Rosalind Picard: Affective Computing, Emotion, Privacy, and Health #24

Transcript

00:00:00 The following is a conversation with Rosalind Picard.

00:00:02 She’s a professor at MIT,

00:00:04 director of the Effective Computing Research Group

00:00:06 at the MIT Media Lab,

00:00:08 and cofounder of two companies, Affectiva and Empatica.

00:00:12 Over two decades ago,

00:00:13 she launched a field of effective computing

00:00:15 with her book of the same name.

00:00:17 This book described the importance of emotion

00:00:20 in artificial and natural intelligence.

00:00:23 The vital role of emotional communication

00:00:25 has to the relationship between people in general

00:00:28 and human robot interaction.

00:00:30 I really enjoy talking with Ros over so many topics,

00:00:34 including emotion, ethics, privacy, wearable computing,

00:00:37 and her recent research in epilepsy,

00:00:39 and even love and meaning.

00:00:42 This conversation is part

00:00:43 of the Artificial Intelligence Podcast.

00:00:46 If you enjoy it, subscribe on YouTube, iTunes,

00:00:48 or simply connect with me on Twitter at Lex Friedman,

00:00:51 spelled F R I D.

00:00:53 And now, here’s my conversation with Rosalind Picard.

00:00:59 More than 20 years ago,

00:01:00 you’ve coined the term effective computing

00:01:03 and led a lot of research in this area since then.

00:01:06 As I understand, the goal is to make the machine detect

00:01:09 and interpret the emotional state of a human being

00:01:12 and adapt the behavior of the machine

00:01:14 based on the emotional state.

00:01:16 So how is your understanding of the problem space

00:01:19 defined by effective computing changed in the past 24 years?

00:01:25 So it’s the scope, the applications, the challenges,

00:01:28 what’s involved, how has that evolved over the years?

00:01:32 Yeah, actually, originally,

00:01:33 when I defined the term affective computing,

00:01:36 it was a bit broader than just recognizing

00:01:40 and responding intelligently to human emotion,

00:01:42 although those are probably the two pieces

00:01:44 that we’ve worked on the hardest.

00:01:47 The original concept also encompassed machines

00:01:50 that would have mechanisms

00:01:52 that functioned like human emotion does inside them.

00:01:55 It would be any computing that relates to arises from

00:01:59 or deliberately influences human emotion.

00:02:02 So the human computer interaction part

00:02:05 is the part that people tend to see,

00:02:07 like if I’m really ticked off at my computer

00:02:11 and I’m scowling at it and I’m cursing at it

00:02:13 and it just keeps acting smiling and happy

00:02:15 like that little paperclip used to do,

00:02:17 dancing, winking, that kind of thing

00:02:22 just makes you even more frustrated, right?

00:02:24 And I thought that stupid thing needs to see my affect.

00:02:29 And if it’s gonna be intelligent,

00:02:30 which Microsoft researchers had worked really hard on,

00:02:33 it actually had some of the most sophisticated AI

00:02:34 in it at the time,

00:02:36 that thing’s gonna actually be smart.

00:02:38 It needs to respond to me and you,

00:02:41 and we can send it very different signals.

00:02:45 So by the way, just a quick interruption,

00:02:47 the Clippy, maybe it’s in Word 95, 98,

00:02:52 I don’t remember when it was born,

00:02:54 but many people, do you find yourself with that reference

00:02:58 that people recognize what you’re talking about

00:03:00 still to this point?

00:03:01 I don’t expect the newest students to these days,

00:03:05 but I’ve mentioned it to a lot of audiences,

00:03:07 like how many of you know this Clippy thing?

00:03:09 And still the majority of people seem to know it.

00:03:11 So Clippy kind of looks at maybe natural language processing

00:03:15 where you were typing and tries to help you complete,

00:03:18 I think.

00:03:19 I don’t even remember what Clippy was, except annoying.

00:03:22 Yeah, some people actually liked it.

00:03:25 I would hear those stories.

00:03:27 You miss it?

00:03:28 Well, I miss the annoyance.

00:03:31 They felt like there’s an element.

00:03:34 Someone was there.

00:03:34 Somebody was there and we were in it together

00:03:36 and they were annoying.

00:03:37 It’s like a puppy that just doesn’t get it.

00:03:40 They keep stripping up the couch kind of thing.

00:03:42 And in fact, they could have done it smarter like a puppy.

00:03:44 If they had done, like if when you yelled at it

00:03:48 or cursed at it,

00:03:49 if it had put its little ears back in its tail down

00:03:51 and shrugged off,

00:03:52 probably people would have wanted it back, right?

00:03:55 But instead, when you yelled at it, what did it do?

00:03:58 It smiled, it winked, it danced, right?

00:04:01 If somebody comes to my office and I yell at them,

00:04:03 they start smiling, winking and dancing.

00:04:04 I’m like, I never want to see you again.

00:04:06 So Bill Gates got a standing ovation

00:04:08 when he said it was going away

00:04:10 because people were so ticked.

00:04:12 It was so emotionally unintelligent, right?

00:04:15 It was intelligent about whether you were writing a letter,

00:04:18 what kind of help you needed for that context.

00:04:20 It was completely unintelligent about,

00:04:23 hey, if you’re annoying your customer,

00:04:25 don’t smile in their face when you do it.

00:04:28 So that kind of mismatch was something

00:04:32 the developers just didn’t think about.

00:04:35 And intelligence at the time was really all about math

00:04:39 and language and chess and games,

00:04:44 problems that could be pretty well defined.

00:04:47 Social emotional interaction is much more complex

00:04:50 than chess or Go or any of the games

00:04:53 that people are trying to solve.

00:04:56 And in order to understand that required skills

00:04:58 that most people in computer science

00:05:00 actually were lacking personally.

00:05:02 Well, let’s talk about computer science.

00:05:03 Have things gotten better since the work,

00:05:06 since the message,

00:05:07 since you’ve really launched the field

00:05:09 with a lot of research work in this space?

00:05:11 I still find as a person like yourself,

00:05:14 who’s deeply passionate about human beings

00:05:16 and yet am in computer science,

00:05:18 there still seems to be a lack of,

00:05:22 sorry to say empathy in as computer scientists.

00:05:26 Yeah, well.

00:05:27 Or hasn’t gotten better.

00:05:28 Let’s just say there’s a lot more variety

00:05:30 among computer scientists these days.

00:05:32 Computer scientists are a much more diverse group today

00:05:35 than they were 25 years ago.

00:05:37 And that’s good.

00:05:39 We need all kinds of people to become computer scientists

00:05:41 so that computer science reflects more what society needs.

00:05:45 And there’s brilliance among every personality type.

00:05:49 So it need not be limited to people

00:05:52 who prefer computers to other people.

00:05:54 How hard do you think it is?

00:05:55 Your view of how difficult it is to recognize emotion

00:05:58 or to create a deeply emotionally intelligent interaction.

00:06:03 Has it gotten easier or harder

00:06:06 as you’ve explored it further?

00:06:07 And how far away are we from cracking this?

00:06:12 If you think of the Turing test solving the intelligence,

00:06:16 looking at the Turing test for emotional intelligence.

00:06:20 I think it is as difficult as I thought it was gonna be.

00:06:25 I think my prediction of its difficulty is spot on.

00:06:29 I think the time estimates are always hard

00:06:33 because they’re always a function of society’s love

00:06:37 and hate of a particular topic.

00:06:39 If society gets excited and you get thousands of researchers

00:06:45 working on it for a certain application,

00:06:49 that application gets solved really quickly.

00:06:52 The general intelligence,

00:06:54 the computer’s complete lack of ability

00:06:58 to have awareness of what it’s doing,

00:07:03 the fact that it’s not conscious,

00:07:05 the fact that there’s no signs of it becoming conscious,

00:07:08 the fact that it doesn’t read between the lines,

00:07:11 those kinds of things that we have to teach it explicitly,

00:07:15 what other people pick up implicitly.

