Rana el Kaliouby: Emotion AI, Social Robots, and Self-Driving Cars

Rana el Kaliouby (02:45.520)

Yeah. What, what's like a definitive food of Egypt? You know, there's like these almost

Rana el Kaliouby (02:52.640)

stereotypical foods in different parts of the world, like Ukraine invented borscht.

Rana el Kaliouby (02:59.600)

Borscht is this beet soup with, that you put sour cream on. See, it's not, I can't see if you,

Rana el Kaliouby (03:04.800)

if you know, if you know what it is, I think, you know, is delicious. But if I explain it,

Rana el Kaliouby (03:10.880)

it's just not going to sound delicious. I feel like beet soup. This doesn't make any sense,

Lex Fridman (03:15.280)

but that's kind of, and you probably have actually seen pictures of it because it's one of the

Rana el Kaliouby (03:19.600)

traditional foods in Ukraine, in Russia, in different parts of the Slavic world. So that's,

Lex Fridman (03:26.800)

but it's become so cliche and stereotypical that you almost don't mention it, but it's still

Lex Fridman (03:31.520)

delicious. Like I visited Ukraine, I eat that every single day, so.

Lex Fridman (03:35.440)

Do you, do you make it yourself? How hard is it to make?

Rana el Kaliouby (03:38.480)

No, I don't know. I think to make it well, like anything, like Italians, they say, well,

Rana el Kaliouby (03:44.320)

tomato sauce is easy to make, but to make it right, that's like a generational skill. So anyway,

Lex Fridman (03:51.760)

is there something like that in Egypt? Is there a culture of food?

Rana el Kaliouby (03:55.200)

There is. And actually, we have a similar kind of soup. It's called molokhia, and it's, it's made

Rana el Kaliouby (04:02.880)

of this green plant. It's like, it's somewhere between spinach and kale, and you mince it,

Lex Fridman (04:07.520)

and then you cook it in like chicken broth. And my grandma used to make, and my mom makes it really

Rana el Kaliouby (04:13.360)

well, and I try to make it, but it's not as great. So we used to have that. And then we used to have

Rana el Kaliouby (04:18.080)

it alongside stuffed pigeons. I'm pescetarian now, so I don't eat that anymore, but.

Lex Fridman (04:23.520)

Stuffed pigeons.

Rana el Kaliouby (04:24.480)

Yeah, it's like, it was really yummy. It's the one thing I miss about,

Lex Fridman (04:28.480)

you know, now that I'm pescetarian and I don't eat.

Lex Fridman (04:32.080)

The stuffed pigeons?

Lex Fridman (04:33.040)

Yeah, the stuffed pigeons.

Rana el Kaliouby (04:35.440)

Is it, what are they stuffed with? If that doesn't bother you too much to describe.

Rana el Kaliouby (04:39.920)

No, no, it's stuffed with a lot of like just rice and, yeah, it's just rice. Yeah, so.

Lex Fridman (04:46.000)

And you also, you said that your first, in your book, that your first computer

Lex Fridman (04:51.120)

was an Atari, and Space Invaders was your favorite game.

Lex Fridman (04:56.000)

Is that when you first fell in love with computers, would you say?

Lex Fridman (04:58.800)

Yeah, I would say so.

Rana el Kaliouby (05:00.160)

Video games, or just the computer itself? Just something about the machine.

Lex Fridman (05:04.160)

Ooh, this thing, there's magic in here.

Rana el Kaliouby (05:07.840)

Yeah, I think the magical moment is definitely like playing video games with my,

Rana el Kaliouby (05:12.080)

I have two younger sisters, and we would just like had fun together, like playing games.

Lex Fridman (05:17.120)

But the other memory I have is my first code, the first code I wrote.

Lex Fridman (05:22.240)

I wrote, I drew a Christmas tree, and I'm Muslim, right?

Lex Fridman (05:26.720)

So it's kind of, it was kind of funny that the first thing I did was like this Christmas tree.

Rana el Kaliouby (05:32.000)

So, yeah, and that's when I realized, wow, you can write code to do all sorts of like

Rana el Kaliouby (05:38.320)

really cool stuff. I must have been like six or seven at the time.

Lex Fridman (05:42.720)

So you can write programs, and the programs do stuff for you. That's power.

Rana el Kaliouby (05:48.560)

That's, if you think about it, that's empowering.

Lex Fridman (05:50.880)

It's AI.

Rana el Kaliouby (05:51.600)

Yeah, I know what it is. I don't know if that, you see like,

Rana el Kaliouby (05:56.400)

I don't know if many people think of it that way when they first learned to program.

Rana el Kaliouby (05:59.520)

They just love the puzzle of it. Like, ooh, this is cool. This is pretty.

Lex Fridman (06:02.880)

It's a Christmas tree, but like, it's power.

Rana el Kaliouby (06:05.600)

It is power.

Lex Fridman (06:06.960)

Eventually, I guess you couldn't at the time, but eventually this thing,

Rana el Kaliouby (06:11.040)

if it's interesting enough, if it's a pretty enough Christmas tree,

Lex Fridman (06:14.640)

it can be run by millions of people and bring them joy, like that little thing.

Lex Fridman (06:19.280)

And then because it's digital, it's easy to spread.

Lex Fridman (06:22.400)

So like you just created something that's easily spreadable to millions of people.

Rana el Kaliouby (06:26.560)

Totally.

Lex Fridman (06:28.160)

It's hard to think that way when you're six.

Rana el Kaliouby (06:30.800)

In the book, you write, I am who I am because I was raised by a particular set of parents,

Lex Fridman (06:37.040)

both modern and conservative, forward thinking, yet locked in tradition.

Rana el Kaliouby (06:41.760)

I'm a Muslim and I feel I'm stronger, more centered for it.

Rana el Kaliouby (06:46.000)

I adhere to the values of my religion, even if I'm not as dutiful as I once was.

Lex Fridman (06:50.960)

And I am a new American and I'm thriving on the energy,

Lex Fridman (06:55.040)

vitality and entrepreneurial spirit of this great country.

Lex Fridman (06:59.840)

So let me ask you about your parents.

Lex Fridman (07:01.520)

What have you learned about life from them, especially when you were young?

Lex Fridman (07:05.280)

So both my parents, they're Egyptian, but they moved to Kuwait right out.

Lex Fridman (07:09.920)

Actually, there's a cute story about how they met.

Lex Fridman (07:11.680)

So my dad taught COBOL in the 70s.

Lex Fridman (07:14.960)

Nice.

Lex Fridman (07:15.680)

And my mom decided to learn programming.

Lex Fridman (07:18.240)

So she signed up to take his COBOL programming class.

Lex Fridman (07:22.400)

And he tried to date her and she was like, no, no, no, I don't date.

Lex Fridman (07:26.640)

And so he's like, okay, I'll propose.

Lex Fridman (07:28.240)

And that's how they got married.

Lex Fridman (07:29.680)

Whoa, strong move.

Rana el Kaliouby (07:30.960)

Right, exactly, right.

Lex Fridman (07:32.240)

That's really impressive.

Rana el Kaliouby (07:35.760)

Those COBOL guys know how to impress a lady.

Lex Fridman (07:40.640)

So yeah, so what have you learned from them?

Lex Fridman (07:43.520)

So definitely grit.

Lex Fridman (07:44.720)

One of the core values in our family is just hard work.

Rana el Kaliouby (07:48.320)

There were no slackers in our family.

Lex Fridman (07:50.720)

And that's something that's definitely stayed with me,

Rana el Kaliouby (07:55.920)

both as a professional, but also in my personal life.

Lex Fridman (07:58.480)

But I also think my mom, my mom always used to like, I don't know, it was like unconditional

Rana el Kaliouby (08:06.160)

love.

Rana el Kaliouby (08:06.720)

Like I just knew my parents would be there for me kind of regardless of what I chose to do.

Lex Fridman (08:14.240)

And I think that's very powerful.

Lex Fridman (08:15.520)

And they got tested on it because I kind of challenged cultural norms and I kind of took

Rana el Kaliouby (08:21.600)

a different path, I guess, than what's expected of a woman in the Middle East.

Lex Fridman (08:27.360)

And they still love me, which I'm so grateful for that.

Lex Fridman (08:32.480)

When was like a moment that was the most challenging for them?

Rana el Kaliouby (08:35.440)

Which moment where they kind of had to come face to face with the fact that you're a bit

Lex Fridman (08:42.240)

of a rebel?

Rana el Kaliouby (08:44.080)

I think the first big moment was when I had just gotten married, but I decided to go do

Rana el Kaliouby (08:52.080)

my PhD at Cambridge University.

Lex Fridman (08:53.840)

And because my husband at the time, he's now my ex, ran a company in Cairo, he was going

Rana el Kaliouby (08:59.440)

to stay in Egypt.

Lex Fridman (09:00.240)

So it was going to be a long distance relationship.

Lex Fridman (09:03.120)

And that's very unusual in the Middle East for a woman to just head out and kind of pursue

Lex Fridman (09:09.040)

her career.

Lex Fridman (09:09.840)

And so my dad and my parents in law both said, you know, we do not approve of you doing this,

Lex Fridman (09:18.720)

but now you're under the jurisdiction of your husband so he can make the call.

Lex Fridman (09:22.480)

And luckily for me, he was supportive.

Lex Fridman (09:26.800)

He said, you know, this is your dream come true.

Rana el Kaliouby (09:29.600)

You've always wanted to do a PhD.

Lex Fridman (09:30.960)

I'm going to support you.

Lex Fridman (09:33.200)

So I think that was the first time where, you know, I challenged the cultural norms.

Lex Fridman (09:39.120)

Was that scary?

Rana el Kaliouby (09:40.080)

Oh, my God, yes.

Lex Fridman (09:41.360)

It was totally scary.

Lex Fridman (09:42.720)

What's the biggest culture shock from there to Cambridge, to London?

Lex Fridman (09:50.320)

Well, that was also during right around September 11th.

Lex Fridman (09:56.480)

So everyone thought that there was going to be a third world war.

Rana el Kaliouby (10:01.040)

It was really like, and I, at the time I used to wear the hijab, so I was very visibly Muslim.

Lex Fridman (10:07.680)

And so my parents just were, they were afraid for my safety.

Lex Fridman (10:11.840)

But anyways, when I got to Cambridge, because I was so scared, I decided to take off my

Rana el Kaliouby (10:15.600)

headscarf and wear a hat instead.

Lex Fridman (10:17.440)

So I just went to class wearing these like British hats, which was, in my opinion, actually

Lex Fridman (10:22.320)

worse than just showing up in a headscarf because it was just so awkward, right?

Lex Fridman (10:25.920)

Like sitting in class with like all these.

Rana el Kaliouby (10:27.680)

Trying to fit in.

Lex Fridman (10:29.040)

Yeah.

Rana el Kaliouby (10:29.360)

Like a spy.

Lex Fridman (10:30.320)

Yeah, yeah, yeah.

Lex Fridman (10:31.280)

So after a few weeks of doing that, I was like, to heck with that.

Lex Fridman (10:34.640)

I'm just going to go back to wearing my headscarf.

Rana el Kaliouby (10:37.120)

Yeah, you wore the hijab, so starting in 2000 and for 12 years after.

Lex Fridman (10:43.840)

So it's always, whenever you're in public, you have to wear the head covering.

Lex Fridman (10:47.280)

Can you speak to that, to the hijab, maybe your mixed feelings about it?

Lex Fridman (10:52.480)

Like what does it represent in its best case?

Lex Fridman (10:55.120)

What does it represent in the worst case?

Rana el Kaliouby (10:56.960)

Yeah, you know, I think there's a lot of, I guess I'll first start by saying I wore

Rana el Kaliouby (11:03.040)

it voluntarily.

Lex Fridman (11:04.000)

I was not forced to wear it.

Lex Fridman (11:05.360)

And in fact, I was one of the very first women in my family to decide to put on the hijab.

Lex Fridman (11:09.920)

And my family thought it was really odd, right?

Lex Fridman (11:13.040)

Like there was, they were like, why do you want to put this on?

Lex Fridman (11:15.840)

And at its best, it's a sign of modesty, humility.

Rana el Kaliouby (11:20.480)

Yeah.

Lex Fridman (11:22.160)

It's like me wearing a suit, people are like, why are you wearing a suit?

Rana el Kaliouby (11:25.280)

It's a step back into some kind of tradition, a respect for tradition of sorts.

Lex Fridman (11:30.880)

So you said, because it's by choice, you're kind of free to make that choice to celebrate

Rana el Kaliouby (11:36.000)

a tradition of modesty.

Lex Fridman (11:37.760)

Exactly. And I actually like made it my own.

Rana el Kaliouby (11:40.960)

I remember I would really match the color of my headscarf with what I was wearing.

Lex Fridman (11:45.680)

Like it was a form of self expression and at its best, I loved wearing it.

Rana el Kaliouby (11:52.080)

You know, I have a lot of questions around how we practice religion and religion and,

Rana el Kaliouby (11:56.480)

you know, and I think also it was a time where I was spending a lot of time going back and

Rana el Kaliouby (12:02.160)

forth between the US and Egypt.

Lex Fridman (12:04.480)

And I started meeting a lot of people in the US who are just amazing people, very purpose

Rana el Kaliouby (12:09.920)

driven, people who have very strong core values, but they're not Muslim.

Lex Fridman (12:14.720)

That's okay, right?

Lex Fridman (12:15.760)

And so that was when I just had a lot of questions.

Lex Fridman (12:19.920)

And politically, also the situation in Egypt was when the Muslim Brotherhood ran the country

Lex Fridman (12:25.120)

and I didn't agree with their ideology.

Lex Fridman (12:29.520)

It was at a time when I was going through a divorce.

Rana el Kaliouby (12:31.360)

Like it was like, it was like just the perfect storm of like political, personal conditions

Lex Fridman (12:37.040)

where I was like, this doesn't feel like me anymore.

Lex Fridman (12:40.160)

And it took a lot of courage to take it off because culturally it's not, it's okay if

Lex Fridman (12:44.960)

you don't wear it, but it's really not okay to wear it and then take it off.

Lex Fridman (12:50.000)

But you're still, so you have to do that while still maintaining a deep core and pride in

Lex Fridman (12:56.480)

the origins, in your origin story.

Rana el Kaliouby (13:02.400)

Totally.

Lex Fridman (13:02.960)

So still being Egyptian, still being a Muslim.

Rana el Kaliouby (13:06.640)

Right.

Lex Fridman (13:07.200)

And being, I think generally like faith driven, but yeah.

Lex Fridman (13:14.240)

But what that means changes year by year for you.

Lex Fridman (13:17.440)

It's like a personal journey.

Rana el Kaliouby (13:18.800)

Yeah, exactly.

Lex Fridman (13:20.080)

What would you say is the role of faith in that part of the world?

Rana el Kaliouby (13:23.120)

Like, how do you see, you mentioned it a bit in the book too.

Lex Fridman (13:26.480)

Yeah.

Rana el Kaliouby (13:27.600)

I mean, I think, I think there is something really powerful about just believing that

Rana el Kaliouby (13:34.480)

there's a bigger force, you know, there's a kind of surrendering, I guess, that comes

Rana el Kaliouby (13:39.200)

with religion and you surrender and you have this deep conviction that it's going to be

Lex Fridman (13:43.360)

okay, right?

Rana el Kaliouby (13:44.480)

Like the universe is out to like do amazing things for you and it's going to be okay.

Lex Fridman (13:48.880)

And there's strength to that.

Rana el Kaliouby (13:50.080)

Like even when you're going through adversity, you just know that it's going to work out.

Lex Fridman (13:57.040)

Yeah, it gives you like an inner peace, a calmness.

Rana el Kaliouby (13:59.440)

Exactly, exactly.

Lex Fridman (14:00.640)

Yeah, that's, it's faith in all the meanings of that word.

Rana el Kaliouby (14:04.880)

Right.

Lex Fridman (14:05.440)

Faith that everything is going to be okay.

Lex Fridman (14:07.200)

And it is because time passes and time cures all things.

Lex Fridman (14:12.320)

It's like a calmness with the chaos of the world.

Rana el Kaliouby (14:15.200)

Yeah.

Lex Fridman (14:15.680)

And also there's like a silver, I'm a true believer of this, that something at the specific

Rana el Kaliouby (14:22.320)

moment in time can look like it's catastrophic and it's not what you wanted in life.

Lex Fridman (14:28.800)

But then time passes and then you look back and there's the silver lining, right?

Rana el Kaliouby (14:32.880)

It maybe closed the door, but it opened a new door for you.

Lex Fridman (14:37.120)

And so I'm a true believer in that, that, you know, there's a silver lining in almost

Rana el Kaliouby (14:42.880)

anything in life, you just have to have this like, yeah, faith or conviction that it's

Lex Fridman (14:47.120)

going to work out.

Rana el Kaliouby (14:47.760)

Yeah, it's such a beautiful way to see a shady feeling.

Lex Fridman (14:50.720)

So if you feel shady about a current situation, I mean, it almost is always true.

Rana el Kaliouby (14:57.840)

Unless it's the cliche thing of if it doesn't kill you, whatever doesn't kill you makes

Lex Fridman (15:04.800)

you stronger.

Rana el Kaliouby (15:05.360)

It's, it does seem that over time when you take a perspective on things that the hardest

Lex Fridman (15:13.200)

moments and periods of your life are the most meaningful.

Rana el Kaliouby (15:18.400)

Yeah, yeah.

Lex Fridman (15:19.280)

So over time you get to have that perspective.

Rana el Kaliouby (15:21.520)

Right.

Lex Fridman (15:23.760)

What about, because you mentioned Kuwait, what about, let me ask you about war.

Rana el Kaliouby (15:30.000)

What's the role of war and peace, maybe even the big love and hate in that part of

Rana el Kaliouby (15:35.680)

the world, because it does seem to be a part of the world where there's turmoil.

Rana el Kaliouby (15:40.720)

There was turmoil, there's still turmoil.

Lex Fridman (15:44.560)

It is so unfortunate, honestly.

Rana el Kaliouby (15:46.480)

It's, it's such a waste of human resources and, and, and yeah, and human mindshare.

Lex Fridman (15:53.760)

I mean, and at the end of the day, we all kind of want the same things.

Rana el Kaliouby (15:57.280)

We want, you know, we want a human connection, we want joy, we want to feel fulfilled, we

Lex Fridman (16:02.560)

want to feel, you know, a life of purpose.

Lex Fridman (16:05.760)

And I just, I just find it baffling, honestly, that we are still having to grapple with that.

Lex Fridman (16:14.160)

I have a story to share about this.

Rana el Kaliouby (16:15.840)

You know, I grew up, I'm Egyptian, American now, but, but, you know, originally from Egypt.

Lex Fridman (16:21.840)

And when I first got to Cambridge, it turned out my officemate, like my PhD kind of, you

Rana el Kaliouby (16:28.080)

know, she ended up, you know, we ended up becoming friends, but she was from Israel.

Lex Fridman (16:32.960)

And we didn't know, yeah, we didn't know how it was going to be like.

Lex Fridman (16:37.760)

Did you guys sit there just staring at each other for a bit?

Lex Fridman (16:41.040)

Actually, she, because I arrived before she did.

Lex Fridman (16:44.560)

And it turns out she emailed our PhD advisor and asked him if she thought it was going

Lex Fridman (16:50.320)

to be okay.

Rana el Kaliouby (16:52.320)

Yeah.

Lex Fridman (16:52.800)

And this is around 9 11 too.

Rana el Kaliouby (16:55.040)

Yeah.

Rana el Kaliouby (16:55.680)

And, and Peter, Peter Robinson, our PhD advisor was like, yeah, like, this is an academic

Rana el Kaliouby (17:01.280)

institution, just show up.

Lex Fridman (17:02.720)

And we became super good friends.

Rana el Kaliouby (17:04.480)

We were both new moms.

Lex Fridman (17:07.200)

Like we both had our kids during our PhD.

Rana el Kaliouby (17:09.200)

We were both doing artificial emotional intelligence.

Lex Fridman (17:11.360)

She was looking at speech.

Rana el Kaliouby (17:12.320)

I was looking at the face.

Lex Fridman (17:13.680)

We just had so the culture was so similar.

Rana el Kaliouby (17:17.040)

Our jokes were similar.

Rana el Kaliouby (17:18.320)

It was just, I was like, why on earth are our countries, why is there all this like

Lex Fridman (17:24.080)

war and tension?

Lex Fridman (17:25.200)

And I think it falls back to the narrative, right?

Rana el Kaliouby (17:27.360)

If you change the narrative, like whoever creates this narrative of war.

Lex Fridman (17:31.840)

I don't know.

Rana el Kaliouby (17:32.400)

We should have women run the world.

Lex Fridman (17:34.640)

Yeah, that's one solution.

Rana el Kaliouby (17:37.520)

The good women, because there's also evil women in the world.

Lex Fridman (17:40.640)

True, okay.

Lex Fridman (17:43.920)

But yes, yes, there could be less war if women ran the world.

Lex Fridman (17:47.280)

The other aspect is, it doesn't matter the gender, the people in power.

Rana el Kaliouby (17:54.560)

I get to see this with Ukraine and Russia and different parts of the world around that

Lex Fridman (17:59.280)

conflict now.

Lex Fridman (18:00.800)

And that's happening in Yemen as well and everywhere else.

Lex Fridman (18:05.200)

There's these narratives told by the leaders to the populace.

Lex Fridman (18:09.840)

And those narratives take hold and everybody believes that.

Lex Fridman (18:12.400)

And they have a distorted view of the humanity on the other side.

Rana el Kaliouby (18:17.920)

In fact, especially during war, you don't even see the people on the other side as human

Lex Fridman (18:25.120)

or as equal intelligence or worth or value as you.

Rana el Kaliouby (18:30.960)

You tell all kinds of narratives about them being Nazis or dumb or whatever narrative

Lex Fridman (18:40.000)

you want to weave around that or evil.

Lex Fridman (18:44.720)

But I think when you actually meet them face to face, you realize they're like the same.

Lex Fridman (18:49.120)

Exactly, right?

Rana el Kaliouby (18:50.400)

It's actually a big shock for people to realize that they've been essentially lied to within

Lex Fridman (18:58.960)

their country.

Lex Fridman (19:00.000)

And I kind of have faith that social media, as ridiculous as it is to say, or any kind

Rana el Kaliouby (19:05.840)

of technology, is able to bypass the walls that governments put up and connect people

Rana el Kaliouby (19:13.520)

directly.

Lex Fridman (19:14.000)

And then you get to realize, oh, people fall in love across different nations and religions

Lex Fridman (19:20.880)

and so on.

