(Whistling) (Whistling ends) (Applause) Thank you. (Applause) Thank you very much. That was whistling. I'm trying to do this in English. What is a chubby, curly-haired guy from Holland -- why is he whistling? Well actually, I've been whistling since the age of four, about four. My dad was always whistling around the house, and I just thought that's part of communication in my family. So I whistled along with him. And actually, until I was 34, I always annoyed and irritated people with whistling, because, to be honest, my whistling is a kind of deviant behavior. I whistled alone, I whistled in the classroom, I whistled on bike, I whistled everywhere. And I also whistled at a Christmas Eve party with my family-in-law. And they had some, in my opinion, terrible Christmas music. And when I hear music that I don't like, I try to make it better. (Laughter) So when "Rudolph the Red-Nosed Reindeer" -- you know it? (Whistling) But it can also sound like this. (Whistling) But during a Christmas party -- at dinner, actually -- it's very annoying. So my sister-in-law asked me a few times, "Please stop whistling." And I just couldn't. And at one point -- and I had some wine, I have to admit that -- at one point I said, "If there was a contest, I would join." And two weeks later, I received a text message: "You're going to America." (Laughter) So, OK, I'm going to America. I would love to, but why? So I immediately called her up, of course. She googled, and she found this World Whistling Championship in America, of course. (Laughter) She didn't expect me to go there. And I would have lost my face. I don't know if that's correct English. But the Dutch people here will understand what I mean. (Laughter) I lost my face. (Applause) And she thought, "He will never go there." But actually, I did. So I went to Louisburg, North Carolina, southeast of the United States, and I entered the world of whistling. And I also entered the World Championship, and I won there, in 2004. (Applause) That was great fun, of course. And to defend my title -- like judokas do and sportsmen -- I thought, well let's go back in 2005 -- and I won again. (Laughter) Then I couldn't participate for a few years. And in 2008, I entered again in Japan, Tokyo, and I won again. So what happened now is I'm standing here in Rotterdam, in the beautiful city, on a big stage, and I'm talking about whistling. And actually, I earn my money whistling, at the moment. So I quit my day job as a nurse. (Applause) And I try to live my dream -- well, actually, it was never my dream, but it sounds so good. (Laughter) OK, I'm not the only one whistling here. You say, "Huh, what do you mean?" Well actually, you are going to whistle along. And then always the same thing happens: people are watching each other and think, "Oh, my God. Why? Can I go away?" No, you can't. (Laughter) Actually, it's very simple. The track that I will whistle is called "Fête de la Belle." It's about 80 minutes long. (Laughter) No, no, no. It's four minutes long. And I want to first rehearse with you your whistling. Yes, so I whistle the tone. (Whistling) (Laughter) Sorry, I forgot one thing -- you whistle the same tone as me. (Laughter) I heard a wide variety of tones. (Geert Chatrou and audience whistling) (Whistling ends) This is very promising. (Laughter) This is very promising. I'll ask the technicians to start the music. And if it's started, I just point where you whistle along, and we will see what happens. (Laughter) Oh, I'm so sorry, technicians. (Laughter) I'm so used to that. (Laughter) I start it myself. (Laughter) OK, here it is. (Laughter) (Music) (Whistling) (Whistling ends) (Music) OK. (Whistling) It's easy, isn't it? (Whistling) Now comes the solo, I propose I do that myself, OK? (Music) (Whistling) (Applause) Max Westerman: Geert Chatrou, the World Champion of Whistling. Geert Chatrou: Thank you. Thank you. So, embryonic stem cells are really incredible cells. They are our body's own repair kits, and they're pluripotent, which means they can morph into all of the cells in our bodies. Soon, we actually will be able to use stem cells to replace cells that are damaged or diseased. But that's not what I want to talk to you about, because right now there are some really extraordinary things that we are doing with stem cells that are completely changing the way we look and model disease, our ability to understand why we get sick, and even develop drugs. I truly believe that stem cell research is going to allow our children to look at Alzheimer's and diabetes and other major diseases the way we view polio today, which is as a preventable disease. So here we have this incredible field, which has enormous hope for humanity, but much like IVF over 35 years ago, until the birth of a healthy baby, Louise, this field has been under siege politically and financially. Critical research is being challenged instead of supported, and we saw that it was really essential to have private safe haven laboratories where this work could be advanced without interference. And so, in 2005, we started the New York Stem Cell Foundation Laboratory so that we would have a small organization that could do this work and support it. What we saw very quickly is the world of both medical research, but also developing drugs and treatments, is dominated by, as you would expect, large organizations, but in a new field, sometimes large organizations really have trouble getting out of their own way, and sometimes they can't ask the right questions, and there is an enormous gap that's just gotten larger between academic research on the one hand and pharmaceutical companies and biotechs that are responsible for delivering all of our drugs and many of our treatments, and so we knew that to really