7-2-1:Never never give up
01:47
And I'm thinking, what are they imagining? That I'll just sort of do some celestial navigation --
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And carry a bowie knife in my mouth, and I'll hunt fish and skin them alive and eat them, and maybe drag a desalinization plant behind me for fresh water.
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And the team is expert, and the team is courageous, and brimming with innovation and scientific discovery, as is true of any major expedition on the planet.
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And we've been on a journey. And the debate has raged, hasn't it, since the Greeks, of isn't it what it's all about? Isn't life about the journey, not really the destination? And here we've been on this journey, and the truth is, it's been thrilling. We haven't reached that other shore, and still, our sense of pride and commitment, unwavering commitment. When I turned 60, the dream was still alive from having tried this in my 20s -- dreamed it and imagined it. The most famous body of water on the Earth today, I imagine,Cuba to Florida. And it was deep. It was deep in my soul.
03:15
When I turned 60, it wasn't so much about the athletic accomplishment, it wasn't the ego of "I want to be the first." That's always there and it's undeniable. But it was deeper. It was "how much life is there left?" Let's face it -- we're all on a one-way street, aren't we? And what are we going to do? What are we going to do as we go forward, to have no regrets looking back? And all this past year in training, I had that Teddy Roosevelt quote to paraphrase it, floating around in my brain. It says, "You go ahead. You go ahead and sit back in your comfortable chair and you be the critic, you be the observer, while the brave one gets in the ring and engages and gets bloody and gets dirty and fails over and over and over again, but yet isn't afraid and isn't timid and lives life in a bold way."
04:10
And so of course I want to make it across. It is the goal, and I should be so shallow to say that this year, the destination was even sweeter than the journey.
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But the journey itself was worthwhile taking. And at this point, by this summer, everybody -- scientists, sports scientists,endurance experts, neurologists, my own team, Bonnie -- said it's impossible. It just simply can't be done, and Bonnie said to me,"But if you're going to take the journey, I'm going to see you through to the end of it, so I'll be there."
04:55
And now we're there. As we're looking out, kind of a surreal moment before the first stroke, standing on the rocks at Marina Hemingway, the Cuban flag is flying above, all my team is out in their boats, hands up in the air, "We're here! We're here for you!"Bonnie and I look at each other and say, this year, the mantra is -- and I've been using it in training -- Find a way. You have a dreamand you have obstacles in front of you, as we all do. None of us ever get through this life without heartache, without turmoil, and if you believe and you have faith and you can get knocked down and get back up again and you believe in perseverance as a great human quality, you find your way. And Bonnie grabbed my shoulders, and she said, "Let's find our way to Florida."
06:01
And we started, and for the next 53 hours, it was an intense, unforgettable life experience. The highs were high, the awe -- I'm not a religious person, but I'll tell you, to be in the azure blue of the Gulf Stream as if, as you're breathing, you're looking down miles and miles and miles, to feel the majesty of this blue planet we live on -- it's awe-inspiring. I have a playlist of about 85 songs, and especially in the middle of the night ... That night, because we use no lights -- lights attract jellyfish, lights attract sharks, lights attract baitfish that attract sharks, so we go in the pitch black of the night. You've never seen black this black. You can't see the front of your hand, and the people on the boat, Bonnie and my team on the boat -- they just hear the slapping of the arms, and they know where I am, because there's no visual at all. And I'm out there kind of tripping out on my little playlist.
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I've got tight rubber caps, I don't hear a thing. I've got goggles and I'm turning my head 50 times a minute, and I'm singing ...
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(Singing) Imagine there's no heaven
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doo doo doo doo doo It's easy if you try doo doo doo doo doo
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And I can sing that song a thousand times in a row.
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Now there's a talent unto itself.
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And each time I get done with,
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(Singing) Oh, you may say I'm a dreamer but I'm not the only one
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(Singing) Imagine there's no heaven
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And when I get through the end of a thousand of John Lennon's "Imagine," I have swum nine hours and 45 minutes ... exactly.