00:07:17 We don’t see that changing yet.

00:07:20 There aren’t breakthroughs yet that lead us to believe

00:07:23 that that’s gonna go any faster,

00:07:25 which means that it’s still gonna be kind of stuck

00:07:28 with a lot of limitations

00:07:31 where it’s probably only gonna do the right thing

00:07:34 in very limited, narrow, prespecified contexts

00:07:37 where we can prescribe pretty much

00:07:40 what’s gonna happen there.

00:07:42 So I don’t see the,

00:07:46 it’s hard to predict a date

00:07:47 because when people don’t work on it, it’s infinite.

00:07:51 When everybody works on it, you get a nice piece of it

00:07:56 well solved in a short amount of time.

00:07:58 I actually think there’s a more important issue right now

00:08:01 than the difficulty of it.

00:08:04 And that’s causing some of us

00:08:05 to put the brakes on a little bit.

00:08:07 Usually we’re all just like step on the gas,

00:08:09 let’s go faster.

00:08:11 This is causing us to pull back and put the brakes on.

00:08:14 And that’s the way that some of this technology

00:08:18 is being used in places like China right now.

00:08:21 And that worries me so deeply

00:08:24 that it’s causing me to pull back myself

00:08:27 on a lot of the things that we could be doing.

00:08:30 And try to get the community to think a little bit more

00:08:33 about, okay, if we’re gonna go forward with that,

00:08:36 how can we do it in a way that puts in place safeguards

00:08:39 that protects people?

00:08:41 So the technology we’re referring to is

00:08:43 just when a computer senses the human being,

00:08:46 like the human face, right?

00:08:48 So there’s a lot of exciting things there,

00:08:51 like forming a deep connection with the human being.

00:08:53 So what are your worries, how that could go wrong?

00:08:57 Is it in terms of privacy?

00:08:59 Is it in terms of other kinds of more subtle things?

00:09:02 But let’s dig into privacy.

00:09:04 So here in the US, if I’m watching a video

00:09:07 of say a political leader,

00:09:09 and in the US we’re quite free as we all know

00:09:13 to even criticize the president of the United States, right?

00:09:17 Here that’s not a shocking thing.

00:09:19 It happens about every five seconds, right?

00:09:22 But in China, what happens if you criticize

00:09:27 the leader of the government, right?

00:09:30 And so people are very careful not to do that.

00:09:34 However, what happens if you’re simply watching a video

00:09:37 and you make a facial expression

00:09:40 that shows a little bit of skepticism, right?

00:09:45 Well, and here we’re completely free to do that.

00:09:47 In fact, we’re free to fly off the handle

00:09:50 and say anything we want, usually.

00:09:54 I mean, there are some restrictions

00:09:56 when the athlete does this

00:09:58 as part of the national broadcast.

00:10:00 Maybe the teams get a little unhappy

00:10:03 about picking that forum to do it, right?

00:10:05 But that’s more a question of judgment.

00:10:08 We have these freedoms,

00:10:11 and in places that don’t have those freedoms,

00:10:14 what if our technology can read

00:10:17 your underlying affective state?

00:10:19 What if our technology can read it even noncontact?

00:10:22 What if our technology can read it

00:10:24 without your prior consent?

00:10:28 And here in the US,

00:10:30 in my first company we started, Affectiva,

00:10:32 we have worked super hard to turn away money

00:10:35 and opportunities that try to read people’s affect

00:10:38 without their prior informed consent.

00:10:41 And even the software that is licensable,

00:10:45 you have to sign things saying

00:10:46 you will only use it in certain ways,

00:10:48 which essentially is get people’s buy in, right?

00:10:52 Don’t do this without people agreeing to it.

00:10:56 There are other countries where they’re not interested

00:10:58 in people’s buy in.

00:10:59 They’re just gonna use it.

00:11:01 They’re gonna inflict it on you.

00:11:03 And if you don’t like it,

00:11:04 you better not scowl in the direction of any censors.

00:11:08 So one, let me just comment on a small tangent.

00:11:11 Do you know with the idea of adversarial examples

00:11:15 and deep fakes and so on,

00:11:18 what you bring up is actually,

00:11:20 in that one sense, deep fakes provide

00:11:23 a comforting protection that you can no longer really trust

00:11:30 that the video of your face was legitimate.

00:11:34 And therefore you always have an escape clause

00:11:37 if a government is trying,

00:11:38 if a stable, balanced, ethical government

00:11:44 is trying to accuse you of something,

00:11:46 at least you have protection.

00:11:47 You can say it was fake news, as is a popular term now.

00:11:50 Yeah, that’s the general thinking of it.

00:11:52 We know how to go into the video

00:11:54 and see, for example, your heart rate and respiration

00:11:58 and whether or not they’ve been tampered with.

00:12:02 And we also can put like fake heart rate and respiration

00:12:05 in your video now too.

00:12:06 We decided we needed to do that.

00:12:10 After we developed a way to extract it,

00:12:12 we decided we also needed a way to jam it.

00:12:15 And so the fact that we took time to do that other step too,

00:12:20 that was time that I wasn’t spending

00:12:22 making the machine more affectively intelligent.

00:12:25 And there’s a choice in how we spend our time,

00:12:28 which is now being swayed a little bit less by this goal

00:12:32 and a little bit more like by concern

00:12:34 about what’s happening in society

00:12:36 and what kind of future do we wanna build.

00:12:38 And as we step back and say,

00:12:41 okay, we don’t just build AI to build AI

00:12:44 to make Elon Musk more money

00:12:46 or to make Amazon Jeff Bezos more money.

00:12:48 Good gosh, you know, that’s the wrong ethic.

00:12:52 Why are we building it?

00:12:54 What is the point of building AI?

00:12:57 It used to be, it was driven by researchers in academia

00:13:01 to get papers published and to make a career for themselves

00:13:04 and to do something cool, right?

00:13:05 Like, cause maybe it could be done.

00:13:08 Now we realize that this is enabling rich people

00:13:12 to get vastly richer, the poor are,

00:13:17 the divide is even larger.

00:13:19 And is that the kind of future that we want?

00:13:22 Maybe we wanna think about, maybe we wanna rethink AI.

00:13:25 Maybe we wanna rethink the problems in society

00:13:29 that are causing the greatest inequity

00:13:32 and rethink how to build AI

00:13:35 that’s not about a general intelligence,

00:13:36 but that’s about extending the intelligence

00:13:39 and capability of the have nots

00:13:41 so that we close these gaps in society.

00:13:43 Do you hope that kind of stepping on the brake

00:13:46 happens organically?

00:13:47 Because I think still majority of the force behind AI

00:13:51 is the desire to publish papers,

00:13:52 is to make money without thinking about the why.

00:13:55 Do you hope it happens organically?

00:13:57 Is there room for regulation?

00:14:01 Yeah, yeah, yeah, great questions.

00:14:02 I prefer the, you know,

00:14:05 they talk about the carrot versus the stick.

00:14:07 I definitely prefer the carrot to the stick.

00:14:09 And, you know, in our free world,

00:14:12 we, there’s only so much stick, right?

00:14:14 You’re gonna find a way around it.

00:14:17 I generally think less regulation is better.

00:14:21 That said, even though my position is classically carrot,

00:14:24 no stick, no regulation,

00:14:26 I think we do need some regulations in this space.

00:14:29 I do think we need regulations

00:14:30 around protecting people with their data,

00:14:33 that you own your data, not Amazon, not Google.

00:14:38 I would like to see people own their own data.

00:14:40 I would also like to see the regulations

00:14:42 that we have right now around lie detection

00:14:44 being extended to emotion recognition in general,

00:14:48 that right now you can’t use a lie detector on an employee

00:14:50 when you’re, on a candidate

00:14:52 when you’re interviewing them for a job.

00:14:54 I think similarly, we need to put in place protection

00:14:57 around reading people’s emotions without their consent

00:15:00 and in certain cases,

00:15:02 like characterizing them for a job and other opportunities.

