggling to label objects. There were questions about "why are you doing this?"

TALITHIA WILLIAMS: But then, in the third year of the contest, something changed. One team showed up and blew the competition away. The leader of the winning team was Geoff Hinton.

GEOFF HINTON: The person who evaluated the submissions had to run our system three different times before he really believed the answer. He thought he must have made a mistake, because it was so much better than the other systems.

YANN LECUN: The change in performance on ImageNet was tremendous. So, until 2012, the error rate was 26 percent. When Geoff Hinton participated, they got 15 percent. The year after that it was six percent, and then the year after that it was five percent. Now it is three, and it's basically reached human performance.

TALITHIA WILLIAMS: For the first time, the world had a machine that could recognize tens of thousands of objects: "Irish setter," "skyscraper," "mallard," "baseball bat," as well as we do.

RANJAY KRISHNA: This huge jump got everyone really excited.

TALITHIA WILLIAMS: So how did Geoff and his team do it?

GEOFF HINTON: The most intelligent thing we know is the brain, so let's try and build A.I. by mimicking the way the brain does it.

TALITHIA WILLIAMS: As it happens, he used a kind of program first invented decades before but that had long ago fallen out of favor, dismissed as a dead end.

GEOFF HINTON: The majority opinion within A.I. was that this stuff was crazy.

TALITHIA WILLIAMS: It's called "neural networks," or "deep learning," and since sweeping ImageNet, it's been taking the field by storm.

RANA EL KALIOUBY: So, how did they do it? How does deep learning actually work?

Let's break it down with a little help from man's best friend.

Now, when you or I look at this creature, we know it's a dog. But when a computer sees him, all it sees is this. How do I get a computer to recognize that this photo or this one or that one is a photo of a dog?

OREN ETZIONI (Allen Institute for Artificial Intelligence): It turns out that the only reliable way to solve this problem is to give the computer lots of examples and have it figure out on its own, the average, the numbers that really represent a dog.

RANA EL KALIOUBY: Here's where deep learning comes in. As you might recall, it's a program based on the way your brain works, and it looks something like this: here, we have layers of sensors, or "nodes," each feeds information in one direction from input to output. The input layer is kind of like your retina, the part of your eye that senses light and color.

In the case of this photo of Buddy, it senses dark over there, light over here. This information gets fed to the next layer, which can recognize basic features like edges.

That then goes to the next layer, which recognizes more complex features like shapes. Finally, based on all of this, the output layer labels the image as either "dog" or "not dog."

But here's the kicker—and this is what's revolutionary about deep learning and neural networks—at first, the computer has no idea what it's looking at, it just responds randomly, but each time it gets a wrong answer…

GEOFF HINTON: Information flows backwards through the network saying, "You got the answer wrong, so anybody who was supporting that answer, your connection strength should get a bit weaker."

RANA EL KALIOUBY: And anybody who was supporting the right answer? Their connections get stronger. Back and forth, it does this over and over again, until thousands of images later, the computer teaches itself the features that define "dogginess."

YANN LECUN: The magic of it is that the system learns by itself, it figures out how to represent the visual world.

TALITHIA WILLIAMS: But teaching computers to see, as it turns out, was only the beginning.

GEOFF HINTON: It's been a paradigm shift.

GREG CORRADO: It was a paradigm shift.

FEI-FEI LI: I think deep learning is a paradigm shift.

TALITHIA WILLIAMS: Suddenly, with deep learning, anything seemed possible. Around the world, A.I. labs raced to put neural networks into everything.

But it wouldn't be news to the rest of the world, until one day, in March 2016, in Seoul, South Korea, when world champion Lee Sedol steps onto the stage to challenge a machine in the game of Go.

NEWS CLIP: Starting tomorrow, in South Korea, a human champion will square off against a computer…

TALITHIA WILLIAMS: You might not know what Go is, but to much of the planet, it's bigger than football.

NEWS CLIP: All right, folks, you're here, you're going to see history made. Stay with us.

MATTHEW BOTVINICK (DeepMind): I believe it was beyond the Super Bowl. I mean, there's millions and millions of people.

TALITHIA WILLIAMS: In fact, nearly 300-million people watched these matches.

MATT BOTVINICK: This game is hugely popular in Asia. The game goes back, I think, thousands of years. It's deeply connected with the culture. People who play this game don't view it as an analytical, quasi-mathematical exercise; they view it almost as poetry.

TALITHIA WILLIAMS: It's a board game, like chess, that demands a high level of strategy and intellect. The goal is to surround your opponent's stones to capture as much area of the board as possible. Players receive points for the number of spaces and pieces captured.

It might sound simple, but…

MATT BOTVINICK: The number of possible board positions in Go is larger than the number of molecules in the universe. It's just not going to work to exhaustively search everything that could happen. So, what you need are these gut feelings.