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And then there are the crises. Of course there are. And the vomiting starts, the seawater -- you're not well. You're wearing a jellyfish mask for the ultimate protection. It's difficult to swim in. It's causing abrasions on the inside of the mouth, but the tentacles can't get you. And the hypothermia sets in. The water's 85 degrees, and yet you're losing weight and using calories. And as you come over toward the side of the boat -- not allowed to touch it, not allowed to get out, but Bonnie and her team hand me nutritionand ask me how I'm doing, am I all right. I am seeing the Taj Mahal --
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Over here. I'm in a very different state --
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And I'm thinking, "Wow! I never thought I'd be running into the Taj Mahal out here. It's gorgeous! I mean, how long did it take them to build that? It's just ... So, uh -- wooo -- you know?
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We kind of have a cardinal rule that I'm never told how far it is, because we don't know how far it is. What's going to happen to you between this point and that point? What's going to happen to the weather and the currents and, God forbid, you're stung, when you don't think you could be stung in all this armor. Bonnie made a decision coming into that third morning that I was suffering,and I was hanging on by a thread. And she said, "Come here," and I came close to the boat, and she said, "Look, look out there."And I saw light, because the day is easier than the night, and I thought we were coming into day. I saw a stream of white light along the horizon, and I said, "It's going to be morning soon." And she said, "No, those are the lights of Key West." It was 15 more hours, which for most swimmers would be a long time.
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You have no idea how many 15-hour training swims I had done.
10:14
So here we go, and I somehow, without a decision, went into no counting of strokes and no singing and no quoting Stephen Hawking on the parameters of the universe. I just went into thinking about this dream, and why and how. As I said, when I turned 60, it wasn't about that concrete "Can you do it?" That's the everyday machinations. That's the discipline, and it's the preparation,and there's a pride in that. But I decided to think, as I went along, about -- you know, the phrase usually is, "reaching for the stars."And in my case, it's reaching for the horizon. And when you reach for the horizon, as I've proven, you may not get there. But what a tremendous build of character and spirit that you lay down; what a foundation you lay down in reaching for those horizons.
11:17
And now, the shore is coming. And there's just a little part of me that's sad. The epic journey is going to be over.
11:25
So many people come up to me now and say, "What's next?"
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"That little tracker on the computer? When are you going to do the next one? We can't wait to follow the next one." Well, they were just there for 53 hours, and I was there for years. And so there won't be another epic journey in the ocean.
11:46
But the point is, and the point was, that every day of our lives is epic. And I'll tell you, when I walked up onto that beach, staggered up onto that beach ... I had so many times, in a very puffed-up ego way, rehearsed what I would say ...
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on the beach. When Bonnie thought the back of my throat was swelling up, she brought the medical team over to our boat to say, "She's really beginning to have trouble breathing; another 12, 24 hours in the saltwater ..." -- the whole thing -- I just thought, in my hallucinatory moment, that I heard the word "tracheotomy."
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Bonnie said to the doctor, "I'm not worried about her not breathing. If she can't talk when she gets to the shore, she's going to be pissed off."
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But the truth is, all those orations that I had practiced, just to get myself through some training swims as motivation -- it wasn't like that. It was a very real moment, with that crowd, with my team. We did it. I didn't do it. We did it. And we'll never forget it. It'll always be part of us.
13:05
The three things I did sort of blurt out when we got there, was first: Never, ever give up. I live it. What's the phrase from today from Socrates?
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Diana Nyad: To be is to do. So I don't stand up and say, "Don't ever give up." I didn't give up. There was action behind these words.
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The second is: You can chase your dreams at any age; you're never too old. Sixty-four; a thing no one, at any age, any gender, could ever do has done it. And there's no doubt in my mind that I am at the prime of my life today.
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And the third thing I said on that beach was, it looks like the most solitary endeavor in the world, and in many ways, of course, it is.And in other ways, and the most important ways, it's a team. And if you think I'm a badass, you want to meet Bonnie.
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Bonnie, where are you? Where are you? There's Bonnie Stoll.
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The Henry David Thoreau quote goes, "When you achieve your dreams, it's not so much what you get as who you have become in achieving them." And yeah, I stand before you now. In the three months since that swim ended, I've sat down with Oprah, and I've been in President Obama's Oval Office; I've been invited to speak in front of esteemed groups such as yourselves; I've signed a wonderful major book contract. All of that's great, and I don't denigrate it. I'm proud of it all, but the truth is, I'm walking around tallbecause I am that bold, fearless person, and I will be, every day, until it's time for these days to be done.