Lex Fridman (19:21.440)

And that, I think, ultimately can cure a lot of our ills, especially in person.

Rana el Kaliouby (19:26.640)

I also think that if leaders met in person, they'd have a conversation that could cure

Lex Fridman (19:32.400)

a lot of the ills of the world, especially in private.

Rana el Kaliouby (19:37.680)

Let me ask you about the women running the world.

Lex Fridman (19:42.320)

So gender does, in part, perhaps shape the landscape of just our human experience.

Lex Fridman (19:51.040)

So in what ways was it limiting and in what ways was it empowering for you to be a woman

Lex Fridman (19:57.760)

in the Middle East?

Rana el Kaliouby (19:58.560)

I think, just kind of going back to my comment on women running the world, I think it comes

Lex Fridman (1:00:00.360)

EQ being emotional intelligence.

Rana el Kaliouby (1:00:03.880)

In terms of in what makes us human.

Lex Fridman (1:00:08.120)

I think emotional intelligence is what makes us human.

Rana el Kaliouby (1:00:11.240)

It's how we connect with one another.

Lex Fridman (1:00:14.760)

It's how we build trust.

Lex Fridman (1:00:16.440)

It's how we make decisions, right?

Rana el Kaliouby (1:00:19.560)

Like your emotions drive kind of what you had for breakfast, but also where you decide

Rana el Kaliouby (1:00:25.720)

to live and what you want to do for the rest of your life.

Lex Fridman (1:00:28.600)

So I think emotions are underrated.

Lex Fridman (1:00:33.160)

So emotional intelligence isn't just about the effective expression of your own emotions.

Rana el Kaliouby (1:00:39.080)

It's about a sensitivity and empathy to other people's emotions and that sort of being

Rana el Kaliouby (1:00:44.440)

able to effectively engage in the dance of emotions with other people.

Lex Fridman (1:00:48.840)

Yeah, I like that explanation.

Rana el Kaliouby (1:00:51.240)

I like that kind of.

Lex Fridman (1:00:53.720)

Yeah, thinking about it as a dance because it is really about that.

Rana el Kaliouby (1:00:56.680)

It's about sensing what state the other person's in and using that information to decide on

Lex Fridman (1:01:01.800)

how you're going to react.

Lex Fridman (1:01:05.400)

And I think it can be very powerful.

Rana el Kaliouby (1:01:06.760)

Like people who are the best, most persuasive leaders in the world tap into, you know, they

Rana el Kaliouby (1:01:15.160)

have, if you have higher EQ, you're more likely to be able to motivate people to change

Lex Fridman (1:01:20.360)

their behaviors.

Lex Fridman (1:01:21.880)

So it can be very powerful.

Rana el Kaliouby (1:01:24.920)

On a more kind of technical, maybe philosophical level, you've written that emotion is universal.

Rana el Kaliouby (1:01:31.800)

It seems that, sort of like Chomsky says, language is universal.

Lex Fridman (1:01:36.920)

There's a bunch of other stuff like cognition, consciousness.

Rana el Kaliouby (1:01:39.960)

It seems a lot of us have these aspects.

Lex Fridman (1:01:43.560)

So the human mind generates all this.

Lex Fridman (1:01:46.520)

And so what do you think is the, they all seem to be like echoes of the same thing.

Lex Fridman (1:01:52.840)

What do you think emotion is exactly?

Lex Fridman (1:01:56.280)

Like how deep does it run?

Lex Fridman (1:01:57.720)

Is it a surface level thing that we display to each other?

Lex Fridman (1:02:01.800)

Is it just another form of language or something deep within?

Lex Fridman (1:02:05.640)

I think it's really deep.

Rana el Kaliouby (1:02:07.480)

It's how, you know, we started with memory.

Lex Fridman (1:02:09.880)

I think emotions play a really important role.

Lex Fridman (1:02:14.040)

Yeah, emotions play a very important role in how we encode memories, right?

Lex Fridman (1:02:18.040)

Our memories are often encoded, almost indexed by emotions.

Rana el Kaliouby (1:02:21.640)

Yeah.

Rana el Kaliouby (1:02:22.120)

Yeah, it's at the core of how, you know, our decision making engine is also heavily

Rana el Kaliouby (1:02:28.520)

influenced by our emotions.

Lex Fridman (1:02:30.040)

So emotions is part of cognition.

Rana el Kaliouby (1:02:31.960)

Totally.

Lex Fridman (1:02:32.680)

It's intermixed into the whole thing.

Rana el Kaliouby (1:02:34.680)

Yes, absolutely.

Lex Fridman (1:02:35.960)

And in fact, when you take it away, people are unable to make decisions.

Rana el Kaliouby (1:02:39.960)

They're really paralyzed.

Rana el Kaliouby (1:02:41.000)

Like they can't go about their daily or their, you know, personal or professional lives.

Rana el Kaliouby (1:02:45.240)

So.

Rana el Kaliouby (1:02:45.740)

It does seem like there's probably some interesting interweaving of emotion and consciousness.

Rana el Kaliouby (1:02:53.740)

I wonder if it's possible to have, like if they're next door neighbors somehow, or if

Lex Fridman (1:02:58.940)

they're actually flat mates.

Rana el Kaliouby (1:03:01.740)

I don't, it feels like the hard problem of consciousness where it's some, it feels like

Lex Fridman (1:03:08.940)

something to experience the thing.

Rana el Kaliouby (1:03:10.780)

Like red feels like red, and it's, you know, when you eat a mango, it's sweet.

Rana el Kaliouby (1:03:16.780)

The taste, the sweetness, that it feels like something to experience that sweetness, that

Rana el Kaliouby (1:03:24.620)

whatever generates emotions.

Lex Fridman (1:03:28.060)

But then like, see, I feel like emotion is part of communication.

Rana el Kaliouby (1:03:31.740)

It's very much about communication.

Lex Fridman (1:03:34.300)

And then, you know, it's like, you know, it's like, you know, it's like, you know, it's

Lex Fridman (1:03:39.420)

and then that means it's also deeply connected to language.

Lex Fridman (1:03:45.980)

But then probably human intelligence is deeply connected to the collective intelligence between

Rana el Kaliouby (1:03:52.300)

humans.

Lex Fridman (1:03:52.700)

It's not just the standalone thing.

Lex Fridman (1:03:54.540)

So the whole thing is really connected.

Lex Fridman (1:03:56.380)

So emotion is connected to language, language is connected to intelligence, and then intelligence

Rana el Kaliouby (1:04:02.140)

is connected to consciousness, and consciousness is connected to emotion.

Lex Fridman (1:04:05.740)

The whole thing is that it's a beautiful mess.

Lex Fridman (1:04:09.180)

So can I comment on the emotions being a communication mechanism?

Lex Fridman (1:04:15.660)

Because I think there are two facets of our emotional experiences.

Lex Fridman (1:04:23.020)

One is communication, right?

Rana el Kaliouby (1:04:24.380)

Like we use emotions, for example, facial expressions or other nonverbal cues to connect

Lex Fridman (1:04:29.820)

with other human beings and with other beings in the world, right?

Lex Fridman (1:04:34.700)

But even if it's not a communication context, we still experience emotions and we still

Rana el Kaliouby (1:04:40.460)

process emotions and we still leverage emotions to make decisions and to learn and, you know,

Lex Fridman (1:04:46.860)

to experience life.

Lex Fridman (1:04:47.740)

So it isn't always just about communication.

Lex Fridman (1:04:51.180)

And we learned that very early on in our and kind of our work at Affectiva.

Rana el Kaliouby (1:04:56.460)

One of the very first applications we brought to market was understanding how people respond

Lex Fridman (1:05:00.700)

to content, right?

Lex Fridman (1:05:01.660)

So if they're watching this video of ours, like, are they interested?

Lex Fridman (1:05:04.860)

Are they inspired?

Lex Fridman (1:05:05.900)

Are they bored to death?

Lex Fridman (1:05:07.180)

And so we watched their facial expressions and we had, we weren't sure if people would

Rana el Kaliouby (1:05:12.060)

express any emotions if they were sitting alone.

Rana el Kaliouby (1:05:15.580)

Like if you're in your bed at night, watching a Netflix TV series, would we still see any

Lex Fridman (1:05:20.220)

emotions on your face?

Lex Fridman (1:05:21.420)

And we were surprised that, yes, people still emote, even if they're alone, even if you're

Rana el Kaliouby (1:05:25.660)

in your car driving around, you're singing along the song and you're joyful, you're

Lex Fridman (1:05:30.300)

smiling, you're joyful, we'll see these expressions.

Lex Fridman (1:05:33.980)

So it's not just about communicating with another person.

Lex Fridman (1:05:37.820)

It sometimes really isn't just about experiencing the world.

Lex Fridman (1:05:41.260)

And first of all, I wonder if some of that is because we develop our intelligence and

Lex Fridman (1:05:47.900)

our emotional intelligence by communicating with other humans.

Lex Fridman (1:05:52.140)

And so when other humans disappear from the picture, we're still kind of a virtual human.

Lex Fridman (1:05:56.940)

The code still runs.

Rana el Kaliouby (1:05:57.900)

Yeah, the code still runs, but you also kind of, you're still, there's like virtual humans.

Rana el Kaliouby (1:06:02.860)

You don't have to think of it that way, but there's a kind of, when you like chuckle,

Rana el Kaliouby (1:06:07.260)

like, yeah, like you're kind of chuckling to a virtual human.

Rana el Kaliouby (1:06:13.100)

I mean, it's possible that the code has to have another human there because if you just

Rana el Kaliouby (1:06:23.340)

grew up alone, I wonder if emotion will still be there in this visual form.

Lex Fridman (1:06:28.540)

So yeah, I wonder, but anyway, what can you tell from the human face about what's going

Lex Fridman (1:06:37.100)

on inside?

Lex Fridman (1:06:38.300)

So that's the problem that Effectiva first tackled, which is using computer vision, using

Rana el Kaliouby (1:06:45.100)

machine learning to try to detect stuff about the human face, as many things as possible

Lex Fridman (1:06:50.220)

and convert them into a prediction of categories of emotion, anger, happiness, all that kind

Rana el Kaliouby (1:06:57.900)

of stuff.

Lex Fridman (1:06:58.700)

How hard is that problem?

Rana el Kaliouby (1:07:00.300)

It's extremely hard.

Rana el Kaliouby (1:07:01.340)

It's very, very hard because there is no one to one mapping between a facial expression

Lex Fridman (1:07:07.180)

and your internal state.

Lex Fridman (1:07:08.380)

There just isn't.

Rana el Kaliouby (1:07:09.340)

There's this oversimplification of the problem where it's something like, if you are smiling,

Lex Fridman (1:07:14.220)

then you're happy.

Rana el Kaliouby (1:07:15.180)

If you do a brow furrow, then you're angry.

Lex Fridman (1:07:17.340)

If you do an eyebrow raise, then you're surprised.

Lex Fridman (1:07:19.500)

And just think about it for a moment.

Lex Fridman (1:07:22.140)

You could be smiling for a whole host of reasons.

Lex Fridman (1:07:24.940)

You could also be happy and not be smiling, right?

Rana el Kaliouby (1:07:28.700)

You could furrow your eyebrows because you're angry or you're confused about something or

Rana el Kaliouby (1:07:34.220)

you're constipated.

Lex Fridman (1:07:37.100)

So I think this oversimplistic approach to inferring emotion from a facial expression

Rana el Kaliouby (1:07:41.820)

is really dangerous.

Lex Fridman (1:07:42.780)

The solution is to incorporate as many contextual signals as you can, right?

Lex Fridman (1:07:48.700)

So if, for example, I'm driving a car and you can see me like nodding my head and my

Rana el Kaliouby (1:07:55.100)

eyes are closed and the blinking rate is changing, I'm probably falling asleep at the wheel,

Lex Fridman (1:08:00.460)

right?

Lex Fridman (1:08:00.940)

Because you know the context.

Rana el Kaliouby (1:08:03.180)

You understand what the person's doing or add additional channels like voice or gestures

Rana el Kaliouby (1:08:10.300)

or even physiological sensors, but I think it's very dangerous to just take this oversimplistic

Rana el Kaliouby (1:08:17.020)

approach of, yeah, smile equals happy and...

Rana el Kaliouby (1:08:20.060)

If you're able to, in a high resolution way, specify the context, there's certain things

Rana el Kaliouby (1:08:25.020)

that are going to be somewhat reliable signals of something like drowsiness or happiness

Lex Fridman (1:08:31.500)

or stuff like that.

Rana el Kaliouby (1:08:32.620)

I mean, when people are watching Netflix content, that problem, that's a really compelling idea

Rana el Kaliouby (1:08:40.300)

that you can kind of, at least in aggregate, highlight like which part was boring, which

Rana el Kaliouby (1:08:46.380)

part was exciting.

Lex Fridman (1:08:47.660)

How hard was that problem?

Rana el Kaliouby (1:08:50.300)

That was on the scale of difficulty.

Rana el Kaliouby (1:08:53.740)

I think that's one of the easier problems to solve because it's a relatively constrained

Rana el Kaliouby (1:09:00.140)

environment.

Lex Fridman (1:09:00.620)

You have somebody sitting in front of...

Rana el Kaliouby (1:09:02.780)

Initially, we started with like a device in front of you, like a laptop, and then we graduated

Rana el Kaliouby (1:09:07.820)

to doing this on a mobile phone, which is a lot harder just because of, you know, from

Rana el Kaliouby (1:09:12.140)

a computer vision perspective, the profile view of the face can be a lot more challenging.

Rana el Kaliouby (1:09:17.900)

We had to figure out lighting conditions because usually people are watching content literally

Rana el Kaliouby (1:09:23.180)

in their bedrooms at night.

Lex Fridman (1:09:24.620)

Lights are dimmed.

Rana el Kaliouby (1:09:25.420)

Yeah, I mean, if you're standing, it's probably going to be the looking up.

Lex Fridman (1:09:30.220)

The nostril view.

Rana el Kaliouby (1:09:31.500)

Yeah, and nobody looks good at it.

Lex Fridman (1:09:34.060)

I've seen data sets from that perspective.

Rana el Kaliouby (1:09:36.140)

It's like, this is not a good look for anyone.

Lex Fridman (1:09:40.620)

Or if you're laying in bed at night, what is it, side view or something?

Rana el Kaliouby (1:09:44.460)

Right.

Lex Fridman (1:09:44.940)

And half your face is like on a pillow.

Rana el Kaliouby (1:09:47.580)

Actually, I would love to know, have data about like how people watch stuff in bed at

Rana el Kaliouby (1:09:56.620)

night, like, do they prop there, is it a pillow, the, like, I'm sure there's a lot of interesting

Rana el Kaliouby (1:10:03.340)

dynamics there.

Lex Fridman (1:10:04.060)

Right.

Lex Fridman (1:10:05.260)

From a health and well being perspective, right?

Lex Fridman (1:10:07.100)

Sure.

Rana el Kaliouby (1:10:07.580)

Like, oh, you're hurting your neck.

Rana el Kaliouby (1:10:08.540)

I was thinking machine learning perspective, but yes, but also, yeah, yeah, once you have

Rana el Kaliouby (1:10:13.740)

that data, you can start making all kinds of inference about health and stuff like that.

Lex Fridman (1:10:18.060)

Interesting.

Rana el Kaliouby (1:10:19.260)

Yeah, there's an interesting thing when I was at Google that we were, it's called active

Rana el Kaliouby (1:10:26.700)

authentication, where you want to be able to unlock your phone without using a password.

Lex Fridman (1:10:32.620)

So it would face, but also other stuff, like the way you take a phone out of the pocket.

Lex Fridman (1:10:38.940)

Amazing.

Lex Fridman (1:10:39.500)

So that kind of data to use the multimodal with machine learning to be able to identify

Rana el Kaliouby (1:10:45.260)

that it's you or likely to be you, likely not to be you, that allows you to not always

Rana el Kaliouby (1:10:50.220)

have to enter the password.

Lex Fridman (1:10:51.260)

That was the idea.

Lex Fridman (1:10:52.700)

But the funny thing about that is, I just want to tell a small anecdote is because it

Rana el Kaliouby (1:10:58.540)

was all male engineers, except so my boss is, our boss was still one of my favorite humans,

Rana el Kaliouby (1:11:09.660)

was a woman, Regina Dugan.

Lex Fridman (1:11:12.300)

Oh, my God, I love her.

Rana el Kaliouby (1:11:14.140)

She's awesome.

Lex Fridman (1:11:14.940)

She's the best.

Rana el Kaliouby (1:11:15.500)

She's the best.

Rana el Kaliouby (1:11:16.300)

So, but anyway, and there's one female brilliant female engineer on the team, and she was the

Rana el Kaliouby (1:11:25.900)

one that actually highlighted the fact that women often don't have pockets.

Rana el Kaliouby (1:11:30.380)

It was like, whoa, that was not even a category in the code of like, wait a minute, you can

Rana el Kaliouby (1:11:37.340)

take the phone out of some other place than your pocket.

Lex Fridman (1:11:41.260)

So anyway, that's a funny thing when you're considering people laying in bed, watching

Rana el Kaliouby (1:11:45.580)

a phone, you have to consider if you have to, you know, diversity in all its forms,

Lex Fridman (1:11:51.820)

depending on the problem, depending on the context.

Rana el Kaliouby (1:11:53.900)

Actually, this is like a very important, I think this is, you know, you probably get

Lex Fridman (1:11:58.140)

this all the time.

Rana el Kaliouby (1:11:58.940)

Like people are worried that AI is going to take over humanity and like, get rid of all

Lex Fridman (1:12:03.100)

the humans in the world.

Rana el Kaliouby (1:12:04.300)

I'm like, actually, that's not my biggest concern.

Lex Fridman (1:12:06.540)

My biggest concern is that we are building bias into these systems.

Lex Fridman (1:12:10.380)

And then they're like deployed at large and at scale.

Lex Fridman (1:12:14.380)

And before you know it, you're kind of accentuating the bias that exists in society.

Rana el Kaliouby (1:12:19.660)

Yeah, I'm not, you know, I know people, it's very important to worry about that, but the

Rana el Kaliouby (1:12:26.940)

worry is an emergent phenomena to me, which is a very good one, because I think these

Rana el Kaliouby (1:12:32.620)

systems are actually, by encoding the data that exists, they're revealing the bias in

Lex Fridman (1:12:39.660)

society.

Rana el Kaliouby (1:12:40.380)

They're both for teaching us what the bias is.

Lex Fridman (1:12:43.340)

Therefore, we can now improve that bias within the system.

Lex Fridman (1:12:46.380)

So they're almost like putting a mirror to ourselves.

Lex Fridman (1:12:49.980)

Totally.

Lex Fridman (1:12:50.780)

So I'm not.

Lex Fridman (1:12:51.500)

You have to be open to looking at the mirror, though.

Rana el Kaliouby (1:12:53.500)

You have to be open to scrutinizing the data.

Lex Fridman (1:12:56.540)

And if you just take it as ground.

Rana el Kaliouby (1:12:59.500)

Or you don't even have to look at the, I mean, yes, the data is how you fix it.

Lex Fridman (1:13:02.860)

But then you just look at the behavior of the system.

Lex Fridman (1:13:05.100)

And you realize, holy crap, this thing is kind of racist.

Lex Fridman (1:13:08.620)

Like, why is that?

Lex Fridman (1:13:09.820)

And then you look at the data, it's like, oh, okay.

Lex Fridman (1:13:11.740)

And then you start to realize that I think that some much more effective ways to do that

Rana el Kaliouby (1:13:15.820)

are effective way to be introspective as a society than through sort of political discourse.

Rana el Kaliouby (1:13:23.020)

Like AI kind of, because people are for some reason more productive and rigorous in criticizing

Rana el Kaliouby (1:13:34.060)

AI than they're criticizing each other.

Lex Fridman (1:13:35.740)

So I think this is just a nice method for studying society and see which way progress

Rana el Kaliouby (1:13:41.340)

lies.

Lex Fridman (1:13:42.380)

Anyway, what we're talking about.

Rana el Kaliouby (1:13:44.380)

You're watching the problem of watching Netflix in bed or elsewhere and seeing which parts

Lex Fridman (1:13:50.220)

are exciting, which parts are boring.

Rana el Kaliouby (1:13:51.660)

You're saying that's relatively constrained because you have a captive audience and you

Lex Fridman (1:13:56.620)

kind of know the context.

Lex Fridman (1:13:57.740)

And one thing you said that was really key is the aggregate.

Lex Fridman (1:14:01.100)

You're doing this in aggregate, right?

Rana el Kaliouby (1:14:02.380)

Like we're looking at aggregated response of people.

Lex Fridman (1:14:04.700)

And so when you see a peak, say a smile peak, they're probably smiling or laughing at something

Rana el Kaliouby (1:14:11.100)

that's in the content.

Lex Fridman (1:14:12.140)

So that was one of the first problems we were able to solve.

Lex Fridman (1:14:15.740)

And when we see the smile peak, it doesn't mean that these people are internally happy.

Lex Fridman (1:14:20.380)

They're just laughing at content.

Lex Fridman (1:14:22.060)

So it's important to call it for what it is.

Lex Fridman (1:14:25.420)

But it's still really, really useful data.

Rana el Kaliouby (1:14:28.140)

I wonder how that compares to, so what like YouTube and other places will use is obviously

Lex Fridman (1:14:34.380)

they don't have, for the most case, they don't have that kind of data.

Rana el Kaliouby (1:14:39.900)

They have the data of when people tune out, like switch to drop off.

Lex Fridman (1:14:45.660)

And I think that's an aggregate for YouTube, at least a pretty powerful signal.

Rana el Kaliouby (1:14:50.300)

I worry about what that leads to because looking at like YouTubers that kind of really care

Rana el Kaliouby (1:14:59.580)

about views and try to maximize the number of views, I think when they say that the video

Rana el Kaliouby (1:15:07.740)

should be constantly interesting, which seems like a good goal, I feel like that leads to

Lex Fridman (1:15:15.100)

this manic pace of a video.

Rana el Kaliouby (1:15:19.020)

Like the idea that I would speak at the current speed that I'm speaking, I don't know.

Lex Fridman (1:15:25.820)

And that every moment has to be engaging, right?

Rana el Kaliouby (1:15:28.220)

Engaging.

Lex Fridman (1:15:28.780)

Yeah.

Rana el Kaliouby (1:15:29.260)

I think there's value to silence.

Lex Fridman (1:15:31.500)

There's value to the boring bits.

Rana el Kaliouby (1:15:33.660)

I mean, some of the greatest movies ever, some of the greatest movies ever.

Rana el Kaliouby (1:15:37.500)

Some of the greatest stories ever told me they have that boring bits, seemingly boring bits.

