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

Episode 2019-06-172019-06-17English

Guests: Rosalind Picard

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§01Transcript · 00:00–09:02

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

00:02She's a professor at MIT,

00:04director of the Effective Computing Research Group

00:06at the MIT Media Lab,

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

00:12Over two decades ago,

00:13she launched a field of effective computing

00:15with her book of the same name.

00:17This book described the importance of emotion

00:20in artificial and natural intelligence.

00:23The vital role of emotional communication

00:25has to the relationship between people in general

00:28and human robot interaction.

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

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

00:37and her recent research in epilepsy,

00:39and even love and meaning.

00:42This conversation is part

00:43of the Artificial Intelligence Podcast.

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

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

00:51spelled F R I D.

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

00:59More than 20 years ago,

01:00you've coined the term effective computing

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

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

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

01:12and adapt the behavior of the machine

01:14based on the emotional state.

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

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

01:25So it's the scope, the applications, the challenges,

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

01:32Yeah, actually, originally,

01:33when I defined the term affective computing,

01:36it was a bit broader than just recognizing

01:40and responding intelligently to human emotion,

01:42although those are probably the two pieces

01:44that we've worked on the hardest.

01:47The original concept also encompassed machines

01:50that would have mechanisms

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

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

01:59or deliberately influences human emotion.

02:02So the human computer interaction part

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

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

02:11and I'm scowling at it and I'm cursing at it

02:13and it just keeps acting smiling and happy

02:15like that little paperclip used to do,

02:17dancing, winking, that kind of thing

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

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

02:29And if it's gonna be intelligent,

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

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

02:34in it at the time,

02:36that thing's gonna actually be smart.

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

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

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

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

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

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

02:58that people recognize what you're talking about

03:00still to this point?

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

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

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

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

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

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

03:18I think.

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

03:22Yeah, some people actually liked it.

03:25I would hear those stories.

03:27You miss it?

03:28Well, I miss the annoyance.

03:31They felt like there's an element.

03:34Someone was there.

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

03:36and they were annoying.

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

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

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

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

03:48or cursed at it,

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

03:51and shrugged off,

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

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

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

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

04:03they start smiling, winking and dancing.

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

04:06So Bill Gates got a standing ovation

04:08when he said it was going away

04:10because people were so ticked.

04:12It was so emotionally unintelligent, right?

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

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

04:20It was completely unintelligent about,

04:23hey, if you're annoying your customer,

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

04:28So that kind of mismatch was something

04:32the developers just didn't think about.

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

04:39and language and chess and games,

04:44problems that could be pretty well defined.

04:47Social emotional interaction is much more complex

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

04:53that people are trying to solve.

04:56And in order to understand that required skills

04:58that most people in computer science

05:00actually were lacking personally.

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

05:03Have things gotten better since the work,

05:06since the message,

05:07since you've really launched the field

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

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

05:14who's deeply passionate about human beings

05:16and yet am in computer science,

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

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

05:26Yeah, well.

05:27Or hasn't gotten better.

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

05:30among computer scientists these days.

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

05:35than they were 25 years ago.

05:37And that's good.

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

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

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

05:49So it need not be limited to people

05:52who prefer computers to other people.

05:54How hard do you think it is?

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

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

06:03Has it gotten easier or harder

06:06as you've explored it further?

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

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

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

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

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

06:29I think the time estimates are always hard

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

06:37and hate of a particular topic.

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

06:45working on it for a certain application,

06:49that application gets solved really quickly.

06:52The general intelligence,

06:54the computer's complete lack of ability

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

07:03the fact that it's not conscious,

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

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

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

07:15what other people pick up implicitly.

07:17We don't see that changing yet.

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

07:23that that's gonna go any faster,

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

07:28with a lot of limitations

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

07:34in very limited, narrow, prespecified contexts

07:37where we can prescribe pretty much

07:40what's gonna happen there.

07:42So I don't see the,

07:46it's hard to predict a date

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

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

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

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

08:01than the difficulty of it.

08:04And that's causing some of us

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

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

08:09let's go faster.

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

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

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

08:21And that worries me so deeply

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

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

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

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

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

08:39that protects people?