00:15:06 So I’m also, I also think that when we’re reading emotion

00:15:09 that’s predictive around mental health,

00:15:11 that that should, even though it’s not medical data,

00:15:14 that that should get the kinds of protections

00:15:16 that our medical data gets.

00:15:18 What most people don’t know yet

00:15:19 is right now with your smartphone use,

00:15:22 and if you’re wearing a sensor

00:15:25 and you wanna learn about your stress and your sleep

00:15:27 and your physical activity

00:15:28 and how much you’re using your phone

00:15:30 and your social interaction,

00:15:32 all of that nonmedical data,

00:15:34 when we put it together with machine learning,

00:15:37 now called AI, even though the founders of AI

00:15:40 wouldn’t have called it that,

00:15:42 that capability can not only tell that you’re calm right now

00:15:48 or that you’re getting a little stressed,

00:15:50 but it can also predict how you’re likely to be tomorrow.

00:15:53 If you’re likely to be sick or healthy,

00:15:55 happy or sad, stressed or calm.

00:15:58 Especially when you’re tracking data over time.

00:16:00 Especially when we’re tracking a week of your data or more.

00:16:03 Do you have an optimism towards,

00:16:05 you know, a lot of people on our phones

00:16:07 are worried about this camera that’s looking at us.

00:16:10 For the most part, on balance,

00:16:12 are you optimistic about the benefits

00:16:16 that can be brought from that camera

00:16:17 that’s looking at billions of us?

00:16:19 Or should we be more worried?

00:16:24 I think we should be a little bit more worried

00:16:28 about who’s looking at us and listening to us.

00:16:32 The device sitting on your countertop in your kitchen,

00:16:36 whether it’s, you know, Alexa or Google Home or Apple, Siri,

00:16:42 these devices want to listen

00:16:47 while they say ostensibly to help us.

00:16:49 And I think there are great people in these companies

00:16:52 who do want to help people.

00:16:54 Let me not brand them all bad.

00:16:56 I’m a user of products from all of these companies

00:16:59 I’m naming all the A companies, Alphabet, Apple, Amazon.

00:17:04 They are awfully big companies, right?

00:17:09 They have incredible power.

00:17:11 And you know, what if China were to buy them, right?

00:17:17 And suddenly all of that data

00:17:19 were not part of free America,

00:17:22 but all of that data were part of somebody

00:17:24 who just wants to take over the world

00:17:26 and you submit to them.

00:17:27 And guess what happens if you so much as smirk the wrong way

00:17:32 when they say something that you don’t like?

00:17:34 Well, they have reeducation camps, right?

00:17:37 That’s a nice word for them.

00:17:39 By the way, they have a surplus of organs

00:17:41 for people who have surgery these days.

00:17:43 They don’t have an organ donation problem

00:17:45 because they take your blood and they know you’re a match.

00:17:48 And the doctors are on record of taking organs

00:17:51 from people who are perfectly healthy and not prisoners.

00:17:55 They’re just simply not the favored ones of the government.

00:17:59 And you know, that’s a pretty freaky evil society.

00:18:04 And we can use the word evil there.

00:18:06 I was born in the Soviet Union.

00:18:07 I can certainly connect to the worry that you’re expressing.

00:18:13 At the same time, probably both you and I

00:18:15 and you very much so,

00:18:19 you know, there’s an exciting possibility

00:18:23 that you can have a deep connection with a machine.

00:18:27 Yeah, yeah.

00:18:28 Right, so.

00:18:30 Those of us, I’ve admitted students who say that they,

00:18:35 you know, when you list like,

00:18:36 who do you most wish you could have lunch with

00:18:39 or dinner with, right?

00:18:41 And they’ll write like, I don’t like people.

00:18:43 I just like computers.

00:18:44 And one of them said to me once

00:18:46 when I had this party at my house,

00:18:49 I want you to know,

00:18:51 this is my only social event of the year,

00:18:53 my one social event of the year.

00:18:55 Like, okay, now this is a brilliant

00:18:57 machine learning person, right?

00:18:59 And we need that kind of brilliance in machine learning.

00:19:01 And I love that computer science welcomes people

00:19:04 who love people and people who are very awkward

00:19:07 around people.

00:19:08 I love that this is a field that anybody could join.

00:19:12 We need all kinds of people

00:19:14 and you don’t need to be a social person.

00:19:16 I’m not trying to force people who don’t like people

00:19:19 to suddenly become social.

00:19:21 At the same time,

00:19:23 if most of the people building the AIs of the future

00:19:26 are the kind of people who don’t like people,

00:19:29 we’ve got a little bit of a problem.

00:19:31 Well, hold on a second.

00:19:31 So let me push back on that.

00:19:33 So don’t you think a large percentage of the world

00:19:38 can, you know, there’s loneliness.

00:19:40 There is a huge problem with loneliness that’s growing.

00:19:44 And so there’s a longing for connection.

00:19:47 Do you…

00:19:49 If you’re lonely, you’re part of a big and growing group.

00:19:51 Yes.

00:19:52 So we’re in it together, I guess.

00:19:54 If you’re lonely, join the group.

00:19:56 You’re not alone.

00:19:56 You’re not alone.

00:19:57 That’s a good line.

00:20:00 But do you think there’s…

00:20:03 You talked about some worry,

00:20:04 but do you think there’s an exciting possibility

00:20:07 that something like Alexa and these kinds of tools

00:20:11 can alleviate that loneliness

00:20:14 in a way that other humans can’t?

00:20:16 Yeah, yeah, definitely.

00:20:18 I mean, a great book can kind of alleviate loneliness

00:20:22 because you just get sucked into this amazing story

00:20:25 and you can’t wait to go spend time with that character.

00:20:27 And they’re not a human character.

00:20:30 There is a human behind it.

00:20:33 But yeah, it can be an incredibly delightful way

00:20:35 to pass the hours and it can meet needs.

00:20:39 Even, you know, I don’t read those trashy romance books,

00:20:43 but somebody does, right?

00:20:44 And what are they getting from this?

00:20:46 Well, probably some of that feeling of being there, right?

00:20:50 Being there in that social moment,

00:20:52 that romantic moment or connecting with somebody.

00:20:56 I’ve had a similar experience

00:20:57 reading some science fiction books, right?

00:20:59 And connecting with the character.

00:21:00 Orson Scott Card, you know, just amazing writing

00:21:04 and Ender’s Game and Speaker for the Dead, terrible title.

00:21:07 But those kind of books that pull you into a character

00:21:11 and you feel like you’re, you feel very social.

00:21:13 It’s very connected, even though it’s not responding to you.

00:21:17 And a computer, of course, can respond to you.

00:21:19 So it can deepen it, right?

00:21:21 You can have a very deep connection,

00:21:25 much more than the movie Her, you know, plays up, right?

00:21:29 Well, much more.

00:21:30 I mean, movie Her is already a pretty deep connection, right?

00:21:34 Well, but it’s just a movie, right?

00:21:36 It’s scripted.

00:21:37 It’s just, you know, but I mean,

00:21:39 like there can be a real interaction

00:21:42 where the character can learn and you can learn.

00:21:46 You could imagine it not just being you and one character.

00:21:49 You could imagine a group of characters.

00:21:51 You can imagine a group of people and characters,

00:21:53 human and AI connecting,

00:21:56 where maybe a few people can’t sort of be friends

00:22:00 with everybody, but the few people

00:22:02 and their AIs can befriend more people.

00:22:07 There can be an extended human intelligence in there

00:22:10 where each human can connect with more people that way.

00:22:14 But it’s still very limited, but there are just,

00:22:19 what I mean is there are many more possibilities

00:22:21 than what’s in that movie.

00:22:22 So there’s a tension here.

00:22:24 So one, you expressed a really serious concern

00:22:27 about privacy, about how governments

00:22:29 can misuse the information,

00:22:31 and there’s the possibility of this connection.

