Playing God? Towards machines that deny their maker
Picard, Rosalind
Today, technology is acquiring the capability to sense and respond skilfully to human emotion, and in some cases to act as if it has emotional processes that make it more intelligent. What is driving this progress? I‘ll give examples of the potential for good with this technology in helping people with autism, epilepsy, and mental health challenges such as anxiety and depression. However, the possibilities are much larger: Affect sensing is being used to make ads less boring, and to sell more products. Social robots will need emotional intelligence, in order to not annoy us. Some organizations want to sense human emotion without people knowing or consenting, for example, to detect terrorists in public settings. A few scientists want to build computers that are vastly superior to humans, capable of powers beyond reproducing their own kind. What do we want to see built? And, how might we make sure that new affective technologies make human lives better?
 Thank you. It's a real honor and a pleasure for me to be here today. I've been enjoying a lot of very cool demonstrations of the technology here and I'm really impressed with how bright everybody is and I've heard how amazing the students are that get to come here as well. As was said in the introduction, I wrote a book called Affective Computing and at the time I was inspired by artificial intelligence and by my senior colleagues who wanted to build a machine that as my friend Marvin Minsky put it, that would be so amazing, it would be so intelligent that we'd be lucky if it kept us around us household pets. And I thought, well, I like the first part of that but I wasn't really keen on being a household pet, being somebody's dog or cat. That did not feel like all humans could be. And when the Media Lab was founded, we started to think not so much about building the monolithic AI, but about how technology with people could make our lives and our futures much better than just in the movies that we had seen. So I started thinking what kind of intelligent machine would I want to build and I started realizing that one of the important skills we needed in human intelligence was emotional intelligence. Now, I really did not want to be associated with emotion in the early days as a woman in science and engineering. I thought, wait, this will kill my career. So I tried to get some guys to work on it. So I tried publishing a paper which was rejected and so then I wrote a book. And, you know, they want to make money off books so they don't reject books. So anyhow, then what I really wanted to do though was build the technology and see if it worked. And so we started to try to build technology that would show respect for people's feelings. And the first feeling I thought was most common in front of computers was when you feel frustration. Okay, you have a deadline, right? You're trying to do something important and the stupid paper clip pops up or some other, you know, right? Now, why can't it see that I'm busy and I'm concentrating and not do that then, right? An intelligent human would look at your feelings, look at what matters to you because what matters is reflected in your emotions and it would change what it does in response. So we tried to figure out how to automate this and we've done lots of experiments where we frustrate people. I could fill hours with them. Here's an example of a person coming in and I'll have to tell you if you try to do experiments in this space, you cannot tell people that you're trying to frustrate them. It doesn't work. You have to tell them something else. So in this case we told him he was testing the usability of a new system and unbeknownst to him, it's going to give him some problems. I am sorry. You seem to be having difficulties getting through this form. Wouldn't you mind looking at the camera and sharing your thoughts on how to improve the form? We can all relate, right? So just so you can see, we frustrated people. Now, at the same time I was taught and I wrote about this in my book and if you have my book you can now cross out this part of the book because I no longer believe it. I was taught that if you want to detect if somebody is truly happy then you look not for the smile of action unit 12, the lip corner pull but you look for the smile of the eyes, action unit 6, the cheek raise, what gives us crow's feet as we age. So the theorist always said if you see both of these then the person is truly happy. So watch what happens in this next situation. We do the same thing you just saw to the student. But look at the smile and the eyes. So what we found was that the smile of delight could show up when people were really frustrated. In fact it showed up 90% of the time when people were frustrated. And this was a surprise. And since then we have found over and over that there's the theory of what people do and then there's what people do. And we need to always be looking and showing more respect for what people really do and then making the theory match the practice. Now we were interested in facial expressions also because we not only do they signal at the largest distance something like delight and it turns out actually we were able to teach a computer how to recognize the delight versus the frustration and do so more accurately than a person could recognize it using some dynamic machine learning that recognized the speed of the expression not just the peak of the expression. But it doesn't mean that machines could always recognize this better than people. It was good at doing it in a very focused directed context. And we were trying to do this to help people on the autism spectrum who when they heard that we were building computers to recognize emotion the brother of a person on the autism spectrum came to me and said can you help my brother? And I said I don't know tell me about your brother. And he said his brother has autism and he has trouble reading other people's emotions. So we realized we could put the little glasses a little camera in the glasses and this was long before Google Glass. This was more than a decade before that. And we also put LEDs in. So the computer would in real time recognize if you were smiling or looking interested I would get a green light while I'm looking at you. If you look at me and you look confused I get a yellow light like slow down Ross. And if if you're like really distracted you know or frowning or looking like you really need to think about something else I get the red light to stop. I know you probably wish you had that for all the professors who lecture. We could do better with that information. So then we worked with children with autism. Hello. Hello. How are you? Check that out. It's me shooting you shooting me. How are you? Good. How are you? Good. I like this thing. You like it? I like this thing. I made a perfect video. You know there are heat sensors on cases now for light detection. If you get the face with it and the tape and analyze that later, that way you can get the tools. So he might not even need the additional three voices with this. He just says he'll use it as a tool later would be very helpful. I did notice that the wires, it would probably be easier if it was more of a self-contained system. You don't really understand something until you teach it to somebody else. So we started to learn from people on the autism spectrum what they wished that we could do. And we realized we had a lot of improvements to make. Early on the technology was pretty good on well-lit, well-controlled lab data, more than 80% accurate, recognizing six different common expressions. But it wasn't anywhere near 100%. And when you went into the real world, the rates, the accuracy went down 50%, 60%. And we had spent, you know, with our partners in the UK, over a million dollars of research funding trying to get this far. And to get the data that we would need to get the accuracy up a lot higher was going to cost millions more that we didn't think anybody would give us. So we started thinking how can we get enough data to make this really accurate and really helpful for people. And here's my co-founder of Affectiva, Rana Elkaliubi, saying a little bit more about what we did. This is on building computers that can read people's emotional states through their facial expressions. We've built technology that looks at people's facial expressions. It identifies the main feature points on your face, like your eyes, your eyebrows, your nose, and then it tracks how they move over time. When I smile, the green smile track goes up, and when I nod my head, the head nod track goes up. And when I shake my head as in disagreement, you see the pink line go up. We use thousands of videos of people showing agreement, showing disagreement in order for the computer to learn what it looks like liking, concentrating, disagreeing. Making this technology online opens a whole lot of new applications. Almost everybody with a computer today has a webcam, or a lot of smartphones have cameras too. So if we enable people to opt in, turn the camera on, they can share their experiences online. It's a more sophisticated version of the like button on Facebook. So we decided to see if people would turn on their cameras online, and we started by deploying the most popular ads and recording people's faces while they smiled. Thank you. Did this Volkswagen ad air over here? It became the number one ad in the U.S., and they paid millions of Super Bowl. And with the smiling that peaked at the end, we could see that when people saw it again, they smiled even more. And after they had become familiar with the ad, they actually smiled before the punchline. So it was a way to sort of detect if they knew the good part was coming. Now by capturing the entertainment, then you could get people to pay people to watch content to see their facial expressions. And with that, we were able to go from hundreds of examples in the lab to millions of examples online and get the accuracy much better. The work to do this, I liken it kind of to taking, you know, we wanted to build this autism technology. We wanted to just run to the front door and open it when the doorbell rang. But it's like these old cartoons where the kids run all over the house before they get there. We had to do a lot of other things in order to get the data. And that now is an app that you can, I think, still download for free to do a demo version of and try out all the different facial affect recognition that it has right now on your iOS or Android device. But don't do it right now in the talk. Do it later. Now, Rana, who you saw was from Cairo, and the Arab Spring was happening as we were starting this. And we realized that while there were a million people in Tahrir Square, Mubarak might have been saying at the time that a million isn't even a majority in Egypt, right? Maybe the million in Tahrir Square don't like me. But Egypt has another 79 million people, and they're not in Tahrir Square, so they must like me. We can often think what we want to think, right? But if we could opt, we could give people the chance to listen to the speech, and anonymously, we record no identifying data, anonymously have their not just smiles recognized like you saw there, but we can recognize a lot of expressions. In particular, we can recognize smirks. We see a lot of these with politics in the US, these asymmetric smiles. So if Mubarak's talking and they're doing this, then it doesn't mean that most of the people listening like it. And that feedback, if it could be kept private in a way that protects people, could be very valuable. There is a question though about could that be done in a way that they couldn't see that people in a particular area were disgruntled and take some action that we regret there. So these are questions we face. The power of the nonverbal voice to give people feedback is very strong, but the truth is also very powerful and can be upsetting to people in power. Now, while we built this for people with autism and wound up doing these other things to get data, I was talking with a friend with autism a little bit later, and she said to me, Ross, you, yes, you know, some people with autism want to recognize other people's facial expressions, but actually you have it wrong. That's not our biggest problem. Our biggest problem is not recognizing other people's emotions. Our biggest problem is you recognizing our emotions. And she's pointing at me and I'm like, oh, great. I'm not doing so well, huh? And she goes, not just you. We, you know, everybody, we all could do better recognizing the emotions of people with autism. In particular, I learned by talking to my friends more that they have a lot of anxiety and stress that was not being recognized by other human beings. And things like the fluorescent lights or the smells or the sounds were extremely stressful, and they wanted help with that. I started getting emails from people who learned that what our technology could do. Here's one from a father who wrote me from Holland. He said, my colleague told me about a new fascinating technology he heard on BBC Radio 4 where computers were able to detect a person's mood. He thought of it as a useful technology to prevent him from smashing his laptop into pieces. But I thought immediately of something else. I have an eight year old son who is diagnosed with ASS. Unfortunately, I only know the Dutch term, but in English it must be something like autism spectrum disorder. In general, he is a normal, intelligent boy. Autism is the last thing that comes to mind when you see him. But sometimes he starts disturbing the classroom or responds too aggressive against other children. It's his third year in school and he is already in his third school. For the moment, he is going to a special school for children with learning disorders, but things still do not go as expected. We are going from one test center to another for four years now, and the pressure on our family is really hard. So I asked my colleague the details of the radio program. And when I listened to the recording, I had to pause it halfway through as I was getting too emotional. I believe this technology can help us in two important ways. And he goes on and he describes if this could help name my son's emotions, the fear, the anger, the concentration and help him recognize them. For example, we discovered lately that when he is hungry, he becomes aggressive without even knowing that he feels hunger. How long can he concentrate in the class and how long is there between the loss of concentration and his aggressive outbursts? We also believe he has a lot of fear. How much would that affect the day? And he went on and asked if we could please build technology that would help him. We would really appreciate your help. My son is an intelligent child and this could be decisive for his school career. Who knows what contribution he can make back towards the future? So I got letters like this and I'm happy in my lab working away. And I think I need to get this technology out of the lab. We need to take it and make it something that can help these children better understand their emotions and better communicate them to other people. Now we knew from measuring emotion in the lab that we could measure the autonomic arousal that often precedes meltdowns and frustration and anger and stress. So we had built versions that could take the usual things like the electrodermal activity often called skin conductance, often measured by skin conductance, an old name for it is galvanic skin response, and measure it in other ways that were less obtrusive and easy to get in daily life. And full disclosure, I'm now a co-founder of Empatica that makes two sensors in the space, which I'm recording myself with right now. My own stress level is up here as we speak. One of them, the E4, gives two kinds of autonomic data, the electrodermal activity, which I'll be saying more about on the top, and the blood volume pulse, which I won't say too much about here other than if you're interested in heart rate variability. It's been benchmarked to ECG and is within 2% error of a very expensive ECG for measuring heart rate variability when you're still. And if you start moving, you get more motion artifacts. Now, what does this electrodermal activity tell us? Here's an example of a very early prototype we built worn on a child's ankle that shows you it going up with the child having a meltdown. I'm going to turn down the volume. This is a little girl with autism. The stuff on her head has nothing to do with what we're measuring here, but she's about to hit a peak and have a meltdown here. The blue window is one minute wide, and that corresponds to that blue stripe at the bottom. The bottom is 45 minutes wide. So here she is, peaking with a meltdown. We also see it's been building slowly the whole time. Now, just because it's building doesn't mean a meltdown is coming, but if it's building and you don't see any reason why it's building, you might want to watch out. Also, it goes down when the arousal declines, when there's calming. And here we see it, first actually we see it peak when she's getting on a swing. Three kinds of stress can make the signal go up. The affective autonomic arousal, the emotional arousal, the physical effort or exertion of motor planning to get on the swing and cognitive load, just tasks like counting backwards by sevens can make it go up. But here we see rhythmic activity tends to make it go down. So when I was in your lab and looking at these beds rocking people, we would expect something like that to make this signal decline. And in autism, I've seen some people rocking, and I've seen this go down like a slide on a playground. Here's the first time we got to look at the electrodermal activity from a MIT student, 24-7. And what we saw was no surprise here that it was peaking with lab activity and MIT homeworks and problem sets, very hard, probably just like here. Intense work. To our surprise, the biggest peak of every day is sleep. And I won't go into that here, but we've got a lot of papers online talking about this now. It peaks during non-rim sleep, not during rim. It peaks during the latter part, usually slow waves, stage three, sometimes stage two, and very rarely in stage one in rim. And it right now appears to be very useful in predicting memory consolidation. So we had the biggest peak during sleep, which should bother you because that doesn't seem arousing at all. But the biggest awake peaks are during homework and lab. Oh, and to the embarrassment of MIT professors, the low point every day is classroom activity. So I will keep trying to get you to laugh, and I really do want you to ask questions because when you do that, everybody's autonomic arousal goes back up. Now, once we built a sensor that wasn't wired, you know, where we had to attach you to something in the lab, people could leave the lab. And get that data like you just saw. And it was the end of the semester, December, when an undergrad came to me and he said, Professor Picard, can I please borrow one of your sensors? My little brother has autism. He can't speak. And I want to see what's stressing him out. And I said, sure, in fact, don't just take one, take two, because they broke very easily back then. And I said, actually, do you need a soldering iron, too? And he says, no, I have a soldering iron. Okay, good. So he's ready to fix it. So he puts the two sensors on his little brother on the wrist in sweat bands like the sweat band shown here. And I'm back at MIT looking at the data in my office on the screen. And this day's data looks pretty normal. And the next day's data looks pretty normal. In fact, this kid didn't even look stressed. And the next day looks pretty normal. And I'm like, you know, this will be quick. I go to the next day and my jaw drops. First of all, he put it on both wrists. Like, I thought you'd do one and then the other first one broke. But no, he disobeyed me. And I'm really glad he did. Because in this day's data, one wrist went so high that I thought the sensor must be broken. We have stressed MIT people out every way imaginable. Professors get to do that. And I've never seen it go so high. And the other side wasn't even responding. All right? So like, how can you be stressed on one half of your body? So, and furthermore, I'm an electrical engineer. So I thought there was an electrical problem. I should be able to debug this. But the data were fine, perfectly fine right before and after and showing a very clear sleep signature afterwards that was symmetric. And I, and it's not always symmetric, but this case it was. And so I tried a few things to explain it. And I finally I gave up and I did something I've never done before as a professor. I called the student at home on vacation. Like, hi. How's your, how's your Christmas? How's your little brother doing? Hey, any idea what happened to him? And I gave him the exact date and time of this weird data. And he says, I don't know. I'll check the diary. Diary? MIT student keeps a diary? Quick prayer. Like, what are the odds he's gonna, like, have written this down? And he comes back. He has the exact date and time. And he says, that was right before he had a grand mal seizure. Now, I didn't know what a seizure was. I mean, I had heard of them, but I didn't really know anything about the neurology. And so I thought I need to learn more about this. And I learned another student's dad was a brain surgeon at Children's Hospital Boston. And he worked on patients with epilepsy who have multiple seizures. So I called him up. Hi, Dr. Madsen. My name is Rosalind Picard. Is it possible that somebody could have a huge, I wanted to sound medical, a huge sympathetic nervous system surge? Because that's what elicits the skin-conducting response. A huge sympathetic nervous system surge. And in this case, it looked like it was 20 minutes before the seizure. I said 20 minutes before a seizure. And he said, probably not. But he said, it's interesting you ask. We've had patients sometimes whose hair stands on end on one arm before a seizure. I'm like, on one arm? And he's like, yeah. And then I told him it was just on one side. And he got very interested. And he said, we can explain that. You could have a seizure in one part of the brain that can give rise to the activity on just one side of the body. So he got excited. And we made a bunch more sensors. We got IRB approval. We enrolled 90 families. We went to Boston Children's Hospital. We measured gold standard EEG, video, ECG, electrocardiogram, and EDA, electrodermal activity on the wrist. And we saw for 100% of the convulsive seizures, the grand mal, generalized