08:41So the technology we're referring to is

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

08:46like the human face, right?

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

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

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

08:57Is it in terms of privacy?

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

§02Transcript · 09:02–18:28

09:02But let's dig into privacy.

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

09:07of say a political leader,

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

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

09:17Here that's not a shocking thing.

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

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

09:27the leader of the government, right?

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

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

09:37and you make a facial expression

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

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

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

09:50and say anything we want, usually.

09:54I mean, there are some restrictions

09:56when the athlete does this

09:58as part of the national broadcast.

10:00Maybe the teams get a little unhappy

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

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

10:08We have these freedoms,

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

10:14what if our technology can read

10:17your underlying affective state?

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

10:22What if our technology can read it

10:24without your prior consent?

10:28And here in the US,

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

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

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

10:38without their prior informed consent.

10:41And even the software that is licensable,

10:45you have to sign things saying

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

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

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

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

10:58in people's buy in.

10:59They're just gonna use it.

11:01They're gonna inflict it on you.

11:03And if you don't like it,

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

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

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

11:15and deep fakes and so on,

11:18what you bring up is actually,

11:20in that one sense, deep fakes provide

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

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

11:34And therefore you always have an escape clause

11:37if a government is trying,

11:38if a stable, balanced, ethical government

11:44is trying to accuse you of something,

11:46at least you have protection.

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

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

11:52We know how to go into the video

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

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

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

12:05in your video now too.

12:06We decided we needed to do that.

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

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

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

12:20that was time that I wasn't spending

12:22making the machine more affectively intelligent.

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

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

12:32and a little bit more like by concern

12:34about what's happening in society

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

12:38And as we step back and say,

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

12:44to make Elon Musk more money

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

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

12:52Why are we building it?

12:54What is the point of building AI?

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

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

13:04and to do something cool, right?

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

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

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

13:17the divide is even larger.

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

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

13:25Maybe we wanna rethink the problems in society

13:29that are causing the greatest inequity

13:32and rethink how to build AI

13:35that's not about a general intelligence,

13:36but that's about extending the intelligence

13:39and capability of the have nots

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

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

13:46happens organically?

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

13:51is the desire to publish papers,

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

13:55Do you hope it happens organically?

13:57Is there room for regulation?

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

14:02I prefer the, you know,

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

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

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

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

14:14You're gonna find a way around it.

14:17I generally think less regulation is better.

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

14:24no stick, no regulation,

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

14:29I do think we need regulations

14:30around protecting people with their data,

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

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

14:40I would also like to see the regulations

14:42that we have right now around lie detection

14:44being extended to emotion recognition in general,

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

14:50when you're, on a candidate

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

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

14:57around reading people's emotions without their consent

15:00and in certain cases,

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

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

15:09that's predictive around mental health,

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

15:14that that should get the kinds of protections

15:16that our medical data gets.

15:18What most people don't know yet

15:19is right now with your smartphone use,

15:22and if you're wearing a sensor

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

15:27and your physical activity

15:28and how much you're using your phone

15:30and your social interaction,

15:32all of that nonmedical data,

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

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

15:40wouldn't have called it that,

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

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

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

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

15:55happy or sad, stressed or calm.

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

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

16:03Do you have an optimism towards,

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

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

16:10For the most part, on balance,

16:12are you optimistic about the benefits

16:16that can be brought from that camera

16:17that's looking at billions of us?

16:19Or should we be more worried?

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

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

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

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

16:42these devices want to listen

16:47while they say ostensibly to help us.

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

16:52who do want to help people.

16:54Let me not brand them all bad.

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

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

17:04They are awfully big companies, right?

17:09They have incredible power.

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

17:17And suddenly all of that data

17:19were not part of free America,

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

17:24who just wants to take over the world

17:26and you submit to them.

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

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

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

17:37That's a nice word for them.

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

17:41for people who have surgery these days.

17:43They don't have an organ donation problem

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

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

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

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

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

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

18:06I was born in the Soviet Union.

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

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

18:15and you very much so,

18:19you know, there's an exciting possibility

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

18:27Yeah, yeah.