00:22:34 So let’s look at Alexa.

00:22:36 So personal assistance.

00:22:37 For the most part, as far as I’m aware,

00:22:40 they ignore your emotion.

00:22:42 They ignore even the context or the existence of you,

00:22:47 the intricate, beautiful, complex aspects of who you are,

00:22:52 except maybe aspects of your voice

00:22:54 that help it recognize for speech recognition.

00:22:58 Do you think they should move towards

00:23:00 trying to understand your emotion?

00:23:03 All of these companies are very interested

00:23:04 in understanding human emotion.

00:23:07 They want, more people are telling Siri every day

00:23:11 they want to kill themselves.

00:23:13 Apple wants to know the difference between

00:23:15 if a person is really suicidal versus if a person

00:23:18 is just kind of fooling around with Siri, right?

00:23:21 The words may be the same, the tone of voice

00:23:25 and what surrounds those words is pivotal to understand

00:23:31 if they should respond in a very serious way,

00:23:34 bring help to that person,

00:23:35 or if they should kind of jokingly tease back,

00:23:40 ah, you just want to sell me for something else, right?

00:23:44 Like, how do you respond when somebody says that?

00:23:47 Well, you do want to err on the side of being careful

00:23:51 and taking it seriously.

00:23:53 People want to know if the person is happy or stressed

00:23:59 in part, well, so let me give you an altruistic reason

00:24:03 and a business profit motivated reason.

00:24:08 And there are people in companies that operate

00:24:11 on both principles.

00:24:12 The altruistic people really care about their customers

00:24:16 and really care about helping you feel a little better

00:24:19 at the end of the day.

00:24:20 And it would just make those people happy

00:24:22 if they knew that they made your life better.

00:24:24 If you came home stressed and after talking

00:24:27 with their product, you felt better.

00:24:29 There are other people who maybe have studied

00:24:32 the way affect affects decision making

00:24:35 and prices people pay.

00:24:36 And they know, I don’t know if I should tell you,

00:24:38 like the work of Jen Lerner on heartstrings and purse strings,

00:24:43 you know, if we manipulate you into a slightly sadder mood,

00:24:47 you’ll pay more, right?

00:24:50 You’ll pay more to change your situation.

00:24:53 You’ll pay more for something you don’t even need

00:24:55 to make yourself feel better.

00:24:58 So, you know, if they sound a little sad,

00:25:00 maybe I don’t want to cheer them up.

00:25:01 Maybe first I want to help them get something,

00:25:04 a little shopping therapy, right?

00:25:07 That helps them.

00:25:08 Which is really difficult for a company

00:25:09 that’s primarily funded on advertisement.

00:25:12 So they’re encouraged to get you to offer you products

00:25:16 or Amazon that’s primarily funded

00:25:17 on you buying things from their store.

00:25:20 So I think we should be, you know,

00:25:22 maybe we need regulation in the future

00:25:24 to put a little bit of a wall between these agents

00:25:27 that have access to our emotion

00:25:29 and agents that want to sell us stuff.

00:25:32 Maybe there needs to be a little bit more

00:25:35 of a firewall in between those.

00:25:38 So maybe digging in a little bit

00:25:40 on the interaction with Alexa,

00:25:42 you mentioned, of course, a really serious concern

00:25:44 about like recognizing emotion,

00:25:46 if somebody is speaking of suicide or depression and so on,

00:25:49 but what about the actual interaction itself?

00:25:55 Do you think, so if I, you know,

00:25:57 you mentioned Clippy and being annoying,

00:26:01 what is the objective function we’re trying to optimize?

00:26:04 Is it minimize annoyingness or minimize or maximize happiness?

00:26:09 Or if we look at human to human relations,

00:26:12 I think that push and pull, the tension, the dance,

00:26:15 you know, the annoying, the flaws, that’s what makes it fun.

00:26:19 So is there a room for, like what is the objective function?

00:26:24 There are times when you want to have a little push and pull,

00:26:26 I think of kids sparring, right?

00:26:29 You know, I see my sons and they,

00:26:31 one of them wants to provoke the other to be upset

00:26:33 and that’s fun.

00:26:34 And it’s actually healthy to learn where your limits are,

00:26:38 to learn how to self regulate.

00:26:40 You can imagine a game where it’s trying to make you mad

00:26:43 and you’re trying to show self control.

00:26:45 And so if we’re doing a AI human interaction

00:26:48 that’s helping build resilience and self control,

00:26:51 whether it’s to learn how to not be a bully

00:26:54 or how to turn the other cheek

00:26:55 or how to deal with an abusive person in your life,

00:26:58 then you might need an AI that pushes your buttons, right?

00:27:04 But in general, do you want an AI that pushes your buttons?

00:27:10 Probably depends on your personality.

00:27:12 I don’t, I want one that’s respectful,

00:27:15 that is there to serve me

00:27:18 and that is there to extend my ability to do things.

00:27:23 I’m not looking for a rival,

00:27:25 I’m looking for a helper.

00:27:27 And that’s the kind of AI I’d put my money on.

00:27:30 Your sense is for the majority of people in the world,

00:27:33 in order to have a rich experience,

00:27:35 that’s what they’re looking for as well.

00:27:37 So they’re not looking,

00:27:37 if you look at the movie Her, spoiler alert,

00:27:40 I believe the program that the woman in the movie Her

00:27:46 leaves the person for somebody else,

00:27:51 says they don’t wanna be dating anymore, right?

00:27:54 Like, do you, your sense is if Alexa said,

00:27:58 you know what, I’m actually had enough of you for a while,

00:28:02 so I’m gonna shut myself off.

00:28:04 You don’t see that as…

00:28:07 I’d say you’re trash, cause I paid for you, right?

00:28:10 You, we’ve got to remember,

00:28:14 and this is where this blending human AI

00:28:18 as if we’re equals is really deceptive

00:28:22 because AI is something at the end of the day

00:28:26 that my students and I are making in the lab.

00:28:28 And we’re choosing what it’s allowed to say,

00:28:33 when it’s allowed to speak, what it’s allowed to listen to,

00:28:36 what it’s allowed to act on given the inputs

00:28:40 that we choose to expose it to,

00:28:43 what outputs it’s allowed to have.

00:28:45 It’s all something made by a human.

00:28:49 And if we wanna make something

00:28:50 that makes our lives miserable, fine.

00:28:52 I wouldn’t invest in it as a business,

00:28:56 unless it’s just there for self regulation training.

00:28:59 But I think we need to think about

00:29:01 what kind of future we want.

00:29:02 And actually your question, I really like the,

00:29:05 what is the objective function?

00:29:06 Is it to calm people down?

00:29:09 Sometimes.

00:29:10 Is it to always make people happy and calm them down?

00:29:14 Well, there was a book about that, right?

00:29:16 The brave new world, make everybody happy,

00:29:18 take your Soma if you’re unhappy, take your happy pill.

00:29:22 And if you refuse to take your happy pill,

00:29:24 well, we’ll threaten you by sending you to Iceland

00:29:28 to live there.

00:29:29 I lived in Iceland three years.

00:29:30 It’s a great place.

00:29:31 Don’t take your Soma, then go to Iceland.

00:29:35 A little TV commercial there.

00:29:37 Now I was a child there for a few years.

00:29:39 It’s a wonderful place.

00:29:40 So that part of the book never scared me.

00:29:43 But really like, do we want AI to manipulate us

00:29:46 into submission, into making us happy?

00:29:49 Well, if you are a, you know,

00:29:52 like a power obsessed sick dictator individual

00:29:56 who only wants to control other people

00:29:57 to get your jollies in life, then yeah,

00:29:59 you wanna use AI to extend your power and your scale

00:30:03 to force people into submission.

00:30:07 If you believe that the human race is better off

00:30:10 being given freedom and the opportunity

00:30:12 to do things that might surprise you,

00:30:15 then you wanna use AI to extend people’s ability to build,

00:30:20 you wanna build AI that extends human intelligence,

00:30:22 that empowers the weak and helps balance the power

00:30:27 between the weak and the strong,

00:30:28 not that gives more power to the strong.