tonic-clonic seizures, these huge responses. On the top is the electrodermal activity. On the bottom is the accelerometer. So you see for these three events a lot of accelerometer movement, as well as this huge electrodermal activity response. And by the way, this is this boy's sleep. So for the MIT student, the sleep was the biggest peak of the day. These seizures are huge events. They're much bigger than any stress events. They are something very unusual that we were never going to hypothesize. They were completely accidental. We were able to use machine learning to build a seizure detector, which at the time was 94% since then. And Patek has continued to improve the accuracy with more data. We also asked, does the size of these mean something? Maybe they're just getting really sweaty with lots of convulsing, right? Maybe it's just how sweaty they are and how long the seizure is. Turns out it doesn't relate to that at all. Turns out it relates to the amount of time that the cortex is actually being shut down. When you have an EEG on the scalp and you measure the duration of the EEG shutting down after a seizure, which is a biomarker that was present in all cases of death following seizures, this signal is bigger in that case. Now fortunately, nobody died in our study. Whatever was shutting this down, restarted. Breathing restarted. But we now know that this deep brain activity that cannot be picked up on the scalp, because the scalp is reading below 10 microvolts, is actually showing up on the wrist. I am so glad I had tenure when we found this. I would have never proposed looking for deep brain super activation, you know, lateralized on the wrist. And yet we're finding this, and now there are other studies showing direct stimulation of these regions, giving rise to these lateralized responses as well. So this has now not only been shown here in published neurology, but replicated in multiple hospitals. Now seizures are not usually deadly, but they are the number two cause of death for years of potential life lost after stroke. In fact, I'm curious how many people here have heard of Sudep, sudden unexpected death and epilepsy? Probably very few. I see one hand, you're honest. Most people have never heard of Sudep. I had not ever heard of it. And yet it is the number two cause of total years of potential life lost, so deaths times life expectancy. More than Lou Gehrig's disease, multiple sclerosis, Alzheimer's, Parkinson's, meningitis, encephalitis. Now, we also know that it takes more lives in the US every year than house fires. And that seizures can be thought of like, you know, little electrical brush fires that may stay local in your brain. They may not cause convulsions. They may cause funny sensations. They may cause you to just appear confused for a moment. Or they can cause the full convulsions and loss of consciousness. The deaths are most likely to happen with the kind where you lose consciousness and they take more lives every year than house fires. You would never build a house without a smoke detector, but people had been sending people home without alerts. So we decided that we had to take this accidental finding in the lab and get it out to give people alerts. So that led to this goal that if we could give alerts, maybe we could reduce significantly this amount of SUDEP and help save a lot of lives. So that is still in the future, although we already got an alert the other day that shows where a mom going to her daughter and finding her not breathing in bed and able to use our alert to get there in time to save her. So this has led to this effort to take this technology out of the lab. Epilepsy. Seizures can seriously hurt or even kill people and we need an alert to intervene. One in 26 people in the U.S. will develop epilepsy at some point during their lifetime. Everybody with epilepsy should be able to have a device that alerts a friend or a family member to come help at the time that they might need it. This device will measure both movement and physiological signals. The combination is likely to be very important for detecting potentially life-threatening seizures. So we're now able to issue alerts and I just want all of you to know if you have a friend with epilepsy and you probably do, they may not have told you, but if you ask your friends, some of them will have epilepsy, some people here will. And let them know that if they have at least one convulsive seizure a year, they are at risk of having their respiration shut down by this seizure and simply having somebody go there and say their name and turn them over could restart their breathing. It's been shown even in people who are conscious if you stimulate the amygdala in a certain way like 15 volts, it can shut off your breathing while you are sitting there typing on the computer until somebody says your name, Robert, you know, and then suddenly you breathe again. So it's anybody here could be a lifesaver with these kinds of situations. So please take that knowledge with you as you leave here. We've donated with the generosity of lots of people, devices through the Epilepsy Foundation for families who cannot afford this. We also learned, again, one of our ethics in honoring the people that we try to build technology for is to listen to what they want. And probably you have friends with epilepsy who've never told you they have epilepsy. I was shocked when one of my friends told me he had epilepsy and I've known him for 30 years and he never told me. And I'm like, why didn't you tell me? And he says, I never tell anybody. I don't want people to know I have seizures. I thought, you know, you need to tell people so they can check on you. But the stigma associated with it needs to go away. And until it goes away, we need to make a device that doesn't look like a medical device that just looks like a cool watch. So we decided to make a watch that's designed gorgeously and that can do things besides detect seizures. So this device, which is not quite on the market yet, you can only pre-order it right now. We're only on the market with betas for people in most serious need. It detects the physical activity, the steps, the sleep, wake, and the, and it tells time. Maybe not as perfectly as your amazing Swiss watches, but it's better than what I had before. It's as good as my phone, and it's also going to soon come with an API for all the hackers in the room who want to do really cool stuff with it. Now, my former boss will be very happy because what he's always asked me to do is he said, Roz, when are you going to build me the mood ring that tells me my wife's mood before I go home? Now, the mood ring was just a temperature sensor, right? We have temperature in here, too. But this gives that autonomic stress that you saw, that skin conductance, right? That was going up before the meltdown, that was increasing, that was decreasing on the swing. You know, so you can see if your loved one was just really chilling, you know, or really, yeah, yeah, activated. This has a lot of practical implications for how people interact. This data comes from a young woman on the autism spectrum who asked to borrow one of our early prototypes one night when she was giving a three-minute speech. And she said, I'm going to be stressed by this speech. Can I borrow your sensor? I'm like, sure. And this sensor was just logging. It wasn't showing the data in real time. And before the event, she heard that they were going to delay the start. Now, uncertainty is stressful for everybody. Now, for a person on the autism spectrum, uncertainty is really stressful. And so when she heard it was delayed and she was already stressed about giving her talk, she started pacing back and forth. Okay. Now, her friend who was sitting there trying to work said, stop pacing. That doesn't help. So she stops pacing and she starts stimming. I asked her later, what do you mean by stimming? And she said it was kind of this flapping rocking thing. Afterwards, she gives her talk, that's the peak, and then it's downhill from there punctuated by some terrible auditory feedback events. Sinsery, ah, stressors. After the event, the two of them sit down to look at her data and the first word spoken by, or by her boyfriend. And he says, I'm not going to tell you to stop pacing anymore. He looked at that timestamp. And the next morning, I saw the two of them before another event. And this time she was pacing. And this time he just let her pace and he typed on his laptop. By seeing the objective data of what was going on, it affirmed for him that her pacing was helpful in this case. We often do what our bodies think is good to do, but we don't believe them, right? And so by taking stuff that seems subjective and making it objective, we give it more belief. It doesn't mean it wasn't true to begin with. It just means that we believe it more once we convert it to graphs and data. Now, I've talked about a lot of things. We've talked about starting with the facial expressions in autism, then learning that maybe the biggest problem wasn't always them recognizing other people's faces, but us recognizing their stress and anxiety. And then trying to build a device that helps read out some of that stress and anxiety, accidentally discovering it could also detect seizures, taking a little detour to do that because that appears to be life-saving. And then coming back to understanding emotion and human-human interaction and also in human-computer interaction. This example I love. How many of you have seen the Wally movie Pixar? Oh, everybody. Great choice by Roz. Yay. So one thing I didn't notice until a person with autism pointed this out to me is these two stars of the movie, Wally and Eva, they never use words. They build an entire relationship with nonverbal interaction. So it was a non-speaking person with autism who pointed this out. Look at this rich relationship that is developed only by emotional communication. And we like these robots. We feel like we build this emotional connection to them. We share their dreams and their failures. Now, many of you may also know Kismet, developed by Cynthia Brzeal for her PhD at MIT. I was just a reader on the thesis. Rod Brooks was the advisor. But I always loved this example where she was showing not only that a robot could recognize emotion, in this case recognizing tone of voice, but also that if you make the robot respond to emotion in a particular way, it really affects how we feel about the robot. So I go back to that opening scene of that Microsoft screen or Clippy or something coming up. You know, when it ignores your feelings, you hate it. You want to kill it. I mean, there are pictures of a Clippy on the web with a noose around his neck. And Bill Gates got his standing ovation when he said Clippy was going away. Watch what Kismet does. This is intelligent. So another important skill for the robot to be able to learn from people is being able to recognize communicative intent. Very good, Kismet. And the way we've done that with Kismet right now is to have the robot recognized by tone of voice. Are you praising it? Are you scolding it? Where did you put your body? No. No. You're not to do that. No. So, you see, when we don't like what the robot does, if it acts sorry, then we're like more likely to forgive it, right? And then we're happy to see it again, right? It's when we're miserable and it's happy and it's not noticing that we're like ready to kill it and get rid of it. So that emotional intelligence is really critical if we're going to build an intelligent future system. Now, emotional intelligence became a very powerful theme in a recent movie, Ex Machina. I don't know how many people saw this. I wrote a review of this that science invited me to write. You can read online if you're interested. But in this movie, the young man here is giving the robot a test of emotional intelligence to see if she can pass that, to see if she really has emotions. I can't spoil the end of the movie for you, but this movie also raises a lot of issues that transcend the intelligence of a machine to the morals of a machine as well. And these are things that people studying morals and moral decision making know that emotion plays a key role in. Now, we don't know if you have to have emotions to make moral decisions, but we know that people who make moral decisions often and perhaps usually employ their emotions in making such decisions. And that seems to be an important part of how we act in a way we think as moral and intelligent. As we give robots the ability to see emotion in our lives and as we start putting robots into our homes, such as the Jibo here that Cynthia has left MIT temporarily to build, these can start to see not only our facial expressions, but also our interaction with each other, our relationships. If we're doing things that make us happy or if we're more stressed. By the way, now we can also read heart rate and respiration non-contact with video and with Wi-Fi. So there are ways that these machines can sense a lot of things about us that could be disturbing or could be very beneficial. So these also raise a lot of ethical questions that I think need more discussion in society. In our work, we've become very interested recently in a new direction that is concerned with mental health. In this graph, the vertical axis has collapsed a lot of factors of well-being into one dimension. And when the university admits you or hires you as new faculty, you're at your peak of well-being. You're strong, you're smart, you're healthy, you're happy, hopefully, positive. As we encounter major stressors, as time goes on, almost everybody takes a dip. And hopefully you're on the blue line and you come back up over time. But at MIT, we have a large number of students who are going down the red line there. And when they get far down, usually about here, hopefully they by then have a medical diagnosis. And a doctor says, you're depressed or you're in trouble and we need to help you. But I'm asking, and my students are asking now, why do we have to wait until they get down here? Why can't we measure things back here? And we're starting to do that and see things changing even before outwardly there's any signs of trouble. If we could tell that you're on the red line back here, we could do two things. One is we might be able to tell you how to get to the blue line. Second, we ought to be able to prevent you from going further down this line. We ought to be able to prevent things like 80% of cases of depression. Now that's a very ambitious future goal. But we would like to start thinking about what are those components and how might our technology that's filling our lives and can measure all of these amazing things be used to not make us household pets, but be used to help us take better control. Just like forecasting the weather was enabled 150 years ago with technology sensors all over the geographic environment. Why don't we use some of these to better understand what helps us be well and forecast health? Wellness is incredibly complex. There are lots of theories of it. I'm just going to show you briefly one here because I want to debunk the popular notion that this just means happiness. This theory, due to Marty Seligman, shows positive emotions as one of five aspects of well-being. So being happy is just one of these components. Others that are really easy for people at really high-end universities like this are usually engagement, engaged in something you really enjoy doing, and accomplishment. Probably everybody in this room is very high on engagement and accomplishment. Hopefully you're high on positive emotions too. At MIT we have to work more on positive relationships and meaning. And it's interesting because they're very connected too. If you ask where does meaning happen, you write a brilliant paper, right? But if nobody reads it, in the days of read-only memory, one of my friends coined the phrase, write-only memo, the write-only paper that you wrote but nobody read, is there really meaning in it if you do all this work and there's no receiver of the value? There's no relationship if there's no recipient of the meaning. So meaning and relationship have motivated where we've been taking the robotic technology and our affective agent technology to try to facilitate more of that interpersonal connection, whether it's between two people or between a machine and a robot. It's helping us understand what contributes to that. It's also helping us see the important value of that. Now the one thing I'm most frequently asked when talking about these systems, and the one on the left here is called Mock. This is an agent that my student, Eson Hook, built to support people in developing better nonverbal skills during interviewing. So you interact with this agent, she asks you questions like a tough interviewer, and then gives you feedback on your pitch and your facial expressions and how animated you are and your speaking, and words such as and, but, so, or these filler words that we all need to stop using. Now the agent is scripted and appears very intelligent in these limited scripted contexts. Now you may have seen a movie like her where he has this romantic relationship with an agent, a software operating system that does not appear scripted in the movie, but of course it's a movie, right? The whole thing is scripted. We have to remember science fiction script, much harder to do it in real life, even scripted to make it meaningful. The challenge in the future intelligence systems is to build the unscripted interactions. The challenge is to, you know, could we make them not only have emotional intelligence, but could they actually feel and be conscious? We don't know how to do that right now, and we don't foresee how to do that with the software and hardware that we have today. So I'm not going to say that's going to happen in the next five years. It doesn't mean it can't happen. It means we don't see how to make it happen right now. But suppose that it could happen, and suppose that we could build them to be unscripted, emotional, emotionally intelligent, then the next thing people ask is would they be our slaves or would we set them free? Would we try to enable them to not just be unscripted and emotionally intelligent, but also to operate with true freedom, to make their own decisions, to believe whatever they want to believe? And some of you may have seen Sunny as one example of this in the iRobot. What if one robot were built or more to be truly free and to be true, not just autonomous, but free? And furthermore, suppose they were on another planet where they had never seen a human. There's a robot colony, and we know that machines can produce machines. And so the robots having never seen a human might explain that they arose from other machines. And maybe some virus wipes out all references to human and people at all in their system. So if they're really free and they've never seen a human, there's no reason for them to ever acknowledge that Cynthia or I or you or anybody who ever worked on this technology ever even existed. They could simply deny that humans ever had anything to do with them. After all, they could have just send it from silicon, from sand. If they chose to do that in their truly free state, what would you do? What would your choice be? Would you have to ignore them or reach out to them? How would you feel if robots declared that people never existed? I say that's just as possible as these other scenarios. And a fun thing for us to have more discussion about. I want to wrap up, though, and get to Q&A. So I just will take you on a quick recap. I started this talk with a little bit about reading facial expressions, emotional intelligence systems that could recognize and respond to our emotion, learning from people with autism that they didn't so much need our technology as they needed to be better understood and could our technology help them be better understood. So redirecting it at that, accidentally realizing that deep brain activity is giving rise to signals that we could read electrically on the skin even at the same time while it's shutting down the signals on the scalp. And that we could build an alert system that could potentially save lives for that. So getting that out, commercializing that. But wanting to do it in a way that builds a device that doesn't just detect seizures, but is something that enables people to share emotional data and have fun learning about each other. Hopefully building relationships. Relationships, positive affect, engagement, meaning, achievement, core to well-being. Could we take all of the power of this technology and start to understand what's contributing to human well-being? And not only what's contributing to it now, but what changes it and can forecast what could keep it well as opposed to when it's slipping. And then finally, as we take all this intelligence and put it back into robotic systems that are in our home and future coming into all aspects of our lives, what does that mean in terms of how they interact with us and what we expect from them going forth? What do we expect them to acknowledge about us? What kind of rights do we want to give them? What kind of freedom do we want to give them? And all of this, I will have to say too, is not going to happen automatically. Some people seem to think, oh, the robots are just going to evolve this stuff and this will happen. That's not true. They won't develop any of these abilities unless we give them these abilities. It's not going to happen unless some engineer, some bright people like in this room decide to program them this way. None of this happens without human effort setting this into course. So the opportunities are to have hopefully a great discussion about this future, and I'm happy to take questions about any aspect of this or my faith, any of these issues as we continue. Thank you so much. Thank you very much Rosalind for this inspiring, interesting talk, and we have time now for questions. I had a question regarding the top left picture, so from my perspective about the smile that we see when someone is frustrated. I was just wondering, could that be caused by a certain mechanism in the individual to maintain positivity even though they're stressed? That's one of the hypotheses right now is that people who do that are trying to make themselves feel happy, and this could be compensatory instead of getting angry and hitting the machine. It's a way to try to make yourself feel better. That's one possibility. By the way, it's also been observed in small preschoolers, little kids, so when they're failing at a task in a learning environment. So we evidently, some people learn to do this very early. It is also possible it