§03Transcript · 18:28–27:33

18:28Right, so.

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

18:35you know, when you list like,

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

18:39or dinner with, right?

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

18:43I just like computers.

18:44And one of them said to me once

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

18:49I want you to know,

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

18:53my one social event of the year.

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

18:57machine learning person, right?

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

19:01And I love that computer science welcomes people

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

19:07around people.

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

19:12We need all kinds of people

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

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

19:19to suddenly become social.

19:21At the same time,

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

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

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

19:31Well, hold on a second.

19:31So let me push back on that.

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

19:38can, you know, there's loneliness.

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

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

19:47Do you...

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

19:51Yes.

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

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

19:56You're not alone.

19:56You're not alone.

19:57That's a good line.

20:00But do you think there's...

20:03You talked about some worry,

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

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

20:11can alleviate that loneliness

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

20:16Yeah, yeah, definitely.

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

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

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

20:27And they're not a human character.

20:30There is a human behind it.

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

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

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

20:43but somebody does, right?

20:44And what are they getting from this?

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

20:50Being there in that social moment,

20:52that romantic moment or connecting with somebody.

20:56I've had a similar experience

20:57reading some science fiction books, right?

20:59And connecting with the character.

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

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

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

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

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

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

21:19So it can deepen it, right?

21:21You can have a very deep connection,

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

21:29Well, much more.

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

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

21:36It's scripted.

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

21:39like there can be a real interaction

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

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

21:49You could imagine a group of characters.

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

21:53human and AI connecting,

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

22:00with everybody, but the few people

22:02and their AIs can befriend more people.

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

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

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

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

22:21than what's in that movie.

22:22So there's a tension here.

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

22:27about privacy, about how governments

22:29can misuse the information,

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

22:34So let's look at Alexa.

22:36So personal assistance.

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

22:40they ignore your emotion.

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

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

22:52except maybe aspects of your voice

22:54that help it recognize for speech recognition.

22:58Do you think they should move towards

23:00trying to understand your emotion?

23:03All of these companies are very interested

23:04in understanding human emotion.

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

23:11they want to kill themselves.

23:13Apple wants to know the difference between

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

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

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

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

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

23:34bring help to that person,

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

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

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

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

23:51and taking it seriously.

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

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

24:03and a business profit motivated reason.

24:08And there are people in companies that operate

24:11on both principles.

24:12The altruistic people really care about their customers

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

24:19at the end of the day.

24:20And it would just make those people happy

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

24:24If you came home stressed and after talking

24:27with their product, you felt better.

24:29There are other people who maybe have studied

24:32the way affect affects decision making

24:35and prices people pay.

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

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

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

24:47you'll pay more, right?

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

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

24:55to make yourself feel better.

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

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

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

25:04a little shopping therapy, right?

25:07That helps them.

25:08Which is really difficult for a company

25:09that's primarily funded on advertisement.

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

25:16or Amazon that's primarily funded

25:17on you buying things from their store.

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

25:22maybe we need regulation in the future

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

25:27that have access to our emotion

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

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

25:35of a firewall in between those.

25:38So maybe digging in a little bit

25:40on the interaction with Alexa,

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

25:44about like recognizing emotion,

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

25:49but what about the actual interaction itself?

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

25:57you mentioned Clippy and being annoying,

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

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

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

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

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

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

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

26:26I think of kids sparring, right?

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

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

26:33and that's fun.

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

26:38to learn how to self regulate.

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

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

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

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

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

26:54or how to turn the other cheek

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

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

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

27:10Probably depends on your personality.

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

27:15that is there to serve me

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

27:23I'm not looking for a rival,

27:25I'm looking for a helper.

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

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

§04Transcript · 27:33–36:29

27:33in order to have a rich experience,

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

27:37So they're not looking,

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

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

27:46leaves the person for somebody else,

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

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

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

28:02so I'm gonna shut myself off.

28:04You don't see that as...

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

28:10You, we've got to remember,

28:14and this is where this blending human AI

28:18as if we're equals is really deceptive

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

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

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

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

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

28:40that we choose to expose it to,

28:43what outputs it's allowed to have.