00:30:32 So in this process of empowering people and sensing people,

00:30:39 what is your sense on emotion

00:30:41 in terms of recognizing emotion?

00:30:42 The difference between emotion that is shown

00:30:44 and emotion that is felt.

00:30:46 So yeah, emotion that is expressed on the surface

00:30:52 through your face, your body, and various other things,

00:30:56 and what’s actually going on deep inside

00:30:58 on the biological level, on the neuroscience level,

00:31:01 or some kind of cognitive level.

00:31:03 Yeah, yeah.

00:31:05 Whoa, no easy questions here.

00:31:07 Well, yeah, I’m sure there’s no definitive answer,

00:31:11 but what’s your sense?

00:31:12 How far can we get by just looking at the face?

00:31:16 We’re very limited when we just look at the face,

00:31:18 but we can get further than most people think we can get.

00:31:21 People think, hey, I have a great poker face,

00:31:25 therefore all you’re ever gonna get from me is neutral.

00:31:28 Well, that’s naive.

00:31:30 We can read with the ordinary camera

00:31:32 on your laptop or on your phone.

00:31:34 We can read from a neutral face if your heart is racing.

00:31:39 We can read from a neutral face

00:31:41 if your breathing is becoming irregular

00:31:44 and showing signs of stress.

00:31:46 We can read under some conditions

00:31:50 that maybe I won’t give you details on,

00:31:53 how your heart rate variability power is changing.

00:31:57 That could be a sign of stress,

00:31:58 even when your heart rate is not necessarily accelerating.

00:32:02 So…

00:32:03 Sorry, from physio sensors or from the face?

00:32:06 From the color changes that you cannot even see,

00:32:09 but the camera can see.

00:32:11 That’s amazing.

00:32:12 So you can get a lot of signal, but…

00:32:15 So we get things people can’t see using a regular camera.

00:32:18 And from that, we can tell things about your stress.

00:32:21 So if you were just sitting there with a blank face

00:32:25 thinking nobody can read my emotion, well, you’re wrong.

00:32:30 Right, so that’s really interesting,

00:32:31 but that’s from sort of visual information from the face.

00:32:34 That’s almost like cheating your way

00:32:37 to the physiological state of the body,

00:32:39 by being very clever with what you can do with vision.

00:32:42 With signal processing.

00:32:43 With signal processing.

00:32:44 So that’s really impressive.

00:32:45 But if you just look at the stuff we humans can see,

00:32:49 the poker, the smile, the smirks,

00:32:52 the subtle, all the facial actions.

00:32:54 So then you can hide that on your face

00:32:55 for a limited amount of time.

00:32:57 Now, if you’re just going in for a brief interview

00:33:00 and you’re hiding it, that’s pretty easy for most people.

00:33:03 If you are, however, surveilled constantly everywhere you go,

00:33:08 then it’s gonna say, gee, you know, Lex used to smile a lot

00:33:13 and now I’m not seeing so many smiles.

00:33:15 And Roz used to laugh a lot

00:33:20 and smile a lot very spontaneously.

00:33:22 And now I’m only seeing

00:33:23 these not so spontaneous looking smiles.

00:33:26 And only when she’s asked these questions.

00:33:28 You know, that’s something’s changed here.

00:33:31 Probably not getting enough sleep.

00:33:33 We could look at that too.

00:33:35 So now I have to be a little careful too.

00:33:37 When I say we, you think we can’t read your emotion

00:33:40 and we can, it’s not that binary.

00:33:42 What we’re reading is more some physiological changes

00:33:45 that relate to your activation.

00:33:48 Now, that doesn’t mean that we know everything

00:33:51 about how you feel.

00:33:52 In fact, we still know very little about how you feel.

00:33:54 Your thoughts are still private.

00:33:56 Your nuanced feelings are still completely private.

00:34:01 We can’t read any of that.

00:34:02 So there’s some relief that we can’t read that.

00:34:07 Even brain imaging can’t read that.

00:34:09 Wearables can’t read that.

00:34:12 However, as we read your body state changes

00:34:16 and we know what’s going on in your environment

00:34:18 and we look at patterns of those over time,

00:34:21 we can start to make some inferences

00:34:24 about what you might be feeling.

00:34:26 And that is where it’s not just the momentary feeling

00:34:31 but it’s more your stance toward things.

00:34:34 And that could actually be a little bit more scary

00:34:37 with certain kinds of governmental control freak people

00:34:42 who want to know more about are you on their team

00:34:46 or are you not?

00:34:48 And getting that information through over time.

00:34:50 So you’re saying there’s a lot of signal

00:34:51 by looking at the change over time.

00:34:53 Yeah.

00:34:54 So you’ve done a lot of exciting work

00:34:56 both in computer vision

00:34:57 and physiological sense like wearables.

00:35:00 What do you think is the best modality for,

00:35:03 what’s the best window into the emotional soul?

00:35:08 Is it the face?

00:35:09 Is it the voice?

00:35:10 Depends what you want to know.

00:35:11 It depends what you want to know.

00:35:13 It depends what you want to know.

00:35:13 Everything is informative.

00:35:15 Everything we do is informative.

00:35:17 So for health and wellbeing and things like that,

00:35:20 do you find the wearable physiotechnical,

00:35:22 measuring physiological signals

00:35:24 is the best for health based stuff?

00:35:29 So here I’m going to answer empirically

00:35:31 with data and studies we’ve been doing.

00:35:34 We’ve been doing studies.

00:35:36 Now these are currently running

00:35:38 with lots of different kinds of people

00:35:39 but where we’ve published data

00:35:41 and I can speak publicly to it,

00:35:44 the data are limited right now

00:35:45 to New England college students.

00:35:47 So that’s a small group.

00:35:50 Among New England college students,

00:35:52 when they are wearing a wearable

00:35:55 like the empathic embrace here

00:35:57 that’s measuring skin conductance, movement, temperature.

00:36:01 And when they are using a smartphone

00:36:05 that is collecting their time of day

00:36:09 of when they’re texting, who they’re texting,

00:36:12 their movement around it, their GPS,

00:36:14 the weather information based upon their location.

00:36:18 And when it’s using machine learning

00:36:19 and putting all of that together

00:36:20 and looking not just at right now

00:36:22 but looking at your rhythm of behaviors

00:36:26 over about a week.

00:36:28 When we look at that,

00:36:29 we are very accurate at forecasting tomorrow’s stress,

00:36:33 mood and happy, sad mood and health.

00:36:38 And when we look at which pieces of that are most useful,

00:36:43 first of all, if you have all the pieces,

00:36:45 you get the best results.

00:36:48 If you have only the wearable,

00:36:50 you get the next best results.

00:36:52 And that’s still better than 80% accurate

00:36:56 at forecasting tomorrow’s levels.

00:37:00 Isn’t that exciting because the wearable stuff

00:37:02 with physiological information,

00:37:05 it feels like it violates privacy less

00:37:08 than the noncontact face based methods.

00:37:12 Yeah, it’s interesting.

00:37:14 I think what people sometimes don’t,

00:37:16 it’s funny in the early days people would say,

00:37:18 oh, wearing something or giving blood is invasive, right?

00:37:22 Whereas a camera is less invasive

00:37:24 because it’s not touching you.

00:37:26 I think on the contrary,

00:37:28 the things that are not touching you are maybe the scariest

00:37:31 because you don’t know when they’re on or off.

00:37:33 And you don’t know who’s behind it, right?

00:37:39 A wearable, depending upon what’s happening

00:37:43 to the data on it, if it’s just stored locally

00:37:46 or if it’s streaming and what it is being attached to,

00:37:52 in a sense, you have the most control over it

00:37:54 because it’s also very easy to just take it off, right?

00:37:59 Now it’s not sensing me.