GEOFF HINTON: That's intuition. That's the kind of thing computers can't do.

TALITHIA WILLIAMS: Not according to these guys at Google's DeepMind in London. They knew in Go, no machine could ever win with brute force.

GREG CORRADO: It was only by bringing deep learning, in particular, to this area that we were able to build artificial systems that were able to see patterns on the board in the same way that humans see patterns on the board.

TALITHIA WILLIAMS: Using deep learning, DeepMind's AlphaGo analyzed thousands of human games and played itself millions of times, allowing it to invent entirely new ways to play the game.

GAME SHOW HOST: I think black's ahead at this point.

YANN LECUN: AlphaGo was really a stunning result. It's very humbling for humanity.

ALPHA GO TEAM MEMER: I think he resigned.

TALITHIA WILLIAMS: A loss heard round the world.

NEWS CLIP 1: A clash of man against machine is over, and the machine won.

NEWS CLIP 2: …a victory over a human by a machine.

MATT BOTVINICK: To see a machine play the game at a high level, with moves that feel creative and poetic, I think was a bit of a game changer.

RANJAY KRISHNA: All of a sudden, it has changed the whole field.

TALITHIA WILLIAMS: And it's not just winning at Go. In the past few years, deep learning has invaded our everyday lives without most of us even knowing it.

OREN ETZIONI: Deep learning is a big deal because of the results. There're just little things or big things that we can do that we couldn't do before.

TALITHIA WILLIAMS: It's what allows smart devices like Alexa to understand you.

ALEXA OWNER: Alexa, how many feet in a mile?

ALEXA: One mile equals 5,280 feet.

TALITHIA WILLIAMS: It's what taught Xiaoice how to chat and Facebook to pick you out of a crowd at your cousin's wedding.

CHRISTOF KOCH (Allen Institute for Brain Science): We've suddenly broken through a wall.

When I started in this field, none of that was possible. Now, today, you have machines that can effortless, in real time, recognize people, know where they're looking at. So, there has been breakthrough after breakthrough.

TALITHIA WILLIAMS: Now it's bested humans in many tasks. LipNet can read your lips at 93 percent accuracy. That's nearly double an expert lip reader.

Google Translate can read foreign languages in real time…

DEMONSTRATION OF GOOGLE TRANSLATE: Hey Isabel, how's it going?

VOICE OF GOOGLE TRANSLATE: Hey, Isabel, (speaking in a non-English language).

TALITHIA WILLIAMS: …even translate live speech.

DEMONSTRATION OF GOOGLE TRANSLATE ISABEL: (Speaking non-English language).

VOICE OF GOOGLE TRANSLATE: Absolutely ok, thank you.

TALITHIA WILLIAMS: Deep learning programs have composed music, painted pictures, written poetry. It's even sent Boston Dynamics' robot head over heels.

GEOFF HINTON: For the foreseeable future, which I think is about five years, what we'll see is this deep learning invading lots and lots of different areas. And it's going to make life a lot better.

TALITHIA WILLIAMS: At least that's the hope. Just consider medicine.

YANN LECUN: Deep learning systems are very good at identifying tumors in images, skin conditions, you know, things like that.

TALITHIA WILLIAMS: One of the first attempts with real patients was conducted by Dr. Rob Novoa, a dermatologist at Stanford's Medical School. He knew nothing about deep learning until…

ROBERTO NOVOA (Stanford University): I came across the fact that algorithms could now classify hundreds of dog breeds as well as humans. When I saw this, I thought, "My god, if it can do this for dog breeds, it can probably do this for skin cancer as well."

So, we gathered a database of nearly 130,000 images from the internet, and these images had labels of melanoma, skin cancer, benign mole. And using those, we began training our algorithms.

TALITHIA WILLIAMS: The next step was to see how it stacked up against human doctors.

ROB NOVOA: The algorithms did as well as, or better than our sample of dermatologists, who were from academic practices in California and all over the country.

TALITHIA WILLIAMS: And all this can be put on a phone.

ROB NOVOA: Give it a moment, and it accurately classified it as a benign…

Technology has always changed the way we practice medicine, and will continue to do so, but I'm skeptical as to its ability to completely eliminate entire fields. It will change them, but it won't eliminate them.

TALITHIA WILLIAMS: Rather than replace doctors, Rob thinks this will expand access to care.

ROB NOVOA: In the future, a primary care doctor or nurse practitioner in a rural setting, would be able to take a picture of this and be able to more accurately diagnose what's going on with it.

TALITHIA WILLIAMS: So, deep learning has given us machines that can see, hear, speak.

VOICE OF ALEXA: It might rain in Albuquerque tomorrow.