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

28:49And if we wanna make something

28:50that makes our lives miserable, fine.

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

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

28:59But I think we need to think about

29:01what kind of future we want.

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

29:05what is the objective function?

29:06Is it to calm people down?

29:09Sometimes.

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

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

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

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

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

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

29:28to live there.

29:29I lived in Iceland three years.

29:30It's a great place.

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

29:35A little TV commercial there.

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

29:39It's a wonderful place.

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

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

29:46into submission, into making us happy?

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

29:52like a power obsessed sick dictator individual

29:56who only wants to control other people

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

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

30:03to force people into submission.

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

30:10being given freedom and the opportunity

30:12to do things that might surprise you,

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

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

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

30:27between the weak and the strong,

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

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

30:39what is your sense on emotion

30:41in terms of recognizing emotion?

30:42The difference between emotion that is shown

30:44and emotion that is felt.

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

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

30:56and what's actually going on deep inside

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

31:01or some kind of cognitive level.

31:03Yeah, yeah.

31:05Whoa, no easy questions here.

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

31:11but what's your sense?

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

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

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

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

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

31:28Well, that's naive.

31:30We can read with the ordinary camera

31:32on your laptop or on your phone.

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

31:39We can read from a neutral face

31:41if your breathing is becoming irregular

31:44and showing signs of stress.

31:46We can read under some conditions

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

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

31:57That could be a sign of stress,

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

32:02So...

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

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

32:09but the camera can see.

32:11That's amazing.

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

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

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

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

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

32:30Right, so that's really interesting,

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

32:34That's almost like cheating your way

32:37to the physiological state of the body,

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

32:42With signal processing.

32:43With signal processing.

32:44So that's really impressive.

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

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

32:52the subtle, all the facial actions.

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

32:55for a limited amount of time.

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

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

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

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

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

33:15And Roz used to laugh a lot

33:20and smile a lot very spontaneously.

33:22And now I'm only seeing

33:23these not so spontaneous looking smiles.

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

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

33:31Probably not getting enough sleep.

33:33We could look at that too.

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

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

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

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

33:45that relate to your activation.

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

33:51about how you feel.

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

33:54Your thoughts are still private.

33:56Your nuanced feelings are still completely private.

34:01We can't read any of that.

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

34:07Even brain imaging can't read that.

34:09Wearables can't read that.

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

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

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

34:21we can start to make some inferences

34:24about what you might be feeling.

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

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

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

34:37with certain kinds of governmental control freak people

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

34:46or are you not?

34:48And getting that information through over time.

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

34:51by looking at the change over time.

34:53Yeah.

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

34:56both in computer vision

34:57and physiological sense like wearables.

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

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

35:08Is it the face?

35:09Is it the voice?

35:10Depends what you want to know.

35:11It depends what you want to know.

35:13It depends what you want to know.

35:13Everything is informative.

35:15Everything we do is informative.

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

35:20do you find the wearable physiotechnical,

35:22measuring physiological signals

35:24is the best for health based stuff?

35:29So here I'm going to answer empirically

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

35:34We've been doing studies.

35:36Now these are currently running

35:38with lots of different kinds of people

35:39but where we've published data

35:41and I can speak publicly to it,

35:44the data are limited right now

35:45to New England college students.

35:47So that's a small group.

35:50Among New England college students,

35:52when they are wearing a wearable

35:55like the empathic embrace here

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

36:01And when they are using a smartphone

36:05that is collecting their time of day

36:09of when they're texting, who they're texting,

36:12their movement around it, their GPS,

36:14the weather information based upon their location.

36:18And when it's using machine learning

36:19and putting all of that together

36:20and looking not just at right now

36:22but looking at your rhythm of behaviors

36:26over about a week.

36:28When we look at that,

§05Transcript · 36:29–44:58

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

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

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

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

36:45you get the best results.

36:48If you have only the wearable,

36:50you get the next best results.

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

36:56at forecasting tomorrow's levels.

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

37:02with physiological information,

37:05it feels like it violates privacy less

37:08than the noncontact face based methods.