00:38:01 So if I’m uncomfortable with what it’s sensing,

00:38:05 now I’m free, right?

00:38:07 If I’m comfortable with what it’s sensing,

00:38:09 then, and I happen to know everything about this one

00:38:12 and what it’s doing with it,

00:38:13 so I’m quite comfortable with it,

00:38:15 then I have control, I’m comfortable.

00:38:20 Control is one of the biggest factors for an individual

00:38:24 in reducing their stress.

00:38:26 If I have control over it,

00:38:28 if I know all there is to know about it,

00:38:30 then my stress is a lot lower

00:38:32 and I’m making an informed choice

00:38:34 about whether to wear it or not,

00:38:36 or when to wear it or not.

00:38:38 I wanna wear it sometimes, maybe not others.

00:38:40 Right, so that control, yeah, I’m with you.

00:38:42 That control, even if, yeah, the ability to turn it off,

00:38:47 that is a really important thing.

00:38:49 It’s huge.

00:38:49 And we need to, maybe, if there’s regulations,

00:38:53 maybe that’s number one to protect

00:38:55 is people’s ability to, it’s easy to opt out as to opt in.

00:38:59 Right, so you’ve studied a bit of neuroscience as well.

00:39:04 How have looking at our own minds,

00:39:08 sort of the biological stuff or the neurobiological,

00:39:12 the neuroscience to get the signals in our brain,

00:39:17 helped you understand the problem

00:39:18 and the approach of effective computing, so?

00:39:21 Originally, I was a computer architect

00:39:23 and I was building hardware and computer designs

00:39:26 and I wanted to build ones that worked like the brain.

00:39:28 So I’ve been studying the brain

00:39:29 as long as I’ve been studying how to build computers.

00:39:33 Have you figured out anything yet?

00:39:36 Very little.

00:39:37 It’s so amazing.

00:39:39 You know, they used to think like,

00:39:40 oh, if you remove this chunk of the brain

00:39:42 and you find this function goes away,

00:39:44 well, that’s the part of the brain that did it.

00:39:45 And then later they realized

00:39:46 if you remove this other chunk of the brain,

00:39:48 that function comes back and,

00:39:50 oh no, we really don’t understand it.

00:39:52 Brains are so interesting and changing all the time

00:39:56 and able to change in ways

00:39:58 that will probably continue to surprise us.

00:40:02 When we were measuring stress,

00:40:04 you may know the story where we found

00:40:07 an unusual big skin conductance pattern on one wrist

00:40:10 in one of our kids with autism.

00:40:14 And in trying to figure out how on earth

00:40:15 you could be stressed on one wrist and not the other,

00:40:17 like how can you get sweaty on one wrist, right?

00:40:20 When you get stressed

00:40:21 with that sympathetic fight or flight response,

00:40:23 like you kind of should like sweat more

00:40:25 in some places than others,

00:40:26 but not more on one wrist than the other.

00:40:27 That didn’t make any sense.

00:40:30 We learned that what had actually happened

00:40:33 was a part of his brain had unusual electrical activity

00:40:37 and that caused an unusually large sweat response

00:40:41 on one wrist and not the other.

00:40:44 And since then we’ve learned

00:40:45 that seizures cause this unusual electrical activity.

00:40:49 And depending where the seizure is,

00:40:51 if it’s in one place and it’s staying there,

00:40:53 you can have a big electrical response

00:40:55 we can pick up with a wearable at one part of the body.

00:40:58 You can also have a seizure

00:40:59 that spreads over the whole brain,

00:41:00 generalized grand mal seizure.

00:41:02 And that response spreads

00:41:04 and we can pick it up pretty much anywhere.

00:41:07 As we learned this and then later built Embrace

00:41:10 that’s now FDA cleared for seizure detection,

00:41:13 we have also built relationships

00:41:15 with some of the most amazing doctors in the world

00:41:18 who not only help people

00:41:20 with unusual brain activity or epilepsy,

00:41:23 but some of them are also surgeons

00:41:24 and they’re going in and they’re implanting electrodes,

00:41:27 not just to momentarily read the strange patterns

00:41:31 of brain activity that we’d like to see return to normal,

00:41:35 but also to read out continuously what’s happening

00:41:37 in some of these deep regions of the brain

00:41:39 during most of life when these patients are not seizing.

00:41:41 Most of the time they’re not seizing,

00:41:42 most of the time they’re fine.

00:41:44 And so we are now working on mapping

00:41:47 those deep brain regions

00:41:49 that you can’t even usually get with EEG scalp electrodes

00:41:53 because the changes deep inside don’t reach the surface.

00:41:58 But interesting when some of those regions

00:42:00 are activated, we see a big skin conductance response.

00:42:04 Who would have thunk it, right?

00:42:05 Like nothing here, but something here.

00:42:07 In fact, right after seizures

00:42:10 that we think are the most dangerous ones

00:42:12 that precede what’s called SUDEP,

00:42:14 Sudden Unexpected Death and Epilepsy,

00:42:16 there’s a period where the brainwaves go flat

00:42:19 and it looks like the person’s brain has stopped,

00:42:21 but it hasn’t.

00:42:23 The activity has gone deep into a region

00:42:26 that can make the cortical activity look flat,

00:42:29 like a quick shutdown signal here.

00:42:32 It can unfortunately cause breathing to stop

00:42:35 if it progresses long enough.

00:42:38 Before that happens, we see a big skin conductance response

00:42:42 in the data that we have.

00:42:43 The longer this flattening, the bigger our response here.

00:42:46 So we have been trying to learn, you know, initially,

00:42:49 like why are we getting a big response here

00:42:51 when there’s nothing here?

00:42:52 Well, it turns out there’s something much deeper.

00:42:55 So we can now go inside the brains

00:42:57 of some of these individuals, fabulous people

00:43:01 who usually aren’t seizing,

00:43:03 and get this data and start to map it.

00:43:05 So that’s the active research that we’re doing right now

00:43:07 with top medical partners.

00:43:09 So this wearable sensor that’s looking at skin conductance

00:43:12 can capture sort of the ripples of the complexity

00:43:17 of what’s going on in our brain.

00:43:18 So this little device, you have a hope

00:43:22 that you can start to get the signal

00:43:24 from the interesting things happening in the brain.

00:43:27 Yeah, we’ve already published the strong correlations

00:43:30 between the size of this response

00:43:32 and the flattening that happens afterwards.

00:43:35 And unfortunately, also in a real SUDEP case

00:43:38 where the patient died because the, well, we don’t know why.

00:43:42 We don’t know if somebody was there,

00:43:43 it would have definitely prevented it.

00:43:45 But we know that most SUDEPs happen

00:43:47 when the person’s alone.

00:43:48 And in this case, a SUDEP is an acronym, S U D E P.

00:43:53 And it stands for the number two cause

00:43:56 of years of life lost actually

00:43:58 among all neurological disorders.

00:44:01 Stroke is number one, SUDEP is number two,

00:44:03 but most people haven’t heard of it.

00:44:05 Actually, I’ll plug my TED talk,

00:44:07 it’s on the front page of TED right now

00:44:09 that talks about this.

00:44:11 And we hope to change that.

00:44:13 I hope everybody who’s heard of SIDS and stroke

00:44:17 will now hear of SUDEP

00:44:18 because we think in most cases it’s preventable

00:44:21 if people take their meds and aren’t alone

00:44:24 when they have a seizure.

00:44:26 Not guaranteed to be preventable.

00:44:27 There are some exceptions,

00:44:29 but we think most cases probably are.

00:44:31 So you had this embrace now in the version two wristband,

00:44:35 right, for epilepsy management.

00:44:39 That’s the one that’s FDA approved?

00:44:41 Yes.

00:44:42 Which is kind of a clear.

00:44:43 FDA cleared, they say.

00:44:45 Sorry.

00:44:46 No, it’s okay.

00:44:46 It essentially means it’s approved for marketing.

00:44:49 Got it.