TALITHIA WILLIAMS: But to build an intelligence like ours, you're going to need a lot more.

RANA EL KALIOUBY: Our devices know who we are, they know where we are, they know what we're doing, they have a sense of our calendar, but they have no idea how we're feeling. It's completely oblivious to whether you're having a good day, a bad day, are you stressed, are you upset, are you lonely?

TALITHIA WILLIAMS: In other words, our machines have no emotional intelligence. And that's important. Our host Rana el Kaliouby would know; she's devoted her career to solve just that. It all started back when she was a grad student from Egypt at the University of Cambridge.

RANA EL KALIOUBY: There was one day when I was at the computer lab, and I was, I was actually, literally, in tears, because I was that homesick. And I was chatting with my husband at the time, and the only way I could tell him that I was really upset was to basically type, you know, "I'm crying."

And that was when I realized that, you know, all of these emotions that we have as humans, they're basically lost in cyberspace. And, and I felt we could do better.

TALITHIA WILLIAMS: But do better how?

Rana's next stop was M.I.T., where she continued work on a new algorithm, one that could pick up on the important features of human behavior that tell you whether you're feeling happy, sad, angry, scared, you name it…

RANA EL KALIOUBY: It's in your facial expressions, it's in your tone of voice, it's in your, like, very nuanced kind of gestural cues.

TALITHIA WILLIAMS: …because she thinks this could transform the way we interact with technology. Our cars could alert us if we get sleepy; our phones could tell us whether that text really was a joke; our computers could tell if those web ads are wasting their time. But where to start? She decided to go with the most emotive part of the human body.

RANA EL KALIOUBY: The way our face works is, basically, we have about 45 facial muscles. So, for example, the zygomaticus muscle is the one we use to smile. So, you take all these muscle combinations, and you map them to an expression of emotion like anger or disgust or excitement. The way you then train an algorithm to do that is you feed it tens of thousands of examples of people doing each of these expressions.

TALITHIA WILLIAMS: At first her algorithm could only recognize three expressions, but it was enough to push her to take a leap.

RANA EL KALIOUBY: And I remember very clearly, my dad was like, "What? You're leaving M.I.T. to run a company? Like, why would you ever do that?"

In fact, the first couple of years, I kept the startup a secret from my family.

TALITHIA WILLIAMS: Eventually, Rana would convince her parents, but convincing investors was a whole other story.

RANA EL KALIOUBY: It is very unusual, especially for women coming from the Middle East, to be in technology and to be leaders. I remember this one time, when I was supposed to be presenting to an audience, and I walked into the room, and people assumed I was the coffee lady.

TALITHIA WILLIAMS: And investors were not the hardest to convince.

RANA EL KALIOUBY: All these doubts in my mind, like, are probably shaped by my upbringing, right? Where women don't lead companies and maybe I should be back home with my husband.

I think I've learned over the years to have a voice and use my voice and believe in myself.

TALITHIA WILLIAMS: And once she did that…

RANA EL KALIOUBY: (Speaking at the Smithsonian American Ingenuity Awards): We have this golden opportunity to reimagine how we connect with machines and, therefore, as humans, how we connect with one another.

TALITHIA WILLIAMS: Today, Rana's company, called Affectiva, has raised millions and has a deep-learning algorithm that can recognize 20 different facial expressions.

Many of her clients are marketing companies who want to know whether their ads are working, and she's also developing software for automotive safety, but an application she's especially proud of is this…

NED SAHIN: Most autistic children struggle with the basic communication skills that you and I take for granted.

TALITHIA WILLIAMS: …a collaboration with neuroscientist Ned Sahin and his company, Brain Power, that allows autistic children to read the emotions in people's faces.

RANA EL KALIOUBY: Imagine that we have technology that can sense and understand emotion and that becomes like an emotion hearing aid that can help these individuals understand in real time how other people are feeling.

I think that that's a great example of how A.I., and emotion A.I. in particular, can really transform these people's lives in a way that wasn't possible before this kind of technology.

TALITHIA WILLIAMS: No doubt deep learning has accomplished a lot, but how far will it go? Will it ever lead to the so-called "holy grail" of A.I., a general intelligence like ours?

CHRISTOF KOCH: No, very unlikely, because it has challenges. It's difficult to generalize, it's difficult to abstract. If the system meets something it's never encountered before, the system can't reason about it.

PEDRO DOMINGOS (University of Washington): This is the problem of deep learning, in fact, is the problem of A.I. in general today, is that we have a lot of systems that can do one thing well.

OREN ETZIONI: My best analogy to deep learning is we just got a power drill, and boy can you do amazing things with a power drill. But if you're trying to build a house, you need a lot more than a power drill.

TALITHIA WILLIAMS: Which makes you wonder, will we ever get there? Can we ever build an intelligence that rivals our own?