can make you feel better because smiling can actually feedback to make you feel better even when you don't feel happy. Thank you. Hi. Thank you very much for the cool talk. So I felt a little uneasy when you showed the graph about how you could measure someone sliding to depression or the girl that had to give a talk. If someone was tracking my emotions, let's say, you know, supposed girlfriend or something, then I would feel even more stressed about that because I feel it's a very nice piece of privacy and freedom that I can smile and at the same time feel terrible and not have to explain myself. So this is one problem, I guess, and the other problem is how, what if, let's say, my boss could track me all the time, right? And how if people can basically just see the stages, such a precious data and so revealing. So which steps do you propose to, let's say, make sure that it's not abused and that people are not made totally transparent? Great question. Thank you. Yeah. First, when the young woman had her data recorded, for example, the first thing I did is said, you know, do you want to see it and do you want him to see it? Right. And she said, yes. And we never show people somebody else's data without their permission or completely in a de-identified way. I think that's a very important ethic to have for protecting people's privacy. I agree. It's very personal data and we have to be extremely careful with how people might interpret it. The boss situation, oh gosh, we have been asked, you know, can you measure my employees' stress, you know, and let me see it? And I'm like, well, what are you going to do with that data? Like, how are you going to help your employee? So we agreed, in one case where we agreed, it was I will measure the employees, we will give the employees' sensors to measure their stress. If they opt in, if there are enough of them that, first of all, they get to see their data, their boss does not see any individual's data. The boss, if there are enough people who opt in and we can aggregate the data, the boss can see when the peak bad times of day or if overdraft Wednesdays really are as stressful as the employees. So we set up some ground rules for the ethics of it and then that worked very well and people were very happy to opt in and nobody was singled out as having any particular characteristic. So I think we have to show the utmost respect for people's feelings in all of this and that is easier to do when it's wearable technology, when it's unobtrusive sensing technology. That's where it becomes much trickier and I'm a lot less comfortable with the way the opportunity is there for abuse in some cases. I think this may be a case where we have to get society saying what do humans value about each other and how do we keep that, you know, acted upon. Hi, Professor. Thank you so much for your awesome talk. So recently human bot interaction is a very hot, I guess, consumer product now with Facebook launching its new M messenger service in the Bay Area and Microsoft launching their new API for humans to communicate with bots. But they seem to feel like text is sufficient in terms of understanding the user need. And clearly from your presentation, there's much more that can be understood from speech and video. I'm wondering where do you think the limit of understanding emotion in text in natural language processing is and why you decide to go in the direction of more multimedia. We have also done a bunch of natural language processing to my student Karthik Deena Karz and I've published a bunch on that recently. You can find on our website here publications of everything I've talked about and a lot more. The text as we know what you say and how you say it can be completely in conflict. And words can be very confusing too. I mean, you know, when one person says the F word, they think something's cool. Another person says that they're really mad, right? Or take the word sick, you know, I don't know what that means to you. To my son, that's like a super cool move, right? And I'm like, sick, you know, somebody needs to go to the doctor. So we have problems with words that you have to use a lot of interpretation around the context. And often it's still ambiguous. So each thing, whether it's words, face, physiology, we've also measured posture, gestures, sitting in chairs, fidgety-ness, all kinds of factors. How hard you're squeezing a steering wheel, how hard you're squeezing a mouse. These things all convey affective information and we can use whatever people are comfortable sharing. With the caveat I always put in that we must, number one, show respect for people's feelings. And in my case, that means, in all of our work, it means you inform people, they get to opt in, and only then do you take that data. There's several down here. I have a question about consciousness in relation to those robots. And I'm not sure if I remember correctly. I think you said that you don't think it's possible that consciousness will appear with robotics in that way as we know it, but maybe I misremember it. Can you just maybe explain a little more about that, the connection between consciousness you talk about robots that will recognize emotions, will they also feel the emotions themselves at some point? Can you talk a little bit more about that? Sure. It's a good question. With any of these things, there's kind of the level at which you evaluate it. If you perceive it, like if I query to the robot, do you have consciousness? Do you have feelings? And it says, oh yes, I'm feeling quite fine right now. How are you feeling? I mean, at that level, it could act, and I perceive you are in the room. I mean, that kind of functional claims of consciousness, that's trivial. But I think what we're thinking of is something much deeper that involves our physiology, our feelings, even things that we can't just measure with physiological sensors. Things that are ineffable. We can't prove those things aren't there, and yet there's evidence, and I know the materialist in the room will not like this, but you can't prove everything's material either, right? What if there's something more? What if there really is something spiritual or something else there that we can experience, like a nurse going into a room and saying, I knew when I walked in that the patient had died, even though there was no signs of vital changes, I knew that they had left the room in some sense. There are so many reports of there being something else, and we can't prove what that something else is, if it's immaterial, because science doesn't measure this immaterial stuff, but we can allow the possibility of it. And when we try to build consciousness, while we can build this perception of it, we don't have any idea how to build these other aspects that we can't even begin to measure. I say, I don't see how to do it right now. I don't see how to do it with current hardware and software right now. I do see how to build a fully functioning conscious human entity. Some of my colleagues would say we're all just machines, and would say, oh, let's just buy an engineer the tissue and make each part of us, each organ, and so forth. And I say, well, if you're going to go to all that trouble, and it's going to be just like us, then why not just make it the same old-fashioned way we've been making them forever? I have three children. I mean, that's a lot more fun than bioengineering all the tissue, right? So I think we're talking about something different than that. And it's not happening with the hardware and software that we see right now. It's not even getting close. We're clearly in a completely different space. So how would you prevent or isn't there a danger that we don't get like senseless, emotionless zombies with all the technology and everything is telling us what we should do, now we should eat, now we should smile, and we are totally dependent on that stuff, and we lose like everything else? You know, that's a future we can choose to build or not, and I hope we don't build that. I think hopefully you agree. I'm reminded of when a colleague was at Foo Camp, Foo, we won't say what Foo stands for, Computer Science, catch all word, and at the conference, there were all these really fascinating, interesting, brilliant people, and there was a break when you think people would want to go talk to each other. And instead, they all grabbed their phones and they hunched over them with shallow breathing and were doing this, and nobody was interacting with each other. And the developer of iOS was there and he said, I feel badly I invented this. I think a lot of times with our technology, we're not thinking what could happen. We're just building something cool and sticky, and we have an opportunity with the future here to ask what do we want to build? Not just what can we build, but what do we want to build? And I hope we're not building something that's turning us all into zombies. You know, when I have been reading some reports about what's been happening in Silicon Valley and about these technological innovators and what they think about communication and what they especially think about people's thinking that at least some of these experts have said that we could actually have robots. Or other artificial intelligence devices think for us and we could actually then have more leisure time and so on. I mean, have the work done for us and so on. And I would really like to know what you think about these, I mean to say the tendencies within the framework of Silicon Valley and whether there are really within this framework, attempts to restrain the use of artificial intelligence not only to restrain it to certain sectors. For example, as you said, people who are mentally challenged or any other things, seizures, and you gave some examples, to restrain it to that and not to just have it to apply it to the whole of Silicon Valley. And I would like to know what you think about the work done by the human society and how it's going to work. And I think that's a great question. I think that's a great question. I think that's a great question. I think that's a great question. It's funny because we're all kind of, I don't know about you, I'm kind of attracted to just curling up with a book and staying there forever. But I also know that my brain doesn't work well when it's so sedentary. And I've also learned that my introverted self that would stay away from people is actually much healthier when I'm interacting with people. And back to those five pillars, if you're out building relationships, you're out engaging in challenges, you're not taking the lazy, easy path. It actually leads to better well-being than just thinking about what you enjoy personally. That positive feeling is only one of the five components. If you start to explore where meaning comes from and engaging with things that matter more than just yourself, you start to feel better also. So while you might be attracted to that, make your life easy and have leisure time because you'll feel better, rarely is that the real secret to well-being and happiness. So I think we need to stop and question what it is we think is good and help each other see that we may actually be much happier not taking that easy path. Thank you. I have a question concerning the empathic bracelets. Do you think it would be an option or whatever happened that some healthcare insurance companies would make that a must for their patients with maybe any of the desired? I predict in the future it will become a must for patients who have convulsive seizures. Probably not just the one one company makes, but that a device that does an alert should become a requirement for people. It should be reimbursable, right? The medical system should pay for this because it could be life-saving for people who have at least one convulsive seizure a year. Yeah. You gave a very nice example in the beginning of how your interest changed from helping autistic people to understand the world with this light to actually the world understanding autistic people. Can you apply that same change in interest also to robotics because I see there's lots of examples right now of robots trying to understand us. Maybe we should also change to us trying to understand what these robots actually do. Is there anything in your research that goes that direction? Actually that trying to make it so that it's symmetric is one of the motives behind making humanoid robots because the thought is we're good at understanding people or some of us are. We could all improve. My husband would make comments about women here. But we think we're good about understanding people. And if the robot acted more like a person, then we wouldn't have so much difficulty understanding it. But if it's spitting out all of this complicated numerical code that we don't understand, you know, state, numeric state information, why doesn't it just say it's frustrated? Right? It may not have feelings and conscious experience like we do, but we might prefer that, you know, if it's banging its head against the wall trying to do something and not succeeding that it instead of it just acting weird, it tell us that it's frustrated that it has this goal and it can't achieve it. So we may accept that anthropomorphic language for understandability. Okay. Yeah. You got it there already. Okay. I got a question regarding the timeframe of artificial intelligence. How far are we away to substitute nurses in hospitals? Oh, I hope we never substitute nurses in hospitals. Nurses are wonderful. I think we, you know, this goes back to questions of what we want. And I think, you know, we need to look at what people are good at and help them do it better. Their jobs, parts of the job each person does that they would love to have automated and their other parts that they would love to still be able to do. And why don't we show respect for that as we develop technology instead trying to replace whole people and whole occupations? Why don't we just help them figure out how to do their jobs in ways that get rid of the part they don't like and facilitate the part that they do like. So again, this goes back to our choices, the future of technology largely shaped by people, bright people like in this room. We have a choice to, you know, I mean, the public who doesn't build technology may think that it's inevitable, but it's not inevitable, right? It's what we choose to do and what values we bring to that. I see that there's still a number of questions, but that's actually a good thing. I think this was actually a very good closing statement and I do believe from what I see is that we are, many of us have really realized that there is no need or use to try to replace things, but to try to have these systems actually assist in a meaningful way and have each part do what they can do best. Before we close, I would, you will have a chance to ask more questions just afterwards, but before we close, I would like to ask Remy, where is she to come up here? She's actually been the driving force behind organizing this talk and she would like to say a few words to you before we close the session. Thank you, Raja. On behalf of ETA, Fawbege, Campus Life and the Zentum Forum, I want to thank Prof Picard for coming and taking the time. Thank you very much. Thank you, Remy.
 Thank you. It's a real honor and a pleasure for me to be here today. I've been enjoying  a lot of very cool demonstrations of the technology here and I'm really impressed with how bright  everybody is and I've heard how amazing the students are that get to come here as well.  As was said in the introduction, I wrote a book called Affective Computing and at the  time I was inspired by artificial intelligence and by my senior colleagues who wanted to  build a machine that as my friend Marvin Minsky put it, that would be so amazing, it would  be so intelligent that we'd be lucky if it kept us around us household pets.  And I thought, well, I like the first part of that but I wasn't really keen on being  a household pet, being somebody's dog or cat. That did not feel like all humans could be.  And when the Media Lab was founded, we started to think not so much about building the monolithic  AI, but about how technology with people could make our lives and our futures much better  than just in the movies that we had seen. So I started thinking what kind of intelligent  machine would I want to build and I started realizing that one of the important skills  we needed in human intelligence was emotional intelligence. Now, I really did not want to  be associated with emotion in the early days as a woman in science and engineering. I thought,  wait, this will kill my career. So I tried to get some guys to work on it. So I tried  publishing a paper which was rejected and so then I wrote a book. And, you know, they  want to make money off books so they don't reject books. So anyhow, then what I really  wanted to do though was build the technology and see if it worked. And so we started to  try to build technology that would show respect for people's feelings. And the first feeling  I thought was most common in front of computers was when you feel frustration. Okay, you have  a deadline, right? You're trying to do something important and the stupid paper clip pops up  or some other, you know, right? Now, why can't it see that I'm busy and I'm concentrating  and not do that then, right? An intelligent human would look at your feelings, look at  what matters to you because what matters is reflected in your emotions and it would change  what it does in response. So we tried to figure out how to automate this and we've done lots  of experiments where we frustrate people. I could fill hours with them. Here's an example  of a person coming in and I'll have to tell you if you try to do experiments in this space,  you cannot tell people that you're trying to frustrate them. It doesn't work. You have  to tell them something else. So in this case we told him he was testing the usability of  a new system and unbeknownst to him, it's going to give him some problems.  I am sorry. You seem to be having difficulties getting through this form. Wouldn't you mind  looking at the camera and sharing your thoughts on how to improve the form?  We can all relate, right? So just so you can see, we frustrated people. Now, at the same  time I was taught and I wrote about this in my book and if you have my book you can now  cross out this part of the book because I no longer believe it. I was taught that if you  want to detect if somebody is truly happy then you look not for the smile of action unit 12,  the lip corner pull but you look for the smile of the eyes, action unit 6, the cheek raise,  what gives us crow's feet as we age. So the theorist always said if you see both of these  then the person is truly happy. So watch what happens in this next situation. We do the same  thing you just saw to the student. But look at the smile and the eyes. So what we found  was that the smile of delight could show up when people were really frustrated. In fact  it showed up 90% of the time when people were frustrated. And this was a surprise. And since  then we have found over and over that there's the theory of what people do and then there's  what people do. And we need to always be looking and showing more respect for what people really  do and then making the theory match the practice. Now we were interested in facial expressions  also because we not only do they signal at the largest distance something like delight  and it turns out actually we were able to teach a computer how to recognize the delight  versus the frustration and do so more accurately than a person could recognize it using some  dynamic machine learning that recognized the speed of the expression not just the peak  of the expression. But it doesn't mean that machines could always recognize this better  than people. It was good at doing it in a very focused directed context. And we were  trying to do this to help people on the autism spectrum who when they heard that we were  building computers to recognize emotion the brother of a person on the autism spectrum  came to me and said can you help my brother? And I said I don't know tell me about your  brother. And he said his brother has autism and he has trouble reading other people's  emotions. So we realized we could put the little glasses a little camera in the glasses  and this was long before Google Glass. This was more than a decade before that. And we  also put LEDs in. So the computer would in real time recognize if you were smiling or  looking interested I would get a green light while I'm looking at you. If you look at me  and you look confused I get a yellow light like slow down Ross. And if if you're like  really distracted you know or frowning or looking like you really need to think about  something else I get the red light to stop. I know you probably wish you had that for  all the professors who lecture. We could do better with that information. So then we worked  with children with autism.  Hello. Hello. How are you? Check that out. It's me shooting you shooting me.  How are you? Good. How are you? Good. I like this thing. You like it? I like this thing.  I made a perfect video. You know there are heat sensors on cases now for light detection.  If you get the face with it and the tape and analyze that later, that way you can get the tools.  So he might not even need the additional three voices with this.  He just says he'll use it as a tool later would be very helpful.  I did notice that the wires, it would probably be easier if it was more of a self-contained system.  You don't really understand something until you teach it to somebody else.  So we started to learn from people on the autism spectrum what they wished that we could do.  And we realized we had a lot of improvements to make.  Early on the technology was pretty good on well-lit, well-controlled lab data,  more than 80% accurate, recognizing six different common expressions.  But it wasn't anywhere near 100%.  And when you went into the real world, the rates, the accuracy went down 50%, 60%.  And we had spent, you know, with our partners in the UK, over a million dollars of research funding trying to get this far.  And to get the data that we would need to get the accuracy up a lot higher was going to cost millions more that we didn't think anybody would give us.  So we started thinking how can we get enough data to make this really accurate and really helpful for people.  And here's my co-founder of Affectiva, Rana Elkaliubi, saying a little bit more about what we did.  This is on building computers that can read people's emotional states through their facial expressions.  We've built technology that looks at people's facial expressions.  