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Thank you very much and enjoy the conference.
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Thank you. Thank you. Thank you. Thank you! Thank you.
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As a boy in Lima,
my grandfather told me a legend of the Spanish conquest of Peru. Atahualpa, emperor of the Inca, had been captured and killed. Pizarro and his conquistadors had grown rich, and tales of their conquest and glory had reached Spain and was bringing new waves of Spaniards, hungry for gold and glory. They would go into towns and ask the Inca, "Where's another civilization we can conquer? Where's more gold?"
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And the Inca, out of vengeance, told them, "Go to the Amazon. You'll find all the gold you want there. In fact, there is a city called Paititi -- El Dorado in Spanish -- made entirely of gold."
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The Spanish set off into the jungle, but the few that return come back with stories, stories of powerful shamans, of warriors with poisoned arrows, of trees so tall they blotted out the sun, spiders that ate birds, snakes that swallowed men whole and a river that boiled.
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All this became a childhood memory. And years passed. I'm working on my PhD at SMU, trying to understand Peru's geothermal energy potential, when I remember this legend, and I began asking that question. Could the boiling river exist?
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I asked colleagues from universities, the government, oil, gas and mining companies, and the answer was a unanimous no. And this makes sense. You see, boiling rivers do exist in the world, but they're generally associated with volcanoes. You need a powerful heat source to produce such a large geothermal manifestation. And as you can see from the red dots here, which are volcanoes, we don't have volcanoes in the Amazon, nor in most of Peru. So it follows: We should not expect to see a boiling river.
02:09
Telling this same story at a family dinner, my aunt tells me, "But no, Andrés, I've been there. I've swum in that river."
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Then my uncle jumps in. "No, Andrés, she's not kidding. You see, you can only swim in it after a very heavy rain, and it's protected by a powerful shaman. Your aunt, she's friends with his wife."
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You know, despite all my scientific skepticism, I found myself hiking into the jungle, guided by my aunt, over 700 kilometers away from the nearest volcanic center, and well, honestly, mentally preparing myself to behold the legendary "warm stream of the Amazon."
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But then ... I heard something, a low surge that got louder and louder as we came closer. It sounded like ocean waves constantly crashing, and as we got closer, I saw smoke, vapor, coming up through the trees. And then, I saw this.
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I immediately grabbed for my thermometer, and the average temperatures in the river were 86 degrees C. This is not quite the 100-degree C boiling but definitely close enough. The river flowed hot and fast. I followed it upriver and was led by, actually, the shaman's apprentice to the most sacred site on the river. And this is what's bizarre -- It starts off as a cold stream. And here, at this site, is the home of the Yacumama, mother of the waters, a giant serpent spirit who births hot and cold water. And here we find a hot spring, mixing with cold stream water underneath her protective motherly jaws and thus bringing their legends to life.
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The next morning, I woke up and --
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I asked for tea. I was handed a mug, a tea bag and, well, pointed towards the river. To my surprise, the water was clean and had a pleasant taste, which is a little weird for geothermal systems.
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What was amazing is that the locals had always known about this place, and that I was by no means the first outsider to see it. It was just part of their everyday life. They drink its water. They take in its vapor. They cook with it, clean with it, even make their medicines with it.
05:09
I met the shaman, and he seemed like an extension of the river and his jungle. He asked for my intentions and listened carefully.Then, to my tremendous relief -- I was freaking out, to be honest with you -- a smile began to snake across his face, and he just laughed.
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I had received the shaman's blessing to study the river, on the condition that after I take the water samples and analyze them in my lab, wherever I was in the world, that I pour the waters back into the ground so that, as the shaman said, the waters could find their way back home.
05:59
I've been back every year since that first visit in 2011, and the fieldwork has been exhilarating, demanding and at times dangerous.One story was even featured in National Geographic Magazine. I was trapped on a small rock about the size of a sheet of paper in sandals and board shorts, in between an 80 degree C river and a hot spring that, well, looked like this, close to boiling. And on top of that, it was Amazon rain forest. Pshh, pouring rain, couldn't see a thing. The temperature differential made it all white. It was a whiteout. Intense.