37:12Yeah, it's interesting.

37:14I think what people sometimes don't,

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

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

37:22Whereas a camera is less invasive

37:24because it's not touching you.

37:26I think on the contrary,

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

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

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

37:39A wearable, depending upon what's happening

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

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

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

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

37:59Now it's not sensing me.

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

38:05now I'm free, right?

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

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

38:12and what it's doing with it,

38:13so I'm quite comfortable with it,

38:15then I have control, I'm comfortable.

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

38:24in reducing their stress.

38:26If I have control over it,

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

38:30then my stress is a lot lower

38:32and I'm making an informed choice

38:34about whether to wear it or not,

38:36or when to wear it or not.

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

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

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

38:47that is a really important thing.

38:49It's huge.

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

38:53maybe that's number one to protect

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

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

39:04How have looking at our own minds,

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

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

39:17helped you understand the problem

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

39:21Originally, I was a computer architect

39:23and I was building hardware and computer designs

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

39:28So I've been studying the brain

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

39:33Have you figured out anything yet?

39:36Very little.

39:37It's so amazing.

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

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

39:42and you find this function goes away,

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

39:45And then later they realized

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

39:48that function comes back and,

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

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

39:56and able to change in ways

39:58that will probably continue to surprise us.

40:02When we were measuring stress,

40:04you may know the story where we found

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

40:10in one of our kids with autism.

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

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

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

40:20When you get stressed

40:21with that sympathetic fight or flight response,

40:23like you kind of should like sweat more

40:25in some places than others,

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

40:27That didn't make any sense.

40:30We learned that what had actually happened

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

40:37and that caused an unusually large sweat response

40:41on one wrist and not the other.

40:44And since then we've learned

40:45that seizures cause this unusual electrical activity.

40:49And depending where the seizure is,

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

40:53you can have a big electrical response

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

40:58You can also have a seizure

40:59that spreads over the whole brain,

41:00generalized grand mal seizure.

41:02And that response spreads

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

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

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

41:13we have also built relationships

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

41:18who not only help people

41:20with unusual brain activity or epilepsy,

41:23but some of them are also surgeons

41:24and they're going in and they're implanting electrodes,

41:27not just to momentarily read the strange patterns

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

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

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

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

41:41Most of the time they're not seizing,

41:42most of the time they're fine.

41:44And so we are now working on mapping

41:47those deep brain regions

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

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

41:58But interesting when some of those regions

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

42:04Who would have thunk it, right?

42:05Like nothing here, but something here.

42:07In fact, right after seizures

42:10that we think are the most dangerous ones

42:12that precede what's called SUDEP,

42:14Sudden Unexpected Death and Epilepsy,

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

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

42:21but it hasn't.

42:23The activity has gone deep into a region

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

42:29like a quick shutdown signal here.

42:32It can unfortunately cause breathing to stop

42:35if it progresses long enough.

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

42:42in the data that we have.

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

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

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

42:51when there's nothing here?

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

42:55So we can now go inside the brains

42:57of some of these individuals, fabulous people

43:01who usually aren't seizing,

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

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

43:07with top medical partners.

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

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

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

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

43:22that you can start to get the signal

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

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

43:30between the size of this response

43:32and the flattening that happens afterwards.

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

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

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

43:43it would have definitely prevented it.

43:45But we know that most SUDEPs happen

43:47when the person's alone.

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

43:53And it stands for the number two cause

43:56of years of life lost actually

43:58among all neurological disorders.

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

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

44:05Actually, I'll plug my TED talk,

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

44:09that talks about this.

44:11And we hope to change that.

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

44:17will now hear of SUDEP

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

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

44:24when they have a seizure.

44:26Not guaranteed to be preventable.

44:27There are some exceptions,

44:29but we think most cases probably are.

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

44:35right, for epilepsy management.

44:39That's the one that's FDA approved?

44:41Yes.

44:42Which is kind of a clear.

44:43FDA cleared, they say.

44:45Sorry.

44:46No, it's okay.

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

44:49Got it.

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

44:52It's essentially getting FDA approval

44:54for computer science technology.

44:57It's so agonizing.