00:44:50 Just a side note, how difficult is that to do?

00:44:52 It’s essentially getting FDA approval

00:44:54 for computer science technology.

00:44:57 It’s so agonizing.

00:44:58 It’s much harder than publishing multiple papers

00:45:01 in top medical journals.

00:45:04 Yeah, we’ve published peer reviewed

00:45:05 top medical journal neurology, best results,

00:45:08 and that’s not good enough for the FDA.

00:45:10 Is that system,

00:45:12 so if we look at the peer review of medical journals,

00:45:14 there’s flaws, there’s strengths,

00:45:16 is the FDA approval process,

00:45:19 how does it compare to the peer review process?

00:45:21 Does it have the strength?

00:45:23 I’ll take peer review over FDA any day.

00:45:25 But is that a good thing?

00:45:26 Is that a good thing for FDA?

00:45:28 You’re saying, does it stop some amazing technology

00:45:31 from getting through?

00:45:32 Yeah, it does.

00:45:33 The FDA performs a very important good role

00:45:36 in keeping people safe.

00:45:37 They keep things,

00:45:39 they put you through tons of safety testing

00:45:41 and that’s wonderful and that’s great.

00:45:44 I’m all in favor of the safety testing.

00:45:46 But sometimes they put you through additional testing

00:45:51 that they don’t have to explain why they put you through it

00:45:54 and you don’t understand why you’re going through it

00:45:56 and it doesn’t make sense.

00:45:58 And that’s very frustrating.

00:46:00 And maybe they have really good reasons

00:46:04 and they just would,

00:46:05 it would do people a service to articulate those reasons.

00:46:09 Be more transparent.

00:46:10 Be more transparent.

00:46:12 So as part of Empatica, you have sensors.

00:46:15 So what kind of problems can we crack?

00:46:17 What kind of things from seizures to autism

00:46:24 to I think I’ve heard you mentioned depression.

00:46:28 What kind of things can we alleviate?

00:46:29 Can we detect?

00:46:30 What’s your hope of what,

00:46:32 how we can make the world a better place

00:46:33 with this wearable tech?

00:46:35 I would really like to see my fellow brilliant researchers

00:46:40 step back and say, what are the really hard problems

00:46:46 that we don’t know how to solve

00:46:47 that come from people maybe we don’t even see

00:46:50 in our normal life because they’re living

00:46:52 in the poor places.

00:46:54 They’re stuck on the bus.

00:46:56 They can’t even afford the Uber or the Lyft

00:46:58 or the data plan or all these other wonderful things

00:47:02 we have that we keep improving on.

00:47:04 Meanwhile, there’s all these folks left behind in the world

00:47:07 and they’re struggling with horrible diseases

00:47:09 with depression, with epilepsy, with diabetes,

00:47:12 with just awful stuff that maybe a little more time

00:47:19 and attention hanging out with them

00:47:20 and learning what are their challenges in life?

00:47:22 What are their needs?

00:47:24 How do we help them have job skills?

00:47:25 How do we help them have a hope and a future

00:47:28 and a chance to have the great life

00:47:31 that so many of us building technology have?

00:47:34 And then how would that reshape the kinds of AI

00:47:37 that we build? How would that reshape the new apps

00:47:41 that we build or the maybe we need to focus

00:47:44 on how to make things more low cost and green

00:47:46 instead of thousand dollar phones?

00:47:49 I mean, come on, why can’t we be thinking more

00:47:52 about things that do more with less for these folks?

00:47:56 Quality of life is not related to the cost of your phone.

00:48:00 It’s not something that, it’s been shown that what about

00:48:03 $75,000 of income and happiness is the same, okay?

00:48:08 However, I can tell you, you get a lot of happiness

00:48:10 from helping other people.

00:48:12 You get a lot more than $75,000 buys.

00:48:15 So how do we connect up the people who have real needs

00:48:19 with the people who have the ability to build the future

00:48:21 and build the kind of future that truly improves the lives

00:48:25 of all the people that are currently being left behind?

00:48:28 So let me return just briefly on a point,

00:48:32 maybe in the movie, Her.

00:48:35 So do you think if we look farther into the future,

00:48:37 you said so much of the benefit from making our technology

00:48:41 more empathetic to us human beings would make them

00:48:46 better tools, empower us, make our lives better.

00:48:50 Well, if we look farther into the future,

00:48:51 do you think we’ll ever create an AI system

00:48:54 that we can fall in love with?

00:48:56 That we can fall in love with and loves us back

00:49:00 on a level that is similar to human to human interaction,

00:49:04 like in the movie Her or beyond?

00:49:07 I think we can simulate it in ways that could,

00:49:13 you know, sustain engagement for a while.

00:49:17 Would it be as good as another person?

00:49:20 I don’t think so, if you’re used to like good people.

00:49:24 Now, if you’ve just grown up with nothing but abuse

00:49:27 and you can’t stand human beings,

00:49:29 can we do something that helps you there

00:49:32 that gives you something through a machine?

00:49:34 Yeah, but that’s pretty low bar, right?

00:49:36 If you’ve only encountered pretty awful people.

00:49:39 If you’ve encountered wonderful, amazing people,

00:49:41 we’re nowhere near building anything like that.

00:49:44 And I would not bet on building it.

00:49:49 I would bet instead on building the kinds of AI

00:49:53 that helps kind of raise all boats,

00:49:56 that helps all people be better people,

00:49:59 helps all people figure out if they’re getting sick tomorrow

00:50:02 and helps give them what they need to stay well tomorrow.

00:50:05 That’s the kind of AI I wanna build

00:50:07 that improves human lives,

00:50:09 not the kind of AI that just walks on The Tonight Show

00:50:11 and people go, wow, look how smart that is.

00:50:14 Really?

00:50:15 And then it goes back in a box, you know?

00:50:18 So on that point,

00:50:19 if we continue looking a little bit into the future,

00:50:23 do you think an AI that’s empathetic

00:50:25 and does improve our lives

00:50:28 need to have a physical presence, a body?

00:50:31 And even let me cautiously say the C word consciousness

00:50:38 and even fear of mortality.

00:50:40 So some of those human characteristics,

00:50:42 do you think it needs to have those aspects

00:50:45 or can it remain simply a machine learning tool

00:50:50 that learns from data of behavior

00:50:53 that learns to make us,

00:50:56 based on previous patterns, feel better?

00:51:00 Or does it need those elements of consciousness?

00:51:02 It depends on your goals.

00:51:03 If you’re making a movie, it needs a body.

00:51:06 It needs a gorgeous body.

00:51:08 It needs to act like it has consciousness.

00:51:10 It needs to act like it has emotion, right?

00:51:11 Because that’s what sells.

00:51:13 That’s what’s gonna get me to show up and enjoy the movie.

00:51:16 Okay.

00:51:17 In real life, does it need all that?

00:51:19 Well, if you’ve read Orson Scott Card,

00:51:21 Ender’s Game, Speaker of the Dead,

00:51:23 it could just be like a little voice in your earring, right?

00:51:26 And you could have an intimate relationship

00:51:28 and it could get to know you.

00:51:29 And it doesn’t need to be a robot.

00:51:34 But that doesn’t make this compelling of a movie, right?

00:51:37 I mean, we already think it’s kind of weird

00:51:38 when a guy looks like he’s talking to himself on the train,

00:51:41 even though it’s earbuds.

00:51:43 So we have these, embodied is more powerful.

00:51:49 Embodied, when you compare interactions

00:51:51 with an embodied robot versus a video of a robot

00:51:55 versus no robot, the robot is more engaging.

00:52:00 The robot gets our attention more.

00:52:01 The robot, when you walk in your house,

00:52:03 is more likely to get you to remember to do the things

00:52:05 that you asked it to do,

00:52:06 because it’s kind of got a physical presence.

00:52:09 You can avoid it if you don’t like it.

00:52:10 It could see you’re avoiding it.

00:52:12 There’s a lot of power to being embodied.