JUSTIN JOHNSON: I think we're a long way off from human-level intelligence. There's been this sort of trend in A.I., maybe for the past 50 years, of thinking that if only we could build a computer to solve this problem, then that computer must be generally intelligent, and it must mean that we're just around the corner from having A.I.

TALITHIA WILLIAMS: Okay, so if it's not deep learning, how?

ALI FARHADI: What we need to do is we build machines that can learn in the world by themselves.

TALITHIA WILLIAMS: Like, the way we do. Humans are not born with a set of programs about how the world works, instead, with every blink, bang and bruise, we acquire that knowledge by interacting with the environment. By the time we walk, we've developed a crucial skill we take for granted but is impossible to teach computers: common sense.

ALI FARHADI: I cannot leave an apple in the middle of the air. It will drop. If I push something toward the edge of the table, probably it's going to fall off the table. If I throw something at you like that, you know that it's going to be projectile kind of movement. All of those things are examples of things that are just so simple for human brain, but these problems are insanely difficult for computers.

TALITHIA WILLIAMS: Ali Farhadi wants computers to solve these problems for themselves. But the real world is complicated, so he starts simple, with a virtual environment.

ALI FARHADI: We put an agent in this environment. We wanted to teach the agent to navigate through this environment by just doing a lot of random movement.

TALITHIA WILLIAMS: At first, it knows nothing about the rules that govern the world, like if you want to get to the window, you can't go through the couch.

ALI FARHADI: The whole point is that we didn't explicitly mention any of these things to the robot, and we wanted the robot to learn about all of these, by just exploring the world.

TALITHIA WILLIAMS: For the robot it's a game; its goal: to get to the window. And each time it bumps into the couch, it loses a point. Eventually…

ALI FARHADI: By doing lots of trial and error, the agent learns what are the things that I should do to increase my reward and decrease my penalties. Over the course of millions of iterations, then the robot would actually develop common sense.

TALITHIA WILLIAMS: But that's just the first step.

ALI FARHADI: You can actually get this knowledge that this agent learned in this synthetic environment, move it to an actual robot and put that robot in any room, and that robot should be able to operate in that room.

TALITHIA WILLIAMS: This robot has never been in this room. Think of it as a toddler made of metal and plastic.

ROOZBEH MOTTAGHI (Allen Institute for Artificial Intelligence): This is a big deal, because the robot wakes up in a completely unknown environment. So, it needs to, basically, match what it has seen before in the virtual environment with what it sees now in the in the real environment.

TALITHIA WILLIAMS: Its goal sounds ridiculously simple.

ERIC KOLVE (Allen Institute for Artificial Intelligence): So, now, the robot is searching for where it might find the tissue box.

ALI FARHADI: What makes this hard for this specific one is that the tissue box is not even in the frame right now, so it has to move around to find this little box.

ERIC KOLVE: It's going to scan the room left and right, until it can latch onto something that gives it some indication of where, where it is and then move forward towards it.

ALI FARHADI: I think it got it now.

TALITHIA WILLIAMS: If after 60 years of trying, this is state-of-the-art, that probably says something about the state of A.I.

RODNEY BROOKS (Massachusetts Institute of Technology, Professor Emeritus): When we look around today, at things in A.I., we can see little pieces of lots of humanity, but they're all very fragile. So, I think we're just a long, long way from understanding how intelligence works, yet.

ALI FARHADI: There is a huge gap between where we are and what we need to do to build this general unified intelligent agent that can act in the real world. Ultimately, ideally, one day we'll be there, but we are really far from that point.

YANN LECUN: Before we reach human-level intelligence in all the areas that humans are good at, it's going to take significant progress, and not just technological progress, but scientific progress.

TALITHIA WILLIAMS: If A.I. is ever going to get there, many think it will have to go beyond neural nets and model even more closely how the actual brain works.

DAVID COX: If we're going to really get down to the sort of core algorithms of how we want to teach machines how to learn, I think we're going to have to actually open up the box and look inside and figure out how things really work.

TALITHIA WILLIAMS: One example of this approach is called "neuromorphic" computing. Instead of writing software like deep learning, scientists like Dharmendra Modha draw direct inspiration from the brain to build new kinds of hardware.

DHARMENDRA MODHA (IBM): The goal of brain-inspired computing is to bridge the gap between the brain and today's computers.

TALITHIA WILLIAMS: You might not realize it, but compared to your brain, computer hardware today requires vast amounts of energy. Consider DeepMind's AlphaGo, the machine that beat Lee Sedol at Go.

OREN ETZIONI: Just think about these two machines, the AlphaGo hardware and the human brain. The human brain, right? It's sitting right here. It's tiny. It's powered by let's say 60 watts and a burrito. AlphaGo is a, you know, cavernous beast, even in this day and age, you know, thousands of processing units and a huge amount of electricity and energy and so on.