§06Transcript · 44:58–54:07

44:58It's much harder than publishing multiple papers

45:01in top medical journals.

45:04Yeah, we've published peer reviewed

45:05top medical journal neurology, best results,

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

45:10Is that system,

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

45:14there's flaws, there's strengths,

45:16is the FDA approval process,

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

45:21Does it have the strength?

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

45:25But is that a good thing?

45:26Is that a good thing for FDA?

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

45:31from getting through?

45:32Yeah, it does.

45:33The FDA performs a very important good role

45:36in keeping people safe.

45:37They keep things,

45:39they put you through tons of safety testing

45:41and that's wonderful and that's great.

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

45:46But sometimes they put you through additional testing

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

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

45:56and it doesn't make sense.

45:58And that's very frustrating.

46:00And maybe they have really good reasons

46:04and they just would,

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

46:09Be more transparent.

46:10Be more transparent.

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

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

46:17What kind of things from seizures to autism

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

46:28What kind of things can we alleviate?

46:29Can we detect?

46:30What's your hope of what,

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

46:33with this wearable tech?

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

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

46:46that we don't know how to solve

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

46:50in our normal life because they're living

46:52in the poor places.

46:54They're stuck on the bus.

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

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

47:02we have that we keep improving on.

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

47:07and they're struggling with horrible diseases

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

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

47:19and attention hanging out with them

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

47:22What are their needs?

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

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

47:28and a chance to have the great life

47:31that so many of us building technology have?

47:34And then how would that reshape the kinds of AI

47:37that we build? How would that reshape the new apps

47:41that we build or the maybe we need to focus

47:44on how to make things more low cost and green

47:46instead of thousand dollar phones?

47:49I mean, come on, why can't we be thinking more

47:52about things that do more with less for these folks?

47:56Quality of life is not related to the cost of your phone.

48:00It's not something that, it's been shown that what about

48:03$75,000 of income and happiness is the same, okay?

48:08However, I can tell you, you get a lot of happiness

48:10from helping other people.

48:12You get a lot more than $75,000 buys.

48:15So how do we connect up the people who have real needs

48:19with the people who have the ability to build the future

48:21and build the kind of future that truly improves the lives

48:25of all the people that are currently being left behind?

48:28So let me return just briefly on a point,

48:32maybe in the movie, Her.

48:35So do you think if we look farther into the future,

48:37you said so much of the benefit from making our technology

48:41more empathetic to us human beings would make them

48:46better tools, empower us, make our lives better.

48:50Well, if we look farther into the future,

48:51do you think we'll ever create an AI system

48:54that we can fall in love with?

48:56That we can fall in love with and loves us back

49:00on a level that is similar to human to human interaction,

49:04like in the movie Her or beyond?

49:07I think we can simulate it in ways that could,

49:13you know, sustain engagement for a while.

49:17Would it be as good as another person?

49:20I don't think so, if you're used to like good people.

49:24Now, if you've just grown up with nothing but abuse

49:27and you can't stand human beings,

49:29can we do something that helps you there

49:32that gives you something through a machine?

49:34Yeah, but that's pretty low bar, right?

49:36If you've only encountered pretty awful people.

49:39If you've encountered wonderful, amazing people,

49:41we're nowhere near building anything like that.

49:44And I would not bet on building it.

49:49I would bet instead on building the kinds of AI

49:53that helps kind of raise all boats,

49:56that helps all people be better people,

49:59helps all people figure out if they're getting sick tomorrow

50:02and helps give them what they need to stay well tomorrow.

50:05That's the kind of AI I wanna build

50:07that improves human lives,

50:09not the kind of AI that just walks on The Tonight Show

50:11and people go, wow, look how smart that is.

50:14Really?

50:15And then it goes back in a box, you know?

50:18So on that point,

50:19if we continue looking a little bit into the future,

50:23do you think an AI that's empathetic

50:25and does improve our lives

50:28need to have a physical presence, a body?

50:31And even let me cautiously say the C word consciousness

50:38and even fear of mortality.