accelerate cures and therapies, we were going to have to address this with two things: new technologies and also a new research model. Because if you don't close that gap, you really are exactly where we are today. And that's what I want to focus on. We've spent the last couple of years pondering this, making a list of the different things that we had to do, and so we developed a new technology, It's software and hardware, that actually can generate thousands and thousands of genetically diverse stem cell lines to create a global array, essentially avatars of ourselves. And we did this because we think that it's actually going to allow us to realize the potential, the promise, of all of the sequencing of the human genome, but it's going to allow us, in doing that, to actually do clinical trials in a dish with human cells, not animal cells, to generate drugs and treatments that are much more effective, much safer, much faster, and at a much lower cost. So let me put that in perspective for you and give you some context. This is an extremely new field. In 1998, human embryonic stem cells were first identified, and just nine years later, a group of scientists in Japan were able to take skin cells and reprogram them with very powerful viruses to create a kind of pluripotent stem cell called an induced pluripotent stem cell, or what we refer to as an IPS cell. This was really an extraordinary advance, because although these cells are not human embryonic stem cells, which still remain the gold standard, they are terrific to use for modeling disease and potentially for drug discovery. So a few months later, in 2008, one of our scientists built on that research. He took skin biopsies, this time from people who had a disease, ALS, or as you call it in the U.K., motor neuron disease. He turned them into the IPS cells that I've just told you about, and then he turned those IPS cells into the motor neurons that actually were dying in the disease. So basically what he did was to take a healthy cell and turn it into a sick cell, and he recapitulated the disease over and over again in the dish, and this was extraordinary, because it was the first time that we had a model of a disease from a living patient in living human cells. And as he watched the disease unfold, he was able to discover that actually the motor neurons were dying in the disease in a different way than the field had previously thought. There was another kind of cell that actually was sending out a toxin and contributing to the death of these motor neurons, and you simply couldn't see it until you had the human model. So you could really say that researchers trying to understand the cause of disease without being able to have human stem cell models were much like investigators trying to figure out what had gone terribly wrong in a plane crash without having a black box, or a flight recorder. They could hypothesize about what had gone wrong, but they really had no way of knowing what led to the terrible events. And stem cells really have given us the black box for diseases, and it's an unprecedented window. It really is extraordinary, because you can recapitulate many, many diseases in a dish, you can see what begins to go wrong in the cellular conversation well before you would ever see symptoms appear in a patient. And this opens up the ability, which hopefully will become something that is routine in the near term, of using human cells to test for drugs. Right now, the way we test for drugs is pretty problematic. To bring a successful drug to market, it takes, on average, 13 years — that's one drug — with a sunk cost of 4 billion dollars, and only one percent of the drugs that start down that road are actually going to get there. You can't imagine other businesses that you would think of going into that have these kind of numbers. It's a terrible business model. But it is really a worse social model because of what's involved and the cost to all of us. So the way we develop drugs now is by testing promising compounds on -- We didn't have disease modeling with human cells, so we'd been testing them on cells of mice or other creatures or cells that we engineer, but they don't have the characteristics of the diseases that we're actually trying to cure. You know, we're not mice, and you can't go into a living person with an illness and just pull out a few brain cells or cardiac cells and then start fooling around in a lab to test for, you know, a promising drug. But what you can do with human stem cells, now, is actually create avatars, and you can create the cells, whether it's the live motor neurons or the beating cardiac cells or liver cells or other kinds of cells, and you can test for drugs, promising compounds, on the actual cells that you're trying to affect, and this is now, and it's absolutely extraordinary, and you're going to know at the beginning, the very early stages of doing your assay development and your testing, you're not going to have to wait 13 years until you've brought a drug to market, only to find out that actually it doesn't work, or even worse, harms people. But it isn't really enough just to look at the cells from a few people or a small group of people, because we have to step back. We've got to look at the big picture. Look around this room. We are all different, and a disease that I might have, if I had Alzheimer's disease or Parkinson's disease, it probably would affect me differently than if one of you had that disease, and if we both had Parkinson's disease, and we took the same medication, but we had different genetic makeup, we probably would have a different result, and it could well be that a drug that worked wonderfully for me was actually ineffective for you, and similarly, it could be that a