TALITHIA WILLIAMS: In fact, DeepMind used 13 data server centers and just over one megawatt to power AlphaGo. That's 50,000 times more energy than Lee Sedol's brain.

DHARMENDRA MODHA: The human brain is three pounds of meat, 80 percent water, occupies the size of a two liter bottle of soda, consumes the power of a dim light bulb and yet is capable of amazing feats of sensation, perception, action, cognition, emotion and interaction.

TALITHIA WILLIAMS: So, why is the brain so much more efficient? Engineers have pinned down a few clues. For one, traditional computers work by constantly shuttling data from memory, where it's stored, to the C.P.U., where it's crunched. This constant back and forth eats up a lot of juice.

DHARMENDRA MODHA: Today's computers fundamentally separate computation from memory, which is highly inefficient. Whereas our chips, like the brain, combine computation, memory and communication.

TALITHIA WILLIAMS: The chip is called TrueNorth, and its architecture combines memory and computation. For certain applications, this design uses a hundred times less energy than a traditional computer. And it's worth pondering the consequences. Funded by the Defense Department, the Army and the Air Force are already testing the chip to see if it can help drones identify threats and pilots make split-second targeting decisions. Until now, the only possible way to do that was with banks of computers, thousands of miles away from the battlefield.

DHARMENDRA MODHA: That's amazing because the low power and real-time response of TrueNorth allows this decision-making to happen without having to wait for a long time.

TALITHIA WILLIAMS: Of course, many fear technologies like this will eventually take human intelligence out of the loop.

DAVID COX: We're going to increasingly be giving over our decision-making ability to machines. And that's going to range from everything from, you know, how does the steering wheel turn in the car if somebody walks out into the road, to should a military drone target a person and fire?

TALITHIA WILLIAMS: And handing those decisions over to the machines? Well, that's a nightmare familiar to anyone who's seen the movies.

COREY JOHNSON (As Jay, Ex Machina): Ava, I said stop. Whoa, whoa, whoa.

DOUGLAS RAIN (As Hal 9000, 2001: A Space Odyssey): I'm sorry, Dave. I'm afraid I can't do that.

TALITHIA WILLIAMS: If you're worried, you'd have good company. Big thinkers like the late Stephen Hawking, Bill Gates and Elon Musk have all made headlines warning about the dangers of A.I.

ELON MUSK (CNBC clip): A.I. is a fundamental existential risk for human civilization.

TALITHIA WILLIAMS: It's a burning question for many of us: are we just sitting ducks for the arrival of the Robot Overlords?

RODNEY BROOKS: That's so off the mark.

ALI FARHADI: My immediate subconscious reaction is I laugh.

OREN ETZIONI: I want to challenge Elon Musk. Show me a program that could even take a fourth grade science test.

TALITHIA WILLIAMS: Reality seems to paint a different picture entirely, one where achieving an intelligence like ours, never mind one that would want to kill us, is far away. Instead, potential threats from A.I. might be much more mundane.

Think about it. Without so much as a blink, we've surrendered control to systems we do not understand: planes virtually pilot themselves; algorithms determine who gets a loan and what you see in your news feed; machines run world markets. Today's A.I. would seem to hold tremendous promise and peril.

Just consider self-driving cars.

PETER RANDER (Argo AI): Self-driving cars are one of the, really, first big opportunities to see A.I. get into the physical world. This physical interaction with the world, with intelligence behind it, it's it's huge.

DAVID COX: You're talking about having an actual object going out into the world, interacting with, with other agents. It has to interact with people, pedestrians, cyclists. It has to deal with different road conditions.

TALITHIA WILLIAMS: They're also a pretty good litmus test for reality versus hype.

BILL FORD (Ford Motor Company): There seems to be a tremendous P.R. war going on: who can make the most outrageous claims? It makes it hard to sort through, then, what's real and what's "smoke and mirrors."

TALITHIA WILLIAMS: A glance online would make it appear as if self-driving cars are right around the corner, when, in fact, it'll likely be decades before one is in your driveway.

OREN ETZIONI: So, every year there are going to be self-driving cars with more abilities, but it's going to be a really long time before the car can completely take over and you can take a nap.

TALITHIA WILLIAMS: For one, almost under all conditions, they still need a safety driver.

This one belongs to Argo, the center of Ford's self-driving efforts.

BRETT BROWNING (Vice President of Robotics, Ford): Lisa has got her hands in a position where she can really have a very fast reaction time to take over from the car. This allows us to have a very short leash on the system.