It identifies the main feature points on your face, like your eyes, your eyebrows, your nose, and then it tracks how they move over time.  When I smile, the green smile track goes up, and when I nod my head, the head nod track goes up.  And when I shake my head as in disagreement, you see the pink line go up.  We use thousands of videos of people showing agreement, showing disagreement in order for the computer to learn what it looks like liking, concentrating, disagreeing.  Making this technology online opens a whole lot of new applications.  Almost everybody with a computer today has a webcam, or a lot of smartphones have cameras too.  So if we enable people to opt in, turn the camera on, they can share their experiences online.  It's a more sophisticated version of the like button on Facebook.  So we decided to see if people would turn on their cameras online, and we started by deploying the most popular ads and recording people's faces while they smiled.  Thank you.  Did this Volkswagen ad air over here?  It became the number one ad in the U.S., and they paid millions of Super Bowl.  And with the smiling that peaked at the end, we could see that when people saw it again, they smiled even more.  And after they had become familiar with the ad, they actually smiled before the punchline.  So it was a way to sort of detect if they knew the good part was coming.  Now by capturing the entertainment, then you could get people to pay people to watch content to see their facial expressions.  And with that, we were able to go from hundreds of examples in the lab to millions of examples online and get the accuracy much better.  The work to do this, I liken it kind of to taking, you know, we wanted to build this autism technology.  We wanted to just run to the front door and open it when the doorbell rang.  But it's like these old cartoons where the kids run all over the house before they get there.  We had to do a lot of other things in order to get the data.  And that now is an app that you can, I think, still download for free to do a demo version of and try out all the different facial affect recognition that it has right now on your iOS or Android device.  But don't do it right now in the talk. Do it later.  Now, Rana, who you saw was from Cairo, and the Arab Spring was happening as we were starting this.  And we realized that while there were a million people in Tahrir Square, Mubarak might have been saying at the time that a million isn't even a majority in Egypt, right?  Maybe the million in Tahrir Square don't like me.  But Egypt has another 79 million people, and they're not in Tahrir Square, so they must like me.  We can often think what we want to think, right?  But if we could opt, we could give people the chance to listen to the speech, and anonymously, we record no identifying data,  anonymously have their not just smiles recognized like you saw there, but we can recognize a lot of expressions.  In particular, we can recognize smirks. We see a lot of these with politics in the US, these asymmetric smiles.  So if Mubarak's talking and they're doing this, then it doesn't mean that most of the people listening like it.  And that feedback, if it could be kept private in a way that protects people, could be very valuable.  There is a question though about could that be done in a way that they couldn't see that people in a particular area were disgruntled and take some action that we regret there.  So these are questions we face. The power of the nonverbal voice to give people feedback is very strong, but the truth is also very powerful and can be upsetting to people in power.  Now, while we built this for people with autism and wound up doing these other things to get data, I was talking with a friend with autism a little bit later,  and she said to me, Ross, you, yes, you know, some people with autism want to recognize other people's facial expressions, but actually you have it wrong.  That's not our biggest problem. Our biggest problem is not recognizing other people's emotions.  Our biggest problem is you recognizing our emotions. And she's pointing at me and I'm like, oh, great. I'm not doing so well, huh?  And she goes, not just you. We, you know, everybody, we all could do better recognizing the emotions of people with autism.  In particular, I learned by talking to my friends more that they have a lot of anxiety and stress that was not being recognized by other human beings.  And things like the fluorescent lights or the smells or the sounds were extremely stressful, and they wanted help with that.  I started getting emails from people who learned that what our technology could do.  Here's one from a father who wrote me from Holland.  He said, my colleague told me about a new fascinating technology he heard on BBC Radio 4 where computers were able to detect a person's mood.  He thought of it as a useful technology to prevent him from smashing his laptop into pieces.  But I thought immediately of something else.  I have an eight year old son who is diagnosed with ASS. Unfortunately, I only know the Dutch term, but in English it must be something like autism spectrum disorder.  In general, he is a normal, intelligent boy. Autism is the last thing that comes to mind when you see him.  But sometimes he starts disturbing the classroom or responds too aggressive against other children.  It's his third year in school and he is already in his third school.  For the moment, he is going to a special school for children with learning disorders, but things still do not go as expected.  We are going from one test center to another for four years now, and the pressure on our family is really hard.  So I asked my colleague the details of the radio program.  And when I listened to the recording, I had to pause it halfway through as I was getting too emotional.  I believe this technology can help us in two important ways.  And he goes on and he describes if this could help name my son's emotions, the fear, the anger, the concentration and help him recognize them.  For example, we discovered lately that when he is hungry, he becomes aggressive without even knowing that he feels hunger.  How long can he concentrate in the class and how long is there between the loss of concentration and his aggressive outbursts?  We also believe he has a lot of fear. How much would that affect the day?  And he went on and asked if we could please build technology that would help him.  We would really appreciate your help.  My son is an intelligent child and this could be decisive for his school career.  Who knows what contribution he can make back towards the future?  So I got letters like this and I'm happy in my lab working away.  And I think I need to get this technology out of the lab.  We need to take it and make it something that can help these children better understand their emotions and better communicate them to other people.  Now we knew from measuring emotion in the lab that we could measure the autonomic arousal that often precedes meltdowns and frustration and anger and stress.  So we had built versions that could take the usual things like the electrodermal activity often called skin conductance,  often measured by skin conductance, an old name for it is galvanic skin response,  and measure it in other ways that were less obtrusive and easy to get in daily life.  And full disclosure, I'm now a co-founder of Empatica that makes two sensors in the space, which I'm recording myself with right now.  My own stress level is up here as we speak.  One of them, the E4, gives two kinds of autonomic data, the electrodermal activity, which I'll be saying more about on the top,  and the blood volume pulse, which I won't say too much about here other than if you're interested in heart rate variability.  It's been benchmarked to ECG and is within 2% error of a very expensive ECG for measuring heart rate variability when you're still.  And if you start moving, you get more motion artifacts.  Now, what does this electrodermal activity tell us?  Here's an example of a very early prototype we built worn on a child's ankle that shows you it going up with the child having a meltdown.  I'm going to turn down the volume.  This is a little girl with autism.  The stuff on her head has nothing to do with what we're measuring here, but she's about to hit a peak and have a meltdown here.  The blue window is one minute wide, and that corresponds to that blue stripe at the bottom.  The bottom is 45 minutes wide.  So here she is, peaking with a meltdown.  We also see it's been building slowly the whole time.  Now, just because it's building doesn't mean a meltdown is coming, but if it's building and you don't see any reason why it's building, you might want to watch out.  Also, it goes down when the arousal declines, when there's calming.  And here we see it, first actually we see it peak when she's getting on a swing.  Three kinds of stress can make the signal go up.  The affective autonomic arousal, the emotional arousal, the physical effort or exertion of motor planning to get on the swing and cognitive load, just tasks like counting backwards by sevens can make it go up.  But here we see rhythmic activity tends to make it go down.  So when I was in your lab and looking at these beds rocking people, we would expect something like that to make this signal decline.  And in autism, I've seen some people rocking, and I've seen this go down like a slide on a playground.  Here's the first time we got to look at the electrodermal activity from a MIT student, 24-7.  And what we saw was no surprise here that it was peaking with lab activity and MIT homeworks and problem sets, very hard, probably just like here.  Intense work.  To our surprise, the biggest peak of every day is sleep.  And I won't go into that here, but we've got a lot of papers online talking about this now.  It peaks during non-rim sleep, not during rim.  It peaks during the latter part, usually slow waves, stage three, sometimes stage two, and very rarely in stage one in rim.  And it right now appears to be very useful in predicting memory consolidation.  So we had the biggest peak during sleep, which should bother you because that doesn't seem arousing at all.  But the biggest awake peaks are during homework and lab.  Oh, and to the embarrassment of MIT professors, the low point every day is classroom activity.  So I will keep trying to get you to laugh, and I really do want you to ask questions because when you do that, everybody's autonomic arousal goes back up.  Now, once we built a sensor that wasn't wired, you know, where we had to attach you to something in the lab, people could leave the lab.  And get that data like you just saw.  And it was the end of the semester, December, when an undergrad came to me and he said,  Professor Picard, can I please borrow one of your sensors?  My little brother has autism. He can't speak. And I want to see what's stressing him out.  And I said, sure, in fact, don't just take one, take two, because they broke very easily back then.  And I said, actually, do you need a soldering iron, too?  And he says, no, I have a soldering iron.  Okay, good. So he's ready to fix it.  So he puts the two sensors on his little brother on the wrist in sweat bands like the sweat band shown here.  And I'm back at MIT looking at the data in my office on the screen.  And this day's data looks pretty normal. And the next day's data looks pretty normal.  In fact, this kid didn't even look stressed.  And the next day looks pretty normal. And I'm like, you know, this will be quick.  I go to the next day and my jaw drops.  First of all, he put it on both wrists. Like, I thought you'd do one and then the other first one broke.  But no, he disobeyed me. And I'm really glad he did.  Because in this day's data, one wrist went so high that I thought the sensor must be broken.  We have stressed MIT people out every way imaginable.  Professors get to do that.  And I've never seen it go so high.  And the other side wasn't even responding. All right? So like, how can you be stressed on one half of your body?  So, and furthermore, I'm an electrical engineer.  So I thought there was an electrical problem. I should be able to debug this.  But the data were fine, perfectly fine right before and after and showing a very clear sleep signature afterwards that was symmetric.  And I, and it's not always symmetric, but this case it was.  And so I tried a few things to explain it. And I finally I gave up and I did something I've never done before as a professor.  I called the student at home on vacation.  Like, hi. How's your, how's your Christmas? How's your little brother doing?  Hey, any idea what happened to him? And I gave him the exact date and time of this weird data.  And he says, I don't know. I'll check the diary.  Diary? MIT student keeps a diary? Quick prayer.  Like, what are the odds he's gonna, like, have written this down?  And he comes back. He has the exact date and time. And he says, that was right before he had a grand mal seizure.  Now, I didn't know what a seizure was. I mean, I had heard of them, but I didn't really know anything about the neurology.  And so I thought I need to learn more about this. And I learned another student's dad was a brain surgeon at Children's Hospital Boston.  And he worked on patients with epilepsy who have multiple seizures.  So I called him up. Hi, Dr. Madsen. My name is Rosalind Picard.  Is it possible that somebody could have a huge, I wanted to sound medical, a huge sympathetic nervous system surge?  Because that's what elicits the skin-conducting response. A huge sympathetic nervous system surge.  And in this case, it looked like it was 20 minutes before the seizure. I said 20 minutes before a seizure.  And he said, probably not. But he said, it's interesting you ask.  We've had patients sometimes whose hair stands on end on one arm before a seizure.  I'm like, on one arm? And he's like, yeah. And then I told him it was just on one side. And he got very interested.  And he said, we can explain that. You could have a seizure in one part of the brain that can give rise to the activity on just one side of the body.  So he got excited. And we made a bunch more sensors. We got IRB approval. We enrolled 90 families.  We went to Boston Children's Hospital. We measured gold standard EEG, video, ECG, electrocardiogram,  and EDA, electrodermal activity on the wrist. And we saw for 100% of the convulsive seizures, the grand mal,  generalized tonic-clonic seizures, these huge responses. On the top is the electrodermal activity.  On the bottom is the accelerometer. So you see for these three events a lot of accelerometer movement,  as well as this huge electrodermal activity response. And by the way, this is this boy's sleep.  So for the MIT student, the sleep was the biggest peak of the day. These seizures are huge events.  They're much bigger than any stress events. They are something very unusual that we were never going to hypothesize.  They were completely accidental. We were able to use machine learning to build a seizure detector,  which at the time was 94% since then. And Patek has continued to improve the accuracy with more data.  We also asked, does the size of these mean something? Maybe they're just getting really sweaty with lots of convulsing, right?  Maybe it's just how sweaty they are and how long the seizure is. Turns out it doesn't relate to that at all.  Turns out it relates to the amount of time that the cortex is actually being shut down.  When you have an EEG on the scalp and you measure the duration of the EEG shutting down after a seizure,  which is a biomarker that was present in all cases of death following seizures, this signal is bigger in that case.  Now fortunately, nobody died in our study. Whatever was shutting this down, restarted. Breathing restarted.  But we now know that this deep brain activity that cannot be picked up on the scalp,  because the scalp is reading below 10 microvolts, is actually showing up on the wrist.  I am so glad I had tenure when we found this. I would have never proposed looking for deep brain super activation, you know, lateralized on the wrist.  And yet we're finding this, and now there are other studies showing direct stimulation of these regions,  giving rise to these lateralized responses as well.  So this has now not only been shown here in published neurology, but replicated in multiple hospitals.  Now seizures are not usually deadly, but they are the number two cause of death for years of potential life lost after stroke.  In fact, I'm curious how many people here have heard of Sudep, sudden unexpected death and epilepsy?  Probably very few. I see one hand, you're honest. Most people have never heard of Sudep.  I had not ever heard of it. And yet it is the number two cause of total years of potential life lost, so deaths times life expectancy.  More than Lou Gehrig's disease, multiple sclerosis, Alzheimer's, Parkinson's, meningitis, encephalitis.  Now, we also know that it takes more lives in the US every year than house fires.  And that seizures can be thought of like, you know, little electrical brush fires that may stay local in your brain.  They may not cause convulsions. They may cause funny sensations. They may cause you to just appear confused for a moment.  Or they can cause the full convulsions and loss of consciousness.  The deaths are most likely to happen with the kind where you lose consciousness and they take more lives every year than house fires.  You would never build a house without a smoke detector, but people had been sending people home without alerts.  So we decided that we had to take this accidental finding in the lab and get it out to give people alerts.  So that led to this goal that if we could give alerts, maybe we could reduce significantly this amount of SUDEP and help save a lot of lives.  So that is still in the future, although we already got an alert the other day that shows where a mom going to her daughter and finding her not breathing in bed  and able to use our alert to get there in time to save her.  So this has led to this effort to take this technology out of the lab.  Epilepsy. Seizures can seriously hurt or even kill people and we need an alert to intervene.  One in 26 people in the U.S. will develop epilepsy at some point during their lifetime.  Everybody with epilepsy should be able to have a device that alerts a friend or a family member to come help at the time that they might need it.  This device will measure both movement and physiological signals.  The combination is likely to be very important for detecting potentially life-threatening seizures.  So we're now able to issue alerts and I just want all of you to know if you have a friend with epilepsy and you probably do, they may not have told you,  but if you ask your friends, some of them will have epilepsy, some people here will.  And let them know that if they have at least one convulsive seizure a year, they are at risk of having their respiration shut down by this seizure  and simply having somebody go there and say their name and turn them over could restart their breathing.  It's been shown even in people who are conscious if you stimulate the amygdala in a certain way like 15 volts, it can shut off your breathing while you are sitting there typing on the computer  until somebody says your name, Robert, you know, and then suddenly you breathe again.  So it's anybody here could be a lifesaver with these kinds of situations.  So please take that knowledge with you as you leave here.  We've donated with the generosity of lots of people, devices through the Epilepsy Foundation for families who cannot afford this.  We also learned, again, one of our ethics in honoring the people that we try to build technology for is to listen to what they want.  And probably you have friends with epilepsy who've never told you they have epilepsy.  I was shocked when one of my friends told me he had epilepsy and I've known him for 30 years and he never told me.  And I'm like, why didn't you tell me?  And he says, I never tell anybody.  I don't want people to know I have seizures.  I thought, you know, you need to tell people so they can check on you.  But the stigma associated with it needs to go away.  And until it goes away, we need to make a device that doesn't look like a medical device that just looks like a cool watch.  So we decided to make a watch that's designed gorgeously and that can do things besides detect seizures.  So this device, which is not quite on the market yet, you can only pre-order it right now.  We're only on the market with betas for people in most serious need.  It detects the physical activity, the steps, the sleep, wake, and the, and it tells time.  Maybe not as perfectly as your amazing Swiss watches, but it's better than what I had before.  It's as good as my phone, and it's also going to soon come with an API for all the hackers in the room who want to do really cool stuff with it.  Now, my former boss will be very happy because what he's always asked me to do is he said,  Roz, when are you going to build me the mood ring that tells me my wife's mood before I go home?  Now, the mood ring was just a temperature sensor, right?  We have temperature in here, too.  But this gives that autonomic stress that you saw, that skin conductance, right?  That was going up before the meltdown, that was increasing, that was decreasing on the swing.  You know, so you can see if your loved one was just really chilling, you know, or really, yeah, yeah, activated.  This has a lot of practical implications for how people interact.  This data comes from a young woman on the autism spectrum who asked to borrow one of our early prototypes one night when she was giving a three-minute speech.  And she said, I'm going to be stressed by this speech. Can I borrow your sensor? I'm like, sure.  And this sensor was just logging. It wasn't showing the data in real time.  And before the event, she heard that they were going to delay the start.  Now, uncertainty is stressful for everybody.  Now, for a person on the autism spectrum, uncertainty is really stressful.  And so when she heard it was delayed and she was already stressed about giving her talk, she started pacing back and forth.  Okay. Now, her friend who was sitting there trying to work said, stop pacing. That doesn't help.  So she stops pacing and she starts stimming.  I asked her later, what do you mean by stimming? And she said it was kind of this flapping rocking thing.  Afterwards, she gives her talk, that's the peak, and then it's downhill from there punctuated by some terrible auditory feedback events.  Sinsery, ah, stressors.  