drug that is harmful for you is safe for me, and, you know, this seems totally obvious, but unfortunately it is not the way that the pharmaceutical industry has been developing drugs because, until now, it hasn't had the tools. And so we need to move away from this one-size-fits-all model. The way we've been developing drugs is essentially like going into a shoe store, no one asks you what size you are, or if you're going dancing or hiking. They just say, "Well, you have feet, here are your shoes." It doesn't work with shoes, and our bodies are many times more complicated than just our feet. So we really have to change this. There was a very sad example of this in the last decade. There's a wonderful drug, and a class of drugs actually, but the particular drug was Vioxx, and for people who were suffering from severe arthritis pain, the drug was an absolute lifesaver, but unfortunately, for another subset of those people, they suffered pretty severe heart side effects, and for a subset of those people, the side effects were so severe, the cardiac side effects, that they were fatal. But imagine a different scenario, where we could have had an array, a genetically diverse array, of cardiac cells, and we could have actually tested that drug, Vioxx, in petri dishes, and figured out, well, okay, people with this genetic type are going to have cardiac side effects, people with these genetic subgroups or genetic shoes sizes, about 25,000 of them, are not going to have any problems. The people for whom it was a lifesaver could have still taken their medicine. The people for whom it was a disaster, or fatal, would never have been given it, and you can imagine a very different outcome for the company, who had to withdraw the drug. So that is terrific, and we thought, all right, as we're trying to solve this problem, clearly we have to think about genetics, we have to think about human testing, but there's a fundamental problem, because right now, stem cell lines, as extraordinary as they are, and lines are just groups of cells, they are made by hand, one at a time, and it takes a couple of months. This is not scalable, and also when you do things by hand, even in the best laboratories, you have variations in techniques, and you need to know, if you're making a drug, that the Aspirin you're going to take out of the bottle on Monday is the same as the Aspirin that's going to come out of the bottle on Wednesday. So we looked at this, and we thought, okay, artisanal is wonderful in, you know, your clothing and your bread and crafts, but artisanal really isn't going to work in stem cells, so we have to deal with this. But even with that, there still was another big hurdle, and that actually brings us back to the mapping of the human genome, because we're all different. We know from the sequencing of the human genome that it's shown us all of the A's, C's, G's and T's that make up our genetic code, but that code, by itself, our DNA, is like looking at the ones and zeroes of the computer code without having a computer that can read it. It's like having an app without having a smartphone. We needed to have a way of bringing the biology to that incredible data, and the way to do that was to find a stand-in, a biological stand-in, that could contain all of the genetic information, but have it be arrayed in such a way as it could be read together and actually create this incredible avatar. We need to have stem cells from all the genetic sub-types that represent who we are. So this is what we've built. It's an automated robotic technology. It has the capacity to produce thousands and thousands of stem cell lines. It's genetically arrayed. It has massively parallel processing capability, and it's going to change the way drugs are discovered, we hope, and I think eventually what's going to happen is that we're going to want to re-screen drugs, on arrays like this, that already exist, all of the drugs that currently exist, and in the future, you're going to be taking drugs and treatments that have been tested for side effects on all of the relevant cells, on brain cells and heart cells and liver cells. It really has brought us to the threshold of personalized medicine. It's here now, and in our family, my son has type 1 diabetes, which is still an incurable disease, and I lost my parents to heart disease and cancer, but I think that my story probably sounds familiar to you, because probably a version of it is your story. At some point in our lives, all of us, or people we care about, become patients, and that's why I think that stem cell research is incredibly important for all of us. Thank you. (Applause) (Applause) [A provocation from Danny Hillis:] [It's time to start talking about engineering our climate] What if there was a way to build a thermostat that allowed you to turn down the temperature of the earth anytime you wanted? Now, you would think if somebody had a plausible idea about how to do that, everybody would be very excited about it, and there would be lots of research on how to do it. But in fact, a lot of people do understand how to do that. But there's not much support for research in this area. And I think part of it is because there are some real misunderstandings about it. So I'm not going to try to convince you today that this is a good idea. But I am going to try to get your curiosity going about it and clear up some of the misunderstandings. So, the basic idea of solar geoengineering is that we can cool things down just by reflecting a little bit more sunlight back into space. And ideas about how to do this have been around literally for decades. Clouds are a great way to do that, these low-lying clouds. Everybody knows it's cooler under a cloud. I like this cloud because it has