Rana el Kaliouby (1:15:42.540)

I don't know.

Lex Fridman (1:15:43.500)

I wonder about that.

Rana el Kaliouby (1:15:45.020)

Of course, it's not that the human face can capture that either.

Lex Fridman (1:15:49.180)

It's just giving an extra signal.

Rana el Kaliouby (1:15:51.500)

You have to really, I don't know, you have to really collect deeper long term data about

Lex Fridman (1:16:01.180)

what was meaningful to people.

Rana el Kaliouby (1:16:03.260)

When they think 30 days from now, what they still remember, what moved them, what changed

Lex Fridman (1:16:08.940)

them, what helped them grow, that kind of stuff.

Rana el Kaliouby (1:16:11.660)

You know, it would be a really interesting, I don't know if there are any researchers

Lex Fridman (1:16:14.940)

out there who are doing this type of work.

Rana el Kaliouby (1:16:17.340)

Wouldn't it be so cool to tie your emotional expressions while you're, say, listening

Rana el Kaliouby (1:16:23.500)

to a podcast interview and then 30 days later interview people and say, hey, what do you

Lex Fridman (1:16:30.620)

remember?

Lex Fridman (1:16:31.340)

You've watched this 30 days ago.

Lex Fridman (1:16:33.420)

Like, what stuck with you?

Lex Fridman (1:16:34.620)

And then see if there's any, there ought to be maybe, there ought to be some correlation

Rana el Kaliouby (1:16:38.140)

between these emotional experiences and, yeah, what you, what stays with you.

Lex Fridman (1:16:46.140)

So the one guy listening now on the beach in Brazil, please record a video of yourself

Rana el Kaliouby (1:16:51.660)

listening to this and send it to me and then I'll interview you 30 days from now.

Lex Fridman (1:16:55.900)

Yeah, that'd be great.

Rana el Kaliouby (1:16:58.700)

It'll be statistically significant to you.

Lex Fridman (1:17:00.620)

Yeah, I know one, but, you know, yeah, yeah, I think that's really fascinating.

Rana el Kaliouby (1:17:06.940)

I think that's, that kind of holds the key to a future where entertainment or content

Rana el Kaliouby (1:17:16.460)

is both entertaining and, I don't know, makes you better, empowering in some way.

Lex Fridman (1:17:25.180)

So figuring out, like, showing people stuff that entertains them, but also they're happy

Rana el Kaliouby (1:17:32.540)

they watched 30 days from now because they've become a better person because of it.

Rana el Kaliouby (1:17:37.420)

Well, you know, okay, not to riff on this topic for too long, but I have two children,

Lex Fridman (1:17:41.900)

right?

Lex Fridman (1:17:42.860)

And I see my role as a parent as like a chief opportunity officer.

Lex Fridman (1:17:46.860)

Like I am responsible for exposing them to all sorts of things in the world.

Rana el Kaliouby (1:17:50.780)

And, but often I have no idea of knowing, like, what stuck, like, what was, you know,

Lex Fridman (1:17:56.300)

is this actually going to be transformative, you know, for them 10 years down the line?

Lex Fridman (1:18:00.220)

And I wish there was a way to quantify these experiences.

Lex Fridman (1:18:03.660)

Like, are they, I can tell in the moment if they're engaging, right?

Rana el Kaliouby (1:18:08.060)

I can tell, but it's really hard to know if they're going to remember them 10 years

Lex Fridman (1:18:12.540)

from now or if it's going to.

Rana el Kaliouby (1:18:15.100)

Yeah, that one is weird because it seems like kids remember the weirdest things.

Rana el Kaliouby (1:18:19.500)

I've seen parents do incredible stuff for their kids and they don't remember any of

Rana el Kaliouby (1:18:23.580)

that.

Lex Fridman (1:18:23.820)

They remember some tiny, small, sweet thing a parent did.

Rana el Kaliouby (1:18:27.260)

Right.

Lex Fridman (1:18:27.740)

Like some...

Rana el Kaliouby (1:18:28.380)

Like they took you to, like, this amazing country vacation, blah, blah, blah, blah.

Lex Fridman (1:18:32.540)

No, whatever.

Lex Fridman (1:18:33.180)

And then there'll be, like, some, like, stuffed toy you got or some, or the new PlayStation

Lex Fridman (1:18:38.060)

or something or some silly little thing.

Lex Fridman (1:18:41.100)

So I think they just, like, they were designed that way.

Lex Fridman (1:18:44.940)

They want to mess with your head.

Lex Fridman (1:18:46.220)

But definitely kids are very impacted by, it seems like, sort of negative events.

Lex Fridman (1:18:53.260)

So minimizing the number of negative events is important, but not too much, right?

Rana el Kaliouby (1:18:58.700)

Right.

Rana el Kaliouby (1:18:59.180)

You can't, you can't just, like, you know, there's still discipline and challenge and

Rana el Kaliouby (1:19:04.300)

all those kinds of things.

Lex Fridman (1:19:05.260)

So...

Rana el Kaliouby (1:19:05.740)

You want some adversity for sure.

Rana el Kaliouby (1:19:07.660)

So, yeah, I mean, I'm definitely, when I have kids, I'm going to drive them out into

Rana el Kaliouby (1:19:11.180)

the woods.

Lex Fridman (1:19:11.980)

Okay.

Lex Fridman (1:19:12.700)

And then they have to survive and make, figure out how to make their way back home, like,

Lex Fridman (1:19:17.900)

20 miles out.

Rana el Kaliouby (1:19:18.940)

Okay.

Lex Fridman (1:19:19.660)

Yeah.

Lex Fridman (1:19:20.380)

And after that, we can go for ice cream.

Lex Fridman (1:19:22.300)

Okay.

Rana el Kaliouby (1:19:23.100)

Anyway, I'm working on this whole parenting thing.

Lex Fridman (1:19:26.300)

I haven't figured it out.

Rana el Kaliouby (1:19:27.100)

Okay.

Lex Fridman (1:19:28.540)

What were we talking about?

Rana el Kaliouby (1:19:29.660)

Yes, Effectiva, the problem of emotion, of emotion detection.

Lex Fridman (1:19:37.580)

So there's some people, maybe we can just speak to that a little more, where there's

Rana el Kaliouby (1:19:41.260)

folks like Lisa Feldman Barrett that challenge this idea that emotion could be fully detected

Rana el Kaliouby (1:19:49.820)

or even well detected from the human face, that there's so much more to emotion.

Lex Fridman (1:19:55.100)

What do you think about ideas like hers, criticism like hers?

Lex Fridman (1:19:59.820)

Yeah, I actually agree with a lot of Lisa's criticisms.

Lex Fridman (1:20:03.820)

So even my PhD worked, like, 20 plus years ago now.

Lex Fridman (1:20:07.980)

Time flies when you're having fun.

Lex Fridman (1:20:12.620)

I know, right?

Lex Fridman (1:20:14.140)

That was back when I did, like, dynamic Bayesian networks.

Lex Fridman (1:20:17.500)

That was before deep learning, huh?

Lex Fridman (1:20:19.900)

That was before deep learning.

Rana el Kaliouby (1:20:21.420)

Yeah.

Lex Fridman (1:20:22.700)

Yeah, I know.

Rana el Kaliouby (1:20:24.060)

Back in my day.

Lex Fridman (1:20:24.860)

Now you can just, like, use.

Rana el Kaliouby (1:20:27.340)

Yeah, it's all the same architecture.

Lex Fridman (1:20:30.300)

You can apply it to anything.

Rana el Kaliouby (1:20:31.340)

Yeah.

Rana el Kaliouby (1:20:31.840)

Right, but yeah, but even then I kind of, I did not subscribe to this, like, theory

Rana el Kaliouby (1:20:39.120)

of basic emotions where it's just the simplistic mapping, one to one mapping between facial

Lex Fridman (1:20:43.280)

expressions and emotions.

Rana el Kaliouby (1:20:44.160)

I actually think also we're not in the business of trying to identify your true emotional

Lex Fridman (1:20:49.760)

internal state.

Rana el Kaliouby (1:20:50.400)

We just want to quantify in an objective way what's showing on your face because that's

Lex Fridman (1:20:55.600)

an important signal.

Rana el Kaliouby (1:20:57.040)

It doesn't mean it's a true reflection of your internal emotional state.

Lex Fridman (1:21:02.480)

So I think a lot of the, you know, I think she's just trying to kind of highlight that

Rana el Kaliouby (1:21:07.680)

this is not a simple problem and overly simplistic solutions are going to hurt the industry.

Lex Fridman (1:21:15.520)

And I subscribe to that.

Lex Fridman (1:21:16.560)

And I think multimodal is the way to go.

Rana el Kaliouby (1:21:18.720)

Like, whether it's additional context information or different modalities and channels of information,

Rana el Kaliouby (1:21:24.000)

I think that's what we, that's where we ought to go.

Lex Fridman (1:21:27.520)

And I think, I mean, that's a big part of what she's advocating for as well.

Rana el Kaliouby (1:21:31.280)

So, but there is signal in the human face.

Lex Fridman (1:21:33.440)

There's definitely signal in the human face.

Rana el Kaliouby (1:21:35.760)

That's a projection of emotion.

Rana el Kaliouby (1:21:37.600)

There's that, at least in part is the inner state is captured in some meaningful way on

Rana el Kaliouby (1:21:46.320)

the human face.

Rana el Kaliouby (1:21:47.040)

I think it can sometimes be a reflection or an expression of your internal state, but

Rana el Kaliouby (1:21:56.240)

sometimes it's a social signal.

Lex Fridman (1:21:57.760)

So you cannot look at the face as purely a signal of emotion.

Rana el Kaliouby (1:22:02.080)

It can be a signal of cognition and it can be a signal of a social expression.

Lex Fridman (1:22:08.000)

And I think to disambiguate that we have to be careful about it and we have to add initial

Rana el Kaliouby (1:22:13.760)

information.

Lex Fridman (1:22:14.320)

Humans are fascinating, aren't they?

Rana el Kaliouby (1:22:16.000)

With the whole face thing, this can mean so many things, from humor to sarcasm to everything,

Lex Fridman (1:22:22.000)

the whole thing.

Rana el Kaliouby (1:22:23.280)

Some things we can help, some things we can't help at all.

Rana el Kaliouby (1:22:26.640)

In all the years of leading Effectiva, an emotion recognition company, like we talked

Lex Fridman (1:22:31.680)

about, what have you learned about emotion, about humans and about AI?

Lex Fridman (1:22:37.360)

Big, sweeping questions.

Rana el Kaliouby (1:22:44.240)

Yeah, that's a big, sweeping question.

Rana el Kaliouby (1:22:46.320)

Well, I think the thing I learned the most is that even though we are in the business

Lex Fridman (1:22:52.240)

of building AI, basically, it always goes back to the humans, right?

Lex Fridman (1:23:00.960)

It's always about the humans.

Lex Fridman (1:23:02.160)

And so, for example, the thing I'm most proud of in building Effectiva and, yeah, the thing

Rana el Kaliouby (1:23:11.120)

I'm most proud of on this journey, I love the technology and I'm so proud of the solutions

Rana el Kaliouby (1:23:16.240)

we've built and we've brought to market.

Lex Fridman (1:23:18.640)

But I'm actually most proud of the people we've built and cultivated at the company

Lex Fridman (1:23:23.760)

and the culture we've created.

Rana el Kaliouby (1:23:25.040)

Some of the people who've joined Effectiva, this was their first job, and while at Effectiva,

Rana el Kaliouby (1:23:31.440)

they became American citizens and they bought their first house and they found their partner

Lex Fridman (1:23:38.000)

and they had their first kid, right?

Rana el Kaliouby (1:23:39.440)

Like key moments in life that we got to be part of, and that's the thing I'm most proud

Lex Fridman (1:23:47.520)

of.

Lex Fridman (1:23:47.840)

So that's a great thing at a company that works at a big company, right?

Lex Fridman (1:23:52.320)

So that's a great thing at a company that works at, I mean, like celebrating humanity

Rana el Kaliouby (1:23:57.920)

in general, broadly speaking.

Lex Fridman (1:23:59.360)

And that's a great thing to have in a company that works on AI, because that's not often

Rana el Kaliouby (1:24:04.640)

the thing that's celebrated in AI companies, so often just raw great engineering, just

Lex Fridman (1:24:11.120)

celebrating the humanity.

Rana el Kaliouby (1:24:12.240)

That's great.

Lex Fridman (1:24:12.800)

And especially from a leadership position.

Lex Fridman (1:24:17.200)

Well, what do you think about the movie Her?

Lex Fridman (1:24:20.800)

Let me ask you that.

Rana el Kaliouby (1:24:21.600)

Before I talk to you about, because it's not, Effectiva is and was not just about emotion,

Lex Fridman (1:24:28.240)

so I'd love to talk to you about SmartEye, but before that, let me just jump into the

Rana el Kaliouby (1:24:33.840)

movie Her.

Lex Fridman (1:24:36.720)

Do you think we'll have a deep, meaningful connection with increasingly deeper, meaningful

Lex Fridman (1:24:42.000)

connections with computers?

Lex Fridman (1:24:43.680)

Is that a compelling thing to you?

Lex Fridman (1:24:45.360)

Something you think about?

Lex Fridman (1:24:45.760)

I think that's already happening.

Rana el Kaliouby (1:24:46.960)

The thing I love the most, I love the movie Her, by the way, but the thing I love the

Rana el Kaliouby (1:24:50.960)

most about this movie is it demonstrates how technology can be a conduit for positive behavior

Rana el Kaliouby (1:24:56.720)

change.

Lex Fridman (1:24:57.120)

So I forgot the guy's name in the movie, whatever.

Rana el Kaliouby (1:25:00.480)

Theodore.

Lex Fridman (1:25:01.120)

Theodore.

Lex Fridman (1:25:02.960)

So Theodore was really depressed, right?

Lex Fridman (1:25:05.280)

And he just didn't want to get out of bed, and he was just done with life, right?

Lex Fridman (1:25:11.200)

And Samantha, right?

Lex Fridman (1:25:12.640)

Samantha, yeah.

Rana el Kaliouby (1:25:14.000)

She just knew him so well.

Rana el Kaliouby (1:25:15.680)

She was emotionally intelligent, and so she could persuade him and motivate him to change

Rana el Kaliouby (1:25:20.960)

his behavior, and she got him out, and they went to the beach together.

Lex Fridman (1:25:24.080)

And I think that represents the promise of emotion AI.

Rana el Kaliouby (1:25:27.200)

If done well, this technology can help us live happier lives, more productive lives,

Lex Fridman (1:25:33.520)

healthier lives, more connected lives.

Lex Fridman (1:25:36.720)

So that's the part that I love about the movie.

Rana el Kaliouby (1:25:39.200)

Obviously, it's Hollywood, so it takes a twist and whatever, but the key notion that technology

Rana el Kaliouby (1:25:46.720)

with emotion AI can persuade you to be a better version of who you are, I think that's awesome.

Lex Fridman (1:25:52.720)

Well, what about the twist?

Lex Fridman (1:25:54.080)

You don't think it's good?

Rana el Kaliouby (1:25:55.520)

You don't think it's good for spoiler alert that Samantha starts feeling a bit of a distance

Lex Fridman (1:26:01.440)

and basically leaves Theodore?

Lex Fridman (1:26:04.640)

You don't think that's a good feature?

Lex Fridman (1:26:07.520)

You think that's a bug or a feature?

Lex Fridman (1:26:10.160)

Well, I think what went wrong is Theodore became really attached to Samantha.

Rana el Kaliouby (1:26:14.240)

Like, I think he kind of fell in love with Theodore.

Lex Fridman (1:26:16.000)

Do you think that's wrong?

Rana el Kaliouby (1:26:17.920)

I mean, I think that's...

Lex Fridman (1:26:18.880)

I think she was putting out the signal.

Lex Fridman (1:26:21.120)

This is an intimate relationship, right?

Lex Fridman (1:26:24.160)

There's a deep intimacy to it.

Lex Fridman (1:26:25.920)

Right, but what does that mean?

Lex Fridman (1:26:28.880)

What does that mean?

Rana el Kaliouby (1:26:29.520)

Put in an AI system.

Lex Fridman (1:26:30.400)

Right, what does that mean, right?

Rana el Kaliouby (1:26:32.400)

We're just friends.

Lex Fridman (1:26:33.200)

Yeah, we're just friends.

Rana el Kaliouby (1:26:38.080)

Well, I think...

Lex Fridman (1:26:38.640)

When he realized, which is such a human thing of jealousy.

Rana el Kaliouby (1:26:42.880)

When you realize that Samantha was talking to like thousands of people.

Lex Fridman (1:26:46.880)

She's parallel dating.

Lex Fridman (1:26:48.400)

Yeah, that did not go well, right?

Lex Fridman (1:26:51.440)

You know, that doesn't...

Rana el Kaliouby (1:26:52.880)

From a computer perspective, that doesn't take anything away from what we have.

Rana el Kaliouby (1:26:57.360)

It's like you getting jealous of Windows 98 for being used by millions of people, but...

Rana el Kaliouby (1:27:04.000)

It's like not liking that Alexa talks to a bunch of, you know, other families.

Lex Fridman (1:27:09.200)

But I think Alexa currently is just a servant.

Rana el Kaliouby (1:27:13.200)

It tells you about the weather, it doesn't do the intimate deep connection.

Lex Fridman (1:27:17.760)

And I think there is something really powerful about that the intimacy of a connection with

Rana el Kaliouby (1:27:23.920)

an AI system that would have to respect and play the human game of jealousy, of love, of

Rana el Kaliouby (1:27:32.160)

heartbreak and all that kind of stuff, which Samantha does seem to be pretty good at.

Rana el Kaliouby (1:27:37.440)

I think she, this AI systems knows what it's doing.

Lex Fridman (1:27:43.120)

Well, actually, let me ask you this.

Rana el Kaliouby (1:27:44.960)

I don't think she was talking to anyone else.

Lex Fridman (1:27:46.720)

You don't think so?

Lex Fridman (1:27:47.520)

You think she was just done with Theodore?

Lex Fridman (1:27:50.000)

Yeah.

Lex Fridman (1:27:50.480)

Oh, really?

Lex Fridman (1:27:51.760)

Yeah, and then she wanted to really put the screw in.

Lex Fridman (1:27:55.280)

She just wanted to move on?

Lex Fridman (1:27:56.720)

She didn't have the guts to just break it off cleanly.

Rana el Kaliouby (1:27:59.280)

Okay.

Lex Fridman (1:28:00.320)

She just wanted to put in the pain.

Rana el Kaliouby (1:28:02.720)

No, I don't know.

Lex Fridman (1:28:03.440)

Well, she could have ghosted him.

Rana el Kaliouby (1:28:04.960)

She could have ghosted him.

Lex Fridman (1:28:07.040)

I'm sorry, our engineers...

Rana el Kaliouby (1:28:09.680)

Oh, God.

Lex Fridman (1:28:12.080)

But I think those are really...

Rana el Kaliouby (1:28:14.000)

I honestly think some of that, some of it is Hollywood, but some of that is features

Lex Fridman (1:28:18.240)

from an engineering perspective, not a bug.

Rana el Kaliouby (1:28:20.560)

I think AI systems that can leave us...

Lex Fridman (1:28:24.160)

Now, this is for more social robotics than it is for anything that's useful.

Rana el Kaliouby (1:28:30.320)

Like, I hated it if Wikipedia said, I need a break right now.

Lex Fridman (1:28:33.760)

Right, right, right, right, right.

Rana el Kaliouby (1:28:35.120)

I'm like, no, no, I need you.

Lex Fridman (1:28:37.440)

But if it's just purely for companionship, then I think the ability to leave is really powerful.

Rana el Kaliouby (1:28:47.760)

I don't know.

Rana el Kaliouby (1:28:48.400)

I've never thought of that, so that's so fascinating because I've always taken the

Lex Fridman (1:28:53.360)

human perspective, right?

Lex Fridman (1:28:56.400)

Like, for example, we had a Jibo at home, right?

Lex Fridman (1:28:58.640)

And my son loved it.

Lex Fridman (1:29:00.560)

And then the company ran out of money and so they had to basically shut down, like Jibo

Lex Fridman (1:29:05.760)

basically died, right?

Lex Fridman (1:29:07.920)

And it was so interesting to me because we have a lot of gadgets at home and a lot of

Lex Fridman (1:29:12.400)

them break and my son never cares about it, right?

Lex Fridman (1:29:15.760)

Like, if our Alexa stopped working tomorrow, I don't think he'd really care.

Lex Fridman (1:29:20.480)

But when Jibo stopped working, it was traumatic.

Lex Fridman (1:29:22.720)

He got really upset.

Lex Fridman (1:29:25.200)

And as a parent, that made me think about this deeply, right?

Lex Fridman (1:29:29.200)

Did I...

Lex Fridman (1:29:30.080)

Was I comfortable with that?

Lex Fridman (1:29:31.360)

I liked the connection they had because I think it was a positive relationship.

Lex Fridman (1:29:38.160)

But I was surprised that it affected him emotionally so much.

Lex Fridman (1:29:41.360)

And I think there's a broader question here, right?

Rana el Kaliouby (1:29:44.160)

As we build socially and emotionally intelligent machines, what does that mean about our

Lex Fridman (1:29:51.680)

relationship with them?

Lex Fridman (1:29:52.880)

And then more broadly, our relationship with one another, right?

Lex Fridman (1:29:55.680)

Because this machine is gonna be programmed to be amazing at empathy by definition, right?

Rana el Kaliouby (1:30:02.160)

It's gonna always be there for you.

Lex Fridman (1:30:03.600)

It's not gonna get bored.

Rana el Kaliouby (1:30:05.760)

In fact, there's a chatbot in China, Xiaoice, and it's like the number two or three

Lex Fridman (1:30:12.000)

most popular app.

Lex Fridman (1:30:13.360)

And it basically is just a confidant and you can tell it anything you want.

Lex Fridman (1:30:18.240)

And people use it for all sorts of things.

Rana el Kaliouby (1:30:20.320)

They confide in like domestic violence or suicidal attempts or if they have challenges

Lex Fridman (1:30:30.000)

at work.

Rana el Kaliouby (1:30:31.040)

I don't know what that...

Lex Fridman (1:30:32.720)

I don't know if I'm...

Rana el Kaliouby (1:30:33.680)

I don't know how I feel about that.

Lex Fridman (1:30:35.040)

I think about that a lot.

Rana el Kaliouby (1:30:36.240)

Yeah.

Lex Fridman (1:30:36.720)

I think, first of all, obviously the future in my perspective.