After the event, the two of them sit down to look at her data and the first word spoken by, or by her boyfriend.  And he says, I'm not going to tell you to stop pacing anymore.  He looked at that timestamp. And the next morning, I saw the two of them before another event.  And this time she was pacing. And this time he just let her pace and he typed on his laptop.  By seeing the objective data of what was going on, it affirmed for him that her pacing was helpful in this case.  We often do what our bodies think is good to do, but we don't believe them, right?  And so by taking stuff that seems subjective and making it objective, we give it more belief.  It doesn't mean it wasn't true to begin with. It just means that we believe it more once we convert it to graphs and data.  Now, I've talked about a lot of things. We've talked about starting with the facial expressions in autism,  then learning that maybe the biggest problem wasn't always them recognizing other people's faces,  but us recognizing their stress and anxiety.  And then trying to build a device that helps read out some of that stress and anxiety,  accidentally discovering it could also detect seizures, taking a little detour to do that because that appears to be life-saving.  And then coming back to understanding emotion and human-human interaction and also in human-computer interaction.  This example I love. How many of you have seen the Wally movie Pixar?  Oh, everybody. Great choice by Roz. Yay.  So one thing I didn't notice until a person with autism pointed this out to me is these two stars of the movie, Wally and Eva,  they never use words. They build an entire relationship with nonverbal interaction.  So it was a non-speaking person with autism who pointed this out.  Look at this rich relationship that is developed only by emotional communication.  And we like these robots. We feel like we build this emotional connection to them.  We share their dreams and their failures.  Now, many of you may also know Kismet, developed by Cynthia Brzeal for her PhD at MIT.  I was just a reader on the thesis. Rod Brooks was the advisor.  But I always loved this example where she was showing not only that a robot could recognize emotion,  in this case recognizing tone of voice, but also that if you make the robot respond to emotion in a particular way,  it really affects how we feel about the robot.  So I go back to that opening scene of that Microsoft screen or Clippy or something coming up.  You know, when it ignores your feelings, you hate it. You want to kill it.  I mean, there are pictures of a Clippy on the web with a noose around his neck.  And Bill Gates got his standing ovation when he said Clippy was going away.  Watch what Kismet does. This is intelligent.  So another important skill for the robot to be able to learn from people is being able to recognize communicative intent.  Very good, Kismet.  And the way we've done that with Kismet right now is to have the robot recognized by tone of voice.  Are you praising it? Are you scolding it?  Where did you put your body?  No. No. You're not to do that. No.  So, you see, when we don't like what the robot does, if it acts sorry,  then we're like more likely to forgive it, right?  And then we're happy to see it again, right?  It's when we're miserable and it's happy and it's not noticing that we're like ready to kill it and get rid of it.  So that emotional intelligence is really critical if we're going to build an intelligent future system.  Now, emotional intelligence became a very powerful theme in a recent movie, Ex Machina.  I don't know how many people saw this.  I wrote a review of this that science invited me to write. You can read online if you're interested.  But in this movie, the young man here is giving the robot a test of emotional intelligence to see if she can pass that,  to see if she really has emotions.  I can't spoil the end of the movie for you, but this movie also raises a lot of issues that transcend the intelligence of a machine  to the morals of a machine as well.  And these are things that people studying morals and moral decision making know that emotion plays a key role in.  Now, we don't know if you have to have emotions to make moral decisions,  but we know that people who make moral decisions often and perhaps usually employ their emotions in making such decisions.  And that seems to be an important part of how we act in a way we think as moral and intelligent.  As we give robots the ability to see emotion in our lives and as we start putting robots into our homes,  such as the Jibo here that Cynthia has left MIT temporarily to build,  these can start to see not only our facial expressions, but also our interaction with each other, our relationships.  If we're doing things that make us happy or if we're more stressed.  By the way, now we can also read heart rate and respiration non-contact with video and with Wi-Fi.  So there are ways that these machines can sense a lot of things about us that could be disturbing or could be very beneficial.  So these also raise a lot of ethical questions that I think need more discussion in society.  In our work, we've become very interested recently in a new direction that is concerned with mental health.  In this graph, the vertical axis has collapsed a lot of factors of well-being into one dimension.  And when the university admits you or hires you as new faculty, you're at your peak of well-being.  You're strong, you're smart, you're healthy, you're happy, hopefully, positive.  As we encounter major stressors, as time goes on, almost everybody takes a dip.  And hopefully you're on the blue line and you come back up over time.  But at MIT, we have a large number of students who are going down the red line there.  And when they get far down, usually about here, hopefully they by then have a medical diagnosis.  And a doctor says, you're depressed or you're in trouble and we need to help you.  But I'm asking, and my students are asking now, why do we have to wait until they get down here?  Why can't we measure things back here?  And we're starting to do that and see things changing even before outwardly there's any signs of trouble.  If we could tell that you're on the red line back here, we could do two things.  One is we might be able to tell you how to get to the blue line.  Second, we ought to be able to prevent you from going further down this line.  We ought to be able to prevent things like 80% of cases of depression.  Now that's a very ambitious future goal.  But we would like to start thinking about what are those components and how might our technology that's filling our lives  and can measure all of these amazing things be used to not make us household pets,  but be used to help us take better control.  Just like forecasting the weather was enabled 150 years ago with technology sensors all over the geographic environment.  Why don't we use some of these to better understand what helps us be well and forecast health?  Wellness is incredibly complex.  There are lots of theories of it.  I'm just going to show you briefly one here because I want to debunk the popular notion that this just means happiness.  This theory, due to Marty Seligman, shows positive emotions as one of five aspects of well-being.  So being happy is just one of these components.  Others that are really easy for people at really high-end universities like this are usually engagement,  engaged in something you really enjoy doing, and accomplishment.  Probably everybody in this room is very high on engagement and accomplishment.  Hopefully you're high on positive emotions too.  At MIT we have to work more on positive relationships and meaning.  And it's interesting because they're very connected too.  If you ask where does meaning happen, you write a brilliant paper, right?  But if nobody reads it, in the days of read-only memory, one of my friends coined the phrase,  write-only memo, the write-only paper that you wrote but nobody read,  is there really meaning in it if you do all this work and there's no receiver of the value?  There's no relationship if there's no recipient of the meaning.  So meaning and relationship have motivated where we've been taking the robotic technology  and our affective agent technology to try to facilitate more of that interpersonal connection,  whether it's between two people or between a machine and a robot.  It's helping us understand what contributes to that.  It's also helping us see the important value of that.  Now the one thing I'm most frequently asked when talking about these systems,  and the one on the left here is called Mock.  This is an agent that my student, Eson Hook, built to support people in developing better nonverbal skills during interviewing.  So you interact with this agent, she asks you questions like a tough interviewer,  and then gives you feedback on your pitch and your facial expressions and how animated you are and your speaking,  and words such as and, but, so, or these filler words that we all need to stop using.  Now the agent is scripted and appears very intelligent in these limited scripted contexts.  Now you may have seen a movie like her where he has this romantic relationship with an agent,  a software operating system that does not appear scripted in the movie, but of course it's a movie, right?  The whole thing is scripted.  We have to remember science fiction script, much harder to do it in real life,  even scripted to make it meaningful.  The challenge in the future intelligence systems is to build the unscripted interactions.  The challenge is to, you know, could we make them not only have emotional intelligence,  but could they actually feel and be conscious?  We don't know how to do that right now, and we don't foresee how to do that with the software and hardware that we have today.  So I'm not going to say that's going to happen in the next five years.  It doesn't mean it can't happen.  It means we don't see how to make it happen right now.  But suppose that it could happen, and suppose that we could build them to be unscripted, emotional, emotionally intelligent,  then the next thing people ask is would they be our slaves or would we set them free?  Would we try to enable them to not just be unscripted and emotionally intelligent,  but also to operate with true freedom, to make their own decisions,  to believe whatever they want to believe?  And some of you may have seen Sunny as one example of this in the iRobot.  What if one robot were built or more to be truly free and to be true, not just autonomous, but free?  And furthermore, suppose they were on another planet where they had never seen a human.  There's a robot colony, and we know that machines can produce machines.  And so the robots having never seen a human might explain that they arose from other machines.  And maybe some virus wipes out all references to human and people at all in their system.  So if they're really free and they've never seen a human,  there's no reason for them to ever acknowledge that Cynthia or I or you or anybody who ever worked on this technology  ever even existed.  They could simply deny that humans ever had anything to do with them.  After all, they could have just send it from silicon, from sand.  If they chose to do that in their truly free state, what would you do?  What would your choice be?  Would you have to ignore them or reach out to them?  How would you feel if robots declared that people never existed?  I say that's just as possible as these other scenarios.  And a fun thing for us to have more discussion about.  I want to wrap up, though, and get to Q&A.  So I just will take you on a quick recap.  I started this talk with a little bit about reading facial expressions,  emotional intelligence systems that could recognize and respond to our emotion,  learning from people with autism that they didn't so much need our technology  as they needed to be better understood and could our technology help them be better understood.  So redirecting it at that, accidentally realizing that deep brain activity is giving rise to signals  that we could read electrically on the skin even at the same time while it's shutting down the signals on the scalp.  And that we could build an alert system that could potentially save lives for that.  So getting that out, commercializing that.  But wanting to do it in a way that builds a device that doesn't just detect seizures,  but is something that enables people to share emotional data and have fun learning about each other.  Hopefully building relationships.  Relationships, positive affect, engagement, meaning, achievement, core to well-being.  Could we take all of the power of this technology and start to understand what's contributing to human well-being?  And not only what's contributing to it now, but what changes it and can forecast what could keep it well  as opposed to when it's slipping.  And then finally, as we take all this intelligence and put it back into robotic systems that are in our home  and future coming into all aspects of our lives, what does that mean in terms of how they interact with us  and what we expect from them going forth?  What do we expect them to acknowledge about us?  What kind of rights do we want to give them? What kind of freedom do we want to give them?  And all of this, I will have to say too, is not going to happen automatically.  Some people seem to think, oh, the robots are just going to evolve this stuff and this will happen.  That's not true.  They won't develop any of these abilities unless we give them these abilities.  It's not going to happen unless some engineer, some bright people like in this room decide to program them this way.  None of this happens without human effort setting this into course.  So the opportunities are to have hopefully a great discussion about this future,  and I'm happy to take questions about any aspect of this or my faith, any of these issues as we continue.  Thank you so much.  Thank you very much Rosalind for this inspiring, interesting talk, and we have time now for questions.  I had a question regarding the top left picture, so from my perspective about the smile that we see when someone is frustrated.  I was just wondering, could that be caused by a certain mechanism in the individual to maintain positivity even though they're stressed?  That's one of the hypotheses right now is that people who do that are trying to make themselves feel happy,  and this could be compensatory instead of getting angry and hitting the machine.  It's a way to try to make yourself feel better.  That's one possibility.  By the way, it's also been observed in small preschoolers, little kids, so when they're failing at a task in a learning environment.  So we evidently, some people learn to do this very early.  It is also possible it can make you feel better because smiling can actually feedback to make you feel better even when you don't feel happy.  Thank you.  Hi. Thank you very much for the cool talk.  So I felt a little uneasy when you showed the graph about how you could measure someone sliding to depression or the girl that had to give a talk.  If someone was tracking my emotions, let's say, you know, supposed girlfriend or something, then I would feel even more stressed about that because  I feel it's a very nice piece of privacy and freedom that I can smile and at the same time feel terrible and not have to explain myself.  So this is one problem, I guess, and the other problem is how, what if, let's say, my boss could track me all the time, right?  And how if people can basically just see the stages, such a precious data and so revealing.  So which steps do you propose to, let's say, make sure that it's not abused and that people are not made totally transparent?  Great question. Thank you.  Yeah. First, when the young woman had her data recorded, for example, the first thing I did is said, you know, do you want to see it and do you want him to see it?  Right. And she said, yes.  And we never show people somebody else's data without their permission or completely in a de-identified way.  I think that's a very important ethic to have for protecting people's privacy.  I agree. It's very personal data and we have to be extremely careful with how people might interpret it.  The boss situation, oh gosh, we have been asked, you know, can you measure my employees' stress, you know, and let me see it?  And I'm like, well, what are you going to do with that data?  Like, how are you going to help your employee?  So we agreed, in one case where we agreed, it was I will measure the employees, we will give the employees' sensors to measure their stress.  If they opt in, if there are enough of them that, first of all, they get to see their data, their boss does not see any individual's data.  The boss, if there are enough people who opt in and we can aggregate the data, the boss can see when the peak bad times of day or if overdraft Wednesdays really are as stressful as the employees.  So we set up some ground rules for the ethics of it and then that worked very well and people were very happy to opt in and nobody was singled out as having any particular characteristic.  So I think we have to show the utmost respect for people's feelings in all of this and that is easier to do when it's wearable technology, when it's unobtrusive sensing technology.  That's where it becomes much trickier and I'm a lot less comfortable with the way the opportunity is there for abuse in some cases.  I think this may be a case where we have to get society saying what do humans value about each other and how do we keep that, you know, acted upon.  Hi, Professor. Thank you so much for your awesome talk.  So recently human bot interaction is a very hot, I guess, consumer product now with Facebook launching its new M messenger service in the Bay Area and Microsoft launching their new API for humans to communicate with bots.  But they seem to feel like text is sufficient in terms of understanding the user need.  And clearly from your presentation, there's much more that can be understood from speech and video.  I'm wondering where do you think the limit of understanding emotion in text in natural language processing is and why you decide to go in the direction of more multimedia.  We have also done a bunch of natural language processing to my student Karthik Deena Karz and I've published a bunch on that recently.  You can find on our website here publications of everything I've talked about and a lot more.  The text as we know what you say and how you say it can be completely in conflict.  And words can be very confusing too. I mean, you know, when one person says the F word, they think something's cool.  Another person says that they're really mad, right? Or take the word sick, you know, I don't know what that means to you.  To my son, that's like a super cool move, right? And I'm like, sick, you know, somebody needs to go to the doctor.  So we have problems with words that you have to use a lot of interpretation around the context.  And often it's still ambiguous. So each thing, whether it's words, face, physiology, we've also measured posture, gestures, sitting in chairs, fidgety-ness, all kinds of factors.  How hard you're squeezing a steering wheel, how hard you're squeezing a mouse.  These things all convey affective information and we can use whatever people are comfortable sharing.  With the caveat I always put in that we must, number one, show respect for people's feelings.  And in my case, that means, in all of our work, it means you inform people, they get to opt in, and only then do you take that data.  There's several down here.  I have a question about consciousness in relation to those robots. And I'm not sure if I remember correctly. I think you said that you don't think it's possible that consciousness will appear with robotics in that way as we know it, but maybe I misremember it.  Can you just maybe explain a little more about that, the connection between consciousness you talk about robots that will recognize emotions, will they also feel the emotions themselves at some point? Can you talk a little bit more about that?  Sure. It's a good question. With any of these things, there's kind of the level at which you evaluate it. If you perceive it, like if I query to the robot, do you have consciousness? Do you have feelings?  And it says, oh yes, I'm feeling quite fine right now. How are you feeling? I mean, at that level, it could act, and I perceive you are in the room.  I mean, that kind of functional claims of consciousness, that's trivial. But I think what we're thinking of is something much deeper that involves our physiology, our feelings, even things that we can't just measure with physiological sensors.  Things that are ineffable. We can't prove those things aren't there, and yet there's evidence, and I know the materialist in the room will not like this, but you can't prove everything's material either, right?  What if there's something more? What if there really is something spiritual or something else there that we can experience, like a nurse going into a room and saying, I knew when I walked in that the patient had died, even though there was no signs of vital changes, I knew that they had left the room in some sense.  There are so many reports of there being something else, and we can't prove what that something else is, if it's immaterial, because science doesn't measure this immaterial stuff, but we can allow the possibility of it.  And when we try to build consciousness, while we can build this perception of it, we don't have any idea how to build these other aspects that we can't even begin to measure.  I say, I don't see how to do it right now. I don't see how to do it with current hardware and software right now.  I do see how to build a fully functioning conscious human entity. Some of my colleagues would say we're all just machines, and would say, oh, let's just buy an engineer the tissue and make each part of us, each organ, and so forth.  And I say, well, if you're going to go to all that trouble, and it's going to be just like us, then why not just make it the same old-fashioned way we've been making them forever?  I have three children. I mean, that's a lot more fun than bioengineering all the tissue, right?  So I think we're talking about something different than that.  And it's not happening with the hardware and software that we see right now.  It's not even getting close. We're clearly in a completely different space.  So how would you prevent or isn't there a danger that we don't get like senseless, emotionless zombies with all the technology and everything is telling us what we should do, now we should eat, now we should smile, and we are totally dependent on that stuff, and we lose like everything else?  