exactly the same water content as the transparent air around it. And it just shows that even a little bit of a change in the flow of the air can cause a cloud to form. We make artificial clouds all the time. These are contrails, which are artificial water clouds that are made by the passing of a jet engine. And so, we're already changing the clouds on earth. By accident. Or, if you like to believe it, by supersecret government conspiracy. (Laughter) But we are already doing this quite a lot. This is a NASA picture of shipping lanes. Passing ships actually cause clouds to form, and this is a big enough effect that it actually helps reduce global warming already by about a degree. So we already are doing solar engineering. There's lots of ideas about how to do this. People have looked at everything, from building giant parasols out into space to fizzing bubble waters in the ocean. And some of these are actually very plausible ideas. One that was published recently by David Keith at Harvard is to take chalk and put dust up into the stratosphere, where it reflects off sunlight. And that's a really neat idea, because chalk is one of the most common minerals on earth, and it's very safe -- it's so safe, we put it into baby food. And basically, if you throw chalk up into the stratosphere, it comes down in a couple of years all by itself, dissolved in rainwater. Now, before you start worrying about all this chalk in your rainwater, let me explain to you how little of it it actually takes. And that turns out to be very easy to calculate. This is a back-of-the-envelope calculation I made. (Laughter) (Applause) I assure you, people have done much more careful calculations, and it comes out with the same answer, which is that you have to put chalk up at the rate of about 10 teragrams a year to undo the effects of the CO2 that we've already done -- just in terms of temperature, not all the effects, but the temperature. So what does that look like? I can't visualize 10 teragrams per year. So I asked the Cambridge Fire Department and Taylor Milsal to lend me a hand. This is a hose pumping water at 10 teragrams a year. And that is how much you would have to pump into the stratosphere to cool the earth back down to pre-industrial levels. And it's amazingly little; it's like one hose for the entire earth. Now of course, you wouldn't really use a hose, you'd fly it up in airplanes or something like that. But it's so little, it would be like putting a handful of chalk into every Olympic swimming pool full of rain. It's almost nothing. So why don't people like this idea? Why isn't it taken more seriously? And there are some very good reasons for that. A lot of people really don't think we should be talking about scott66@example.net. And, in fact, I have some very good friends in the audience who I respect a lot, who really don't think I should be talking about this. And the reason is that they're concerned that if people imagine there's some easy way out, that we won't give up our addiction to fossil fuels. And I do worry about that. I think it's actually a serious problem. But there's also, I think, a deeper problem, which is: nobody likes the idea of messing with the entire earth -- I certainly don't. I love this planet, I really do. And I don't want to mess with it. But we're already changing our atmosphere, we're already messing with it. And so I think it makes sense for us to look for ways to mitigate that impact. And we need to do research to do that. We need to understand the science behind that. I've noticed that there's a theme that's kind of developed at TED, which is kind of, "fear versus hope," or "creativity versus caution." And of course, we need both of those. So there aren't any silver bullets. This is certainly not a silver bullet. But we need science to tell us what our options are; that informs both our creativity and our caution. So I am an optimist about our future selves, but I'm not an optimist because I think our problems are small. I'm an optimist because I think our capacity to deal with our problems is much greater than we imagine. Thank you very much. (Applause) This talk sparked a lot of controversy at TED2017, and we encourage you to look at discussions online to see other points of view. I'd like to share with you the story of one of my patients called Celine. Celine is a housewife and lives in a rural district of Cameroon in west Central Africa. Six years ago, at the time of her HIV diagnosis, she was recruited to participate in the clinical trial which was running in her health district at the time. When I first met Celine, a little over a year ago, she had gone for 18 months without any antiretroviral therapy, and she was very ill. She told me that she stopped coming to the clinic when the trial ended because she had no money for the bus fare and was too ill to walk the 35-kilometer distance. Now during the clinical trial, she'd been given all her antiretroviral drugs free of charge, and her transportation costs had been covered by the research funds. All of these ended once the trial was completed, leaving Celine with no alternatives. She was unable to tell me the names of the drugs she'd received during the trial, or even what the trial had been about. I didn't bother to ask her what the results of the trial were because it seemed obvious to me that she would have no clue. Yet what puzzled me most was Celine had given her informed consent to be a part of this trial, yet she clearly did not understand the implications of being a participant or what would happen to her once the trial had been completed. Now, I have shared this story with you as an example of what can happen to participants in the clinical trial when it is poorly conducted. Maybe this particular trial