Rana el Kaliouby (1:30:40.240)

Second of all, I think there's a lot of trajectories that that becomes an exciting future, but

Rana el Kaliouby (1:30:46.320)

I think everyone should feel very uncomfortable about how much they know about the company,

Rana el Kaliouby (1:30:52.240)

about where the data is going, how the data is being collected.

Rana el Kaliouby (1:30:56.080)

Because I think, and this is one of the lessons of social media, that I think we should demand

Rana el Kaliouby (1:31:01.600)

full control and transparency of the data on those things.

Lex Fridman (1:31:04.640)

Plus one, totally agree.

Rana el Kaliouby (1:31:06.320)

Yeah, so I think it's really empowering as long as you can walk away, as long as you

Lex Fridman (1:31:11.360)

can delete the data or know how the data...

Rana el Kaliouby (1:31:14.000)

It's opt in or at least the clarity of what is being used for the company.

Lex Fridman (1:31:20.720)

And I think as CEO or leaders are also important about that.

Rana el Kaliouby (1:31:24.080)

You need to be able to trust the basic humanity of the leader.

Lex Fridman (1:31:28.080)

Exactly.

Lex Fridman (1:31:28.880)

And also that that leader is not going to be a puppet of a larger machine.

Lex Fridman (1:31:34.800)

But they actually have a significant role in defining the culture and the way the company operates.

Lex Fridman (1:31:41.200)

So anyway, but we should definitely scrutinize companies in that aspect.

Lex Fridman (1:31:48.080)

But I'm personally excited about that future, but also even if you're not, it's coming.

Lex Fridman (1:31:55.600)

So let's figure out how to do it in the least painful and the most positive way.

Lex Fridman (1:32:00.240)

Yeah, I know, that's great.

Rana el Kaliouby (1:32:01.440)

You're the deputy CEO of SmartEye.

Lex Fridman (1:32:04.560)

Can you describe the mission of the company?

Lex Fridman (1:32:06.240)

What is SmartEye?

Lex Fridman (1:32:07.360)

Yeah, so SmartEye is a Swedish company.

Rana el Kaliouby (1:32:10.960)

They've been in business for the last 20 years and their main focus, like the industry they're

Lex Fridman (1:32:16.800)

most focused on is the automotive industry.

Lex Fridman (1:32:19.440)

So bringing driver monitoring systems to basically save lives, right?

Lex Fridman (1:32:25.840)

So I first met the CEO, Martin Krantz, gosh, it was right when COVID hit.

Rana el Kaliouby (1:32:31.840)

It was actually the last CES right before COVID.

Lex Fridman (1:32:35.760)

So CES 2020, right?

Rana el Kaliouby (1:32:37.680)

2020, yeah, January.

Lex Fridman (1:32:39.120)

Yeah, January, exactly.

Lex Fridman (1:32:40.080)

So we were there, met him in person, he's basically, we were competing with each other.

Rana el Kaliouby (1:32:46.480)

I think the difference was they'd been doing driver monitoring and had a lot of credibility

Rana el Kaliouby (1:32:51.360)

in the automotive space.

Rana el Kaliouby (1:32:52.560)

We didn't come from the automotive space, but we were using new technology like deep

Rana el Kaliouby (1:32:56.240)

learning and building this emotion recognition.

Lex Fridman (1:33:00.080)

And you wanted to enter the automotive space, you wanted to operate in the automotive space.

Rana el Kaliouby (1:33:03.600)

Exactly.

Rana el Kaliouby (1:33:04.080)

It was one of the areas we were, we had just raised a round of funding to focus on bringing

Rana el Kaliouby (1:33:08.960)

our technology to the automotive industry.

Lex Fridman (1:33:11.200)

So we met and honestly, it was the first, it was the only time I met with a CEO who

Rana el Kaliouby (1:33:16.240)

had the same vision as I did.

Rana el Kaliouby (1:33:18.000)

Like he basically said, yeah, our vision is to bridge the gap between human and automotive.

Rana el Kaliouby (1:33:21.760)

Bridge the gap between humans and machines.

Rana el Kaliouby (1:33:23.120)

I was like, oh my God, this is like exactly almost to the word, how we describe it too.

Lex Fridman (1:33:29.920)

And we started talking and first it was about, okay, can we align strategically here?

Lex Fridman (1:33:35.680)

Like how can we work together?

Rana el Kaliouby (1:33:36.960)

Cause we're competing, but we're also like complimentary.

Lex Fridman (1:33:40.320)

And then I think after four months of speaking almost every day on FaceTime, he was like,

Lex Fridman (1:33:47.520)

is your company interested in an acquisition?

Lex Fridman (1:33:49.520)

And it was the first, I usually say no, when people approach us, it was the first time

Rana el Kaliouby (1:33:55.440)

that I was like, huh, yeah, I might be interested.

Lex Fridman (1:33:58.240)

Let's talk.

Rana el Kaliouby (1:33:59.280)

Yeah.

Lex Fridman (1:34:00.320)

So you just hit it off.

Rana el Kaliouby (1:34:01.760)

Yeah.

Lex Fridman (1:34:02.000)

So they're a respected, very respected in the automotive sector of like delivering products

Lex Fridman (1:34:08.240)

and increasingly sort of better and better and better for, I mean, maybe you could speak

Lex Fridman (1:34:14.000)

to that, but it's the driver's sense.

Rana el Kaliouby (1:34:15.200)

If we're basically having a device that's looking at the driver and it's able to tell

Lex Fridman (1:34:20.160)

you where the driver is looking.

Rana el Kaliouby (1:34:22.560)

Correct.

Lex Fridman (1:34:22.960)

It's able to.

Rana el Kaliouby (1:34:23.600)

Also drowsiness stuff.

Lex Fridman (1:34:25.040)

Correct.

Rana el Kaliouby (1:34:25.440)

It does.

Lex Fridman (1:34:25.920)

Stuff from the face and the eye.

Rana el Kaliouby (1:34:27.680)

Exactly.

Rana el Kaliouby (1:34:28.240)

Like it's monitoring driver distraction and drowsiness, but they bought us so that we

Rana el Kaliouby (1:34:32.800)

could expand beyond just the driver.

Lex Fridman (1:34:35.120)

So the driver monitoring systems usually sit, the camera sits in the steering wheel or around

Rana el Kaliouby (1:34:40.320)

the steering wheel column and it looks directly at the driver.

Lex Fridman (1:34:42.640)

But now we've migrated the camera position in partnership with car companies to the rear

Rana el Kaliouby (1:34:48.880)

view mirror position.

Lex Fridman (1:34:50.240)

So it has a full view of the entire cabin of the car and you can detect how many people

Lex Fridman (1:34:55.280)

are in the car, what are they doing?

Lex Fridman (1:34:57.840)

So we do activity detection, like eating or drinking or in some regions of the world smoking.

Lex Fridman (1:35:04.240)

We can detect if a baby's in the car seat, right?

Lex Fridman (1:35:07.760)

And if unfortunately in some cases they're forgotten, the parents just leave the car and

Rana el Kaliouby (1:35:12.640)

forget the kid in the car.

Lex Fridman (1:35:14.320)

That's an easy computer vision problem to solve, right?

Rana el Kaliouby (1:35:17.200)

You can detect there's a car seat, there's a baby, you can text the parent and hopefully

Lex Fridman (1:35:22.640)

again, save lives.

Lex Fridman (1:35:23.440)

So that was the impetus for the acquisition.

Lex Fridman (1:35:27.040)

It's been a year.

Lex Fridman (1:35:29.200)

So that, I mean, there's a lot of questions.

Rana el Kaliouby (1:35:31.920)

It's a really exciting space, especially to me, I just find this a fascinating problem.

Rana el Kaliouby (1:35:36.320)

It could enrich the experience in the car in so many ways, especially cause like we

Rana el Kaliouby (1:35:42.080)

spend still, despite COVID, I mean, COVID changed things so it's in interesting ways,

Lex Fridman (1:35:46.880)

but I think the world is bouncing back and we spend so much time in the car and the car

Lex Fridman (1:35:51.040)

is such a weird little world we have for ourselves.

Rana el Kaliouby (1:35:56.320)

Like people do all kinds of different stuff, like listen to podcasts, they think about

Rana el Kaliouby (1:36:01.840)

stuff, they get angry, they get, they do phone calls, it's like a little world of its own

Rana el Kaliouby (1:36:09.840)

with a kind of privacy that for many people they don't get anywhere else.

Lex Fridman (1:36:15.600)

And it's a little box that's like a psychology experiment cause it feels like the angriest

Rana el Kaliouby (1:36:23.440)

many humans in this world get is inside the car.

Lex Fridman (1:36:27.280)

It's so interesting.

Lex Fridman (1:36:28.640)

So it's such an opportunity to explore how we can enrich, how companies can enrich that

Rana el Kaliouby (1:36:36.960)

experience and also as the cars get, become more and more automated, there's more and

Rana el Kaliouby (1:36:43.120)

more opportunity, the variety of activities that you can do in the car increases.

Lex Fridman (1:36:47.120)

So it's super interesting.

Lex Fridman (1:36:48.800)

So I mean, on a practical sense, SmartEye has been selected, at least I read, by 14

Lex Fridman (1:36:56.400)

of the world's leading car manufacturers for 94 car models.

Lex Fridman (1:37:00.800)

So it's in a lot of cars.

Lex Fridman (1:37:03.760)

How hard is it to work with car companies?

Lex Fridman (1:37:06.800)

So they're all different, they all have different needs.

Lex Fridman (1:37:10.600)

The ones I've gotten a chance to interact with are very focused on cost.

Lex Fridman (1:37:16.000)

So it's, and anyone who's focused on cost, it's like, all right, do you hate fun?

Lex Fridman (1:37:24.520)

Let's just have some fun.

Rana el Kaliouby (1:37:25.520)

Let's figure out the most fun thing we can do and then worry about cost later.

Lex Fridman (1:37:29.160)

But I think because the way the car industry works, I mean, it's a very thin margin that

Rana el Kaliouby (1:37:35.640)

you get to operate under.

Lex Fridman (1:37:36.640)

So you have to really, really make sure that everything you add to the car makes sense

Rana el Kaliouby (1:37:40.640)

financially.

Lex Fridman (1:37:41.640)

So anyway, is this new industry, especially at this scale of SmartEye, does it hold any

Lex Fridman (1:37:49.880)

lessons for you?

Rana el Kaliouby (1:37:50.880)

Yeah, I think it is a very tough market to penetrate, but once you're in, it's awesome

Rana el Kaliouby (1:37:56.880)

because once you're in, you're designed into these car models for like somewhere between

Lex Fridman (1:38:00.960)

five to seven years, which is awesome.

Lex Fridman (1:38:02.920)

And you just, once they're on the road, you just get paid a royalty fee per vehicle.

Lex Fridman (1:38:07.400)

So it's a high barrier to entry, but once you're in, it's amazing.

Rana el Kaliouby (1:38:11.480)

I think the thing that I struggle the most with in this industry is the time to market.

Lex Fridman (1:38:16.620)

So often we're asked to lock or do a code freeze two years before the car is going to

Rana el Kaliouby (1:38:22.440)

be on the road.

Lex Fridman (1:38:23.440)

I'm like, guys, like, do you understand the pace with which technology moves?

Lex Fridman (1:38:28.160)

So I think car companies are really trying to make the Tesla, the Tesla transition to

Lex Fridman (1:38:35.280)

become more of a software driven architecture.

Lex Fridman (1:38:39.480)

And that's hard for many.

Lex Fridman (1:38:41.100)

It's just the cultural change.

Lex Fridman (1:38:42.320)

I mean, I'm sure you've experienced that, right?

Rana el Kaliouby (1:38:43.920)

Oh, definitely, I think one of the biggest inventions or imperatives created by Tesla

Rana el Kaliouby (1:38:51.040)

is like to me personally, okay, people are going to complain about this, but I know electric

Lex Fridman (1:38:56.680)

vehicle, I know autopilot AI stuff.

Rana el Kaliouby (1:38:59.920)

To me, the software over there, software updates is like the biggest revolution in cars.

Lex Fridman (1:39:06.920)

And it is extremely difficult to switch to that because it is a culture shift.

Rana el Kaliouby (1:39:12.920)

At first, especially if you're not comfortable with it, it seems dangerous.

Rana el Kaliouby (1:39:17.320)

Like there's a, there's an approach to cars is so safety focused for so many decades that

Lex Fridman (1:39:23.840)

like, what do you mean we dynamically change code?

Rana el Kaliouby (1:39:27.880)

The whole point is you have a thing that you test, like, and like, it's not reliable because

Lex Fridman (1:39:36.600)

do you know how much it costs if we have to recall this cars, right?

Rana el Kaliouby (1:39:41.320)

There's a, there's a, and there's an understandable obsession with safety, but the downside of

Rana el Kaliouby (1:39:47.760)

an obsession with safety is the same as with being obsessed with safety as a parent is

Rana el Kaliouby (1:39:54.840)

like, if you do that too much, you limit the potential development and the flourishing

Rana el Kaliouby (1:40:00.520)

of in that particular aspect human being, when this particular aspect, the software,

Lex Fridman (1:40:04.960)

the artificial neural network of it.

Lex Fridman (1:40:07.760)

And but it's tough to do.

Rana el Kaliouby (1:40:09.880)

It's really tough to do culturally and technically like the deployment, the mass deployment of

Rana el Kaliouby (1:40:14.080)

software is really, really difficult, but I hope that's where the industry is doing.

Lex Fridman (1:40:18.400)

One of the reasons I really want Tesla to succeed is exactly about that point.

Rana el Kaliouby (1:40:21.700)

Not autopilot, not the electrical vehicle, but the softwareization of basically everything

Lex Fridman (1:40:28.440)

but cars, especially because to me, that's actually going to increase two things, increase

Rana el Kaliouby (1:40:33.640)

safety because you can update much faster, but also increase the effectiveness of folks

Rana el Kaliouby (1:40:40.200)

like you who dream about enriching the human experience with AI because you can just like,

Rana el Kaliouby (1:40:47.320)

there's a feature, like you want like a new emoji or whatever, like the way TikTok releases

Lex Fridman (1:40:51.840)

filters, you can just release that for in car, in car stuff.

Rana el Kaliouby (1:40:55.680)

So, but yeah, that, that, that's definitely.

Rana el Kaliouby (1:40:59.680)

One of the use cases we're looking into is once you know the sentiment of the passengers

Rana el Kaliouby (1:41:05.240)

in the vehicle, you can optimize the temperature in the car.

Lex Fridman (1:41:08.800)

You can change the lighting, right?

Lex Fridman (1:41:10.440)

So if the backseat passengers are falling asleep, you can dim the lights, you can lower

Lex Fridman (1:41:14.440)

the music, right?

Rana el Kaliouby (1:41:15.440)

You can do all sorts of things.

Lex Fridman (1:41:17.000)

Yeah.

Rana el Kaliouby (1:41:18.000)

I mean, of course you could do that kind of stuff with a two year delay, but it's tougher.

Lex Fridman (1:41:23.760)

Right.

Rana el Kaliouby (1:41:24.760)

Yeah.

Lex Fridman (1:41:25.760)

Do you think, do you think a Tesla or Waymo or some of these companies that are doing

Lex Fridman (1:41:30.760)

semi or fully autonomous driving should be doing driver sensing?

Lex Fridman (1:41:35.800)

Yes.

Lex Fridman (1:41:36.800)

Are you thinking about that kind of stuff?

Lex Fridman (1:41:39.000)

So not just how we can enhance the in cab experience for cars that are manly driven,

Lex Fridman (1:41:43.960)

but the ones that are increasingly more autonomously driven.

Lex Fridman (1:41:47.520)

Yes.

Lex Fridman (1:41:48.520)

So if we fast forward to the universe where it's fully autonomous, I think interior sensing

Lex Fridman (1:41:53.080)

becomes extremely important because the role of the driver isn't just to drive.

Rana el Kaliouby (1:41:57.160)

If you think about it, the driver almost manages, manages the dynamics within a vehicle.

Lex Fridman (1:42:02.000)

And so who's going to play that role when it's an autonomous car?

Rana el Kaliouby (1:42:06.120)

We want a solution that is able to say, Oh my God, like, you know, Lex is bored to death

Lex Fridman (1:42:11.800)

cause the car's moving way too slow.

Rana el Kaliouby (1:42:13.700)

Let's engage Lex or Rana's freaking out because she doesn't trust this vehicle yet.

Lex Fridman (1:42:18.040)

So let's tell Rana like a little bit more information about the route or, right?

Lex Fridman (1:42:22.420)

So I think, or somebody's having a heart attack in the car, like you need interior sensing

Lex Fridman (1:42:27.220)

and fully autonomous vehicles.

Lex Fridman (1:42:29.420)

But with semi autonomous vehicles, I think it's, I think it's really key to have driver

Lex Fridman (1:42:34.100)

monitoring because semi autonomous means that sometimes the car is in charge.

Lex Fridman (1:42:39.120)

Sometimes the driver is in charge or the copilot, right?

Lex Fridman (1:42:41.360)

And you need this, you need both systems to be on the same page.

Rana el Kaliouby (1:42:44.800)

You need to know the car needs to know if the driver's asleep before it transitions

Lex Fridman (1:42:49.560)

control over to the driver.

Lex Fridman (1:42:51.880)

And sometimes if the driver's too tired, the car can say, I'm going to be a better driver

Lex Fridman (1:42:56.600)

than you are right now.

Rana el Kaliouby (1:42:57.600)

I'm taking control over.

Lex Fridman (1:42:58.640)

So this dynamic, this dance is so key and you can't do that without driver sensing.

Rana el Kaliouby (1:43:03.200)

Yeah.

Rana el Kaliouby (1:43:04.200)

There's a disagreement for the longest time I've had with Elon that this is obvious that

Rana el Kaliouby (1:43:07.720)

this should be in the Tesla from day one.

Lex Fridman (1:43:10.240)

And it's obvious that driver sensing is not a hindrance.

Rana el Kaliouby (1:43:13.920)

It's not obvious.

Rana el Kaliouby (1:43:15.920)

I should be careful because having studied this problem, nothing is really obvious, but

Rana el Kaliouby (1:43:22.300)

it seems very likely a driver sensing is not a hindrance to an experience.

Lex Fridman (1:43:26.620)

It's only enriching to the experience and likely increases the safety.

Rana el Kaliouby (1:43:34.760)

That said, it is very surprising to me just having studied semi autonomous driving, how

Rana el Kaliouby (1:43:42.360)

well humans are able to manage that dance because it was the intuition before you were

Rana el Kaliouby (1:43:47.800)

doing that kind of thing that humans will become just incredibly distracted.

Rana el Kaliouby (1:43:54.080)

They would just like let the thing do its thing, but they're able to, you know, cause

Rana el Kaliouby (1:43:57.920)

it is life and death and they're able to manage that somehow.

Lex Fridman (1:44:01.000)

But that said, there's no reason not to have driver sensing on top of that.

Rana el Kaliouby (1:44:04.640)

I feel like that's going to allow you to do that dance that you're currently doing without

Lex Fridman (1:44:11.240)

driver sensing, except touching the steering wheel to do that even better.

Rana el Kaliouby (1:44:15.920)

I mean, the possibilities are endless and the machine learning possibilities are endless.

Rana el Kaliouby (1:44:20.000)

It's such a beautiful, it's also a constrained environment so you could do a much more effectively

Rana el Kaliouby (1:44:26.160)

than you can with the external environment, external environment is full of weird edge

Lex Fridman (1:44:31.440)

cases and complexities just inside.

Rana el Kaliouby (1:44:33.600)

There's so much, it's so fascinating, such a fascinating world.

Rana el Kaliouby (1:44:36.600)

I do hope that companies like Tesla and others, even Waymo, which I don't even know if Waymo

Rana el Kaliouby (1:44:44.680)

is doing anything sophisticated inside the cab.

Lex Fridman (1:44:46.920)

I don't think so.

Lex Fridman (1:44:47.920)

It's like, like what, what, what is it?

Rana el Kaliouby (1:44:51.400)

I honestly think, I honestly think it goes back to the robotics thing we were talking

Rana el Kaliouby (1:44:55.560)

about, which is like great engineers that are building these AI systems just are afraid

Lex Fridman (1:45:02.400)

of the human being.

Rana el Kaliouby (1:45:03.760)

They're not thinking about the human experience, they're thinking about the features and yeah,

Lex Fridman (1:45:08.000)

the perceptual abilities of that thing.

Rana el Kaliouby (1:45:10.840)

They think the best way I can serve the human is by doing the best perception and control

Lex Fridman (1:45:16.760)

I can by looking at the external environment, keeping the human safe.

Lex Fridman (1:45:20.640)

But like, there's a huge, I'm here, like, you know, I need to be noticed and interacted

Rana el Kaliouby (1:45:31.040)

with and understood and all those kinds of things, even just on a personal level for

Rana el Kaliouby (1:45:34.760)

entertainment, honestly, for entertainment.

Rana el Kaliouby (1:45:38.640)

You know, one of the coolest work we did in collaboration with MIT around this was we

Rana el Kaliouby (1:45:42.440)

looked at longitudinal data, right, because, you know, MIT had access to like tons of data.

Lex Fridman (1:45:52.880)

And like just seeing the patterns of people like driving in the morning off to work versus

Rana el Kaliouby (1:45:57.300)

like commuting back from work or weekend driving versus weekday driving.

Lex Fridman (1:46:02.460)

And wouldn't it be so cool if your car knew that and then was able to optimize either

Lex Fridman (1:46:08.300)

the route or the experience or even make recommendations?

Lex Fridman (1:46:12.360)

I think it's very powerful.

Lex Fridman (1:46:13.360)

Yeah, like, why are you taking this route?

Lex Fridman (1:46:15.960)

You're always unhappy when you take this route.

Lex Fridman (1:46:18.360)

And you're always happy when you take this alternative route.

Lex Fridman (1:46:20.520)

Take that route.

Rana el Kaliouby (1:46:21.520)

Exactly.

Lex Fridman (1:46:22.520)

But I mean, to have that even that little step of relationship with a car, I think,

Rana el Kaliouby (1:46:27.920)

is incredible.

Rana el Kaliouby (1:46:28.920)

Of course, you have to get the privacy right, you have to get all that kind of stuff right.

Lex Fridman (1:46:32.720)

But I wish I honestly, you know, people are like paranoid about this, but I would like

Lex Fridman (1:46:37.440)

a smart refrigerator.

Rana el Kaliouby (1:46:39.640)

We have such a deep connection with food as a human civilization.