06:42
Now, after years of work, I'll soon be submitting my geophysical and geochemical studies for publication. And I'd like to share, today, with all of you here, on the TED stage, for the first time, some of these discoveries.
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Well, first off, it's not a legend. Surprise!
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When I first started the research, the satellite imagery was too low-resolution to be meaningful. There were just no good maps.Thanks to the support of the Google Earth team, I now have this. Not only that, the indigenous name of the river, Shanay-timpishka, "boiled with the heat of the sun," indicating that I'm not the first to wonder why the river boils, and showing that humanity has always sought to explain the world around us.
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It actually took me three years to get that footage.
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Fault-fed hot springs. As we have hot blood running through our veins and arteries, so, too, the earth has hot water running through its cracks and faults. Where these arteries come to the surface, these earth arteries, we'll get geothermal manifestations:fumaroles, hot springs and in our case, the boiling river.
08:16
What's truly incredible, though, is the scale of this place. Next time you cross the road, think about this. The river flows wider than a two-lane road along most of its path. It flows hot for 6.24 kilometers. Truly impressive. There are thermal pools larger than this TED stage, and that waterfall that you see there is six meters tall -- and all with near-boiling water.
08:52
We mapped the temperatures along the river, and this was by far the most demanding part of the fieldwork. And the results were just awesome. Sorry -- the geoscientist in me coming out. And it showed this amazing trend. You see, the river starts off cold. It then heats up, cools back down, heats up, cools back down, heats up again, and then has this beautiful decay curve until it smashes into this cold river.
09:18
Now, I understand not all of you are geothermal scientists, so to put it in more everyday terms: Everyone loves coffee. Yes? Good.Your regular cup of coffee, 54 degrees C, an extra-hot one, well, 60. So, put in coffee shop terms, the boiling river plots like this.There you have your hot coffee. Here you have your extra-hot coffee, and you can see that there's a bit point there where the river is still hotter than even the extra-hot coffee. And these are average water temperatures. We took these in the dry season to ensure the purest geothermal temperatures.
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But there's a magic number here that's not being shown, and that number is 47 degrees C, because that's where things start to hurt, and I know this from very personal experience. Above that temperature, you don't want to get in that water. You need to be careful. It can be deadly.
10:16
I've seen all sorts of animals fall in, and what's shocking to me, is the process is pretty much the same. So they fall in and the first thing to go are the eyes. Eyes, apparently, cook very quickly. They turn this milky-white color. The stream is carrying them. They're trying to swim out, but their meat is cooking on the bone because it's so hot. So they're losing power, losing power, until finally they get to a point where hot water goes into their mouths and they cook from the inside out.
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A bit sadistic, aren't we? Jeez. Leave them marinating for a little longer. What's, again, amazing are these temperatures. They're similar to things that I've seen on volcanoes all over the world and even super-volcanoes like Yellowstone.
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But here's the thing: the data is showing that the boiling river exists independent of volcanism. It's neither magmatic or volcanic in origin, and again, over 700 kilometers away from the nearest volcanic center.
11:30
How can a boiling river exist like this? I've asked geothermal experts and volcanologists for years, and I'm still unable to find another non-volcanic geothermal system of this magnitude. It's unique. It's special on a global scale. So, still -- how does it work?Where do we get this heat? There's still more research to be done to better constrain the problem and better understand the system, but from what the data is telling us now, it looks to be the result of a large hydrothermal system.
12:13
Basically, it works like this: So, the deeper you go into the earth, the hotter it gets. We refer to this as the geothermal gradient. The waters could be coming from as far away as glaciers in the Andes, then seeping down deep into the earth and coming out to form the boiling river after getting heated up from the geothermal gradient, all due to this unique geologic setting.
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Now, we found that in and around the river -- this is working with colleagues from National Geographic, Dr. Spencer Wells, and Dr. Jon Eisen from UC Davis -- we genetically sequenced the extremophile lifeforms living in and around the river, and have found new lifeforms, unique species living in the boiling river.