You know, that's a future we can choose to build or not, and I hope we don't build that. I think hopefully you agree.  I'm reminded of when a colleague was at Foo Camp, Foo, we won't say what Foo stands for, Computer Science, catch all word, and at the conference, there were all these really fascinating, interesting, brilliant people, and there was a break when you think people would want to go talk to each other.  And instead, they all grabbed their phones and they hunched over them with shallow breathing and were doing this, and nobody was interacting with each other.  And the developer of iOS was there and he said, I feel badly I invented this.  I think a lot of times with our technology, we're not thinking what could happen.  We're just building something cool and sticky, and we have an opportunity with the future here to ask what do we want to build?  Not just what can we build, but what do we want to build?  And I hope we're not building something that's turning us all into zombies.  You know, when I have been reading some reports about what's been happening in Silicon Valley and about these technological innovators and what they think about communication and what they especially think about people's thinking that at least some of these experts have said that we could actually have robots.  Or other artificial intelligence devices think for us and we could actually then have more leisure time and so on.  I mean, have the work done for us and so on.  And I would really like to know what you think about these, I mean to say the tendencies within the framework of Silicon Valley and whether there are really within this framework,  attempts to restrain the use of artificial intelligence not only to restrain it to certain sectors.  For example, as you said, people who are mentally challenged or any other things, seizures, and you gave some examples, to restrain it to that and not to just have it to apply it to the whole of Silicon Valley.  And I would like to know what you think about the work done by the human society and how it's going to work.  And I think that's a great question.  I think that's a great question.  I think that's a great question.  I think that's a great question.  It's funny because we're all kind of, I don't know about you, I'm kind of attracted to just curling up with a book and staying there forever.  But I also know that my brain doesn't work well when it's so sedentary.  And I've also learned that my introverted self that would stay away from people is actually much healthier when I'm interacting with people.  And back to those five pillars, if you're out building relationships, you're out engaging in challenges, you're not taking the lazy, easy path.  It actually leads to better well-being than just thinking about what you enjoy personally.  That positive feeling is only one of the five components.  If you start to explore where meaning comes from and engaging with things that matter more than just yourself, you start to feel better also.  So while you might be attracted to that, make your life easy and have leisure time because you'll feel better, rarely is that the real secret to well-being and happiness.  So I think we need to stop and question what it is we think is good and help each other see that we may actually be much happier not taking that easy path.  Thank you.  I have a question concerning the empathic bracelets.  Do you think it would be an option or whatever happened that some healthcare insurance companies would make that a must for their patients with maybe any of the desired?  I predict in the future it will become a must for patients who have convulsive seizures.  Probably not just the one one company makes, but that a device that does an alert should become a requirement for people.  It should be reimbursable, right? The medical system should pay for this because it could be life-saving for people who have at least one convulsive seizure a year.  Yeah.  You gave a very nice example in the beginning of how your interest changed from helping autistic people to understand the world with this light to actually the world understanding autistic people.  Can you apply that same change in interest also to robotics because I see there's lots of examples right now of robots trying to understand us.  Maybe we should also change to us trying to understand what these robots actually do.  Is there anything in your research that goes that direction?  Actually that trying to make it so that it's symmetric is one of the motives behind making humanoid robots because the thought is we're good at understanding people or some of us are.  We could all improve.  My husband would make comments about women here.  But we think we're good about understanding people.  And if the robot acted more like a person, then we wouldn't have so much difficulty understanding it.  But if it's spitting out all of this complicated numerical code that we don't understand, you know, state, numeric state information, why doesn't it just say it's frustrated?  Right?  It may not have feelings and conscious experience like we do, but we might prefer that, you know, if it's banging its head against the wall trying to do something and not succeeding that it instead of it just acting weird, it tell us that it's frustrated that it has this goal and it can't achieve it.  So we may accept that anthropomorphic language for understandability.  Okay.  Yeah.  You got it there already.  Okay.  I got a question regarding the timeframe of artificial intelligence.  How far are we away to substitute nurses in hospitals?  Oh, I hope we never substitute nurses in hospitals.  Nurses are wonderful.  I think we, you know, this goes back to questions of what we want.  And I think, you know, we need to look at what people are good at and help them do it better.  Their jobs, parts of the job each person does that they would love to have automated and their other parts that they would love to still be able to do.  And why don't we show respect for that as we develop technology instead trying to replace whole people and whole occupations?  Why don't we just help them figure out how to do their jobs in ways that get rid of the part they don't like and facilitate the part that they do like.  So again, this goes back to our choices, the future of technology largely shaped by people, bright people like in this room.  We have a choice to, you know, I mean, the public who doesn't build technology may think that it's inevitable, but it's not inevitable, right?  It's what we choose to do and what values we bring to that.  I see that there's still a number of questions, but that's actually a good thing.  I think this was actually a very good closing statement and I do believe from what I see is that we are, many of us have really realized that there is no need or use to try to replace things,  but to try to have these systems actually assist in a meaningful way and have each part do what they can do best.  Before we close, I would, you will have a chance to ask more questions just afterwards, but before we close, I would like to ask Remy, where is she to come up here?  She's actually been the driving force behind organizing this talk and she would like to say a few words to you before we close the session.  Thank you, Raja.  On behalf of ETA, Fawbege, Campus Life and the Zentum Forum, I want to thank Prof Picard for coming and taking the time.  Thank you very much.  Thank you, Remy.
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Thank you. It's a real honor and a pleasure for me to be here today. I've been enjoying a lot of very cool demonstrations of the technology here and I'm really impressed with how bright everybody is and I've heard how amazing the students are that get to come here as well. As was said in the introduction, I wrote a book called Affective Computing and at the time I was inspired by artificial intelligence and by my senior colleagues who wanted to build a machine that as my friend Marvin Minsky put it, that would be so amazing, it would be so intelligent that we'd be lucky if it kept us around us household pets. And I thought, well, I like the first part of that but I wasn't really keen on being a household pet, being somebody's dog or cat. That did not feel like all humans could be. And when the Media Lab was founded, we started to think not so much about building the monolithic AI, but about how technology with people could make our lives and our futures much better than just in the movies that we had seen. So I started thinking what kind of intelligent machine would I want to build and I started realizing that one of the important skills we needed in human intelligence was emotional intelligence. Now, I really did not want to be associated with emotion in the early days as a woman in science and engineering. I thought, wait, this will kill my career. So I tried to get some guys to work on it. So I tried publishing a paper which was rejected and so then I wrote a book. And, you know, they want to make money off books so they don't reject books. So anyhow, then what I really wanted to do though was build the technology and see if it worked. And so we started to try to build technology that would show respect for people's feelings. And the first feeling I thought was most common in front of computers was when you feel frustration. Okay, you have a deadline, right? You're trying to do something important and the stupid paper clip pops up or some other, you know, right? Now, why can't it see that I'm busy and I'm concentrating and not do that then, right? An intelligent human would look at your feelings, look at what matters to you because what matters is reflected in your emotions and it would change what it does in response. So we tried to figure out how to automate this and we've done lots of experiments where we frustrate people. I could fill hours with them. Here's an example of a person coming in and I'll have to tell you if you try to do experiments in this space, you cannot tell people that you're trying to frustrate them. It doesn't work. You have to tell them something else. So in this case we told him he was testing the usability of a new system and unbeknownst to him, it's going to give him some problems. I am sorry. You seem to be having difficulties getting through this form. Wouldn't you mind looking at the camera and sharing your thoughts on how to improve the form? We can all relate, right? So just so you can see, we frustrated people. Now, at the same time I was taught and I wrote about this in my book and if you have my book you can now cross out this part of the book because I no longer believe it. I was taught that if you want to detect if somebody is truly happy then you look not for the smile of action unit 12, the lip corner pull but you look for the smile of the eyes, action unit 6, the cheek raise, what gives us crow's feet as we age. So the theorist always said if you see both of these then the person is truly happy. So watch what happens in this next situation. We do the same thing you just saw to the student. But look at the smile and the eyes. So what we found was that the smile of delight could show up when people were really frustrated. In fact it showed up 90% of the time when people were frustrated. And this was a surprise. And since then we have found over and over that there's the theory of what people do and then there's what people do. And we need to always be looking and showing more respect for what people really do and then making the theory match the practice. Now we were interested in facial expressions also because we not only do they signal at the largest distance something like delight and it turns out actually we were able to teach a computer how to recognize the delight versus the frustration and do so more accurately than a person could recognize it using some dynamic machine learning that recognized the speed of the expression not just the peak of the expression. But it doesn't mean that machines could always recognize this better than people. It was good at doing it in a very focused directed context. And we were trying to do this to help people on the autism spectrum who when they heard that we were building computers to recognize emotion the brother of a person on the autism spectrum came to me and said can you help my brother? And I said I don't know tell me about your brother. And he said his brother has autism and he has trouble reading other people's emotions. So we realized we could put the little glasses a little camera in the glasses and this was long before Google Glass. This was more than a decade before that. And we also put LEDs in. So the computer would in real time recognize if you were smiling or looking interested I would get a green light while I'm looking at you. If you look at me and you look confused I get a yellow light like slow down Ross. And if if you're like really distracted you know or frowning or looking like you really need to think about something else I get the red light to stop. I know you probably wish you had that for all the professors who lecture. We could do better with that information. So then we worked with children with autism. Hello. Hello. How are you? Check that out. It's me shooting you shooting me. How are you? Good. How are you? Good. I like this thing. You like it? I like this thing. I made a perfect video. You know there are heat sensors on cases now for light detection. If you get the face with it and the tape and analyze that later, that way you can get the tools. So he might not even need the additional three voices with this. He just says he'll use it as a tool later would be very helpful. I did notice that the wires, it would probably be easier if it was more of a self-contained system. You don't really understand something until you teach it to somebody else. So we started to learn from people on the autism spectrum what they wished that we could do. And we realized we had a lot of improvements to make. Early on the technology was pretty good on well-lit, well-controlled lab data, more than 80% accurate, recognizing six different common expressions. But it wasn't anywhere near 100%. And when you went into the real world, the rates, the accuracy went down 50%, 60%. And we had spent, you know, with our partners in the UK, over a million dollars of research funding trying to get this far. And to get the data that we would need to get the accuracy up a lot higher was going to cost millions more that we didn't think anybody would give us. So we started thinking how can we get enough data to make this really accurate and really helpful for people. And here's my co-founder of Affectiva, Rana Elkaliubi, saying a little bit more about what we did. This is on building computers that can read people's emotional states through their facial expressions. We've built technology that looks at people's facial expressions. It identifies the main feature points on your face, like your eyes, your eyebrows, your nose, and then it tracks how they move over time. When I smile, the green smile track goes up, and when I nod my head, the head nod track goes up. And when I shake my head as in disagreement, you see the pink line go up. We use thousands of videos of people showing agreement, showing disagreement in order for the computer to learn what it looks like liking, concentrating, disagreeing. Making this technology online opens a whole lot of new applications. Almost everybody with a computer today has a webcam, or a lot of smartphones have cameras too. So if we enable people to opt in, turn the camera on, they can share their experiences online. It's a more sophisticated version of the like button on Facebook. So we decided to see if people would turn on their cameras online, and we started by deploying the most popular ads and recording people's faces while they smiled. Thank you. Did this Volkswagen ad air over here? It became the number one ad in the U.S., and they paid millions of Super Bowl. And with the smiling that peaked at the end, we could see that when people saw it again, they smiled even more. And after they had become familiar with the ad, they actually smiled before the punchline. So it was a way to sort of detect if they knew the good part was coming. Now by capturing the entertainment, then you could get people to pay people to watch content to see their facial expressions. And with that, we were able to go from hundreds of examples in the lab to millions of examples online and get the accuracy much better. The work to do this, I liken it kind of to taking, you know, we wanted to build this autism technology. We wanted to just run to the front door and open it when the doorbell rang. But it's like these old cartoons where the kids run all over the house before they get there. We had to do a lot of other things in order to get the data. And that now is an app that you can, I think, still download for free to do a demo version of and try out all the different facial affect recognition that it has right now on your iOS or Android device. But don't do it right now in the talk. Do it later. Now, Rana, who you saw was from Cairo, and the Arab Spring was happening as we were starting this. And we realized that while there were a million people in Tahrir Square, Mubarak might have been saying at the time that a million isn't even a majority in Egypt, right? Maybe the million in Tahrir Square don't like me. But Egypt has another 79 million people, and they're not in Tahrir Square, so they must like me. We can often think what we want to think, right? But if we could opt, we could give people the chance to listen to the speech, and anonymously, we record no identifying data, anonymously have their not just smiles recognized like you saw there, but we can recognize a lot of expressions. In particular, we can recognize smirks. We see a lot of these with politics in the US, these asymmetric smiles. So if Mubarak's talking and they're doing this, then it doesn't mean that most of the people listening like it. And that feedback, if it could be kept private in a way that protects people, could be very valuable. There is a question though about could that be done in a way that they couldn't see that people in a particular area were disgruntled and take some action that we regret there. So these are questions we face. The power of the nonverbal voice to give people feedback is very strong, but the truth is also very powerful and can be upsetting to people in power. Now, while we built this for people with autism and wound up doing these other things to get data, I was talking with a friend with autism a little bit later, and she said to me, Ross, you, yes, you know, some people with autism want to recognize other people's facial expressions, but actually you have it wrong. That's not our biggest problem. Our biggest problem is not recognizing other people's emotions. Our biggest problem is you recognizing our emotions. And she's pointing at me and I'm like, oh, great. I'm not doing so well, huh? And she goes, not just you. We, you know, everybody, we all could do better recognizing the emotions of people with autism. In particular, I learned by talking to my friends more that they have a lot of anxiety and stress that was not being recognized by other human beings. And things like the fluorescent lights or the smells or the sounds were extremely stressful, and they wanted help with that. I started getting emails from people who learned that what our technology could do. Here's one from a father who wrote me from Holland. He said, my colleague told me about a new fascinating technology he heard on BBC Radio 4 where computers were able to detect a person's mood. He thought of it as a useful technology to prevent him from smashing his laptop into pieces. But I thought immediately of something else. I have an eight year old son who is diagnosed with ASS. Unfortunately, I only know the Dutch term, but in English it must be something like autism spectrum disorder. In general, he is a normal, intelligent boy. Autism is the last thing that comes to mind when you see him. But sometimes he starts disturbing the classroom or responds too aggressive against other children. It's his third year in school and he is already in his third school. For the moment, he is going to a special school for children with learning disorders, but things still do not go as expected. We are going from one test center to another for four years now, and the pressure on our family is really hard. So I asked my colleague the details of the radio program. And when I listened to the recording, I had to pause it halfway through as I was getting too emotional. I believe this technology can help us in two important ways. And he goes on and he describes if this could help name my son's emotions, the fear, the anger, the concentration and help him recognize them. For example, we discovered lately that when he is hungry, he becomes aggressive without even knowing that he feels hunger. How long can he concentrate in the class and how long is there between the loss of concentration and his aggressive outbursts? We also believe he has a lot of fear. How much would that affect the day? And he went on and asked if we could please build technology that would help him. We would really appreciate your help. My son is an intelligent child and this could be decisive for his school career. Who knows what contribution he can make back towards the future? So I got letters like this and I'm happy in my lab working away. And I think I need to get this technology out of the lab. We need to take it and make it something that can help these children better understand their emotions and better communicate them to other people. Now we knew from measuring emotion in the lab that we could measure the autonomic arousal that often precedes meltdowns and frustration and anger and stress. So we had built versions that could take the usual things like the electrodermal activity often called skin conductance, often measured by skin conductance, an old name for it is galvanic skin response, and measure it in other ways that were less obtrusive and easy to get in daily life. And full disclosure, I'm now a co-founder of Empatica that makes two sensors in the space, which I'm recording myself with right now. My own stress level is up here as we speak. One of them, the E4, gives two kinds of autonomic data, the electrodermal activity, which I'll be saying more about on the top, and the blood volume pulse, which I won't say too much about here other than if you're interested in heart rate variability. It's been benchmarked to ECG and is within 2% error of a very expensive ECG for measuring heart rate variability when you're still. And if you start