yielded exciting results. Maybe it even got published in a high-profile scientific journal. Maybe it would inform clinicians around the world on how to improve on the clinical management of HIV patients. But it would have done so at a price to hundreds of patients who, like Celine, were left to their own devices once the research had been completed. I do not stand here today to suggest in any way that conducting HIV clinical trials in developing countries is bad. On the contrary, clinical trials are extremely useful tools, and are much needed to address the burden of disease in developing countries. However, the inequalities that exist between richer countries and developing countries in terms of funding pose a real risk for exploitation, especially in the context of externally-funded research. Sadly enough, the fact remains that a lot of the studies that are conducted in developing countries could never be authorized in the richer countries which fund the research. I'm sure you must be asking yourselves what makes developing countries, especially those in sub-Saharan Africa, so attractive for these HIV clinical trials? Well, in order for a clinical trial to generate valid and widely applicable results, they need to be conducted with large numbers of study participants and preferably on a population with a high incidence of new HIV infections. Sub-Saharan Africa largely fits this description, with 22 million people living with HIV, an estimated 70 percent of the 30 million people who are infected worldwide. Also, research within the continent is a lot easier to conduct due to widespread poverty, endemic diseases and inadequate health care systems. A clinical trial that is considered to be potentially beneficial to the population is more likely to be authorized, and in the absence of good health care systems, almost any offer of medical assistance is accepted as better than nothing. Even more problematic reasons include lower risk of litigation, less rigorous ethical reviews, and populations that are willing to participate in almost any study that hints at a cure. As funding for HIV research increases in developing countries and ethical review in richer countries become more strict, you can see why this context becomes very, very attractive. The high prevalence of HIV drives researchers to conduct research that is sometimes scientifically acceptable but on many levels ethically questionable. How then can we ensure that, in our search for the cure, we do not take an unfair advantage of those who are already most affected by the pandemic? I invite you to consider four areas I think we can focus on in order to improve the way in which things are done. The first of these is informed consent. Now, in order for a clinical trial to be considered ethically acceptable, participants must be given the relevant information in a way in which they can understand, and must freely consent to participate in the trial. This is especially important in developing countries, where a lot of participants consent to research because they believe it is the only way in which they can receive medical care or other benefits. Consent procedures that are used in richer countries are often inappropriate or ineffective in a lot of developing countries. For example, it is counterintuitive to have an illiterate study participant, like Celine, sign a lengthy consent form that they are unable to read, let alone understand. Local communities need to be more involved in establishing the criteria for recruiting participants in clinical trials, as well as the incentives for participation. The information in these trials needs to be given to the potential participants in linguistically and culturally acceptable formats. The second point I would like for you to consider is the standard of care that is provided to participants within any clinical trial. Now, this is subject to a lot of debate and controversy. Should the control group in the clinical trial be given the best current treatment which is available anywhere in the world? Or should they be given an alternative standard of care, such as the best current treatment available in the country in which the research is being conducted? Is it fair to evaluate a treatment regimen which may not be affordable or accessible to the study participants once the research has been completed? Now, in a situation where the best current treatment is inexpensive and simple to deliver, the answer is straightforward. However, the best current treatment available anywhere in the world is often very difficult to provide in developing countries. It is important to assess the potential risks and benefits of the standard of care which is to be provided to participants in any clinical trial, and establish one which is relevant for the context of the study and most beneficial for the participants within the study. That brings us to the third point I want you think about: the ethical review of research. An effective system for reviewing the ethical suitability of clinical trials is primordial to safeguard participants within any clinical trial. Unfortunately, this is often lacking or inefficient in a lot of developing countries. Local governments need to set up effective systems for reviewing the ethical issues around the clinical trials which are authorized in different developing countries, and they need to do this by setting up ethical review committees that are independent of the government and research sponsors. Public accountability needs to be promoted through transparency and independent review by nongovernmental and international organizations as appropriate. The final point I would like for you to consider tonight is what happens to participants in the clinical trial once the research