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But again, despite all of these studies, all of these discoveries and the legends, a question remains: What is the significance of the boiling river? What is the significance of this stationary cloud that always hovers over this patch of jungle? And what is the significance of a detail in a childhood legend?
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To the shaman and his community, it's a sacred site. To me, as a geoscientist, it's a unique geothermal phenomenon. But to the illegal loggers and cattle farmers, it's just another resource to exploit. And to the Peruvian government, it's just another stretch of unprotected land ready for development.
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My goal is to ensure that whoever controls this land understands the boiling river's uniqueness and significance. Because that's the question, one of significance. And the thing there is, we define significance. It's us. We have that power. We are the ones who draw that line between the sacred and the trivial. And in this age, where everything seems mapped, measured and studied, in this age of information, I remind you all that discoveries are not just made in the black void of the unknown but in the white noise of overwhelming data.
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There remains so much to explore. We live in an incredible world. So go out. Be curious. Because we do live in a world where shamans still sing to the spirits of the jungle, where rivers do boil and where legends do come to life.
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7-2-3:Machine intelligence makes human morals more important
So, I started my first job as a computer programmer in my very first year of college -- basically, as a teenager.
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Soon after I started working, writing software in a company, a manager who worked at the company came down to where I was,and he whispered to me, "Can he tell if I'm lying?" There was nobody else in the room.
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"Can who tell if you're lying? And why are we whispering?"
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The manager pointed at the computer in the room. "Can he tell if I'm lying?" Well, that manager was having an affair with the receptionist.
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And I was still a teenager. So I whisper-shouted back to him, "Yes, the computer can tell if you're lying."
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Well, I laughed, but actually, the laugh's on me. Nowadays, there are computational systems that can suss out emotional states and even lying from processing human faces. Advertisers and even governments are very interested.
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I had become a computer programmer because I was one of those kids crazy about math and science. But somewhere along the line I'd learned about nuclear weapons, and I'd gotten really concerned with the ethics of science. I was troubled. However, because of family circumstances, I also needed to start working as soon as possible. So I thought to myself, hey, let me pick a technical field where I can get a job easily and where I don't have to deal with any troublesome questions of ethics. So I picked computers.
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Well, ha, ha, ha! All the laughs are on me. Nowadays, computer scientists are building platforms that control what a billion people see every day. They're developing cars that could decide who to run over. They're even building machines, weapons, that might kill human beings in war. It's ethics all the way down.
02:07
Machine intelligence is here. We're now using computation to make all sort of decisions, but also new kinds of decisions. We're asking questions to computation that have no single right answers, that are subjective and open-ended and value-laden.
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We're asking questions like, "Who should the company hire?" "Which update from which friend should you be shown?" "Which convict is more likely to reoffend?" "Which news item or movie should be recommended to people?"
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Look, yes, we've been using computers for a while, but this is different. This is a historical twist, because we cannot anchor computation for such subjective decisions the way we can anchor computation for flying airplanes, building bridges, going to the moon. Are airplanes safer? Did the bridge sway and fall? There, we have agreed-upon, fairly clear benchmarks, and we have laws of nature to guide us. We have no such anchors and benchmarks for decisions in messy human affairs.
03:13
To make things more complicated, our software is getting more powerful, but it's also getting less transparent and more complex.Recently, in the past decade, complex algorithms have made great strides. They can recognize human faces. They can decipher handwriting. They can detect credit card fraud and block spam and they can translate between languages. They can detect tumors in medical imaging. They can beat humans in chess and Go.
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Much of this progress comes from a method called "machine learning." Machine learning is different than traditional programming,where you give the computer detailed, exact, painstaking instructions. It's more like you take the system and you feed it lots of data, including unstructured data, like the kind we generate in our digital lives. And the system learns by churning through this data. And also, crucially, these systems don't operate under a single-answer logic. They don't produce a simple answer; it's more probabilistic: "This one is probably more like what you're looking for."
04:20
Now, the upside is: this method is really powerful. The head of Google's AI systems called it, "the unreasonable effectiveness of data." The downside is, we don't really understand what the system learned. In fact, that's its power. This is less like giving instructions to a computer; it's more like training a puppy-machine-creature we don't really understand or control. So this is our problem. It's a problem when this artificial intelligence system gets things wrong. It's also a problem when it gets things right,because we don't even know which is which when it's a subjective problem. We don't know what this thing is thinking.