moving, you get more motion artifacts. Now, what does this electrodermal activity tell us? Here's an example of a very early prototype we built worn on a child's ankle that shows you it going up with the child having a meltdown. I'm going to turn down the volume. This is a little girl with autism. The stuff on her head has nothing to do with what we're measuring here, but she's about to hit a peak and have a meltdown here. The blue window is one minute wide, and that corresponds to that blue stripe at the bottom. The bottom is 45 minutes wide. So here she is, peaking with a meltdown. We also see it's been building slowly the whole time. Now, just because it's building doesn't mean a meltdown is coming, but if it's building and you don't see any reason why it's building, you might want to watch out. Also, it goes down when the arousal declines, when there's calming. And here we see it, first actually we see it peak when she's getting on a swing. Three kinds of stress can make the signal go up. The affective autonomic arousal, the emotional arousal, the physical effort or exertion of motor planning to get on the swing and cognitive load, just tasks like counting backwards by sevens can make it go up. But here we see rhythmic activity tends to make it go down. So when I was in your lab and looking at these beds rocking people, we would expect something like that to make this signal decline. And in autism, I've seen some people rocking, and I've seen this go down like a slide on a playground. Here's the first time we got to look at the electrodermal activity from a MIT student, 24-7. And what we saw was no surprise here that it was peaking with lab activity and MIT homeworks and problem sets, very hard, probably just like here. Intense work. To our surprise, the biggest peak of every day is sleep. And I won't go into that here, but we've got a lot of papers online talking about this now. It peaks during non-rim sleep, not during rim. It peaks during the latter part, usually slow waves, stage three, sometimes stage two, and very rarely in stage one in rim. And it right now appears to be very useful in predicting memory consolidation. So we had the biggest peak during sleep, which should bother you because that doesn't seem arousing at all. But the biggest awake peaks are during homework and lab. Oh, and to the embarrassment of MIT professors, the low point every day is classroom activity. So I will keep trying to get you to laugh, and I really do want you to ask questions because when you do that, everybody's autonomic arousal goes back up. Now, once we built a sensor that wasn't wired, you know, where we had to attach you to something in the lab, people could leave the lab. And get that data like you just saw. And it was the end of the semester, December, when an undergrad came to me and he said, Professor Picard, can I please borrow one of your sensors? My little brother has autism. He can't speak. And I want to see what's stressing him out. And I said, sure, in fact, don't just take one, take two, because they broke very easily back then. And I said, actually, do you need a soldering iron, too? And he says, no, I have a soldering iron. Okay, good. So he's ready to fix it. So he puts the two sensors on his little brother on the wrist in sweat bands like the sweat band shown here. And I'm back at MIT looking at the data in my office on the screen. And this day's data looks pretty normal. And the next day's data looks pretty normal. In fact, this kid didn't even look stressed. And the next day looks pretty normal. And I'm like, you know, this will be quick. I go to the next day and my jaw drops. First of all, he put it on both wrists. Like, I thought you'd do one and then the other first one broke. But no, he disobeyed me. And I'm really glad he did. Because in this day's data, one wrist went so high that I thought the sensor must be broken. We have stressed MIT people out every way imaginable. Professors get to do that. And I've never seen it go so high. And the other side wasn't even responding. All right? So like, how can you be stressed on one half of your body? So, and furthermore, I'm an electrical engineer. So I thought there was an electrical problem. I should be able to debug this. But the data were fine, perfectly fine right before and after and showing a very clear sleep signature afterwards that was symmetric. And I, and it's not always symmetric, but this case it was. And so I tried a few things to explain it. And I finally I gave up and I did something I've never done before as a professor. I called the student at home on vacation. Like, hi. How's your, how's your Christmas? How's your little brother doing? Hey, any idea what happened to him? And I gave him the exact date and time of this weird data. And he says, I don't know. I'll check the diary. Diary? MIT student keeps a diary? Quick prayer. Like, what are the odds he's gonna, like, have written this down? And he comes back. He has the exact date and time. And he says, that was right before he had a grand mal seizure. Now, I didn't know what a seizure was. I mean, I had heard of them, but I didn't really know anything about the neurology. And so I thought I need to learn more about this. And I learned another student's dad was a brain surgeon at Children's Hospital Boston. And he worked on patients with epilepsy who have multiple seizures. So I called him up. Hi, Dr. Madsen. My name is Rosalind Picard. Is it possible that somebody could have a huge, I wanted to sound medical, a huge sympathetic nervous system surge? Because that's what elicits the skin-conducting response. A huge sympathetic nervous system surge. And in this case, it looked like it was 20 minutes before the seizure. I said 20 minutes before a seizure. And he said, probably not. But he said, it's interesting you ask. We've had patients sometimes whose hair stands on end on one arm before a seizure. I'm like, on one arm? And he's like, yeah. And then I told him it was just on one side. And he got very interested. And he said, we can explain that. You could have a seizure in one part of the brain that can give rise to the activity on just one side of the body. So he got excited. And we made a bunch more sensors. We got IRB approval. We enrolled 90 families. We went to Boston Children's Hospital. We measured gold standard EEG, video, ECG, electrocardiogram, and EDA, electrodermal activity on the wrist. And we saw for 100% of the convulsive seizures, the grand mal, generalized tonic-clonic seizures, these huge responses. On the top is the electrodermal activity. On the bottom is the accelerometer. So you see for these three events a lot of accelerometer movement, as well as this huge electrodermal activity response. And by the way, this is this boy's sleep. So for the MIT student, the sleep was the biggest peak of the day. These seizures are huge events. They're much bigger than any stress events. They are something very unusual that we were never going to hypothesize. They were completely accidental. We were able to use machine learning to build a seizure detector, which at the time was 94% since then. And Patek has continued to improve the accuracy with more data. We also asked, does the size of these mean something? Maybe they're just getting really sweaty with lots of convulsing, right? Maybe it's just how sweaty they are and how long the seizure is. Turns out it doesn't relate to that at all. Turns out it relates to the amount of time that the cortex is actually being shut down. When you have an EEG on the scalp and you measure the duration of the EEG shutting down after a seizure, which is a biomarker that was present in all cases of death following seizures, this signal is bigger in that case. Now fortunately, nobody died in our study. Whatever was shutting this down, restarted. Breathing restarted. But we now know that this deep brain activity that cannot be picked up on the scalp, because the scalp is reading below 10 microvolts, is actually showing up on the wrist. I am so glad I had tenure when we found this. I would have never proposed looking for deep brain super activation, you know, lateralized on the wrist. And yet we're finding this, and now there are other studies showing direct stimulation of these regions, giving rise to these lateralized responses as well. So this has now not only been shown here in published neurology, but replicated in multiple hospitals. Now seizures are not usually deadly, but they are the number two cause of death for years of potential life lost after stroke. In fact, I'm curious how many people here have heard of Sudep, sudden unexpected death and epilepsy? Probably very few. I see one hand, you're honest. Most people have never heard of Sudep. I had not ever heard of it. And yet it is the number two cause of total years of potential life lost, so deaths times life expectancy. More than Lou Gehrig's disease, multiple sclerosis, Alzheimer's, Parkinson's, meningitis, encephalitis. Now, we also know that it takes more lives in the US every year than house fires. And that seizures can be thought of like, you know, little electrical brush fires that may stay local in your brain. They may not cause convulsions. They may cause funny sensations. They may cause you to just appear confused for a moment. Or they can cause the full convulsions and loss of consciousness. The deaths are most likely to happen with the kind where you lose consciousness and they take more lives every year than house fires. You would never build a house without a smoke detector, but people had been sending people home without alerts. So we decided that we had to take this accidental finding in the lab and get it out to give people alerts. So that led to this goal that if we could give alerts, maybe we could reduce significantly this amount of SUDEP and help save a lot of lives. So that is still in the future, although we already got an alert the other day that shows where a mom going to her daughter and finding her not breathing in bed and able to use our alert to get there in time to save her. So this has led to this effort to take this technology out of the lab. Epilepsy. Seizures can seriously hurt or even kill people and we need an alert to intervene. One in 26 people in the U.S. will develop epilepsy at some point during their lifetime. Everybody with epilepsy should be able to have a device that alerts a friend or a family member to come help at the time that they might need it. This device will measure both movement and physiological signals. The combination is likely to be very important for detecting potentially life-threatening seizures. So we're now able to issue alerts and I just want all of you to know if you have a friend with epilepsy and you probably do, they may not have told you, but if you ask your friends, some of them will have epilepsy, some people here will. And let them know that if they have at least one convulsive seizure a year, they are at risk of having their respiration shut down by this seizure and simply having somebody go there and say their name and turn them over could restart their breathing. It's been shown even in people who are conscious if you stimulate the amygdala in a certain way like 15 volts, it can shut off your breathing while you are sitting there typing on the computer until somebody says your name, Robert, you know, and then suddenly you breathe again. So it's anybody here could be a lifesaver with these kinds of situations. So please take that knowledge with you as you leave here. We've donated with the generosity of lots of people, devices through the Epilepsy Foundation for families who cannot afford this. We also learned, again, one of our ethics in honoring the people that we try to build technology for is to listen to what they want. And probably you have friends with epilepsy who've never told you they have epilepsy. I was shocked when one of my friends told me he had epilepsy and I've known him for 30 years and he never told me. And I'm like, why didn't you tell me? And he says, I never tell anybody. I don't want people to know I have seizures. I thought, you know, you need to tell people so they can check on you. But the stigma associated with it needs to go away. And until it goes away, we need to make a device that doesn't look like a medical device that just looks like a cool watch. So we decided to make a watch that's designed gorgeously and that can do things besides detect seizures. So this device, which is not quite on the market yet, you can only pre-order it right now. We're only on the market with betas for people in most serious need. It detects the physical activity, the steps, the sleep, wake, and the, and it tells time. Maybe not as perfectly as your amazing Swiss watches, but it's better than what I had before. It's as good as my phone, and it's also going to soon come with an API for all the hackers in the room who want to do really cool stuff with it. Now, my former boss will be very happy because what he's always asked me to do is he said, Roz, when are you going to build me the mood ring that tells me my wife's mood before I go home? Now, the mood ring was just a temperature sensor, right? We have temperature in here, too. But this gives that autonomic stress that you saw, that skin conductance, right? That was going up before the meltdown, that was increasing, that was decreasing on the swing. You know, so you can see if your loved one was just really chilling, you know, or really, yeah, yeah, activated. This has a lot of practical implications for how people interact. This data comes from a young woman on the autism spectrum who asked to borrow one of our early prototypes one night when she was giving a three-minute speech. And she said, I'm going to be stressed by this speech. Can I borrow your sensor? I'm like, sure. And this sensor was just logging. It wasn't showing the data in real time. And before the event, she heard that they were going to delay the start. Now, uncertainty is stressful for everybody. Now, for a person on the autism spectrum, uncertainty is really stressful. And so when she heard it was delayed and she was already stressed about giving her talk, she started pacing back and forth. Okay. Now, her friend who was sitting there trying to work said, stop pacing. That doesn't help. So she stops pacing and she starts stimming. I asked her later, what do you mean by stimming? And she said it was kind of this flapping rocking thing. Afterwards, she gives her talk, that's the peak, and then it's downhill from there punctuated by some terrible auditory feedback events. Sinsery, ah, stressors. After the event, the two of them sit down to look at her data and the first word spoken by, or by her boyfriend. And he says, I'm not going to tell you to stop pacing anymore. He looked at that timestamp. And the next morning, I saw the two of them before another event. And this time she was pacing. And this time he just let her pace and he typed on his laptop. By seeing the objective data of what was going on, it affirmed for him that her pacing was helpful in this case. We often do what our bodies think is good to do, but we don't believe them, right? And so by taking stuff that seems subjective and making it objective, we give it more belief. It doesn't mean it wasn't true to begin with. It just means that we believe it more once we convert it to graphs and data. Now, I've talked about a lot of things. We've talked about starting with the facial expressions in autism, then learning that maybe the biggest problem wasn't always them recognizing other people's faces, but us recognizing their stress and anxiety. And then trying to build a device that helps read out some of that stress and anxiety, accidentally discovering it could also detect seizures, taking a little detour to do that because that appears to be life-saving. And then coming back to understanding emotion and human-human interaction and also in human-computer interaction. This example I love. How many of you have seen the Wally movie Pixar? Oh, everybody. Great choice by Roz. Yay. So one thing I didn't notice until a person with autism pointed this out to me is these two stars of the movie, Wally and Eva, they never use words. They build an entire relationship with nonverbal interaction. So it was a non-speaking person with autism who pointed this out. Look at this rich relationship that is developed only by emotional communication. And we like these robots. We feel like we build this emotional connection to them. We share their dreams and their failures. Now, many of you may also know Kismet, developed by Cynthia Brzeal for her PhD at MIT. I was just a reader on the thesis. Rod Brooks was the advisor. But I always loved this example where she was showing not only that a robot could recognize emotion, in this case recognizing tone of voice, but also that if you make the robot respond to emotion in a particular way, it really affects how we feel about the robot. So I go back to that opening scene of that Microsoft screen or Clippy or something coming up. You know, when it ignores your feelings, you hate it. You want to kill it. I mean, there are pictures of a Clippy on the web with a noose around his neck. And Bill Gates got his standing ovation when he said Clippy was going away. Watch what Kismet does. This is intelligent. So another important skill for the robot to be able to learn from people is being able to recognize communicative intent. Very good, Kismet. And the way we've done that with Kismet right now is to have the robot recognized by tone of voice. Are you praising it? Are you scolding it? Where did you put your body? No. No. You're not to do that. No. So, you see, when we don't like what the robot does, if it acts sorry, then we're like more likely to forgive it, right? And then we're happy to see it again, right? It's when we're miserable and it's happy and it's not noticing that we're like ready to kill it and get rid of it. So that emotional intelligence is really critical if we're going to build an intelligent future system. Now, emotional intelligence became a very powerful theme in a recent movie, Ex Machina. I don't know how many people saw this. I wrote a review of this that science invited me to write. You can read online if you're interested. But in this movie, the young man here is giving the robot a test of emotional intelligence to see if she can pass that, to see if she really has emotions. I can't spoil the end of the movie for you, but this movie also raises a lot of issues that transcend the intelligence of a machine to the morals of a machine as well. And these are things that people studying morals and moral decision making know that emotion plays a key role in. Now, we don't know if you have to have emotions to make moral decisions, but we know that people who make moral decisions often and perhaps usually employ their emotions in making such decisions. And that seems to be an important part of how we act in a way we think as moral and intelligent. As we give robots the ability to see emotion in our lives and as we start putting robots into our homes, such as the Jibo here that Cynthia has left MIT temporarily to build, these can start to see not only our facial expressions, but also our interaction with each other, our relationships. If we're doing things that make us happy or if we're more stressed. By the way, now we can also read heart rate and respiration non-contact with video and with Wi-Fi. So there are ways that these machines can sense a lot of things about us that could be disturbing or could be very beneficial. So these also raise a lot of ethical questions that I think need more discussion in society. In our work, we've become very interested recently in a new direction that is concerned with mental health. In this graph, the vertical axis has collapsed a lot of factors of well-being into one dimension. And when the university admits you or hires you as new faculty, you're at your peak of well-being. You're strong, you're smart, you're healthy, you're happy, hopefully, positive. As we encounter major stressors, as time goes on, almost everybody takes a dip. And hopefully you're on the blue line and you come back up over time. But at MIT, we have a large number of students who are going down the red line there. And when they get far down, usually about here, hopefully they by then have a medical diagnosis. And a doctor says, you're depressed or you're in trouble and we need to help you. But I'm asking, and my students are asking now, why do we have to wait until they get down here? Why can't we measure things back here? And we're starting to do that and see things changing even before outwardly there's any signs of trouble. If we could tell that you're on the red line back here, we could do two things. One is we might be able to tell you how to get to the blue line. Second, we ought to be able to prevent you from going further down this line. We ought to be able to prevent things like 80% of cases of depression. Now that's a very ambitious future goal. But we would like to start thinking about what are those components and how might our technology that's filling our lives and can measure all of these amazing things be used to not make us household pets, but be used to help us take better control. Just like forecasting the weather was enabled 150 years ago with technology sensors all over the geographic environment. Why don't we use some of these to better understand what helps us be well and forecast health? Wellness is incredibly complex. There are lots of theories of it. I'm just going to show you briefly one here because I want to debunk the popular notion that this just means happiness. This theory, due to Marty Seligman, shows positive emotions as one of five aspects of well-being. So being happy is just one of these components. Others that are really easy for people at really high-end universities like this are usually engagement, engaged in something you really enjoy doing, and accomplishment. Probably everybody in this room is very high on engagement and accomplishment. Hopefully you're high on positive emotions too. At MIT we have to work more on positive relationships and meaning. And it's interesting because they're very connected too. If you ask where does meaning happen, you write a brilliant paper, right? But if nobody reads it, in the days of read-only memory, one of my friends coined the phrase, write-only memo, the write-only paper that you wrote but nobody read, is there really meaning in it if you do all this work and there's no receiver of the value? There's no relationship if there's no recipient of the meaning. So meaning and relationship have motivated where we've been taking the robotic technology and our affective agent