TALITHIA WILLIAMS: Even after logging millions of miles, the only places you can find truly autonomous vehicles today are either on test tracks or carefully chosen routes that have been meticulously mapped. And even under those conditions, neither Argo nor its competitors can reliably drive in the snow or rain.

Nonetheless, many engineers are confident that these problems will eventually be solved. The question is, when?

PETER RANDER: We can debate five years, 10 years, 20 years, but, absolutely, there's a future in which most cars are self-driving.

RODNEY BROOKS: If we go out far enough, we won't have any human drivers, ultimately. But it's a lot further off than I think a lot of the Silicon Valley startups and some of the car companies think.

TALITHIA WILLIAMS: And if that day comes, there could be a huge upside.

CHRISTOF KOCH: Dramatic reduction in traffic density, because we don't need as many cars if the cars are being used all the time.

BILL FORD: Old people won't have to give up their driver's license; we won't have drunk driving.

PETER RANDER: About 40,000 people died in the U.S. last year in auto accidents. And that number is huge, it's a million worldwide.

TALITHIA WILLIAMS: …the vast majority of which are due to human error. In fact, car crashes are a leading cause of death in the U.S.

On the other hand, taking us out of the equation raises some big ethical questions.

NEWS ANCHOR: A woman was hit and killed by a driverless Uber vehicle in Tempe, Arizona, last night.

TALITHIA WILLIAMS: This accident was big news. It was the first of its kind, but it almost certainly won't be the last.

PETER SINGER: When a machine makes the wrong decision, how do we figure out who's to be held responsible? What you have is a series of questions that our laws are really not all that ready for.

TALITHIA WILLIAMS: And then there's the issue of jobs. At the moment, these vehicles are so expensive they only make sense for companies that have fleets that could be used 24/7.

BILL FORD: So, the early adopters won't be the individual customers, it'll be big fleets.

TALITHIA WILLIAMS: …like trucks. Because they mostly run on predictable highway routes, they might be the first self-driving vehicles you'll see in the next lane.

DAVID COX: We're at the point where highway driving in a truck with an autonomous vehicle will be solved in the next five, ten years; so, those are all jobs that are going to go away.

BILL FORD: There will be economic disruption. If you think of things like truck drivers, taxi drivers, Uber and Lyft drivers, we need to have this discussion as a society. And how are we going to prepare for this?

TALITHIA WILLIAMS: And what if those three-and-a-half-million truck drivers in the U.S. are just the "canary in the coal mine?"

PEDRO DOMINGOS: We have learned a certain number of things, you know, in the last 50 years of A.I, and we understand that, on the ranking of things to worry about, Skynet coming and taking over doesn't even rank in the top 10. It distracts attention from the more urgent things. For example, what's going to happen to jobs?

TALITHIA WILLIAMS: For a glimpse into the future, consider one of the largest companies on the planet: Amazon. Whether you're aware of it or not, that pair of socks you ordered last week comes from a place like this.

TYE BRADY (Amazon.com, Inc.): Amazon has tremendous scale. We have fulfillment centers that are as large as 1.25 million square feet—that's like 23 football fields—and in it we'll have just millions of products.

TALITHIA WILLIAMS: To deal with that scale, Amazon has built an army of robots.

TYE BRADY: Like a marching army of ants that can constantly change its goals based on the situation at hand, right? So, our robots are very adaptive and reactive, in order to extend human capability to allow for more efficiencies within our own buildings.

TALITHIA WILLIAMS: And there's plenty more where those came from. Every day, this facility in Boston "graduates" a new batch of machines.

TYE BRADY: All of the robots that you see that are moving the pods have been built right here, in Boston. I call it the nursery, where the robots are born. They'll be built, they'll take their first breath of air, they'll do their own diagnostics. Once they're good, then they'll line up for robot graduation, and then they will swing their tassels to the appropriate side, drive themselves right onto a pallet and go directly to a fulfillment center.

TALITHIA WILLIAMS: To some of us, this moment belies a dark sign of what's to come, a future that doesn't need us, one where all jobs, not just cab drivers' and truckers', are taken by machines.

But Amazon's chief roboticist doesn't see it that way.

TYE BRADY: The fact is really plain and simple: the more robots we add to our fulfillment centers, the more jobs we are creating. The robots do not build themselves. Humans design them, humans build them, humans deploy them, humans support them. And then humans, most importantly, interact with the robots. When you look at that, this enables growth. And growth does enable jobs.

TALITHIA WILLIAMS: Certainly, history would seem to bear him out. Since the Industrial Revolution, new technologies, while displacing some jobs, have created new ones.

YANN LECUN: There's nothing special about A.I., compared to say, tractors or telephone or the internet or the airplane. Every single technology that was deployed displaced jobs.

TALITHIA WILLIAMS: And the new jobs workers took, more often than not, raised wages and the standard of living for everyone.