50:40So some of those human characteristics,

50:42do you think it needs to have those aspects

50:45or can it remain simply a machine learning tool

50:50that learns from data of behavior

50:53that learns to make us,

50:56based on previous patterns, feel better?

51:00Or does it need those elements of consciousness?

51:02It depends on your goals.

51:03If you're making a movie, it needs a body.

51:06It needs a gorgeous body.

51:08It needs to act like it has consciousness.

51:10It needs to act like it has emotion, right?

51:11Because that's what sells.

51:13That's what's gonna get me to show up and enjoy the movie.

51:16Okay.

51:17In real life, does it need all that?

51:19Well, if you've read Orson Scott Card,

51:21Ender's Game, Speaker of the Dead,

51:23it could just be like a little voice in your earring, right?

51:26And you could have an intimate relationship

51:28and it could get to know you.

51:29And it doesn't need to be a robot.

51:34But that doesn't make this compelling of a movie, right?

51:37I mean, we already think it's kind of weird

51:38when a guy looks like he's talking to himself on the train,

51:41even though it's earbuds.

51:43So we have these, embodied is more powerful.

51:49Embodied, when you compare interactions

51:51with an embodied robot versus a video of a robot

51:55versus no robot, the robot is more engaging.

52:00The robot gets our attention more.

52:01The robot, when you walk in your house,

52:03is more likely to get you to remember to do the things

52:05that you asked it to do,

52:06because it's kind of got a physical presence.

52:09You can avoid it if you don't like it.

52:10It could see you're avoiding it.

52:12There's a lot of power to being embodied.

52:14There will be embodied AIs.

52:17They have great power and opportunity and potential.

52:22There will also be AIs that aren't embodied,

52:24that just are little software assistants

52:28that help us with different things

52:30that may get to know things about us.

52:33Will they be conscious?

52:34There will be attempts to program them

52:36to make them appear to be conscious.

52:39We can already write programs that make it look like,

52:41oh, what do you mean?

52:42Of course I'm aware that you're there, right?

52:43I mean, it's trivial to say stuff like that.

52:45It's easy to fool people,

52:48but does it actually have conscious experience like we do?

52:53Nobody has a clue how to do that yet.

52:55That seems to be something that is beyond

52:58what any of us knows how to build now.

53:01Will it have to have that?

53:03I think you can get pretty far

53:05with a lot of stuff without it.

53:07But will we accord it rights?

53:10Well, that's more a political game

53:13than it is a question of real consciousness.

53:16Yeah, can you go to jail for turning off Alexa

53:18is the question for an election maybe a few decades from now.

53:24Well, Sophia Robot's already been given rights

53:27as a citizen in Saudi Arabia, right?

53:30Even before women have full rights.

53:33Then the robot was still put back in the box

53:36to be shipped to the next place

53:39where it would get a paid appearance, right?

53:42Yeah, it's dark and almost comedic, if not absurd.

53:50So I've heard you speak about your journey in finding faith.

53:54Sure.

53:55And how you discovered some wisdoms about life

54:00and beyond from reading the Bible.

54:03And I've also heard you say that,

54:05you said scientists too often assume

§07Transcript · 54:07–1:00:11

54:07that nothing exists beyond what can be currently measured.

54:11Yeah, materialism.

54:12Materialism.

54:13And scientism, yeah.

54:14So in some sense, this assumption enables

54:17the near term scientific method,

54:20assuming that we can uncover the mysteries of this world

54:25by the mechanisms of measurement that we currently have.

54:28But we easily forget that we've made this assumption.

54:33So what do you think we miss out on

54:35by making that assumption?

54:38It's fine to limit the scientific method

54:42to things we can measure and reason about and reproduce.

54:47That's fine.

54:49I think we have to recognize

54:51that sometimes we scientists also believe

54:53in things that happen historically.

54:55Like I believe the Holocaust happened.

54:57I can't prove events from past history scientifically.

55:03You prove them with historical evidence, right?

55:06With the impact they had on people,

55:08with eyewitness testimony and things like that.

55:11So a good thinker recognizes that science

55:15is one of many ways to get knowledge.

55:19It's not the only way.