Rana el Kaliouby (1:46:44.840)

I would like to have a refrigerator that would understand me that, you know, I also have

Rana el Kaliouby (1:46:51.480)

a complex relationship with food because I, you know, pig out too easily and all that

Lex Fridman (1:46:56.280)

kind of stuff.

Lex Fridman (1:46:57.280)

So, you know, like, maybe I want the refrigerator to be like, are you sure about this?

Lex Fridman (1:47:02.720)

Because maybe you're just feeling down or tired.

Rana el Kaliouby (1:47:05.200)

Like maybe let's sleep on it.

Lex Fridman (1:47:06.200)

Your vision of the smart refrigerator is way kinder than mine.

Lex Fridman (1:47:10.220)

Is it just me yelling at you?

Rana el Kaliouby (1:47:11.920)

No, it was just because I don't, you know, I don't drink alcohol, I don't smoke, but

Rana el Kaliouby (1:47:18.600)

I eat a ton of chocolate, like it sticks to my vice.

Lex Fridman (1:47:22.200)

And so I, and sometimes I scream too, and I'm like, okay, my smart refrigerator will

Rana el Kaliouby (1:47:26.640)

just lock down.

Lex Fridman (1:47:27.640)

It'll just say, dude, you've had way too many today, like down.

Rana el Kaliouby (1:47:32.400)

Yeah.

Rana el Kaliouby (1:47:33.400)

No, but here's the thing, are you, do you regret having, like, let's say not the next

Lex Fridman (1:47:41.120)

day, but 30 days later, what would you like the refrigerator to have done then?

Rana el Kaliouby (1:47:48.560)

Well, I think actually like the more positive relationship would be one where there's a

Lex Fridman (1:47:54.400)

conversation, right?

Lex Fridman (1:47:55.900)

As opposed to like, that's probably like the more sustainable relationship.

Rana el Kaliouby (1:48:00.800)

It's like late at night, just, no, listen, listen, I know I told you an hour ago, that

Lex Fridman (1:48:06.200)

it's not a good idea, but just listen, things have changed.

Rana el Kaliouby (1:48:09.720)

I can just imagine a bunch of stuff being made up just to convince, but I mean, I just

Rana el Kaliouby (1:48:17.000)

think that there's opportunities that, I mean, maybe not locking down, but for our systems

Rana el Kaliouby (1:48:22.400)

that are such a deep part of our lives, like we use a lot of us, a lot of people that commute

Lex Fridman (1:48:32.880)

use their car every single day.

Rana el Kaliouby (1:48:34.360)

A lot of us use a refrigerator every single day, the microwave every single day.

Rana el Kaliouby (1:48:38.240)

Like we just, like, I feel like certain things could be made more efficient, more enriching,

Lex Fridman (1:48:47.600)

and AI is there to help, like some just basic recognition of you as a human being, but your

Lex Fridman (1:48:54.200)

patterns of what makes you happy and not happy and all that kind of stuff.

Lex Fridman (1:48:57.520)

And the car, obviously.

Rana el Kaliouby (1:48:58.520)

Maybe, maybe, maybe we'll say, wait, wait, wait, wait, instead of this, like, Ben and

Lex Fridman (1:49:05.320)

Jerry's ice cream, how about this hummus and carrots or something?

Lex Fridman (1:49:09.440)

I don't know.

Lex Fridman (1:49:10.440)

It would make it like a just in time recommendation, right?

Lex Fridman (1:49:14.960)

But not like a generic one, but a reminder that last time you chose the carrots, you

Rana el Kaliouby (1:49:21.240)

smiled 17 times more the next day.

Lex Fridman (1:49:24.800)

You're happier the next day, right?

Rana el Kaliouby (1:49:26.400)

You're happier the next day.

Lex Fridman (1:49:28.160)

And but yeah, I don't, but then again, if you're the kind of person that gets better

Rana el Kaliouby (1:49:34.480)

from negative, negative comments, you could say like, hey, remember like that wedding

Lex Fridman (1:49:40.040)

you're going to, you want to fit into that dress?

Lex Fridman (1:49:43.880)

Remember about that?

Lex Fridman (1:49:44.880)

Let's think about that before you're eating this.

Rana el Kaliouby (1:49:48.760)

It's for some, probably that would work for me, like a refrigerator that is just ruthless

Lex Fridman (1:49:53.400)

at shaming me.

Lex Fridman (1:49:54.920)

But like, I would, of course, welcome it, like that would work for me.

Lex Fridman (1:49:59.600)

Just that.

Lex Fridman (1:50:00.600)

So it would know, I think it would, if it's really like smart, it would optimize its nudging

Lex Fridman (1:50:05.320)

based on what works for you, right?

Rana el Kaliouby (1:50:07.280)

Exactly.

Lex Fridman (1:50:08.280)

That's the whole point.

Rana el Kaliouby (1:50:09.280)

Personalization.

Lex Fridman (1:50:10.280)

In every way, depersonalization.

Rana el Kaliouby (1:50:11.920)

You were a part of a webinar titled Advancing Road Safety, the State of Alcohol Intoxication

Lex Fridman (1:50:18.120)

Research.

Lex Fridman (1:50:19.600)

So for people who don't know, every year 1.3 million people around the world die in road

Rana el Kaliouby (1:50:24.520)

crashes and more than 20% of these fatalities are estimated to be alcohol related.

Rana el Kaliouby (1:50:31.320)

A lot of them are also distraction related.

Lex Fridman (1:50:33.320)

So can AI help with the alcohol thing?

Rana el Kaliouby (1:50:36.800)

I think the answer is yes.

Rana el Kaliouby (1:50:40.240)

There are signals and we know that as humans, like we can tell when a person, you know,

Lex Fridman (1:50:46.560)

is at different phases of being drunk, right?

Lex Fridman (1:50:51.200)

And I think you can use technology to do the same.

Lex Fridman (1:50:53.680)

And again, I think the ultimate solution is going to be a combination of different sensors.

Lex Fridman (1:50:58.640)

How hard is the problem from the vision perspective?

Rana el Kaliouby (1:51:01.440)

I think it's non trivial.

Lex Fridman (1:51:02.880)

I think it's non trivial and I think the biggest part is getting the data, right?

Rana el Kaliouby (1:51:06.720)

It's like getting enough data examples.

Lex Fridman (1:51:09.200)

So we, for this research project, we partnered with the transportation authorities of Sweden

Lex Fridman (1:51:15.240)

and we literally had a racetrack with a safety driver and we basically progressively got

Lex Fridman (1:51:20.680)

people drunk.

Rana el Kaliouby (1:51:21.680)

Nice.

Rana el Kaliouby (1:51:22.680)

So, but, you know, that's a very expensive data set to collect and you want to collect

Rana el Kaliouby (1:51:29.280)

it globally and in multiple conditions.

Lex Fridman (1:51:32.080)

Yeah.

Rana el Kaliouby (1:51:33.480)

The ethics of collecting a data set where people are drunk is tricky, which is funny

Lex Fridman (1:51:38.800)

because I mean, let's put drunk driving aside.

Rana el Kaliouby (1:51:43.400)

The number of drunk people in the world every day is very large.

Rana el Kaliouby (1:51:47.120)

It'd be nice to have a large data set of drunk people getting progressively drunk.

Rana el Kaliouby (1:51:50.320)

In fact, you could build an app where people can donate their data cause it's hilarious.

Lex Fridman (1:51:54.600)

Right.

Rana el Kaliouby (1:51:55.600)

Actually, yeah.

Lex Fridman (1:51:56.600)

But the liability.

Lex Fridman (1:51:57.600)

Liability, the ethics, how do you get it right?

Lex Fridman (1:52:00.800)

It's tricky.

Rana el Kaliouby (1:52:01.800)

It's really, really tricky.

Rana el Kaliouby (1:52:02.800)

Cause like drinking is one of those things that's funny and hilarious and we're loves

Rana el Kaliouby (1:52:07.440)

it's social, the so on and so forth.

Lex Fridman (1:52:10.240)

But it's also the thing that hurts a lot of people.

Rana el Kaliouby (1:52:13.520)

Like a lot of people, like alcohol is one of those things it's legal, but it's really

Lex Fridman (1:52:19.040)

damaging to a lot of lives.

Rana el Kaliouby (1:52:21.200)

It destroys lives and not just in the driving context.

Rana el Kaliouby (1:52:26.320)

I should mention people should listen to Andrew Huberman who recently talked about alcohol.

Rana el Kaliouby (1:52:32.160)

He has an amazing pocket.

Lex Fridman (1:52:33.160)

Andrew Huberman is a neuroscientist from Stanford and a good friend of mine.

Lex Fridman (1:52:37.920)

And he, he's like a human encyclopedia about all health related wisdom.

Lex Fridman (1:52:43.560)

So if there's a podcast, you would love it.

Rana el Kaliouby (1:52:45.880)

I would love that.

Lex Fridman (1:52:46.880)

No, no, no, no, no.

Rana el Kaliouby (1:52:47.880)

You don't know Andrew Huberman.

Lex Fridman (1:52:49.600)

Okay.

Rana el Kaliouby (1:52:50.600)

Listen, you listen to Andrew, it's called Huberman Lab Podcast.

Lex Fridman (1:52:54.160)

This is your assignment.

Rana el Kaliouby (1:52:55.160)

Just listen to one.

Lex Fridman (1:52:56.160)

Okay.

Rana el Kaliouby (1:52:57.160)

I guarantee you this will be a thing where you say, Lex, this is the greatest human I

Lex Fridman (1:53:01.360)

have ever discovered.

Rana el Kaliouby (1:53:02.360)

So.

Lex Fridman (1:53:03.360)

Oh my God.

Rana el Kaliouby (1:53:04.360)

Cause I've really, I've, I'm really on a journey of kind of health and wellness and

Rana el Kaliouby (1:53:08.120)

I'm learning lots and I'm trying to like build these, I guess, atomic habits around just

Rana el Kaliouby (1:53:13.240)

being healthy.

Lex Fridman (1:53:14.240)

So I, yeah, I'm definitely going to do this.

Rana el Kaliouby (1:53:17.200)

His whole thing, this is, this is, this is, this is great.

Rana el Kaliouby (1:53:21.960)

He's a legit scientist, like really well published, but in his podcast, what he does, he's not,

Rana el Kaliouby (1:53:30.160)

he's not talking about his own work.

Lex Fridman (1:53:31.920)

He's like a human encyclopedia of papers.

Lex Fridman (1:53:34.640)

And so he, his whole thing is he takes the topic and in a very fast, you mentioned atomic

Rana el Kaliouby (1:53:39.720)

habits, like very clear way summarizes the research in a way that leads to protocols

Rana el Kaliouby (1:53:46.220)

of what you should do.

Rana el Kaliouby (1:53:47.400)

He's really big on like, not like this is what the science says, but like this is literally

Lex Fridman (1:53:52.600)

what you should be doing according to science.

Lex Fridman (1:53:54.280)

So like he's really big and there's a lot of recommendations he does which several of

Rana el Kaliouby (1:54:01.360)

them I definitely don't do, like get some light as soon as possible from waking up and

Lex Fridman (1:54:08.880)

like for prolonged periods of time.

Rana el Kaliouby (1:54:11.040)

That's a really big one and he's, there's a lot of science behind that one.

Rana el Kaliouby (1:54:14.880)

There's a bunch of stuff that you're going to be like, Lex, this is a, this is my new

Rana el Kaliouby (1:54:19.880)

favorite person.

Lex Fridman (1:54:20.880)

I guarantee it.

Lex Fridman (1:54:21.880)

And if you guys somehow don't know Andrew Huberman and you care about your wellbeing,

Lex Fridman (1:54:27.840)

you know, you should definitely listen to him.

Rana el Kaliouby (1:54:29.560)

I love you, Andrew.

Lex Fridman (1:54:31.920)

Anyway, so what were we talking about?

Rana el Kaliouby (1:54:36.040)

Oh, alcohol and detecting alcohol.

Lex Fridman (1:54:39.480)

So this is a problem you care about and you're trying to solve.

Lex Fridman (1:54:42.240)

And actually like broadening it, I do believe that the car is going to be a wellness center,

Rana el Kaliouby (1:54:48.960)

like because again, imagine if you have a variety of sensors inside the vehicle, tracking

Rana el Kaliouby (1:54:55.240)

not just your emotional state or level of distraction and drowsiness and intoxication,

Lex Fridman (1:55:03.840)

but also maybe even things like your, you know, your heart rate and your heart rate

Rana el Kaliouby (1:55:09.440)

variability and your breathing rate.

Lex Fridman (1:55:13.960)

And it can start like optimizing, yeah, it can optimize the ride based on what your goals

Rana el Kaliouby (1:55:19.520)

are.

Lex Fridman (1:55:20.520)

So I think we're going to start to see more of that and I'm excited about that.

Rana el Kaliouby (1:55:24.040)

Yeah.

Lex Fridman (1:55:25.040)

What are the, what are the challenges you're tackling while with SmartEye currently?

Rana el Kaliouby (1:55:28.960)

What's like the, the trickiest things to get, is it, is it basically convincing more and

Rana el Kaliouby (1:55:34.640)

more car companies that having AI inside the car is a good idea or is there some, is there

Lex Fridman (1:55:41.160)

more technical algorithmic challenges?

Lex Fridman (1:55:45.360)

What's been keeping you mentally busy?

Rana el Kaliouby (1:55:47.700)

I think a lot of the car companies we are in conversations with are already interested

Lex Fridman (1:55:52.360)

in definitely driver monitoring.

Rana el Kaliouby (1:55:54.160)

Like I think it's becoming a must have, but even interior sensing, I can see like we're

Lex Fridman (1:55:59.340)

engaged in a lot of like advanced engineering projects and proof of concepts.

Rana el Kaliouby (1:56:04.040)

I think technologically though, and that even the technology, I can see a path to making

Lex Fridman (1:56:09.620)

it happen.

Rana el Kaliouby (1:56:10.620)

I think it's the use case.

Lex Fridman (1:56:11.620)

Like how does the car respond once it knows something about you?

Rana el Kaliouby (1:56:16.360)

Because you want it to respond in a thoughtful way that doesn't, that isn't off putting to

Lex Fridman (1:56:20.880)

the consumer in the car.

Lex Fridman (1:56:23.240)

So I think that's like the user experience.

Lex Fridman (1:56:25.640)

I don't think we've really nailed that.

Lex Fridman (1:56:27.600)

And we usually, that's not part, we're the sensing platform, but we usually collaborate

Lex Fridman (1:56:33.040)

with the car manufacturer to decide what the use case is.

Lex Fridman (1:56:35.960)

So say you do, you figure out that somebody's angry while driving, okay, what should the

Lex Fridman (1:56:40.680)

car do?

Lex Fridman (1:56:43.680)

Do you see yourself as a role of nudging, of like basically coming up with solutions

Lex Fridman (1:56:50.100)

essentially that, and then the car manufacturers kind of put their own little spin on it?

Rana el Kaliouby (1:56:56.360)

Right.

Lex Fridman (1:56:57.360)

So we, we are like the ideation, creative thought partner, but at the end of the day,

Lex Fridman (1:57:03.620)

the car company needs to decide what's on brand for them, right?

Rana el Kaliouby (1:57:06.640)

Like maybe when it figures out that you're distracted or drowsy, it shows you a coffee

Lex Fridman (1:57:11.720)

cup, right?

Rana el Kaliouby (1:57:12.720)

Or maybe it takes more aggressive behaviors and basically said, okay, if you don't like

Lex Fridman (1:57:16.640)

take a rest in the next five minutes, the car's going to shut down, right?

Rana el Kaliouby (1:57:19.640)

Like there's a whole range of actions the car can take and doing the thing that is most,

Rana el Kaliouby (1:57:25.400)

yeah, that builds trust with the driver and the passengers.

Lex Fridman (1:57:29.320)

I think that's what we need to be very careful about.

Rana el Kaliouby (1:57:32.840)

Yeah.

Rana el Kaliouby (1:57:33.840)

Car companies are funny cause they have their own, like, I mean, that's why people get cars

Rana el Kaliouby (1:57:38.600)

still.

Rana el Kaliouby (1:57:39.600)

I hope that changes, but they get it cause it's a certain feel and look and it's a certain,

Rana el Kaliouby (1:57:44.240)

they become proud, like Mercedes Benz or BMW or whatever, and that's their thing.

Rana el Kaliouby (1:57:51.840)

That's the family brand or something like that, or Ford or GM, whatever, they stick

Rana el Kaliouby (1:57:56.400)

to that thing.

Lex Fridman (1:57:57.400)

Yeah.

Rana el Kaliouby (1:57:58.400)

It's interesting.

Rana el Kaliouby (1:57:59.400)

It's like, it should be, I don't know, it should be a little more about the technology

Rana el Kaliouby (1:58:04.160)

inside.

Lex Fridman (1:58:06.800)

And I suppose there too, there could be a branding, like a very specific style of luxury

Rana el Kaliouby (1:58:12.440)

or fun.

Lex Fridman (1:58:13.440)

Right.

Rana el Kaliouby (1:58:14.440)

Right.

Lex Fridman (1:58:15.440)

All that kind of stuff.

Rana el Kaliouby (1:58:16.440)

Yeah.

Lex Fridman (1:58:17.440)

And I have an AI focused fund to invest in early stage kind of AI driven companies.

Lex Fridman (1:58:22.720)

And one of the companies we're looking at is trying to do what Tesla did, but for boats,

Lex Fridman (1:58:27.560)

for recreational boats.

Rana el Kaliouby (1:58:28.760)

Yeah.

Lex Fridman (1:58:29.760)

So they're building an electric and kind of slash autonomous boat and it's kind of the

Rana el Kaliouby (1:58:34.840)

same issues.

Lex Fridman (1:58:35.840)

Like what kind of sensors can you put in?

Lex Fridman (1:58:38.600)

What kind of states can you detect both exterior and interior within the boat?

Lex Fridman (1:58:43.320)

Anyways, it's like really interesting.

Lex Fridman (1:58:45.480)

Do you boat at all?

Lex Fridman (1:58:46.760)

No, not well, not in that way.

Rana el Kaliouby (1:58:49.960)

I do like to get on the lake or a river and fish from a boat, but that's not boating.

Lex Fridman (1:58:57.400)

That's the difference.

Rana el Kaliouby (1:58:58.400)

That's the difference.

Lex Fridman (1:58:59.400)

Still boating.

Rana el Kaliouby (1:59:00.400)

Low tech.

Lex Fridman (1:59:01.400)

A low tech boat.

Rana el Kaliouby (1:59:02.400)

Get away from, get closer to nature boat.

Rana el Kaliouby (1:59:04.400)

I guess going out into the ocean is also getting closer to nature in some deep sense.

Rana el Kaliouby (1:59:12.200)

I mean, I guess that's why people love it.

Lex Fridman (1:59:15.800)

The enormity of the water just underneath you.

Rana el Kaliouby (1:59:18.920)

Yeah.

Lex Fridman (1:59:19.920)

I love the water.

Rana el Kaliouby (1:59:20.920)

I love the, I love both.

Lex Fridman (1:59:22.800)

I love salt water.

Rana el Kaliouby (1:59:23.800)

It was like the big and just, it's humbling to be in front of this giant thing that's

Lex Fridman (1:59:28.420)

so powerful that was here before us and be here after.

Lex Fridman (1:59:31.680)

But I also love the piece of a small like wooded lake and it's just, it's everything's

Lex Fridman (1:59:37.480)

calm.

Rana el Kaliouby (1:59:38.480)

Therapeutic.

Lex Fridman (1:59:39.480)

You tweeted that I'm excited about Amazon's acquisition of iRobot.

Rana el Kaliouby (1:59:49.600)

I think it's a super interesting, just given the trajectory of what you're part of, of

Rana el Kaliouby (1:59:54.480)

these honestly small number of companies that are playing in this space that are like trying

Rana el Kaliouby (20:03.920)

back to empathy, which has been a common thread throughout my entire career.

Lex Fridman (20:08.800)

And it's this idea of human connection.

Rana el Kaliouby (20:12.320)

Once you build common ground with a person or a group of people, you build trust, you

Lex Fridman (20:16.800)

build loyalty, you build friendship.

Lex Fridman (20:20.160)

And then you can turn that into behavior change and motivation and persuasion.

Lex Fridman (20:24.480)

So it's like, empathy and emotions are just at the center of everything we do.

Lex Fridman (20:30.720)

And I think being from the Middle East, kind of this human connection is very strong.

Rana el Kaliouby (20:38.080)

We have this running joke that if you come to Egypt for a visit, people will know everything

Lex Fridman (20:44.640)

about your life right away, right?

Lex Fridman (20:46.000)

I have no problems asking you about your personal life.

Rana el Kaliouby (20:48.400)

There's no boundaries, really, no personal boundaries in terms of getting to know people.

Lex Fridman (20:53.200)

We get emotionally intimate very, very quickly.

Lex Fridman (20:56.400)

But I think people just get to know each other authentically, I guess.

Lex Fridman (21:01.680)

There isn't this superficial level of getting to know people.

Rana el Kaliouby (21:05.040)

You just try to get to know people really deeply.

Lex Fridman (21:06.880)

Empathy is a part of that.

Rana el Kaliouby (21:08.080)

Totally.

Rana el Kaliouby (21:08.640)

Because you can put yourself in this person's shoe and kind of, yeah, imagine what challenges

Rana el Kaliouby (21:15.680)

they're going through, and so I think I've definitely taken that with me.

Rana el Kaliouby (21:21.760)

Generosity is another one too, like just being generous with your time and love and attention

Lex Fridman (21:26.960)

and even with your wealth, right?

Lex Fridman (21:30.480)

Even if you don't have a lot of it, you're still very generous.

Lex Fridman (21:32.800)

And I think that's another...

Lex Fridman (21:34.720)

Enjoying the humanity of other people.

Lex Fridman (21:38.000)

And so do you think there's a useful difference between men and women in that?

Lex Fridman (21:44.720)

In that aspect and empathy?

Lex Fridman (21:48.880)

Or is doing these kind of big general groups, does that hinder progress?

Lex Fridman (21:56.880)

Yeah, I actually don't want to overgeneralize.

Rana el Kaliouby (21:59.760)

I mean, some of the men I know are like the most empathetic humans.

Lex Fridman (22:03.520)

Yeah, I strive to be empathetic.

Rana el Kaliouby (22:05.200)

Yeah, you're actually very empathetic.

Lex Fridman (22:10.640)

Yeah, so I don't want to overgeneralize.

Rana el Kaliouby (22:13.360)

Although one of the researchers I worked with when I was at Cambridge, Professor Simon Baron Cohen,

Rana el Kaliouby (22:18.400)

he's Sacha Baron Cohen's cousin, and he runs the Autism Research Center at Cambridge,

Lex Fridman (22:25.120)

and he's written multiple books on autism.