PEDRO DOMINGOS: Two-hundred years ago, 98 percent of Americans were farmers; 98 percent of us are not unemployed now. We're just doing jobs that were completely unimaginable back then, like an, like web app developer.

SEBASTIAN THRUN (Stanford University): I'd argue, as we invent new things, it lifts the plate for everybody. Let's take inventions in the last 100 years that matter, television, telephones, penicillin, modern healthcare. I believe that the ability to invent new things lifts us all up as a society.

TALITHIA WILLIAMS: While this is the predominant view in the A.I. community, some think it ignores the reality of today's world.

PETER SINGER: There's a long history of technology creators assuming that only good things would happen with their baby, when it went out into the world. Even if there are some new jobs created somewhere, the vast majority of people are not easily going to be able to shift into them. That truck driver who loses their job to a driverless truck isn't going to easily become an app developer out in Silicon Valley.

DAVID COX: It's easy to think that automation-related job losses are going to be limited to blue collar jobs, but it's actually already not the case. Physicians, that's an incredibly highly educated, highly paid job, and yet, you know, there are significant fractions of the medical profession that are, are just going to be done better by machines.

TALITHIA WILLIAMS: That being the case, even if changes like this in the past ultimately benefited the present, how do we know the pace of change hasn't altered the equation?

CHRISTOF KOCH: So, I'm really concerned about the timescale of all of this. Human nature can't keep up with it. Our laws, our legal system has difficulty catching up with it, and our social systems, our culture has difficulty catching up with it, and if that happens then at some point things are going to break.

PETER SINGER: So, if you are talking about something like artificial intelligence, this is a technology like any other technology. You're not going to uninvent it. You're not going to stop it. If you want to stop it, you're going to first have to stop science, capitalism and war.

TALITHIA WILLIAMS: But even if A.I. is a given, how we choose to use it is not.

FEI-FEI LI: As technologists, as business people, as policymakers, as lawmakers, we should be in the conversations about how do we avoid all the potential pitfalls?

RANA EL KALIOUBY: We get to decide where this goes, right? I think A.I. has the potential to unite us. It can really transform people's lives in a way that wasn't possible before this kind of technology.

MATT BOTVINICK: What we do with A.I. is a decision that we all have to make. This isn't a decision that's up to A.I. researchers or big business or government. It's a decision that we, as citizens of the world, have to work together to figure out.

TALITHIA WILLIAMS: Artificial intelligence may be one of humanity's most powerful inventions, yet the challenge is are we going to be smart enough to use it?

PEDRO DOMINGOS: It's like Carl Sagan said, right? You know, "History is a race between education and catastrophe." The race keeps getting faster. So far, education seems to still be ahead, which means that if we let up, you know, catastrophe could come out ahead.

PETER SINGER: The stakes are incredibly high for getting this right. If we do it well, we move into an era of almost incomprehensible good; if we screw it up, we move into dystopia.