05:03
So, consider a hiring algorithm -- a system used to hire people, using machine-learning systems. Such a system would have been trained on previous employees' data and instructed to find and hire people like the existing high performers in the company.Sounds good. I once attended a conference that brought together human resources managers and executives, high-level people,using such systems in hiring. They were super excited. They thought that this would make hiring more objective, less biased, and give women and minorities a better shot against biased human managers.
05:43
And look -- human hiring is biased. I know. I mean, in one of my early jobs as a programmer, my immediate manager would sometimes come down to where I was really early in the morning or really late in the afternoon, and she'd say, "Zeynep, let's go to lunch!" I'd be puzzled by the weird timing. It's 4pm. Lunch? I was broke, so free lunch. I always went. I later realized what was happening. My immediate managers had not confessed to their higher-ups that the programmer they hired for a serious job was a teen girl who wore jeans and sneakers to work. I was doing a good job, I just looked wrong and was the wrong age and gender.
06:29
So hiring in a gender- and race-blind way certainly sounds good to me. But with these systems, it is more complicated, and here's why: Currently, computational systems can infer all sorts of things about you from your digital crumbs, even if you have not disclosed those things. They can infer your sexual orientation, your personality traits, your political leanings. They have predictive power with high levels of accuracy. Remember -- for things you haven't even disclosed. This is inference.
07:05
I have a friend who developed such computational systems to predict the likelihood of clinical or postpartum depression from social media data. The results are impressive. Her system can predict the likelihood of depression months before the onset of any symptoms -- months before. No symptoms, there's prediction. She hopes it will be used for early intervention. Great! But now put this in the context of hiring.
07:36
So at this human resources managers conference, I approached a high-level manager in a very large company, and I said to her, "Look, what if, unbeknownst to you, your system is weeding out people with high future likelihood of depression? They're not depressed now, just maybe in the future, more likely. What if it's weeding out women more likely to be pregnant in the next year or two but aren't pregnant now? What if it's hiring aggressive people because that's your workplace culture?" You can't tell this by looking at gender breakdowns. Those may be balanced. And since this is machine learning, not traditional coding, there is no variable there labeled "higher risk of depression," "higher risk of pregnancy," "aggressive guy scale." Not only do you not know what your system is selecting on, you don't even know where to begin to look. It's a black box. It has predictive power, but you don't understand it.
08:40
"What safeguards," I asked, "do you have to make sure that your black box isn't doing something shady?" She looked at me as if I had just stepped on 10 puppy tails.
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She stared at me and she said, "I don't want to hear another word about this." And she turned around and walked away. Mind you -- she wasn't rude. It was clearly: what I don't know isn't my problem, go away, death stare.
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Look, such a system may even be less biased than human managers in some ways. And it could make monetary sense. But it could also lead to a steady but stealthy shutting out of the job market of people with higher risk of depression. Is this the kind of society we want to build, without even knowing we've done this, because we turned decision-making to machines we don't totally understand?
09:41
Another problem is this: these systems are often trained on data generated by our actions, human imprints. Well, they could just be reflecting our biases, and these systems could be picking up on our biases and amplifying them and showing them back to us,while we're telling ourselves, "We're just doing objective, neutral computation."
10:06
Researchers found that on Google, women are less likely than men to be shown job ads for high-paying jobs. And searching for African-American names is more likely to bring up ads suggesting criminal history, even when there is none. Such hidden biases and black-box algorithms that researchers uncover sometimes but sometimes we don't know, can have life-altering consequences.
10:37
In Wisconsin, a defendant was sentenced to six years in prison for evading the police. You may not know this, but algorithms are increasingly used in parole and sentencing decisions. He wanted to know: How is this score calculated? It's a commercial black box. The company refused to have its algorithm be challenged in open court. But ProPublica, an investigative nonprofit, audited that very algorithm with what public data they could find, and found that its outcomes were biased and its predictive power was dismal, barely better than chance, and it was wrongly labeling black defendants as future criminals at twice the rate of white defendants.