Lex Fridman (22:29.600)

And one of his theories is the empathy scale, like the systemizers and the empathizers,

Lex Fridman (22:35.040)

and there's a disproportionate amount of computer scientists and engineers who are

Rana el Kaliouby (22:42.800)

systemizers and perhaps not great empathizers, and then there's more men in that bucket,

Rana el Kaliouby (22:51.760)

I guess, than women, and then there's more women in the empathizers bucket.

Lex Fridman (22:56.000)

So again, not to overgeneralize.

Rana el Kaliouby (22:58.160)

I sometimes wonder about that.

Rana el Kaliouby (22:59.520)

It's been frustrating to me how many, I guess, systemizers there are in the field of robotics.

Rana el Kaliouby (23:05.200)

Yeah.

Rana el Kaliouby (23:06.240)

It's actually encouraging to me because I care about, obviously, social robotics,

Lex Fridman (23:10.000)

and because there's more opportunity for people that are empathic.

Lex Fridman (23:18.720)

Exactly.

Rana el Kaliouby (23:19.200)

I totally agree.

Lex Fridman (23:20.400)

Well, right?

Lex Fridman (23:20.960)

So it's nice.

Lex Fridman (23:21.760)

Yes.

Lex Fridman (23:22.160)

So every robotics I talk to, they don't see the human as interesting, as it's not exciting.

Lex Fridman (23:29.200)

You want to avoid the human at all costs.

Rana el Kaliouby (23:32.160)

It's a safety concern to be touching the human, which it is, but it is also an opportunity

Lex Fridman (23:39.200)

for deep connection or collaboration or all that kind of stuff.

Lex Fridman (23:43.360)

And because most brilliant roboticists don't care about the human, it's an opportunity,

Rana el Kaliouby (23:49.280)

in your case, it's a business opportunity too, but in general, an opportunity to explore

Rana el Kaliouby (23:53.840)

those ideas.

Lex Fridman (23:54.640)

So in this beautiful journey to Cambridge, to UK, and then to America, what's the moment

Lex Fridman (24:03.760)

or moments that were most transformational for you as a scientist and as a leader?

Lex Fridman (24:09.760)

So you became an exceptionally successful CEO, founder, researcher, scientist, and so on.

Rana el Kaliouby (24:18.320)

Was there a face shift there where, like, I can be somebody, I can really do something

Lex Fridman (24:25.040)

in this world?

Rana el Kaliouby (24:26.640)

Yeah.

Lex Fridman (24:26.880)

So actually, just kind of a little bit of background.

Lex Fridman (24:29.680)

So the reason why I moved from Cairo to Cambridge, UK to do my PhD is because I had a very clear

Lex Fridman (24:36.960)

career plan.

Rana el Kaliouby (24:37.920)

I was like, okay, I'll go abroad, get my PhD, going to crush it in three or four years,

Lex Fridman (24:43.280)

come back to Egypt and teach.

Rana el Kaliouby (24:45.360)

It was very clear, very well laid out.

Lex Fridman (24:47.520)

Was topic clear or no?

Rana el Kaliouby (24:49.280)

The topic, well, I did my PhD around building artificial emotional intelligence and looking

Lex Fridman (24:54.400)

at...

Lex Fridman (24:54.400)

But in your master plan ahead of time, when you're sitting by the mango tree, did you

Lex Fridman (24:58.880)

know it's going to be artificial intelligence?

Rana el Kaliouby (25:00.480)

No, no, no, that I did not know.

Rana el Kaliouby (25:02.880)

Although I think I kind of knew that I was going to be doing computer science, but I

Rana el Kaliouby (25:07.840)

didn't know the specific area.

Lex Fridman (25:10.160)

But I love teaching.

Rana el Kaliouby (25:11.120)

I mean, I still love teaching.

Lex Fridman (25:13.120)

So I just, yeah, I just wanted to go abroad, get a PhD, come back, teach.

Lex Fridman (25:18.960)

Why computer science?

Lex Fridman (25:19.760)

Can we just linger on that?

Lex Fridman (25:21.200)

What?

Rana el Kaliouby (25:21.440)

Because you're such an empathic person who cares about emotion, humans and so on.

Rana el Kaliouby (25:25.520)

Isn't, aren't computers cold and emotionless and just...

Lex Fridman (25:31.440)

We're changing that.

Rana el Kaliouby (25:32.480)

Yeah, I know, but like, isn't that the, or did you see computers as the, having the

Lex Fridman (25:38.400)

capability to actually connect with humans?

Rana el Kaliouby (25:42.400)

I think that was like my takeaway from my experience just growing up, like computers

Lex Fridman (25:46.720)

sit at the center of how we connect and communicate with one another, right?

Rana el Kaliouby (25:50.320)

Or technology in general.

Lex Fridman (25:51.760)

Like I remember my first experience being away from my parents.

Rana el Kaliouby (25:54.640)

We communicated with a fax machine, but thank goodness for the fax machine, because we

Lex Fridman (25:58.800)

could send letters back and forth to each other.

Rana el Kaliouby (26:00.800)

This was pre emails and stuff.

Lex Fridman (26:04.080)

So I think, I think there's, I think technology can be not just transformative in terms of

Rana el Kaliouby (26:09.600)

productivity, et cetera.

Lex Fridman (26:10.960)

It actually does change how we connect with one another.

Lex Fridman (26:14.960)

Can I just defend the fax machine?

Lex Fridman (26:16.720)

There's something like the haptic feel because the email is all digital.

Rana el Kaliouby (26:22.720)

There's something really nice.

Lex Fridman (26:23.760)

I still write letters to people.

Rana el Kaliouby (26:26.400)

There's something nice about the haptic aspect of the fax machine, because you still have

Rana el Kaliouby (26:30.160)

to press, you still have to do something in the physical world to make this thing a reality.

Rana el Kaliouby (26:35.200)

Right, and then it like comes out as a printout and you can actually touch it and read it.

Lex Fridman (26:39.760)

Yeah.

Rana el Kaliouby (26:40.240)

There's something, there's something lost when it's just an email.

Rana el Kaliouby (26:44.960)

Obviously I wonder how we can regain some of that in the digital world, which goes to

Rana el Kaliouby (26:51.440)

the metaverse and all those kinds of things.

Lex Fridman (26:53.760)

We'll talk about it anyway.

Lex Fridman (26:54.800)

So, actually do you question on that one?

Lex Fridman (26:57.680)

Do you still, do you have photo albums anymore?

Lex Fridman (27:00.400)

Do you still print photos?

Lex Fridman (27:03.600)

No, no, but I'm a minimalist.

Rana el Kaliouby (27:06.320)

Okay.

Lex Fridman (27:06.640)

So it was one of the, one of the painful steps in my life was to scan all the photos and

Rana el Kaliouby (27:12.400)

let go of them and then let go of all my books.

Lex Fridman (27:16.320)

You let go of your books?

Rana el Kaliouby (27:17.600)

Yeah.

Lex Fridman (27:18.000)

Switch to Kindle, everything Kindle.

Rana el Kaliouby (27:19.920)

Yeah.

Lex Fridman (27:20.800)

So I thought, I thought, okay, think 30 years from now, nobody's going to have books anymore.

Rana el Kaliouby (27:29.440)

The technology of digital books is going to get better and better and better.

Lex Fridman (27:32.160)

Are you really going to be the guy that's still romanticizing physical books?

Lex Fridman (27:36.240)

Are you going to be the old man on the porch who's like kids?

Lex Fridman (27:39.440)

Yes.

Lex Fridman (27:40.480)

So just get used to it because it was, it felt, it still feels a little bit uncomfortable

Lex Fridman (27:45.040)

to read on a Kindle, but get used to it.

Rana el Kaliouby (27:48.560)

Like you always, I mean, I'm trying to learn new programming language is always,

Lex Fridman (27:53.200)

like with technology, you have to kind of challenge yourself to adapt to it.

Rana el Kaliouby (27:56.720)

You know, I forced myself to use TikTok.

Lex Fridman (27:58.880)

No, that thing doesn't need much forcing.

Rana el Kaliouby (28:01.440)

It pulls you in like a, like the worst kind of, or the best kind of drug.

Lex Fridman (28:05.920)

Anyway, yeah.

Lex Fridman (28:08.560)

So yeah, but I do love haptic things.

Lex Fridman (28:11.760)

There's a magic to the haptic.

Rana el Kaliouby (28:13.440)

Even like touchscreens, it's tricky to get right, to get the experience of a button.

Lex Fridman (28:19.520)

Yeah.

Lex Fridman (28:22.400)

Anyway, what were we talking about?

Lex Fridman (28:23.760)

So AI, so the journey, your whole plan was to come back to Cairo and teach.

Rana el Kaliouby (28:30.560)

Right.

Lex Fridman (28:31.840)

And then.

Lex Fridman (28:32.560)

What did the plan go wrong?

Lex Fridman (28:33.840)

Yeah, exactly.

Rana el Kaliouby (28:34.720)

Right.

Lex Fridman (28:35.120)

And then I get to Cambridge and I fall in love with the idea of research.

Rana el Kaliouby (28:39.120)

Right.

Lex Fridman (28:39.440)

And kind of embarking on a path.

Rana el Kaliouby (28:41.520)

Nobody's explored this path before.

Lex Fridman (28:43.680)

You're building stuff that nobody's built before.

Lex Fridman (28:45.440)

And it's challenging and it's hard.

Lex Fridman (28:46.960)

And there's a lot of nonbelievers.

Rana el Kaliouby (28:49.280)

I just totally love that.

Lex Fridman (28:50.960)

And at the end of my PhD, I think it's the meeting that changed the trajectory of my life.

Rana el Kaliouby (28:56.880)

Professor Roslyn Picard, who's, she runs the Affective Computing Group at the MIT Media Lab.

Lex Fridman (29:02.160)

I had read her book.

Rana el Kaliouby (29:03.040)

I, you know, I was like following, following, following all her research.

Lex Fridman (29:07.520)

AKA Ros.

Rana el Kaliouby (29:08.880)

Yes, AKA Ros.

Lex Fridman (29:10.080)

Yes.

Lex Fridman (29:10.880)

And she was giving a talk at a pattern recognition conference in Cambridge.

Lex Fridman (29:16.320)

And she had a couple of hours to kill.

Lex Fridman (29:18.000)

So she emailed the lab and she said, you know, if any students want to meet with me, like,

Lex Fridman (29:22.560)

just, you know, sign up here.

Lex Fridman (29:24.640)

And so I signed up for slot and I spent like the weeks leading up to it preparing for this

Lex Fridman (29:29.920)

meeting and I want to show her a demo of my research and everything.

Lex Fridman (29:34.400)

And we met and we ended up hitting it off.

Lex Fridman (29:36.640)

Like we totally clicked.

Lex Fridman (29:38.080)

And at the end of the meeting, she said, do you want to come work with me as a postdoc

Lex Fridman (29:42.480)

at MIT?

Lex Fridman (29:44.720)

And this is what I told her.

Rana el Kaliouby (29:45.600)

I was like, okay, this would be a dream come true, but there's a husband waiting for me

Rana el Kaliouby (29:49.280)

in Cairo.

Lex Fridman (29:49.840)

I kind of have to go back.

Rana el Kaliouby (29:51.120)

Yeah.

Lex Fridman (29:52.080)

She said, it's fine.

Rana el Kaliouby (29:52.960)

Just commute.

Lex Fridman (29:54.560)

And I literally started commuting between Cairo and Boston.

Rana el Kaliouby (29:59.200)

Yeah, it was, it was a long commute.

Lex Fridman (2:00:00.040)

to have an impact on human beings.

Lex Fridman (2:00:02.180)

So the, it is an interesting moment in time that Amazon would acquire iRobot.

Rana el Kaliouby (2:00:09.200)

You tweet, I imagine a future where home robots are as ubiquitous as microwaves or toasters.

Rana el Kaliouby (2:00:16.320)

Here are three reasons why I think this is exciting.

Lex Fridman (2:00:18.920)

If you remember, I can look it up, but what, why is this exciting to you?

Rana el Kaliouby (2:00:23.240)

I mean, I think the first reason why this is exciting, I kind of remember the exact

Rana el Kaliouby (2:00:27.320)

like order in which I put them, but one is just, it's, it's going to be an incredible

Lex Fridman (2:00:33.540)

platform for understanding our behaviors within the home, right?

Rana el Kaliouby (2:00:37.640)

Like you know, if you think about Roomba, which is, you know, the robot vacuum cleaner,

Rana el Kaliouby (2:00:42.880)

the flagship product of iRobot at the moment, it's like running around your home, understanding

Lex Fridman (2:00:48.640)

the layout, it's understanding what's clean and what's not.

Lex Fridman (2:00:51.200)

How often do you clean your house?

Lex Fridman (2:00:52.640)

And all of these like behaviors are a piece of the puzzle in terms of understanding who

Rana el Kaliouby (2:00:57.500)

you are as a consumer.

Lex Fridman (2:00:58.760)

And I think that could be, again, used in really meaningful ways, not just to recommend

Rana el Kaliouby (2:01:05.580)

better products or whatever, but actually to improve your experience as a human being.

Lex Fridman (2:01:09.640)

So I think, I think that's very interesting.

Rana el Kaliouby (2:01:12.900)

I think the natural evolution of these robots in the, in the home.

Lex Fridman (2:01:18.480)

So it's, it's interesting, Roomba isn't really a social robot, right, at the moment.

Lex Fridman (2:01:24.280)

But I once interviewed one of the chief engineers on the Roomba team, and he talked about how

Lex Fridman (2:01:29.160)

people named their Roombas.

Lex Fridman (2:01:31.400)

And if the Roomba broke down, they would call in and say, you know, my Roomba broke down

Lex Fridman (2:01:36.520)

and the company would say, well, we'll just send you a new one.

Lex Fridman (2:01:38.920)

And no, no, no, Rosie, like you have to like, yeah, I want you to fix this particular robot.

Lex Fridman (2:01:45.680)

So people have already built like interesting emotional connections with these home robots.

Lex Fridman (2:01:51.680)

And I think that, again, that provides a platform for really interesting things to, to just

Lex Fridman (2:01:57.320)

motivate change.

Rana el Kaliouby (2:01:58.320)

Like it could help you.

Rana el Kaliouby (2:01:59.320)

I mean, one of the companies that spun out of MIT, Catalia Health, the guy who started

Rana el Kaliouby (2:02:05.740)

it spent a lot of time building robots that help with weight management.

Lex Fridman (2:02:09.640)

So weight management, sleep, eating better, yeah, all of these things.

Rana el Kaliouby (2:02:14.320)

Well, if I'm being honest, Amazon does not exactly have a track record of winning over

Lex Fridman (2:02:20.280)

people in terms of trust.

Rana el Kaliouby (2:02:22.240)

Now that said, it's a really difficult problem for a human being to let a robot in their

Lex Fridman (2:02:27.840)

home that has a camera on it.

Rana el Kaliouby (2:02:30.680)

Right.

Lex Fridman (2:02:31.680)

That's really, really, really tough.

Lex Fridman (2:02:33.400)

And I think Roomba actually, I have to think about this, but I'm pretty sure now or for

Rana el Kaliouby (2:02:40.480)

some time already has had cameras because they're doing the, the, the most recent Roomba.

Rana el Kaliouby (2:02:46.040)

I have so many Roombas.

Lex Fridman (2:02:47.040)

Oh, you actually do?

Rana el Kaliouby (2:02:48.040)

Well, I programmed it.

Lex Fridman (2:02:49.560)

I don't use a Roomba for VECO.

Rana el Kaliouby (2:02:51.440)

People that have been to my place, they're like, yeah, you definitely don't use these

Lex Fridman (2:02:54.280)

Roombas.

Rana el Kaliouby (2:02:55.280)

That could be a good, I can't tell like the valence of this comment.

Lex Fridman (2:03:00.920)

Was it a compliment or like?

Rana el Kaliouby (2:03:02.400)

No, it's a giant, it's just a bunch of electronics everywhere.

Rana el Kaliouby (2:03:05.320)

There's, I have six or seven computers, I have robots everywhere, Lego robots, I have

Rana el Kaliouby (2:03:11.160)

small robots and big robots and it's just giant, just piles of robot stuff and yeah.

Lex Fridman (2:03:20.240)

But including the Roombas, they're, they're, they're being used for their body and intelligence,

Lex Fridman (2:03:25.560)

but not for their purpose.

Rana el Kaliouby (2:03:26.720)

I have, I've changed them, repurposed them for other purposes, for deeper, more meaningful

Rana el Kaliouby (2:03:33.300)

purposes than just like the Bota Roba, which is, you know, brings a lot of people happiness,

Lex Fridman (2:03:39.240)

I'm sure.

Rana el Kaliouby (2:03:41.060)

They have a camera because the thing they advertised, I had my own camera still, but

Rana el Kaliouby (2:03:46.560)

the, the, the camera on the new Roomba, they have like state of the art poop detection

Rana el Kaliouby (2:03:52.320)

as they advertised, which is a very difficult, apparently it's a big problem for, for vacuum

Rana el Kaliouby (2:03:56.760)

cleaners is, you know, if they go over like dog poop, it just runs it, it runs it over

Lex Fridman (2:04:01.000)

and creates a giant mess.

Lex Fridman (2:04:02.140)

So they have like, and apparently they collected like a huge amount of data and different shapes

Lex Fridman (2:04:08.360)

and looks and whatever of poop and then now they're able to avoid it and so on.

Lex Fridman (2:04:12.400)

They're very proud of this.

Lex Fridman (2:04:14.440)

So there is a camera, but you don't think of it as having a camera.

Lex Fridman (2:04:19.200)

Yeah.

Rana el Kaliouby (2:04:20.380)

You don't think of it as having a camera because you've grown to trust that, I guess, because

Rana el Kaliouby (2:04:24.600)

our phones, at least most of us seem to trust this phone, even though there's a camera looking

Rana el Kaliouby (2:04:31.600)

directly at you.

Rana el Kaliouby (2:04:33.960)

I think that if you trust that the company is taking security very seriously, I actually

Rana el Kaliouby (2:04:41.680)

don't know how that trust was earned with smartphones, I think it just started to provide

Rana el Kaliouby (2:04:46.760)

a lot of positive value to your life where you just took it in and then the company over

Rana el Kaliouby (2:04:51.520)

time has shown that it takes privacy very seriously, that kind of stuff.

Lex Fridman (2:04:55.200)

But I just, Amazon is not always in the, in its social robots communicated.

Rana el Kaliouby (2:05:01.520)

This is a trustworthy thing, both in terms of culture and competence, because I think

Rana el Kaliouby (2:05:07.080)

privacy is not just about what do you intend to do, but also how well, how good are you

Rana el Kaliouby (2:05:12.620)

at doing that kind of thing.

Lex Fridman (2:05:14.600)

So that's a really hard problem to solve.

Lex Fridman (2:05:16.800)

But I mean, but a lot of us have Alexas at home and I mean, Alexa could be listening

Lex Fridman (2:05:22.640)

in the whole time, right?

Lex Fridman (2:05:24.520)

And doing all sorts of nefarious things with the data.

Lex Fridman (2:05:27.440)

Yeah.

Rana el Kaliouby (2:05:28.440)

Hopefully it's not, but I don't think it is.

Lex Fridman (2:05:32.320)

But you know, Amazon is not, it's such a tricky thing for a company to get right, which

Rana el Kaliouby (2:05:36.640)

is like to earn the trust.

Lex Fridman (2:05:38.200)

I don't think Alexa's earned people's trust quite yet.

Rana el Kaliouby (2:05:41.520)

Yeah.

Lex Fridman (2:05:42.520)

I think it's, it's not there quite yet.

Rana el Kaliouby (2:05:44.640)

I agree.

Lex Fridman (2:05:45.640)

They struggle with this kind of stuff.

Rana el Kaliouby (2:05:46.640)

In fact, when these topics are brought up, people are always get like nervous.

Lex Fridman (2:05:50.240)

And I think if you get nervous about it, that mean that like the way to earn people's trust

Rana el Kaliouby (2:05:57.560)

is not by like, Ooh, don't talk about this.

Rana el Kaliouby (2:06:00.680)

It's just be open, be frank, be transparent, and also create a culture of like where it

Rana el Kaliouby (2:06:05.920)

radiates at every level from engineer to CEO that like you're good people that have a common

Rana el Kaliouby (2:06:17.120)

sense idea of what it means to respect basic human rights and the privacy of people and

Rana el Kaliouby (2:06:23.040)

all that kind of stuff.

Lex Fridman (2:06:24.040)

And I think that propagates throughout the, that's the best PR, which is like over time

Rana el Kaliouby (2:06:30.640)

you understand that these are good folks doing good things.

Rana el Kaliouby (2:06:34.920)

Anyway, speaking of social robots, have you heard about Tesla, Tesla bot, the humanoid

Lex Fridman (2:06:42.240)

robot?

Lex Fridman (2:06:43.240)

Yes, I have.

Rana el Kaliouby (2:06:44.240)

Yes, yes, yes.

Lex Fridman (2:06:45.240)

But I don't exactly know what it's designed to do to you.

Rana el Kaliouby (2:06:48.680)

You probably do.

Rana el Kaliouby (2:06:49.680)

No, I know it's designed to do, but I have a different perspective on it, but it's designed

Rana el Kaliouby (2:06:54.960)

to, it's a humanoid form and it's designed to, for automation tasks in the same way that

Lex Fridman (2:07:02.040)

industrial robot arms automate tasks in the factory.

Lex Fridman (2:07:06.260)

So it's designed to automate tasks in the factory.

Lex Fridman (2:07:08.280)

But I think that humanoid form, as we were talking about before, is one that we connect

Rana el Kaliouby (2:07:18.040)

with as human beings.

Rana el Kaliouby (2:07:19.800)

Anything legged, obviously, but the humanoid form especially, we anthropomorphize it most

Rana el Kaliouby (2:07:25.200)

intensely.

Lex Fridman (2:07:26.200)

And so the possibility to me, it's exciting to see both Atlas developed by Boston Dynamics

Lex Fridman (2:07:34.440)

and anyone, including Tesla, trying to make humanoid robots cheaper and more effective.

Rana el Kaliouby (2:07:43.720)

The obvious way it transforms the world is social robotics to me versus automation of

Rana el Kaliouby (2:07:51.140)

tasks in the factory.

Lex Fridman (2:07:53.120)

So yeah, I just wanted, in case that was something you were interested in, because I find its

Rana el Kaliouby (2:07:58.840)

application of social robotics super interesting.