has been completed. I think it is absolutely wrong for research to begin in the first place without a clear plan for what would happen to the participants once the trial has ended. Now, researchers need to make every effort to ensure that an intervention that has been shown to be beneficial during a clinical trial is accessible to the participants of the trial once the trial has been completed. In addition, they should be able to consider the possibility of introducing and maintaining effective treatments in the wider community once the trial ends. If, for any reason, they feel that this might not be possible, then I think they should have to ethically justify why the clinical trial should be conducted in the first place. Now, fortunately for Celine, our meeting did not end in my office. I was able to get her enrolled into a free HIV treatment program closer to her home, and with a support group to help her cope. Her story has a positive ending, but there are thousands of others in similar situations who are much less fortunate. Although she may not know this, my encounter with Celine has completely changed the way in which I view HIV clinical trials in developing countries, and made me even more determined to be part of the movement to change the way in which things are done. I believe that every single person listening to me tonight can be part of that change. If you are a researcher, I hold you to a higher standard of moral conscience, to remain ethical in your research, and not compromise human welfare in your search for answers. If you work for a funding agency or pharmaceutical company, I challenge you to hold your employers to fund research that is ethically sound. If you come from a developing country like myself, I urge you to hold your government to a more thorough review of the clinical trials which are authorized in your country. Yes, there is a need for us to find a cure for HIV, to find an effective vaccine for malaria, to find a diagnostic tool that works for T.B., but I believe that we owe it to those who willingly and selflessly consent to participate in these clinical trials to do this in a humane way. Thank you. Hello, I'm Joy, a poet of code, on a mission to stop an unseen force that's rising, a force that I called "the coded gaze," my term for algorithmic bias. Algorithmic bias, like human bias, results in unfairness. However, algorithms, like viruses, can spread bias on a massive scale at a rapid pace. Algorithmic bias can also lead to exclusionary experiences and discriminatory practices. Let me show you what I mean. (Video) Joy Buolamwini: Hi, camera. I've got a face. Can you see my face? No-glasses face? You can see her face. What about my face? I've got a mask. Can you see my mask? Joy Buolamwini: So how did this happen? Why am I sitting in front of a computer in a white mask, trying to be detected by a cheap webcam? Well, when I'm not fighting the coded gaze as a poet of code, I'm a graduate student at the MIT Media Lab, and there I have the opportunity to work on all sorts of whimsical projects, including the Aspire Mirror, a project I did so I could project digital masks onto my reflection. So in the morning, if I wanted to feel powerful, I could put on a lion. If I wanted to be uplifted, I might have a quote. So I used generic facial recognition software to build the system, but found it was really hard to test it unless I wore a white mask. Unfortunately, I've run into this issue before. When I was an undergraduate at Georgia Tech studying computer science, I used to work on social robots, and one of my tasks was to get a robot to play peek-a-boo, a simple turn-taking game where partners cover their face and then uncover it saying, "Peek-a-boo!" The problem is, peek-a-boo doesn't really work if I can't see you, and my robot couldn't see me. But I borrowed my roommate's face to get the project done, submitted the assignment, and figured, you know what, somebody else will solve this problem. Not too long after, I was in Hong Kong for an entrepreneurship competition. The organizers decided to take participants on a tour of local start-ups. One of the start-ups had a social robot, and they decided to do a demo. The demo worked on everybody until it got to me, and you can probably guess it. It couldn't detect my face. I asked the developers what was going on, and it turned out we had used the same generic facial recognition software. Halfway around the world, I learned that algorithmic bias can travel as quickly as it takes to download some files off of the internet. So what's going on? Why isn't my face being detected? Well, we have to look at how we give machines sight. Computer vision uses machine learning techniques to do facial recognition. So how this works is, you create a training set with examples of faces. This is a face. This is a face. This is not a face. And over time, you can teach a computer how to recognize other faces. However, if the training sets aren't really that diverse, any face that deviates too much from the established norm will be harder to detect, which is what was happening to me. But don't worry -- there's some good news. Training sets don't just materialize out of nowhere. We actually can create them. So there's an opportunity to create full-spectrum training sets that reflect a richer portrait of humanity. Now you've seen in my examples how social robots was how I found out about exclusion with algorithmic bias. But algorithmic bias can also lead to discriminatory practices. Across the US, police departments are starting to use facial recognition software in their crime-fighting arsenal. Georgetown Law published a report showing that one in two adults in the US -- that's 117 million people -- have their faces in facial