11:23
So, consider this case: This woman was late picking up her godsister from a school in Broward County, Florida, running down the street with a friend of hers. They spotted an unlocked kid's bike and a scooter on a porch and foolishly jumped on it. As they were speeding off, a woman came out and said, "Hey! That's my kid's bike!" They dropped it, they walked away, but they were arrested.
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She was wrong, she was foolish, but she was also just 18. She had a couple of juvenile misdemeanors. Meanwhile, that man had been arrested for shoplifting in Home Depot -- 85 dollars' worth of stuff, a similar petty crime. But he had two prior armed robbery convictions. But the algorithm scored her as high risk, and not him. Two years later, ProPublica found that she had not reoffended.It was just hard to get a job for her with her record. He, on the other hand, did reoffend and is now serving an eight-year prison term for a later crime. Clearly, we need to audit our black boxes and not have them have this kind of unchecked power.
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Audits are great and important, but they don't solve all our problems. Take Facebook's powerful news feed algorithm -- you know, the one that ranks everything and decides what to show you from all the friends and pages you follow. Should you be shown another baby picture?
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A sullen note from an acquaintance? An important but difficult news item? There's no right answer. Facebook optimizes for engagement on the site: likes, shares, comments.
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In August of 2014, protests broke out in Ferguson, Missouri, after the killing of an African-American teenager by a white police officer, under murky circumstances. The news of the protests was all over my algorithmically unfiltered Twitter feed, but nowhere on my Facebook. Was it my Facebook friends? I disabled Facebook's algorithm, which is hard because Facebook keeps wanting to make you come under the algorithm's control, and saw that my friends were talking about it. It's just that the algorithm wasn't showing it to me. I researched this and found this was a widespread problem.
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The story of Ferguson wasn't algorithm-friendly. It's not "likable." Who's going to click on "like?" It's not even easy to comment on.Without likes and comments, the algorithm was likely showing it to even fewer people, so we didn't get to see this. Instead, that week, Facebook's algorithm highlighted this, which is the ALS Ice Bucket Challenge. Worthy cause; dump ice water, donate to charity, fine. But it was super algorithm-friendly. The machine made this decision for us. A very important but difficult conversationmight have been smothered, had Facebook been the only channel.
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Now, finally, these systems can also be wrong in ways that don't resemble human systems. Do you guys remember Watson, IBM's machine-intelligence system that wiped the floor with human contestants on Jeopardy? It was a great player. But then, for Final Jeopardy, Watson was asked this question: "Its largest airport is named for a World War II hero, its second-largest for a World War II battle."
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Chicago. The two humans got it right. Watson, on the other hand, answered "Toronto" -- for a US city category! The impressive system also made an error that a human would never make, a second-grader wouldn't make.
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Our machine intelligence can fail in ways that don't fit error patterns of humans, in ways we won't expect and be prepared for. It'd be lousy not to get a job one is qualified for, but it would triple suck if it was because of stack overflow in some subroutine.
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In May of 2010, a flash crash on Wall Street fueled by a feedback loop in Wall Street's "sell" algorithm wiped a trillion dollars of value in 36 minutes. I don't even want to think what "error" means in the context of lethal autonomous weapons.
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So yes, humans have always made biases. Decision makers and gatekeepers, in courts, in news, in war ... they make mistakes; but that's exactly my point. We cannot escape these difficult questions. We cannot outsource our responsibilities to machines.
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Artificial intelligence does not give us a "Get out of ethics free" card.
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Data scientist Fred Benenson calls this math-washing. We need the opposite. We need to cultivate algorithm suspicion, scrutiny and investigation. We need to make sure we have algorithmic accountability, auditing and meaningful transparency. We need to accept that bringing math and computation to messy, value-laden human affairs does not bring objectivity; rather, the complexity of human affairs invades the algorithms. Yes, we can and we should use computation to help us make better decisions. But we have to own up to our moral responsibility to judgment, and use algorithms within that framework, not as a means to abdicate and outsource our responsibilities to one another as human to human.
17:13
Machine intelligence is here. That means we must hold on ever tighter to human values and human ethics.
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