technology to try to facilitate more of that interpersonal connection, whether it's between two people or between a machine and a robot. It's helping us understand what contributes to that. It's also helping us see the important value of that. Now the one thing I'm most frequently asked when talking about these systems, and the one on the left here is called Mock. This is an agent that my student, Eson Hook, built to support people in developing better nonverbal skills during interviewing. So you interact with this agent, she asks you questions like a tough interviewer, and then gives you feedback on your pitch and your facial expressions and how animated you are and your speaking, and words such as and, but, so, or these filler words that we all need to stop using. Now the agent is scripted and appears very intelligent in these limited scripted contexts. Now you may have seen a movie like her where he has this romantic relationship with an agent, a software operating system that does not appear scripted in the movie, but of course it's a movie, right? The whole thing is scripted. We have to remember science fiction script, much harder to do it in real life, even scripted to make it meaningful. The challenge in the future intelligence systems is to build the unscripted interactions. The challenge is to, you know, could we make them not only have emotional intelligence, but could they actually feel and be conscious? We don't know how to do that right now, and we don't foresee how to do that with the software and hardware that we have today. So I'm not going to say that's going to happen in the next five years. It doesn't mean it can't happen. It means we don't see how to make it happen right now. But suppose that it could happen, and suppose that we could build them to be unscripted, emotional, emotionally intelligent, then the next thing people ask is would they be our slaves or would we set them free? Would we try to enable them to not just be unscripted and emotionally intelligent, but also to operate with true freedom, to make their own decisions, to believe whatever they want to believe? And some of you may have seen Sunny as one example of this in the iRobot. What if one robot were built or more to be truly free and to be true, not just autonomous, but free? And furthermore, suppose they were on another planet where they had never seen a human. There's a robot colony, and we know that machines can produce machines. And so the robots having never seen a human might explain that they arose from other machines. And maybe some virus wipes out all references to human and people at all in their system. So if they're really free and they've never seen a human, there's no reason for them to ever acknowledge that Cynthia or I or you or anybody who ever worked on this technology ever even existed. They could simply deny that humans ever had anything to do with them. After all, they could have just send it from silicon, from sand. If they chose to do that in their truly free state, what would you do? What would your choice be? Would you have to ignore them or reach out to them? How would you feel if robots declared that people never existed? I say that's just as possible as these other scenarios. And a fun thing for us to have more discussion about. I want to wrap up, though, and get to Q&A. So I just will take you on a quick recap. I started this talk with a little bit about reading facial expressions, emotional intelligence systems that could recognize and respond to our emotion, learning from people with autism that they didn't so much need our technology as they needed to be better understood and could our technology help them be better understood. So redirecting it at that, accidentally realizing that deep brain activity is giving rise to signals that we could read electrically on the skin even at the same time while it's shutting down the signals on the scalp. And that we could build an alert system that could potentially save lives for that. So getting that out, commercializing that. But wanting to do it in a way that builds a device that doesn't just detect seizures, but is something that enables people to share emotional data and have fun learning about each other. Hopefully building relationships. Relationships, positive affect, engagement, meaning, achievement, core to well-being. Could we take all of the power of this technology and start to understand what's contributing to human well-being? And not only what's contributing to it now, but what changes it and can forecast what could keep it well as opposed to when it's slipping. And then finally, as we take all this intelligence and put it back into robotic systems that are in our home and future coming into all aspects of our lives, what does that mean in terms of how they interact with us and what we expect from them going forth? What do we expect them to acknowledge about us? What kind of rights do we want to give them? What kind of freedom do we want to give them? And all of this, I will have to say too, is not going to happen automatically. Some people seem to think, oh, the robots are just going to evolve this stuff and this will happen. That's not true. They won't develop any of these abilities unless we give them these abilities. It's not going to happen unless some engineer, some bright people like in this room decide to program them this way. None of this happens without human effort setting this into course. So the opportunities are to have hopefully a great discussion about this future, and I'm happy to take questions about any aspect of this or my faith, any of these issues as we continue. Thank you so much. Thank you very much Rosalind for this inspiring, interesting talk, and we have time now for questions. I had a question regarding the top left picture, so from my perspective about the smile that we see when someone is frustrated. I was just wondering, could that be caused by a certain mechanism in the individual to maintain positivity even though they're stressed? That's one of the hypotheses right now is that people who do that are trying to make themselves feel happy, and this could be compensatory instead of getting angry and hitting the machine. It's a way to try to make yourself feel better. That's one possibility. By the way, it's also been observed in small preschoolers, little kids, so when they're failing at a task in a learning environment. So we evidently, some people learn to do this very early. It is also possible it can make you feel better because smiling can actually feedback to make you feel better even when you don't feel happy. Thank you. Hi. Thank you very much for the cool talk. So I felt a little uneasy when you showed the graph about how you could measure someone sliding to depression or the girl that had to give a talk. If someone was tracking my emotions, let's say, you know, supposed girlfriend or something, then I would feel even more stressed about that because I feel it's a very nice piece of privacy and freedom that I can smile and at the same time feel terrible and not have to explain myself. So this is one problem, I guess, and the other problem is how, what if, let's say, my boss could track me all the time, right? And how if people can basically just see the stages, such a precious data and so revealing. So which steps do you propose to, let's say, make sure that it's not abused and that people are not made totally transparent? Great question. Thank you. Yeah. First, when the young woman had her data recorded, for example, the first thing I did is said, you know, do you want to see it and do you want him to see it? Right. And she said, yes. And we never show people somebody else's data without their permission or completely in a de-identified way. I think that's a very important ethic to have for protecting people's privacy. I agree. It's very personal data and we have to be extremely careful with how people might interpret it. The boss situation, oh gosh, we have been asked, you know, can you measure my employees' stress, you know, and let me see it? And I'm like, well, what are you going to do with that data? Like, how are you going to help your employee? So we agreed, in one case where we agreed, it was I will measure the employees, we will give the employees' sensors to measure their stress. If they opt in, if there are enough of them that, first of all, they get to see their data, their boss does not see any individual's data. The boss, if there are enough people who opt in and we can aggregate the data, the boss can see when the peak bad times of day or if overdraft Wednesdays really are as stressful as the employees. So we set up some ground rules for the ethics of it and then that worked very well and people were very happy to opt in and nobody was singled out as having any particular characteristic. So I think we have to show the utmost respect for people's feelings in all of this and that is easier to do when it's wearable technology, when it's unobtrusive sensing technology. That's where it becomes much trickier and I'm a lot less comfortable with the way the opportunity is there for abuse in some cases. I think this may be a case where we have to get society saying what do humans value about each other and how do we keep that, you know, acted upon. Hi, Professor. Thank you so much for your awesome talk. So recently human bot interaction is a very hot, I guess, consumer product now with Facebook launching its new M messenger service in the Bay Area and Microsoft launching their new API for humans to communicate with bots. But they seem to feel like text is sufficient in terms of understanding the user need. And clearly from your presentation, there's much more that can be understood from speech and video. I'm wondering where do you think the limit of understanding emotion in text in natural language processing is and why you decide to go in the direction of more multimedia. We have also done a bunch of natural language processing to my student Karthik Deena Karz and I've published a bunch on that recently. You can find on our website here publications of everything I've talked about and a lot more. The text as we know what you say and how you say it can be completely in conflict. And words can be very confusing too. I mean, you know, when one person says the F word, they think something's cool. Another person says that they're really mad, right? Or take the word sick, you know, I don't know what that means to you. To my son, that's like a super cool move, right? And I'm like, sick, you know, somebody needs to go to the doctor. So we have problems with words that you have to use a lot of interpretation around the context. And often it's still ambiguous. So each thing, whether it's words, face, physiology, we've also measured posture, gestures, sitting in chairs, fidgety-ness, all kinds of factors. How hard you're squeezing a steering wheel, how hard you're squeezing a mouse. These things all convey affective information and we can use whatever people are comfortable sharing. With the caveat I always put in that we must, number one, show respect for people's feelings. And in my case, that means, in all of our work, it means you inform people, they get to opt in, and only then do you take that data. There's several down here. I have a question about consciousness in relation to those robots. And I'm not sure if I remember correctly. I think you said that you don't think it's possible that consciousness will appear with robotics in that way as we know it, but maybe I misremember it. Can you just maybe explain a little more about that, the connection between consciousness you talk about robots that will recognize emotions, will they also feel the emotions themselves at some point? Can you talk a little bit more about that? Sure. It's a good question. With any of these things, there's kind of the level at which you evaluate it. If you perceive it, like if I query to the robot, do you have consciousness? Do you have feelings? And it says, oh yes, I'm feeling quite fine right now. How are you feeling? I mean, at that level, it could act, and I perceive you are in the room. I mean, that kind of functional claims of consciousness, that's trivial. But I think what we're thinking of is something much deeper that involves our physiology, our feelings, even things that we can't just measure with physiological sensors. Things that are ineffable. We can't prove those things aren't there, and yet there's evidence, and I know the materialist in the room will not like this, but you can't prove everything's material either, right? What if there's something more? What if there really is something spiritual or something else there that we can experience, like a nurse going into a room and saying, I knew when I walked in that the patient had died, even though there was no signs of vital changes, I knew that they had left the room in some sense. There are so many reports of there being something else, and we can't prove what that something else is, if it's immaterial, because science doesn't measure this immaterial stuff, but we can allow the possibility of it. And when we try to build consciousness, while we can build this perception of it, we don't have any idea how to build these other aspects that we can't even begin to measure. I say, I don't see how to do it right now. I don't see how to do it with current hardware and software right now. I do see how to build a fully functioning conscious human entity. Some of my colleagues would say we're all just machines, and would say, oh, let's just buy an engineer the tissue and make each part of us, each organ, and so forth. And I say, well, if you're going to go to all that trouble, and it's going to be just like us, then why not just make it the same old-fashioned way we've been making them forever? I have three children. I mean, that's a lot more fun than bioengineering all the tissue, right? So I think we're talking about something different than that. And it's not happening with the hardware and software that we see right now. It's not even getting close. We're clearly in a completely different space. So how would you prevent or isn't there a danger that we don't get like senseless, emotionless zombies with all the technology and everything is telling us what we should do, now we should eat, now we should smile, and we are totally dependent on that stuff, and we lose like everything else? You know, that's a future we can choose to build or not, and I hope we don't build that. I think hopefully you agree. I'm reminded of when a colleague was at Foo Camp, Foo, we won't say what Foo stands for, Computer Science, catch all word, and at the conference, there were all these really fascinating, interesting, brilliant people, and there was a break when you think people would want to go talk to each other. And instead, they all grabbed their phones and they hunched over them with shallow breathing and were doing this, and nobody was interacting with each other. And the developer of iOS was there and he said, I feel badly I invented this. I think a lot of times with our technology, we're not thinking what could happen. We're just building something cool and sticky, and we have an opportunity with the future here to ask what do we want to build? Not just what can we build, but what do we want to build? And I hope we're not building something that's turning us all into zombies. You know, when I have been reading some reports about what's been happening in Silicon Valley and about these technological innovators and what they think about communication and what they especially think about people's thinking that at least some of these experts have said that we could actually have robots. Or other artificial intelligence devices think for us and we could actually then have more leisure time and so on. I mean, have the work done for us and so on. And I would really like to know what you think about these, I mean to say the tendencies within the framework of Silicon Valley and whether there are really within this framework, attempts to restrain the use of artificial intelligence not only to restrain it to certain sectors. For example, as you said, people who are mentally challenged or any other things, seizures, and you gave some examples, to restrain it to that and not to just have it to apply it to the whole of Silicon Valley. And I would like to know what you think about the work done by the human society and how it's going to work. And I think that's a great question. I think that's a great question. I think that's a great question. I think that's a great question. It's funny because we're all kind of, I don't know about you, I'm kind of attracted to just curling up with a book and staying there forever. But I also know that my brain doesn't work well when it's so sedentary. And I've also learned that my introverted self that would stay away from people is actually much healthier when I'm interacting with people. And back to those five pillars, if you're out building relationships, you're out engaging in challenges, you're not taking the lazy, easy path. It actually leads to better well-being than just thinking about what you enjoy personally. That positive feeling is only one of the five components. If you start to explore where meaning comes from and engaging with things that matter more than just yourself, you start to feel better also. So while you might be attracted to that, make your life easy and have leisure time because you'll feel better, rarely is that the real secret to well-being and happiness. So I think we need to stop and question what it is we think is good and help each other see that we may actually be much happier not taking that easy path. Thank you. I have a question concerning the empathic bracelets. Do you think it would be an option or whatever happened that some healthcare insurance companies would make that a must for their patients with maybe any of the desired? I predict in the future it will become a must for patients who have convulsive seizures. Probably not just the one one company makes, but that a device that does an alert should become a requirement for people. It should be reimbursable, right? The medical system should pay for this because it could be life-saving for people who have at least one convulsive seizure a year. Yeah. You gave a very nice example in the beginning of how your interest changed from helping autistic people to understand the world with this light to actually the world understanding autistic people. Can you apply that same change in interest also to robotics because I see there's lots of examples right now of robots trying to understand us. Maybe we should also change to us trying to understand what these robots actually do. Is there anything in your research that goes that direction? Actually that trying to make it so that it's symmetric is one of the motives behind making humanoid robots because the thought is we're good at understanding people or some of us are. We could all improve. My husband would make comments about women here. But we think we're good about understanding people. And if the robot acted more like a person, then we wouldn't have so much difficulty understanding it. But if it's spitting out all of this complicated numerical code that we don't understand, you know, state, numeric state information, why doesn't it just say it's frustrated? Right? It may not have feelings and conscious experience like we do, but we might prefer that, you know, if it's banging its head against the wall trying to do something and not succeeding that it instead of it just acting weird, it tell us that it's frustrated that it has this goal and it can't achieve it. So we may accept that anthropomorphic language for understandability. Okay. Yeah. You got it there already. Okay. I got a question regarding the timeframe of artificial intelligence. How far are we away to substitute nurses in hospitals? Oh, I hope we never substitute nurses in hospitals. Nurses are wonderful. I think we, you know, this goes back to questions of what we want. And I think, you know, we need to look at what people are good at and help them do it better. Their jobs, parts of the job each person does that they would love to have automated and their other parts that they would love to still be able to do. And why don't we show respect for that as we develop technology instead trying to replace whole people and whole occupations? Why don't we just help them figure out how to do their jobs in ways that get rid of the part they don't like and facilitate the part that they do like. So again, this goes back to our choices, the future of technology largely shaped by people, bright people like in this room. We have a choice to, you know, I mean, the public who doesn't build technology may think that it's inevitable, but it's not inevitable, right? It's what we choose to do and what values we bring to that. I see that there's still a number of questions, but that's actually a good thing. I think this was actually a very good closing statement and I do believe from what I see is that we are, many of us have really realized that there is no need or use to try to replace things, but to try to have these systems actually assist in a meaningful way and have each part do what they can do best. Before we close, I would, you will have a chance to ask more questions just afterwards, but before we close, I would like to ask Remy, where is she to come up here? She's actually been the driving force behind organizing this talk and she would like to say a few words to you before we close the session. Thank you, Raja. On behalf of ETA, Fawbege, Campus Life and the Zentum Forum, I want to thank Prof Picard for coming and taking the time. Thank you very much. Thank you, Remy.
agreement, showing disagreement in order for the people with autism want to recognize other computers were able to detect a person's mood. He think I need to get this technology out of the have epilepsy, some people here will. And let anxiety. And then trying to build a device that robot to be able to learn from people is being rid of it. So that emotional intelligence is the ability to see emotion in our lives and as we start putting robots into our homes, such as the things that make us happy or if we're more new direction that is concerned with mental components and how might our technology that's amazing things be used to not make us household true freedom, to make their own decisions, to respond to our emotion, learning from people with understand what's contributing to human do we want to give them? What kind of freedom do you propose to, let's say, make sure that it's opportunity is there for abuse in some cases. I are good at and help them do it better. Their