00:52:14 There will be embodied AIs.

00:52:17 They have great power and opportunity and potential.

00:52:22 There will also be AIs that aren’t embodied,

00:52:24 that just are little software assistants

00:52:28 that help us with different things

00:52:30 that may get to know things about us.

00:52:33 Will they be conscious?

00:52:34 There will be attempts to program them

00:52:36 to make them appear to be conscious.

00:52:39 We can already write programs that make it look like,

00:52:41 oh, what do you mean?

00:52:42 Of course I’m aware that you’re there, right?

00:52:43 I mean, it’s trivial to say stuff like that.

00:52:45 It’s easy to fool people,

00:52:48 but does it actually have conscious experience like we do?

00:52:53 Nobody has a clue how to do that yet.

00:52:55 That seems to be something that is beyond

00:52:58 what any of us knows how to build now.

00:53:01 Will it have to have that?

00:53:03 I think you can get pretty far

00:53:05 with a lot of stuff without it.

00:53:07 But will we accord it rights?

00:53:10 Well, that’s more a political game

00:53:13 than it is a question of real consciousness.

00:53:16 Yeah, can you go to jail for turning off Alexa

00:53:18 is the question for an election maybe a few decades from now.

00:53:24 Well, Sophia Robot’s already been given rights

00:53:27 as a citizen in Saudi Arabia, right?

00:53:30 Even before women have full rights.

00:53:33 Then the robot was still put back in the box

00:53:36 to be shipped to the next place

00:53:39 where it would get a paid appearance, right?

00:53:42 Yeah, it’s dark and almost comedic, if not absurd.

00:53:50 So I’ve heard you speak about your journey in finding faith.

00:53:54 Sure.

00:53:55 And how you discovered some wisdoms about life

00:54:00 and beyond from reading the Bible.

00:54:03 And I’ve also heard you say that,

00:54:05 you said scientists too often assume

00:54:07 that nothing exists beyond what can be currently measured.

00:54:11 Yeah, materialism.

00:54:12 Materialism.

00:54:13 And scientism, yeah.

00:54:14 So in some sense, this assumption enables

00:54:17 the near term scientific method,

00:54:20 assuming that we can uncover the mysteries of this world

00:54:25 by the mechanisms of measurement that we currently have.

00:54:28 But we easily forget that we’ve made this assumption.

00:54:33 So what do you think we miss out on

00:54:35 by making that assumption?

00:54:38 It’s fine to limit the scientific method

00:54:42 to things we can measure and reason about and reproduce.

00:54:47 That’s fine.

00:54:49 I think we have to recognize

00:54:51 that sometimes we scientists also believe

00:54:53 in things that happen historically.

00:54:55 Like I believe the Holocaust happened.

00:54:57 I can’t prove events from past history scientifically.

00:55:03 You prove them with historical evidence, right?

00:55:06 With the impact they had on people,

00:55:08 with eyewitness testimony and things like that.

00:55:11 So a good thinker recognizes that science

00:55:15 is one of many ways to get knowledge.

00:55:19 It’s not the only way.

00:55:21 And there’s been some really bad philosophy

00:55:24 and bad thinking recently, you can call it scientism,

00:55:27 where people say science is the only way to get to truth.

00:55:31 And it’s not, it just isn’t.

00:55:33 There are other ways that work also.

00:55:35 Like knowledge of love with someone.

00:55:38 You don’t prove your love through science, right?

00:55:43 So history, philosophy, love,

00:55:48 a lot of other things in life show us

00:55:50 that there’s more ways to gain knowledge and truth

00:55:55 if you’re willing to believe there is such a thing,

00:55:57 and I believe there is, than science.

00:56:01 I do, I am a scientist, however.

00:56:03 And in my science, I do limit my science

00:56:05 to the things that the scientific method can do.

00:56:09 But I recognize that it’s myopic

00:56:11 to say that that’s all there is.

00:56:13 Right, there’s, just like you listed,

00:56:15 there’s all the why questions.

00:56:17 And really we know, if we’re being honest with ourselves,

00:56:20 the percent of what we really know is basically zero

00:56:25 relative to the full mystery of the…

00:56:28 Measure theory, a set of measure zero,

00:56:30 if I have a finite amount of knowledge, which I do.

00:56:34 So you said that you believe in truth.

00:56:37 So let me ask that old question.

00:56:40 What do you think this thing is all about?

00:56:42 What’s the life on earth?

00:56:44 Life, the universe, and everything?

00:56:46 And everything, what’s the meaning?

00:56:47 I can’t quote Douglas Adams 42.

00:56:49 It’s my favorite number.

00:56:51 By the way, that’s my street address.

00:56:52 My husband and I guessed the exact same number

00:56:54 for our house, we got to pick it.

00:56:57 And there’s a reason we picked 42, yeah.

00:57:00 So is it just 42 or is there,

00:57:02 do you have other words that you can put around it?

00:57:05 Well, I think there’s a grand adventure

00:57:07 and I think this life is a part of it.

00:57:09 I think there’s a lot more to it than meets the eye

00:57:12 and the heart and the mind and the soul here.

00:57:14 I think we see but through a glass dimly in this life.

00:57:18 We see only a part of all there is to know.

00:57:22 If people haven’t read the Bible, they should,

00:57:25 if they consider themselves educated

00:57:27 and you could read Proverbs

00:57:30 and find tremendous wisdom in there

00:57:33 that cannot be scientifically proven.

00:57:35 But when you read it, there’s something in you,

00:57:38 like a musician knows when the instruments played right

00:57:41 and it’s beautiful.

00:57:42 There’s something in you that comes alive

00:57:45 and knows that there’s a truth there

00:57:47 that it’s like your strings are being plucked by the master

00:57:50 instead of by me, right, playing when I pluck it.

00:57:54 But probably when you play, it sounds spectacular, right?

00:57:57 And when you encounter those truths,

00:58:01 there’s something in you that sings

00:58:03 and knows that there is more

00:58:06 than what I can prove mathematically

00:58:09 or program a computer to do.

00:58:11 Don’t get me wrong, the math is gorgeous.

00:58:13 The computer programming can be brilliant.

00:58:16 It’s inspiring, right?

00:58:17 We wanna do more.

00:58:19 None of this squashes my desire to do science

00:58:21 or to get knowledge through science.

00:58:23 I’m not dissing the science at all.

00:58:26 I grow even more in awe of what the science can do

00:58:29 because I’m more in awe of all there is we don’t know.

00:58:33 And really at the heart of science,

00:58:36 you have to have a belief that there’s truth,

00:58:38 that there’s something greater to be discovered.

00:58:41 And some scientists may not wanna use the faith word,

00:58:44 but it’s faith that drives us to do science.

00:58:47 It’s faith that there is truth,

00:58:49 that there’s something to know that we don’t know,

00:58:52 that it’s worth knowing, that it’s worth working hard,

00:58:56 and that there is meaning,

00:58:58 that there is such a thing as meaning,

00:58:59 which by the way, science can’t prove either.

00:59:02 We have to kind of start with some assumptions

00:59:04 that there’s things like truth and meaning.

00:59:06 And these are really questions philosophers own, right?

00:59:10 This is their space,

00:59:11 of philosophers and theologians at some level.

00:59:14 So these are things science,

00:59:19 when people claim that science will tell you all truth,

00:59:23 there’s a name for that.

00:59:23 It’s its own kind of faith.

00:59:25 It’s scientism and it’s very myopic.

00:59:29 Yeah, there’s a much bigger world out there to be explored

00:59:32 in ways that science may not,

00:59:34 at least for now, allow us to explore.

00:59:37 Yeah, and there’s meaning and purpose and hope

00:59:40 and joy and love and all these awesome things

00:59:43 that make it all worthwhile too.

00:59:45 I don’t think there’s a better way to end it, Roz.

00:59:47 Thank you so much for talking today.

00:59:49 Thanks Lex, what a pleasure.

00:59:50 Great questions.