recognition networks. Police departments can currently look at these networks unregulated, using algorithms that have not been audited for accuracy. Yet we know facial recognition is not fail proof, and labeling faces consistently remains a challenge. You might have seen this on Facebook. My friends and I laugh all the time when we see other people mislabeled in our photos. But misidentifying a suspected criminal is no laughing matter, nor is breaching civil liberties. Machine learning is being used for facial recognition, but it's also extending beyond the realm of computer vision. In her book, "Weapons of Math Destruction," data scientist Cathy O'Neil talks about the rising new WMDs -- widespread, mysterious and destructive algorithms that are increasingly being used to make decisions that impact more aspects of our lives. So who gets hired or fired? Do you get that loan? Do you get insurance? Are you admitted into the college you wanted to get into? Do you and I pay the same price for the same product purchased on the same platform? Law enforcement is also starting to use machine learning for predictive policing. Some judges use machine-generated risk scores to determine how long an individual is going to spend in prison. So we really have to think about these decisions. Are they fair? And we've seen that algorithmic bias doesn't necessarily always lead to fair outcomes. So what can we do about it? Well, we can start thinking about how we create more inclusive code and employ inclusive coding practices. It really starts with people. So who codes matters. Are we creating full-spectrum teams with diverse individuals who can check each other's blind spots? On the technical side, how we code matters. Are we factoring in fairness as we're developing systems? And finally, why we code matters. We've used tools of computational creation to unlock immense wealth. We now have the opportunity to unlock even greater equality if we make social change a priority and not an afterthought. And so these are the three tenets that will make up the "incoding" movement. Who codes matters, how we code matters and why we code matters. So to go towards incoding, we can start thinking about building platforms that can identify bias by collecting people's experiences like the ones I shared, but also auditing existing software. We can also start to create more inclusive training sets. Imagine a "Selfies for Inclusion" campaign where you and I can help developers test and create more inclusive training sets. And we can also start thinking more conscientiously about the social impact of the technology that we're developing. To get the incoding movement started, I've launched the Algorithmic Justice League, where anyone who cares about fairness can help fight the coded gaze. On codedgaze.com, you can report bias, request audits, become a tester and join the ongoing conversation, #codedgaze. So I invite you to join me in creating a world where technology works for all of us, not just some of us, a world where we value inclusion and center social change. Thank you. (Applause) But I have one question: Will you join me in the fight? (Laughter) (Applause) Today, more than half of the world's population lives in cities. The urbanization process started in the late 1700s and has been increasing since then. The prediction is that by 2050, 66 percent of the population will live in cities and the United Nations, the World Health Organization, the World Economic Forum, are warning us, if we don't plan for the increased density, current problems in our cities, like inequality, congestion, crime can only get worse. As a result, urban planners and city developers are putting a lot of effort and creativity in designing our future, denser, bigger cities. But I have a different opinion. I think urbanization is actually reaching the end of its cycle, and now people are going to start moving back to the countryside. And you may think, "But what about the trend?" Well, let me tell you, socioeconomic trends don't last forever. You know, 12,000 years ago everybody was perfectly happy roaming the land, hunting and gathering. And then, the trend changes, and the new thing is to live in a farm and have cattle, until it changes again. When we get to the industrial revolution. Actually, that is what started the urbanization process. And you know what triggered it? Steam power, machines, new chemical processes -- in two words, technological innovation. And I believe technology can also bring the end of this cycle. I've been working on innovation for most of my career. I love it. I love my job. It allows me to work with drones, with 3D printers and smart glasses, and not just those you can buy in the shop but also prototypes. It's a lot of fun sometimes. Now, some of these technologies are opening new possibilities that will radically change the way we did things before and in a few years, they may allow us to enjoy the benefits of city life from anywhere. Think about it. If you could live in a place with a lower crime rate and more space and a lower cost of living and less traffic, of course many people would want that, but they feel they don't have a choice. You have to live in the city. Well, in the past, people moved to the cities not because they loved the city itself but for the things you could have in a city, more job opportunities, easier access to services and goods and a rich social life. So let's dive deeper. More jobs and career opportunities. Is that still true today, because the office people are starting to realize that working in the office and being in the office may not be the same thing anymore. According to a study by Global Workplace Analytics, more than 80 percent of the US workforce would like to work from home. And do you know how much it costs for a company to even have an office? 