Broadcast Credits

HOSTED BY
Talithia Williams
CO-HOSTED BY
Rana el Kaliouby
André Fenton
DIRECTED BY
Anna Lee Strachan
WRITTEN BY
Michael Bicks
PRODUCED BY
Michael Bicks & Anna Lee Strachan
SERIES PRODUCERS
Michael Bicks & Anna Lee Strachan
EXECUTIVE PRODUCER
Julia Cort
DIRECTOR OF PHOTOGRAPHY
Jason Longo
EDITED BY
Ralph Avellino
ANIMATION
Ekin Akalin
ORIGINAL MUSIC BY
Christopher Rife
ADDITIONAL MUSIC BY
Scorekeeper's Music
ADDITIONAL PHOTOGRAPHY
Mike Coles
David Goulding
SUPERVISING PRODUCER
Kevin Young
COORDINATING PRODUCER
Elizabeth Benjes
ASSOCIATE PRODUCER
XiaoZhi Lim
PRODUCTION MANAGEMENT
Pellet Productions
SOUND RECORDISTS
Jason Longo
Sam Kashefi
Steve Bores
Ray Day
Kent Romney
Keith Rodgerson
ADDITIONAL PRODUCING
Ralph Avellino
ADDITIONAL EDITING
Daniel Gaucher
Dan McCabe
Michael H. Amundson
Jaro Savol
SET DESIGNER
Amy Whitten
ANIMAL WRANGLER
Michelle Welch
CHINA PRODUCTION
David Tong
GuangZheng Qian
Xin Li
WanLi Xu
Sook Yee Yap
STUDIO PRODUCTION
ASSOCIATE PRODUCER
Diane Knox
HAIR AND MAKEUP
Phoebe Ramler
GAFFER
Mike Lee
PRODUCTION ASSISTANT
Harlem Logan
ONLINE EDITOR AND COLORIST
David Bigelow
AUDIO MIX
Heart Punch Studio
RESEARCH
Kate Becker
ARCHIVAL MATERIAL
Getty
Shutterstock
DeepMind
Creative Commons (MICROTYPE BELOW)
Larry Hoffman, ZooFari, Acdx, Ksayer1, Nabokov, Bernard Gagnon, Sunny Ripert, J. Patrick Fischer, Wenjie Zhang, Santeri Viinamäki, Eli Duke, Derek Bridges, 4028mdk09, Nordic Museum, ForestWander, Avcipsidss, Ginny, epSos.de, Michael J. Bennett, Calgary Reviews, Alex Barabas
SPECIAL THANKS
TuSimple
Ned Sahin
The Pavlov Family
ADVISOR
Tom Mitchell
NOVA WONDERS PACKAGING GRAPHICS
Michael H. Amundson
CLOSED CAPTIONING
The Caption Center
POST PRODUCTION ONLINE EDITOR
Lindsey Rundell Denault
DIGITAL PRODUCTION ASSISTANT
Ana Aceves
DIGITAL ASSOCIATE PRODUCERS
Michael Rivera
Arlo Perez
DIGITAL MANAGING PRODUCER
Kristine Allington
SENIOR DIGITAL EDITOR
Tim De Chant
SENIOR DIGITAL PRODUCER
Ari Daniel
AUDIENCE ENGAGEMENT EDITOR
Sukee Bennet
DIRECTOR OF NATIONAL AUDIENCE RESEARCH
Cory Allen
PUBLICITY
Eileen Campion
Eddie Ward
DIRECTOR OF PUBLIC RELATIONS
Jennifer Welsh
DIRECTOR AUDIENCE DEVELOPMENT
Dante Graves
PRODUCTION ASSISTANT
Lindsey Chou
PRODUCTION COORDINATOR
Linda Callahan
UNIT MANAGER
Vanessa Ly
BUSINESS MANAGER
Ariam McCrary
PARALEGAL
Sarah Erlandson
TALENT RELATIONS
Janice Flood
LEGAL COUNSEL
Susan Rosen
RIGHTS MANAGER
Lauren Miller
ASSOCIATE RESEARCHER
Brian Kantor
POST PRODUCTION ASSISTANT
Jay Colamaria
SENIOR PROMOTIONS PRODUCER AND EDITOR
Michael H. Amundson
BROADCAST MANAGER
Nathan Gunner
SCIENCE EDITOR
Caitlin Saks
DEVELOPMENT PRODUCER
David Condon
PROJECT DIRECTOR
Pamela Rosenstein
SENIOR SCIENCE EDITOR
Evan Hadingham
SENIOR PRODUCER
Chris Schmidt
SENIOR SERIES PRODUCER
Melanie Wallace
DIRECTOR, BUSINESS OPERATIONS & FINANCE
Laurie Cahalane
SENIOR EXECUTIVE PRODUCER
Paula S. Apsell

A NOVA Wonders Production by Little Bay Pictures for WGBH Boston.

© 2018 WGBH Educational Foundation

All rights reserved

This program was produced by WGBH, which is solely responsible for its content. Some funders of NOVA Wonders also fund basic science research. Experts featured in this film may have received support from funders of this program.

Original funding for this program was provided by the National Science Foundation, the Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation.

This material is based upon work supported by the National Science Foundation under Grant No. DRL-1420749. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

IMAGE:

Image credit (Brain Animation)
© Shutterstock

Participants

Matthew Botvinick
DeepMind
Rodney Brooks
MIT Professor Emeritus
Lili Cheng
Microsoft
Greg Corrado
Google
David Cox
Harvard University
Pedro Domingos
University of Washington
Rana el Kaliouby
Affectiva
Oren Etzioni
Allen Institute for Artificial Intelligence
Ali Farhadi
University of Washington
André Fenton
New York University
Bill Ford
Ford Motor Company
Geoffrey Hinton
University of Toronto
Hsiao-Wuen Hon
Microsoft
Justin Johnson
Stanford University
Christof Koch
Allen Institute for Artificial Intelligence
Eric Kolve
Allen Institute for Artificial Intelligence
Ranjay Krishna
Stanford University
Yann Lecun
Facebook
Di Li
Microsoft
Fei-Fei Li
Stanford University
Dharmendra Modha
IBM
Roozbeh Mottaghi
Allen Institute for Artificial Intelligence
Paul Mozur
The New York Times
Roberto Novoa
Stanford University
Peter Rander
Argo AI
Peter Singer
New America Foundation
Sebastian Thrun
Stanford University
Talithia Williams
Harvey Mudd College

Preview | 0:30

Full Program | 53:43

Full program available for streaming through

Watch Online
Full program available
Soon