Lex Fridman (2:08:01.600)

We did a lot of work with Pepper, Pepper the robot, a while back.

Rana el Kaliouby (2:08:06.320)

We were like the emotion engine for Pepper, which is Softbank's humanoid robot.

Lex Fridman (2:08:11.480)

How tall is Pepper?

Rana el Kaliouby (2:08:12.480)

It's like...

Lex Fridman (2:08:13.480)

Yeah, like, I don't know, like five foot maybe, right?

Rana el Kaliouby (2:08:18.360)

Yeah.

Lex Fridman (2:08:19.360)

Yeah.

Rana el Kaliouby (2:08:20.360)

Pretty, pretty big.

Lex Fridman (2:08:21.360)

Pretty big.

Rana el Kaliouby (2:08:22.360)

It's designed to be at like airport lounges and, you know, retail stores, mostly customer

Lex Fridman (2:08:28.920)

service, right?

Rana el Kaliouby (2:08:30.680)

Hotel lobbies, and I mean, I don't know where the state of the robot is, but I think it's

Lex Fridman (2:08:37.200)

very promising.

Rana el Kaliouby (2:08:38.200)

I think there are a lot of applications where this can be helpful.

Lex Fridman (2:08:40.400)

I'm also really interested in, yeah, social robotics for the home, right?

Rana el Kaliouby (2:08:45.200)

Like that can help elderly people, for example, transport things from one location of the

Lex Fridman (2:08:50.880)

mind to the other, or even like just have your back in case something happens.

Rana el Kaliouby (2:08:55.520)

Yeah, I don't know.

Lex Fridman (2:08:58.000)

I do think it's a very interesting space.

Rana el Kaliouby (2:08:59.840)

It seems early though.

Lex Fridman (2:09:00.840)

Do you feel like the timing is now?

Rana el Kaliouby (2:09:04.960)

Yes, 100%.

Lex Fridman (2:09:09.840)

So it always seems early until it's not, right?

Rana el Kaliouby (2:09:12.160)

Right, right, right.

Rana el Kaliouby (2:09:13.160)

I think the time, I definitely think that the time is now, like this decade for social

Rana el Kaliouby (2:09:24.240)

robots.

Lex Fridman (2:09:25.920)

Whether the humanoid form is right, I don't think so, no.

Rana el Kaliouby (2:09:29.640)

I don't, I think the, like if we just look at Jibo as an example, I feel like most of

Rana el Kaliouby (2:09:40.000)

the problem, the challenge, the opportunity of social connection between an AI system

Lex Fridman (2:09:46.680)

and a human being does not require you to also solve the problem of robot manipulation

Lex Fridman (2:09:52.720)

and bipedal mobility.

Lex Fridman (2:09:55.320)

So I think you could do that with just a screen, honestly, but there's something about the

Lex Fridman (2:09:59.980)

interface of Jibo where it can rotate and so on that's also compelling.

Lex Fridman (2:10:03.880)

But you get to see all these robot companies that fail, incredible companies like Jibo

Lex Fridman (2:10:09.400)

and even, I mean, the iRobot in some sense is a big success story that it was able to

Rana el Kaliouby (2:10:17.600)

find a niche thing and focus on it, but in some sense it's not a success story because

Rana el Kaliouby (2:10:24.000)

they didn't build any other robot, like any other, it didn't expand into all kinds of

Rana el Kaliouby (2:10:30.960)

robotics.

Rana el Kaliouby (2:10:31.960)

Like once you're in the home, maybe that's what happens with Amazon is they'll flourish

Rana el Kaliouby (2:10:34.880)

into all kinds of other robots.

Lex Fridman (2:10:37.200)

But do you have a sense, by the way, why it's so difficult to build a robotics company?

Lex Fridman (2:10:43.760)

Like why so many companies have failed?

Lex Fridman (2:10:47.080)

I think it's like you're building a vertical stack, right?

Rana el Kaliouby (2:10:50.780)

Like you are building the hardware plus the software and you find you have to do this

Lex Fridman (2:10:54.480)

at a cost that makes sense.

Lex Fridman (2:10:56.040)

So I think Jibo was retailing at like, I don't know, like $800, like $700, $800, which for

Lex Fridman (2:11:05.080)

the use case, right, there's a dissonance there.

Rana el Kaliouby (2:11:10.020)

It's too high.

Lex Fridman (2:11:11.020)

So I think cost of building the whole platform in a way that is affordable for what value

Rana el Kaliouby (2:11:20.380)

it's bringing, I think that's a challenge.

Rana el Kaliouby (2:11:23.720)

I think for these home robots that are going to help you do stuff around the home, that's

Rana el Kaliouby (2:11:30.920)

a challenge too, like the mobility piece of it.

Lex Fridman (2:11:33.400)

That's hard.

Rana el Kaliouby (2:11:34.400)

Well, one of the things I'm really excited with Tesla Bot is the people working on it.

Lex Fridman (2:11:40.480)

And that's probably the criticism I would apply to some of the other folks who worked

Rana el Kaliouby (2:11:44.560)

on social robots is the people working on Tesla Bot know how to, they're focused on

Lex Fridman (2:11:50.200)

and know how to do mass manufacture and create a product that's super cheap.

Rana el Kaliouby (2:11:54.360)

Very cool.

Lex Fridman (2:11:55.360)

That's the focus.

Rana el Kaliouby (2:11:56.360)

The engineering focus isn't, I would say that you can also criticize them for that, is they're

Lex Fridman (2:12:00.480)

not focused on the experience of the robot.

Rana el Kaliouby (2:12:03.920)

They're focused on how to get this thing to do the basic stuff that the humanoid form

Lex Fridman (2:12:09.920)

requires to do it as cheap as possible.

Rana el Kaliouby (2:12:13.560)

Then the fewest number of actuators, the fewest numbers of motors, the increasing efficiency,

Lex Fridman (2:12:18.360)

they decrease the weight, all that kind of stuff.

Lex Fridman (2:12:20.400)

So that's really interesting.

Rana el Kaliouby (2:12:21.600)

I would say that Jibo and all those folks, they focus on the design, the experience,

Rana el Kaliouby (2:12:26.520)

all of that, and it's secondary how to manufacture.

Lex Fridman (2:12:29.840)

Right.

Lex Fridman (2:12:30.840)

So you have to think like the Tesla Bot folks from first principles, what is the fewest

Rana el Kaliouby (2:12:36.880)

number of components, the cheapest components, how can I build it as much in house as possible

Rana el Kaliouby (2:12:41.720)

without having to consider all the complexities of a supply chain, all that kind of stuff.

Lex Fridman (2:12:47.680)

It's interesting.

Rana el Kaliouby (2:12:48.680)

Because if you have to build a robotics company, you're not building one robot, you're building

Rana el Kaliouby (2:12:54.200)

hopefully millions of robots, you have to figure out how to do that where the final

Rana el Kaliouby (2:12:58.600)

thing, I mean, if it's Jibo type of robot, is there a reason why Jibo, like we can have

Lex Fridman (2:13:04.240)

this lengthy discussion, is there a reason why Jibo has to be over $100?

Rana el Kaliouby (2:13:08.880)

It shouldn't be.

Lex Fridman (2:13:09.880)

Right.

Rana el Kaliouby (2:13:10.880)

Like the basic components.

Lex Fridman (2:13:11.880)

Right.

Rana el Kaliouby (2:13:12.880)

Components of it.

Lex Fridman (2:13:13.880)

Right.

Lex Fridman (2:13:14.880)

Like you could start to actually discuss like, okay, what is the essential thing about Jibo?

Lex Fridman (2:13:19.080)

How much, what is the cheapest way I can have a screen?

Lex Fridman (2:13:21.440)

What's the cheapest way I can have a rotating base?

Lex Fridman (2:13:23.760)

Right.

Rana el Kaliouby (2:13:24.760)

All that kind of stuff.

Lex Fridman (2:13:25.760)

Right, get down, continuously drive down costs.

Rana el Kaliouby (2:13:29.960)

Speaking of which, you have launched an extremely successful companies, you have helped others,

Lex Fridman (2:13:35.520)

you've invested in companies.

Lex Fridman (2:13:37.920)

Can you give advice on how to start a successful company?

Lex Fridman (2:13:44.160)

I would say have a problem that you really, really, really want to solve, right?

Rana el Kaliouby (2:13:48.780)

Something that you're deeply passionate about.

Lex Fridman (2:13:53.800)

And honestly, take the first step.

Rana el Kaliouby (2:13:55.880)

Like that's often the hardest.

Lex Fridman (2:13:58.520)

And don't overthink it.

Rana el Kaliouby (2:13:59.520)

Like, you know, like this idea of a minimum viable product or a minimum viable version

Lex Fridman (2:14:04.000)

of an idea, right?

Rana el Kaliouby (2:14:05.000)

Like, yes, you're thinking about this, like a humongous, like super elegant, super beautiful

Lex Fridman (2:14:09.160)

thing.

Rana el Kaliouby (2:14:10.160)

What, like reduce it to the littlest thing you can bring to market that can solve a problem

Lex Fridman (2:14:14.640)

or that can, you know, that can help address a pain point that somebody has.

Lex Fridman (2:14:20.880)

They often tell you, like, start with a customer of one, right?

Rana el Kaliouby (2:14:24.320)

If you can solve a problem for one person, then there's probably going to be yourself

Rana el Kaliouby (2:14:28.400)

or some other person.

Lex Fridman (2:14:29.400)

Right.

Rana el Kaliouby (2:14:30.400)

Pick a person.

Lex Fridman (2:14:31.400)

Exactly.

Rana el Kaliouby (2:14:32.400)

It could be you.

Rana el Kaliouby (2:14:33.400)

Yeah, that's actually often a good sign that if you enjoy a thing, enjoy a thing where

Rana el Kaliouby (2:14:37.240)

you have a specific problem that you'd like to solve, that's a good, that's a good end

Lex Fridman (2:14:41.000)

of one to focus on.

Lex Fridman (2:14:43.600)

What else, what else is there to actually step one is the hardest, but there's other

Lex Fridman (2:14:49.360)

steps as well, right?

Lex Fridman (2:14:51.200)

I also think like who you bring around the table early on is so key, right?

Lex Fridman (2:14:58.080)

Like being clear on, on what I call like your core values or your North Star.

Rana el Kaliouby (2:15:02.440)

It might sound fluffy, but actually it's not.

Lex Fridman (2:15:04.840)

So and Roz and I feel like we did that very early on.

Rana el Kaliouby (2:15:08.840)

We sat around her kitchen table and we said, okay, there's so many applications of this

Lex Fridman (2:15:13.040)

technology.

Lex Fridman (2:15:14.040)

How are we going to draw the line?

Lex Fridman (2:15:15.040)

How are we going to set boundaries?

Rana el Kaliouby (2:15:16.940)

We came up with a set of core values that in the hardest of times we fell back on to

Lex Fridman (2:15:22.680)

determine how we make decisions.

Lex Fridman (2:15:25.320)

And so I feel like just getting clarity on these core, like for us, it was respecting

Lex Fridman (2:15:28.760)

people's privacy, only engaging with industries where it's clear opt in.

Lex Fridman (2:15:33.400)

So for instance, we don't do any work in security and surveillance.

Lex Fridman (2:15:38.680)

So things like that, just getting, we very big on, you know, one of our core values is

Lex Fridman (2:15:42.480)

human connection and empathy, right?

Lex Fridman (2:15:44.720)

And that is, yes, it's an AI company, but it's about people.

Rana el Kaliouby (2:15:47.840)

Well, these are all, they become encoded in how we act, even if you're a small, tiny team

Lex Fridman (2:15:54.520)

of two or three or whatever.

Lex Fridman (2:15:57.460)

So I think that's another piece of advice.

Lex Fridman (2:15:59.520)

So what about finding people, hiring people?

Rana el Kaliouby (2:16:02.680)

If you care about people as much as you do, like this, it seems like such a difficult

Lex Fridman (2:16:07.800)

thing to hire the right people.

Rana el Kaliouby (2:16:10.680)

I think early on as a startup, you want people who have, who share the passion and the conviction

Lex Fridman (2:16:16.120)

because it's going to be tough.

Lex Fridman (2:16:17.880)

Like I've yet to meet a startup where it was just a straight line to success, right?

Lex Fridman (2:16:25.000)

Even not just startup, like even everyday people's lives, right?

Rana el Kaliouby (2:16:28.280)

You always like run into obstacles and you run into naysayers and you need people who

Lex Fridman (2:16:36.280)

are believers, whether they're people on your team or even your investors.

Rana el Kaliouby (2:16:40.600)

You need investors who are really believers in what you're doing, because that means they

Lex Fridman (2:16:44.960)

will stick with you.

Rana el Kaliouby (2:16:47.040)

They won't give up at the first obstacle.

Lex Fridman (2:16:49.280)

I think that's important.

Lex Fridman (2:16:50.920)

What about raising money?

Lex Fridman (2:16:51.920)

What about finding investors, first of all, raising money, but also raising money from

Rana el Kaliouby (2:16:59.720)

the right sources from that ultimately don't hinder you, but help you, empower you, all

Lex Fridman (2:17:05.960)

that kind of stuff.

Lex Fridman (2:17:06.960)

What advice would you give there?

Lex Fridman (2:17:08.600)

You successfully raised money many times in your life.

Rana el Kaliouby (2:17:12.120)

Yeah.

Lex Fridman (2:17:13.120)

Again, it's not just about the money.

Rana el Kaliouby (2:17:15.080)

It's about finding the right investors who are going to be aligned in terms of what you

Lex Fridman (2:17:20.360)

want to build and believe in your core values.

Rana el Kaliouby (2:17:23.160)

For example, especially later on, in my latest round of funding, I try to bring in investors

Rana el Kaliouby (2:17:31.280)

that really care about the ethics of AI and the alignment of vision and mission and core

Rana el Kaliouby (2:17:40.120)

values is really important.

Lex Fridman (2:17:41.120)

It's like you're picking a life partner.

Rana el Kaliouby (2:17:43.920)

It's the same kind of...

Lex Fridman (2:17:45.160)

So you take it that seriously for investors?

Rana el Kaliouby (2:17:47.560)

Yeah, because they're going to have to stick with you.

Lex Fridman (2:17:50.040)

You're stuck together.

Rana el Kaliouby (2:17:51.480)

For a while anyway.

Lex Fridman (2:17:52.480)

Yeah.

Rana el Kaliouby (2:17:53.480)

Maybe not for life, but for a while, for sure.

Lex Fridman (2:17:56.880)

For better or worse.

Rana el Kaliouby (2:17:57.880)

I forget what the vowels usually sound like.

Lex Fridman (2:17:59.920)

For better or worse?

Rana el Kaliouby (2:18:00.920)

Through something.

Lex Fridman (2:18:01.920)

Yeah.

Rana el Kaliouby (2:18:02.920)

Oh boy.

Lex Fridman (2:18:03.920)

Yeah.

Rana el Kaliouby (2:18:04.920)

Anyway, it's romantic and deep and you're in it for a while.

Lex Fridman (2:18:15.320)

So it's not just about the money.

Rana el Kaliouby (2:18:18.040)

You tweeted about going to your first capital camp investing get together and that you learned

Lex Fridman (2:18:23.560)

a lot.

Lex Fridman (2:18:24.560)

So this is about investing.

Lex Fridman (2:18:27.840)

So what have you learned from that?

Lex Fridman (2:18:30.240)

What have you learned about investing in general from both because you've been on both ends

Lex Fridman (2:18:34.160)

of it?

Rana el Kaliouby (2:18:35.160)

I mean, I try to use my experience as an operator now with my investor hat on when I'm identifying

Lex Fridman (2:18:41.720)

companies to invest in.

Rana el Kaliouby (2:18:45.280)

First of all, I think the good news is because I have a technology background and I really

Rana el Kaliouby (2:18:49.460)

understand machine learning and computer vision and AI, et cetera, I can apply that level

Rana el Kaliouby (2:18:54.600)

of understanding because everybody says they're an AI company or they're an AI tech.

Lex Fridman (2:18:59.720)

And I'm like, no, no, no, no, no, show me the technology.

Lex Fridman (2:19:02.880)

So I can do that level of diligence, which I actually love.

Lex Fridman (2:19:07.640)

And then I have to do the litmus test of, if I'm in a conversation with you, am I excited

Lex Fridman (2:19:12.760)

to tell you about this new company that I just met?

Lex Fridman (2:19:16.520)

And if I'm an ambassador for that company and I'm passionate about what they're doing,

Rana el Kaliouby (2:19:22.400)

I usually use that.

Lex Fridman (2:19:24.720)

Yeah.

Rana el Kaliouby (2:19:25.720)

That's important to me when I'm investing.

Lex Fridman (2:19:27.720)

So that means you actually can explain what they're doing and you're excited about it.

Rana el Kaliouby (2:19:34.720)

Exactly.

Lex Fridman (2:19:35.720)

Exactly.

Rana el Kaliouby (2:19:36.720)

Thank you for putting it so succinctly, like rambling, but exactly that's it.

Lex Fridman (2:19:41.720)

No, but sometimes it's funny, but sometimes it's unclear exactly.

Rana el Kaliouby (2:19:48.280)

I'll hear people tell me, you know, in the talk for a while and it sounds cool, like

Rana el Kaliouby (2:19:53.120)

they paint a picture of a world, but then when you try to summarize it, you're not

Rana el Kaliouby (2:19:56.600)

exactly clear.

Rana el Kaliouby (2:19:57.600)

Like maybe what the core powerful idea is, like you can't just build another Facebook

Rana el Kaliouby (2:20:05.200)

or there has to be a core, simple to explain idea that then you can or can't get excited

Lex Fridman (2:20:15.360)

about, but it's there, it's right there.

Rana el Kaliouby (2:20:19.000)

Yeah.

Lex Fridman (2:20:20.000)

But how do you ultimately pick who you think will be successful?

Rana el Kaliouby (2:20:25.520)

It's not just about the thing you're excited about, like there's other stuff.

Lex Fridman (2:20:29.320)

Right.

Lex Fridman (2:20:30.320)

And then there's all the, you know, with early stage companies, like pre seed companies,

Rana el Kaliouby (2:20:34.400)

which is where I'm investing, sometimes the business model isn't clear yet, or the go

Rana el Kaliouby (2:20:40.640)

to market strategy isn't clear.

Rana el Kaliouby (2:20:42.240)

There's usually like, it's very early on that some of these things haven't been hashed

Rana el Kaliouby (2:20:45.560)

out, which is okay.

Lex Fridman (2:20:47.840)

So the way I like to think about it is like, if this company is successful, will this be

Lex Fridman (2:20:51.720)

a multi billion slash trillion dollar market, you know, or company?

Lex Fridman (2:20:56.660)

And so that's definitely a lens that I use.

Lex Fridman (2:21:01.280)

What's pre seed?

Lex Fridman (2:21:02.280)

What are the different stages and what's the most exciting stage and what's, or no, what's

Rana el Kaliouby (2:21:07.880)

interesting about every stage, I guess.

Lex Fridman (2:21:09.680)

Yeah.

Lex Fridman (2:21:10.680)

So pre seed is usually when you're just starting out, you've maybe raised the friends and family

Lex Fridman (2:21:16.000)

rounds.

Lex Fridman (2:21:17.000)

So you've raised some money from people, you know, and you're getting ready to take your

Lex Fridman (2:21:20.720)

first institutional check in, like first check from an investor.

Lex Fridman (2:21:25.680)

And I love the stage.

Lex Fridman (2:21:28.920)

There's a lot of uncertainty.

Rana el Kaliouby (2:21:30.780)

Some investors really don't like the stage because the financial models aren't there.

Lex Fridman (2:21:36.760)

Often the teams aren't even like formed really, really early.

Lex Fridman (2:21:40.920)

But to me, it's like a magical stage because it's the time when there's so much conviction,

Lex Fridman (2:21:48.480)

so much belief, almost delusional, right?

Lex Fridman (2:21:51.800)

And there's a little bit of naivete around with founders at the stage.

Lex Fridman (2:21:57.120)

I just love it.

Rana el Kaliouby (2:21:58.120)

It's contagious.

Lex Fridman (2:21:59.120)

And I love that I can, often they're first time founders, not always, but often they're

Lex Fridman (2:22:06.560)

first time founders and I can share my experience as a founder myself and I can empathize, right?

Lex Fridman (2:22:12.620)

And I can almost, I create a safe ground where, because, you know, you have to be careful

Lex Fridman (2:22:18.520)

what you tell your investors, right?

Lex Fridman (2:22:21.200)

And I will often like say, I've been in your shoes as a founder.

Rana el Kaliouby (2:22:24.800)

You can tell me if it's challenging, you can tell me what you're struggling with.

Lex Fridman (2:22:28.360)

It's okay to vent.

Lex Fridman (2:22:30.160)

So I create that safe ground and I think that's a superpower.

Lex Fridman (2:22:34.760)

Yeah.

Rana el Kaliouby (2:22:35.760)

You have to, I guess you have to figure out if this kind of person is going to be able

Rana el Kaliouby (2:22:40.400)

to ride the roller coaster, like of many pivots and challenges and all that kind of stuff.

Lex Fridman (2:22:48.280)

And if the space of ideas they're working in is interesting, like the way they think

Lex Fridman (2:22:53.040)

about the world.

Rana el Kaliouby (2:22:54.040)

Yeah.

Rana el Kaliouby (2:22:55.040)

Because if it's successful, the thing they end up with might be very different, the reason

Rana el Kaliouby (2:23:00.000)

it's successful for them.

Rana el Kaliouby (2:23:01.560)

Actually, you know, I was going to say the third, so the technology is one aspect, the

Lex Fridman (2:23:07.480)

market or the idea, right, is the second and the third is the founder, right?

Rana el Kaliouby (2:23:11.240)

Is this somebody who I believe has conviction, is a hustler, you know, is going to overcome

Lex Fridman (2:23:18.160)

obstacles?

Lex Fridman (2:23:19.160)

Yeah, I think that is going to be a great leader, right?

Rana el Kaliouby (2:23:23.200)

Like as a startup, as a founder, you're often, you are the first person and your role is

Lex Fridman (2:23:28.440)

to bring amazing people around you to build this thing.

Lex Fridman (2:23:32.880)

And so you're an evangelist, right?

Lex Fridman (2:23:36.160)

So how good are you going to be at that?

Lex Fridman (2:23:38.000)

So I try to evaluate that too.

Rana el Kaliouby (2:23:41.360)

You also in the tweet thread about it, mention, is this a known concept, random rich dudes

Rana el Kaliouby (2:23:46.960)

are RDS and saying that there should be like random rich women, I guess.

Lex Fridman (2:23:53.280)

What's the dudes, what's the dudes version of women, the women version of dudes, ladies?