11,000 dollars per employee per year. If only half of those workers would telework even 50 percent of the time, the savings in the states would exceed 500 billion dollars, and it could reduce greenhouse gases by 54 million tons. That is the equivalent of 10 million cars off the streets for a whole year. But even though most people would want to telework, current technology makes the experience isolating. It's not comfortable. It doesn't feel like being there. But that is going to change by the convergence of two technologies: augmented reality and telepresence robots. Augmented reality already today allows you to take your office environment everywhere with you. All you need is a wearable computer, a pair of smart glasses, and you can take your emails and your spreadsheets with you wherever you go. And video conferences and video calls have become very common these days, but they still need improvement. I mean, all those little faces on a flat screen, sometimes you don't even know who is talking. Now, we already have something way better than static videocalls: your average telepresence robot. I call it tablet on a stick. (Laughter) You can control, you can move around, you can control what you're looking at. It's way better, but far from perfect. You know how they say that most human communication is nonverbal? Well, the robot doesn't give you any of that. It looks like an alien. But with advances in augmented reality, it will be easy to wrap the robot in a nice hologram that actually looks and moves like a person. That will do it. Or else, forget the robot. We go full VR, and everybody meets in cyberspace. Give it a couple of years and that will feel so real, you won't tell the difference. So what was the next reason why people move to cities? Access to services and goods. But today you can do all that online. According to a study made by comScore, online shoppers in the US last year did more than half of their retail purchases online, and the global market for e-commerce is estimated to be at two trillion dollars. And it's expected to reach 2.38 by the end of 2017, according to eMarketer. Now, from a logistics standpoint, density is good for deliveries. Supplying goods to a shopping mall is easy. You can send big shipments to the shop, and people will go there, pick it up and take it home themselves. E-commerce means we need to ship onesies and have them home delivered. That's more expensive. It's like the difference between having a birthday party for 20 people or bringing a piece of the cake to each of your 20 friends at their place. But at least in the city, they live close to each other. Density helps. Now, e-commerce deliveries in the countryside, those take forever. The truck sometimes needs to drive miles between one address and the next one. Those are the most expensive deliveries of all. But we already have a solution for that: drones. A vehicle carrying a squadron of drones. The driver does some of the deliveries while the drones are flying back and forth from the truck as it moves. That way, the average cost for delivery is reduced, and voila: affordable e-commerce services in the countryside. You will see: the new homes of our teleworkers will probably have a drone pod in the yard. So once the final mile delivery is not a problem, you don't need to be in the city to buy things anymore. So that's two. Now, what was the third reason why people move to cities? A rich social life. They would need to be in the city for that these days. Because people these days, they make friends, they chat, gossip and flirt from the comfort of their sofa. (Laughter) And while wearing their favorite pajamas. (Laughter) There are over two billion active social media users in the world. In a way, that makes you think like we are connected no matter where we are. But OK, not completely. Sometimes you still need some real human contact. Ironically, the city, with its population density, is not always the best for that. Actually, as social groups become smaller, they grow stronger. A recent study made in the UK by the Office for National Statistics showed a higher life satisfaction rating among people living in rural areas. So as people settle in the countryside, well, they will buy local groceries, fresh groceries, foodstuff, maintenance services. So handymen, small workshops, service companies will thrive. Maybe some of the industrial workers from the cities displaced by the automation will find a nice alternative job here, and they will move too. And as people move to the countryside, how is that going to be? Think about autonomous, off-the-grid houses with solar panels, with wind turbines and waste recycling utilities, our new homes producing their own energy and using it to also power the family car. I mean, cities have always been regarded as being more energy-efficient, but let me tell you, repopulating the countryside can be eco too. By now, you're probably thinking of all the advantages of country living. (Laughter) I did it myself. Six years ago, my wife and I, we packed our stuff, we sold our little apartment in Spain, and for the same money we bought a house with a garden and little birds that come singing in the morning. (Laughter) It's so nice there. And we live in a small village, not really the countryside yet. That is going to be my next move: a refurbished farmhouse, not too far from a city, not too close. And now we'll make sure to have a good spot for drones to land. (Laughter) But hey, that's me. It doesn't have to be you, because it would seem like I'm trying to convince somebody to come join us in the country. I'm not. (Laughter) I don't need more people to come. (Laughter) I just think they will