55:21And there's been some really bad philosophy

55:24and bad thinking recently, you can call it scientism,

55:27where people say science is the only way to get to truth.

55:31And it's not, it just isn't.

55:33There are other ways that work also.

55:35Like knowledge of love with someone.

55:38You don't prove your love through science, right?

55:43So history, philosophy, love,

55:48a lot of other things in life show us

55:50that there's more ways to gain knowledge and truth

55:55if you're willing to believe there is such a thing,

55:57and I believe there is, than science.

56:01I do, I am a scientist, however.

56:03And in my science, I do limit my science

56:05to the things that the scientific method can do.

56:09But I recognize that it's myopic

56:11to say that that's all there is.

56:13Right, there's, just like you listed,

56:15there's all the why questions.

56:17And really we know, if we're being honest with ourselves,

56:20the percent of what we really know is basically zero

56:25relative to the full mystery of the...

56:28Measure theory, a set of measure zero,

56:30if I have a finite amount of knowledge, which I do.

56:34So you said that you believe in truth.

56:37So let me ask that old question.

56:40What do you think this thing is all about?

56:42What's the life on earth?

56:44Life, the universe, and everything?

56:46And everything, what's the meaning?

56:47I can't quote Douglas Adams 42.

56:49It's my favorite number.

56:51By the way, that's my street address.

56:52My husband and I guessed the exact same number

56:54for our house, we got to pick it.

56:57And there's a reason we picked 42, yeah.

57:00So is it just 42 or is there,

57:02do you have other words that you can put around it?

57:05Well, I think there's a grand adventure

57:07and I think this life is a part of it.

57:09I think there's a lot more to it than meets the eye

57:12and the heart and the mind and the soul here.

57:14I think we see but through a glass dimly in this life.

57:18We see only a part of all there is to know.

57:22If people haven't read the Bible, they should,

57:25if they consider themselves educated

57:27and you could read Proverbs

57:30and find tremendous wisdom in there

57:33that cannot be scientifically proven.

57:35But when you read it, there's something in you,

57:38like a musician knows when the instruments played right

57:41and it's beautiful.

57:42There's something in you that comes alive

57:45and knows that there's a truth there

57:47that it's like your strings are being plucked by the master

57:50instead of by me, right, playing when I pluck it.

57:54But probably when you play, it sounds spectacular, right?

57:57And when you encounter those truths,

58:01there's something in you that sings

58:03and knows that there is more

58:06than what I can prove mathematically

58:09or program a computer to do.

58:11Don't get me wrong, the math is gorgeous.

58:13The computer programming can be brilliant.

58:16It's inspiring, right?

58:17We wanna do more.

58:19None of this squashes my desire to do science

58:21or to get knowledge through science.

58:23I'm not dissing the science at all.

58:26I grow even more in awe of what the science can do

58:29because I'm more in awe of all there is we don't know.

58:33And really at the heart of science,

58:36you have to have a belief that there's truth,

58:38that there's something greater to be discovered.

58:41And some scientists may not wanna use the faith word,

58:44but it's faith that drives us to do science.

58:47It's faith that there is truth,

58:49that there's something to know that we don't know,

58:52that it's worth knowing, that it's worth working hard,

58:56and that there is meaning,

58:58that there is such a thing as meaning,

58:59which by the way, science can't prove either.

59:02We have to kind of start with some assumptions

59:04that there's things like truth and meaning.

59:06And these are really questions philosophers own, right?

59:10This is their space,

59:11of philosophers and theologians at some level.

59:14So these are things science,

59:19when people claim that science will tell you all truth,

59:23there's a name for that.

59:23It's its own kind of faith.

59:25It's scientism and it's very myopic.

59:29Yeah, there's a much bigger world out there to be explored

59:32in ways that science may not,

59:34at least for now, allow us to explore.

59:37Yeah, and there's meaning and purpose and hope

59:40and joy and love and all these awesome things

59:43that make it all worthwhile too.

59:45I don't think there's a better way to end it, Roz.

59:47Thank you so much for talking today.

59:49Thanks Lex, what a pleasure.

59:50Great questions.

— A LITTLE MORE TO THINK ABOUT. —