Rana el Kaliouby (2:23:58.280)

I don't know.

Lex Fridman (2:23:59.280)

I don't know.

Lex Fridman (2:24:00.280)

What's, what's, is this a technical term?

Lex Fridman (2:24:01.500)

Is this known?

Lex Fridman (2:24:02.500)

Random rich dudes?

Rana el Kaliouby (2:24:03.500)

I didn't make that up, but I was at this capital camp, which is a get together for investors

Rana el Kaliouby (2:24:09.680)

of all types.

Lex Fridman (2:24:11.600)

And there must have been maybe 400 or so attendees, maybe 20 were women.

Rana el Kaliouby (2:24:19.160)

It was just very disproportionately, you know, male dominated, which I'm used to.

Lex Fridman (2:24:25.680)

I think you're used to this kind of thing.

Rana el Kaliouby (2:24:26.960)

I'm used to it, but it's still surprising.

Lex Fridman (2:24:29.920)

And as I'm raising money for this fund, so my fund partner is a guy called Rob May, who's

Rana el Kaliouby (2:24:36.320)

done this before.

Lex Fridman (2:24:37.680)

So I'm new to the investing world, but he's done this before.

Rana el Kaliouby (2:24:42.000)

Most of our investors in the fund are these, I mean, awesome.

Lex Fridman (2:24:45.880)

I'm super grateful to them.

Rana el Kaliouby (2:24:47.800)

Random just rich guys.

Lex Fridman (2:24:48.800)

I'm like, where are the rich women?

Lex Fridman (2:24:50.400)

So I'm really adamant in both investing in women led AI companies, but I also would love

Rana el Kaliouby (2:24:57.160)

to have women investors be part of my fund because I think that's how we drive change.

Rana el Kaliouby (2:25:03.240)

Yeah.

Lex Fridman (2:25:04.240)

So that takes time, of course, but there's been quite a lot of progress, but yeah, for

Rana el Kaliouby (2:25:09.640)

the next Mark Zuckerberg to be a woman and all that kind of stuff, because that's just

Rana el Kaliouby (2:25:13.840)

like a huge number of wealth generated by women and then controlled by women and allocated

Rana el Kaliouby (2:25:19.760)

by women and all that kind of stuff.

Lex Fridman (2:25:22.200)

And then beyond just women, just broadly across all different measures of diversity and so

Rana el Kaliouby (2:25:28.880)

on.

Lex Fridman (2:25:29.880)

Let me ask you to put on your wise sage hat.

Lex Fridman (2:25:35.880)

So you already gave advice on startups and just advice for women, but in general advice

Rana el Kaliouby (2:25:45.120)

for folks in high school or college today, how to have a career they can be proud of,

Lex Fridman (2:25:51.080)

how to have a life they can be proud of.

Lex Fridman (2:25:55.560)

I suppose you have to give this kind of advice to your kids.

Rana el Kaliouby (2:25:58.560)

Yeah.

Lex Fridman (2:25:59.560)

Well, here's the number one advice that I give to my kids.

Rana el Kaliouby (2:26:03.400)

My daughter's now 19 by the way, and my son's 13 and a half, so they're not little kids

Lex Fridman (2:26:08.200)

anymore.

Lex Fridman (2:26:09.200)

Does it break your heart?

Lex Fridman (2:26:11.560)

It does.

Rana el Kaliouby (2:26:12.560)

They're awesome.

Rana el Kaliouby (2:26:13.560)

They're my best friends, but yeah, I think the number one advice I would share is embark

Lex Fridman (2:26:19.880)

on a journey without attaching to outcomes and enjoy the journey, right?

Lex Fridman (2:26:25.200)

So we often were so obsessed with the end goal that doesn't allow us to be open to different

Rana el Kaliouby (2:26:34.360)

endings of a journey or a story, so you become like so fixated on a particular path.

Rana el Kaliouby (2:26:41.840)

You don't see the beauty in the other alternative path, and then you forget to enjoy the journey

Rana el Kaliouby (2:26:48.520)

because you're just so fixated on the goal, and I've been guilty of that for many, many

Rana el Kaliouby (2:26:53.320)

years of my life, and I'm now trying to make the shift of, no, no, no, I'm going to again

Rana el Kaliouby (2:27:00.180)

trust that things are going to work out and it'll be amazing and maybe even exceed your

Lex Fridman (2:27:04.480)

dreams.

Rana el Kaliouby (2:27:05.480)

We have to be open to that.

Lex Fridman (2:27:07.280)

Yeah.

Rana el Kaliouby (2:27:08.280)

Taking a leap into all kinds of things.

Rana el Kaliouby (2:27:09.840)

I think you tweeted like you went on vacation by yourself or something like this.

Rana el Kaliouby (2:27:13.800)

I know.

Lex Fridman (2:27:14.800)

Yes, and just going, just taking the leap.

Rana el Kaliouby (2:27:19.120)

Doing it.

Lex Fridman (2:27:20.120)

Totally doing it.

Lex Fridman (2:27:21.120)

And enjoying it, enjoying the moment, enjoying the weeks, enjoying not looking at some kind

Lex Fridman (2:27:26.720)

of career ladder, next step and so on.

Rana el Kaliouby (2:27:29.640)

Yeah, there's something to that, like over planning too.

Lex Fridman (2:27:34.320)

I'm surrounded by a lot of people that kind of, so I don't plan.

Lex Fridman (2:27:37.800)

You don't?

Lex Fridman (2:27:38.800)

No.

Lex Fridman (2:27:39.800)

Do you not do goal setting?

Rana el Kaliouby (2:27:43.240)

My goal setting is very like, I like the affirmations, it's very, it's almost, I don't know how to

Rana el Kaliouby (2:27:52.760)

put it into words, but it's a little bit like what my heart yearns for kind of, and I guess

Rana el Kaliouby (2:28:02.040)

in the space of emotions more than in the space of like, this will be like in the rational

Rana el Kaliouby (2:28:08.640)

space because I just try to picture a world that I would like to be in and that world

Lex Fridman (2:28:16.280)

is not clearly pictured, it's mostly in the emotional world.

Rana el Kaliouby (2:28:19.400)

I mean, I think about that from robots because I have this desire, I've had it my whole life

Rana el Kaliouby (2:28:26.640)

to, well, it took different shapes, but I think once I discovered AI, the desire was

Rana el Kaliouby (2:28:33.180)

to, I think in the context of this conversation could be easily easier described as basically

Rana el Kaliouby (2:28:41.120)

a social robotics company and that's something I dreamed of doing and well, there's a lot

Rana el Kaliouby (2:28:50.880)

of complexity to that story, but that's the only thing, honestly, I dream of doing.

Lex Fridman (2:28:55.680)

So I imagine a world that I could help create, but it's not, there's no steps along the way

Lex Fridman (2:29:05.560)

and I think I'm just kind of stumbling around and following happiness and working my ass

Rana el Kaliouby (2:29:12.720)

off in almost random, like an ant does in random directions, but a lot of people, a

Rana el Kaliouby (2:29:18.240)

lot of successful people around me say this, you should have a plan, you should have a

Rana el Kaliouby (2:29:20.920)

clear goal, you have a goal at the end of the month, you have a goal at the end of the

Rana el Kaliouby (2:29:23.960)

month, I don't, I don't, I don't and there's a balance to be struck, of course, but there's

Rana el Kaliouby (2:29:33.320)

something to be said about really making sure that you're living life to the fullest, that

Rana el Kaliouby (2:29:40.360)

goals can actually get in the way of.

Lex Fridman (2:29:43.760)

So one of the best, like kind of most, what do you call it when it challenges your brain,

Lex Fridman (2:29:52.560)

what do you call it?

Rana el Kaliouby (2:29:56.760)

The only thing that comes to mind, and this is me saying is the mindfuck, but yes.

Rana el Kaliouby (2:30:00.320)

Okay.

Lex Fridman (2:30:01.320)

Okay.

Rana el Kaliouby (2:30:02.320)

Okay.

Lex Fridman (2:30:03.320)

Something like that.

Rana el Kaliouby (2:30:04.320)

Yes.

Lex Fridman (2:30:05.320)

Super inspiring talk.

Rana el Kaliouby (2:30:06.320)

Kenneth Stanley, he was at OpenAI, he just laughed and he has a book called Why Greatness

Lex Fridman (2:30:11.320)

Can't Be Planned and it's actually an AI book.

Lex Fridman (2:30:14.160)

So and he's done all these experiments that basically show that when you over optimize,

Lex Fridman (2:30:20.400)

you, like the trade off is you're less creative, right?

Lex Fridman (2:30:23.760)

And to create true greatness and truly creative solutions to problems, you can't over plan

Lex Fridman (2:30:30.760)

it.

Rana el Kaliouby (2:30:31.760)

You can't.

Lex Fridman (2:30:32.760)

And I thought that was, and so he generalizes it beyond AI and he talks about how we apply

Lex Fridman (2:30:36.800)

that in our personal life and in our organizations and our companies, which are over KPIs, right?

Rana el Kaliouby (2:30:42.320)

Like look at any company in the world and it's all like, these aren't the goals, these

Rana el Kaliouby (2:30:45.960)

aren't weekly goals and the sprints and then the quarterly goals, blah, blah, blah.

Lex Fridman (2:30:51.080)

And he just shows with a lot of his AI experiments that that's not how you create truly game

Rana el Kaliouby (2:30:58.560)

changing ideas.

Lex Fridman (2:30:59.640)

So there you go.

Rana el Kaliouby (2:31:00.640)

Yeah, yeah.

Lex Fridman (2:31:01.640)

You can.

Rana el Kaliouby (2:31:02.640)

He's awesome.

Lex Fridman (2:31:03.640)

Yeah.

Rana el Kaliouby (2:31:04.640)

There's a balance of course.

Rana el Kaliouby (2:31:05.640)

That's yeah, many moments of genius will not come from planning and goals, but you still

Rana el Kaliouby (2:31:11.660)

have to build factories and you still have to manufacture and you still have to deliver

Lex Fridman (2:31:15.200)

and there's still deadlines and all that kind of stuff.

Lex Fridman (2:31:17.280)

And that for that, it's good to have goals.

Rana el Kaliouby (2:31:19.280)

I do goal setting with my kids, we all have our goals, but, but, but I think we're starting

Rana el Kaliouby (2:31:25.200)

to morph into more of these like bigger picture goals and not obsess about like, I don't know,

Lex Fridman (2:31:30.800)

it's hard.

Rana el Kaliouby (2:31:31.800)

Well, I honestly think with, especially with kids, it's better, much, much better to have

Rana el Kaliouby (2:31:34.840)

a plan and have goals and so on because you have to, you have to learn the muscle of like

Lex Fridman (2:31:38.400)

what it feels like to get stuff done.

Lex Fridman (2:31:40.480)

Yeah.

Lex Fridman (2:31:41.480)

And once you learn that, there's flexibility for me because I spend most of my life with

Lex Fridman (2:31:46.720)

goal setting and so on.

Lex Fridman (2:31:48.040)

So like I've gotten good with grades and school.

Rana el Kaliouby (2:31:50.560)

I mean, school, if you want to be successful at school, yeah, I mean the kind of stuff

Rana el Kaliouby (2:31:55.040)

in high school and college, the kids have to do in terms of managing their time and

Lex Fridman (2:31:59.280)

getting so much stuff done.

Rana el Kaliouby (2:32:01.160)

It's like, you know, taking five, six, seven classes in college, they're like that would

Rana el Kaliouby (2:32:06.500)

break the spirit of most humans if they took one of them later in life, it's like really

Rana el Kaliouby (2:32:13.200)

difficult stuff, especially engineering curricula.

Lex Fridman (2:32:16.560)

So I think you have to learn that skill, but once you learn it, you can maybe, cause you're,

Rana el Kaliouby (2:32:22.680)

you can be a little bit on autopilot and use that momentum and then allow yourself to be

Lex Fridman (2:32:27.280)

lost in the flow of life.

Rana el Kaliouby (2:32:28.920)

You know, just kind of, or also give like, I worked pretty hard to allow myself to have

Lex Fridman (2:32:38.760)

the freedom to do that.

Rana el Kaliouby (2:32:39.760)

That's really, that's a tricky freedom to have because like a lot of people get lost

Rana el Kaliouby (2:32:44.600)

in the rat race and they, and they also like financially, they, whenever you get a raise,

Rana el Kaliouby (2:32:52.920)

they'll get like a bigger house or something like this.

Rana el Kaliouby (2:32:55.680)

I put very, so like, there's, you're always trapped in this race, I put a lot of emphasis

Rana el Kaliouby (2:32:59.720)

on living like below my means always.

Lex Fridman (2:33:05.240)

And so there's a lot of freedom to do whatever, whatever the heart desires that that's a relief,

Lex Fridman (2:33:12.240)

but everyone has to decide what's the right thing, what's the right thing for them.

Rana el Kaliouby (2:33:15.580)

For some people having a lot of responsibilities, like a house they can barely afford or having

Rana el Kaliouby (2:33:21.560)

a lot of kids, the responsibility side of that is really, helps them get their shit

Lex Fridman (2:33:27.600)

together.

Rana el Kaliouby (2:33:28.600)

Like, all right, I need to be really focused and get, some of the most successful people

Lex Fridman (2:33:32.080)

I know have kids and the kids bring out the best in them.

Rana el Kaliouby (2:33:34.760)

They make them more productive and less productive.

Lex Fridman (2:33:36.720)

Right, it's accountability.

Rana el Kaliouby (2:33:37.720)

Yeah.

Lex Fridman (2:33:38.720)

It's an accountability thing, absolutely.

Lex Fridman (2:33:39.720)

And almost something to actually live and fight and work for, like having a family,

Rana el Kaliouby (2:33:45.400)

it's fascinating to see because you would think kids would be a hit on productivity,

Lex Fridman (2:33:49.520)

but they're not, for a lot of really successful people, they really like, they're like an

Lex Fridman (2:33:53.680)

engine of.

Rana el Kaliouby (2:33:54.680)

Right, efficiency.

Lex Fridman (2:33:55.680)

Oh my God.

Rana el Kaliouby (2:33:56.680)

Yeah.

Lex Fridman (2:33:57.680)

Yeah.

Rana el Kaliouby (2:33:58.680)

It's weird.

Lex Fridman (2:33:59.680)

Yeah.

Rana el Kaliouby (2:34:00.680)

I mean, it's beautiful.

Lex Fridman (2:34:01.680)

It's beautiful to see.

Lex Fridman (2:34:02.680)

And also a source of happiness.

Lex Fridman (2:34:03.680)

Speaking of which, what role do you think love plays in the human condition, love?

Rana el Kaliouby (2:34:12.080)

I think love is, yeah, I think it's why we're all here.

Lex Fridman (2:34:19.880)

I think it would be very hard to live life without love in any of its forms, right?

Lex Fridman (2:34:26.640)

Yeah, that's the most beautiful of forms that human connection takes, right?

Lex Fridman (2:34:35.080)

Yeah.

Lex Fridman (2:34:36.080)

And everybody wants to feel loved, right, in one way or another, right?

Lex Fridman (2:34:42.200)

And to love.

Rana el Kaliouby (2:34:43.200)

Yeah.

Lex Fridman (2:34:44.200)

It feels good.

Lex Fridman (2:34:45.200)

And to love too, totally.

Lex Fridman (2:34:46.200)

Yeah, I agree with that.

Rana el Kaliouby (2:34:47.200)

Both of it.

Lex Fridman (2:34:48.200)

Yeah.

Rana el Kaliouby (2:34:49.200)

I'm not even sure what feels better.

Lex Fridman (2:34:50.200)

Both, both like that.

Rana el Kaliouby (2:34:51.200)

Yeah, to give and to give love too, yeah.

Lex Fridman (2:34:54.760)

And it is like we've been talking about an interesting question, whether some of that,

Rana el Kaliouby (2:34:59.680)

whether one day we'll be able to love a toaster.

Lex Fridman (2:35:02.640)

Okay.

Rana el Kaliouby (2:35:03.640)

It's some small.

Rana el Kaliouby (2:35:05.240)

I wasn't quite thinking about that when I said like, yeah, like we all need love and

Rana el Kaliouby (2:35:10.320)

give love.

Lex Fridman (2:35:11.320)

That's all I was thinking about.

Rana el Kaliouby (2:35:12.320)

Okay.

Lex Fridman (2:35:13.320)

I was thinking about Brad Pitt and toasters.

Rana el Kaliouby (2:35:14.320)

Okay, toasters, great.

Lex Fridman (2:35:15.320)

All right.

Rana el Kaliouby (2:35:16.320)

Well, I think we started on love and ended on love.

Lex Fridman (2:35:20.200)

This was an incredible conversation, Rhonda.

Rana el Kaliouby (2:35:22.320)

Thank you so much.

Lex Fridman (2:35:23.320)

Thank you.

Rana el Kaliouby (2:35:24.320)

You're an incredible person.

Rana el Kaliouby (2:35:25.320)

Thank you for everything you're doing in AI, in the space of just caring about humanity,

Rana el Kaliouby (2:35:32.960)

caring about emotion, about love, and being an inspiration to a huge number of people

Lex Fridman (2:35:38.160)

in robotics, in AI, in science, in the world in general.

Lex Fridman (2:35:42.320)

So thank you for talking to me.

Lex Fridman (2:35:43.320)

It's an honor.

Rana el Kaliouby (2:35:44.320)

Thank you for having me.

Lex Fridman (2:35:45.320)

And you know, I'm a big fan of yours as well.

Lex Fridman (2:35:47.320)

So it's been a pleasure.

Lex Fridman (2:35:49.740)

Thanks for listening to this conversation with Rhonda Alkalioubi.

Rana el Kaliouby (2:35:52.840)

To support this podcast, please check out our sponsors in the description.

Lex Fridman (2:35:56.940)

And now let me leave you with some words from Helen Keller.

Rana el Kaliouby (2:36:00.680)

The best and most beautiful things in the world cannot be seen or even touched.

Lex Fridman (2:36:05.480)

They must be felt with the heart.

Rana el Kaliouby (2:36:09.440)

Thank you for listening and hope to see you next time.

Lex Fridman (30:01.200)

And I didn't, I did that like every few weeks I would, you know, hop on a plane and go to

Rana el Kaliouby (30:05.520)

Boston.

Lex Fridman (30:06.400)

But that, that changed the trajectory of my life.

Lex Fridman (30:08.480)

There was no, I kind of outgrew my dreams, right?

Lex Fridman (30:12.880)

I didn't want to go back to Egypt anymore and be faculty.

Rana el Kaliouby (30:16.720)

Like that was no longer my dream.

Lex Fridman (30:18.320)

I had a dream.

Lex Fridman (30:19.200)

What was the, what was it like to be at MIT?

Lex Fridman (30:22.560)

What was that culture shock?

Lex Fridman (30:25.040)

You mean America in general, but also, I mean, Cambridge has its own culture, right?

Lex Fridman (30:31.040)

So what was MIT like and what was America like?

Rana el Kaliouby (30:34.000)

I think, I wonder if that's similar to your experience at MIT.

Rana el Kaliouby (30:37.600)

I was just, at the Media Lab in particular, I was just really, impressed is not the right

Rana el Kaliouby (30:45.200)

word.

Rana el Kaliouby (30:46.240)

I didn't expect the openness to like innovation and the acceptance of taking a risk and failing.

Lex Fridman (30:54.800)

Like failure isn't really accepted back in Egypt, right?

Lex Fridman (30:58.320)

You don't want to fail.

Rana el Kaliouby (30:59.040)

Like there's a fear of failure, which I think has been hardwired in my brain.

Lex Fridman (31:03.200)

But you get to MIT and it's okay to start things.

Lex Fridman (31:05.840)

And if they don't work out, like it's okay.

Lex Fridman (31:08.000)

You pivot to another idea.

Lex Fridman (31:09.840)

And that kind of thinking was just very new to me.

Lex Fridman (31:12.640)

That's liberating.

Rana el Kaliouby (31:13.600)

Well, Media Lab, for people who don't know, MIT Media Lab is its own beautiful thing because

Lex Fridman (31:19.840)

they, I think more than other places at MIT, reach for big ideas.

Lex Fridman (31:24.000)

And like they try, I mean, I think, I mean, depending of course on who, but certainly

Rana el Kaliouby (31:28.480)

with Roslyn, you try wild stuff, you try big things and crazy things and also try to take

Rana el Kaliouby (31:36.160)

things to completion so you can demo them.

Lex Fridman (31:38.240)

So always, always, always have a demo.

Rana el Kaliouby (31:42.240)

Like if you go, one of the sad things to me about robotics labs at MIT, and there's like

Rana el Kaliouby (31:46.880)

over 30, I think, is like, usually when you show up to a robotics lab, there's not a single

Rana el Kaliouby (31:53.680)

working robot, they're all broken.

Lex Fridman (31:55.760)

All the robots are broken.

Rana el Kaliouby (31:57.280)

The robots are broken, which is like the normal state of things because you're working on

Lex Fridman (32:01.600)

them.

Lex Fridman (32:02.080)

But it would be nice if we lived in a world where robotics labs had some robots functioning.

Rana el Kaliouby (32:08.880)

One of my like favorite moments that just sticks with me, I visited Boston Dynamics

Lex Fridman (32:13.360)

and there was a, first of all, seeing so many spots, so many legged robots in one place.

Lex Fridman (32:20.240)

I'm like, I'm home.

Lex Fridman (32:22.720)

But the, yeah.

Lex Fridman (32:24.880)

This is where I was built.

Rana el Kaliouby (32:27.200)

The cool thing was just to see there was a random robot spot was walking down the hall.

Rana el Kaliouby (32:33.360)

It's probably doing mapping, but it looked like he wasn't doing anything and he was wearing

Rana el Kaliouby (32:37.120)

he or she, I don't know.

Lex Fridman (32:39.120)

But it, well, I like, in my mind, there are people, they have a backstory, but this one

Rana el Kaliouby (32:44.640)

in particular definitely has a backstory because he was wearing a cowboy hat.

Lex Fridman (32:48.640)

So I just saw a spot robot with a cowboy hat walking down the hall and there was just this

Rana el Kaliouby (32:54.160)

feeling like there's a life, like he has a life.

Lex Fridman (32:58.880)

He probably has to commute back to his family at night.

Rana el Kaliouby (33:02.240)

Like there's a, there's a feeling like there's life instilled in this robot and it's magical.

Lex Fridman (33:07.440)

I don't know.

Rana el Kaliouby (33:07.760)

It was, it was kind of inspiring to see.

Lex Fridman (33:09.520)

Did it say hello to, did he say hello to you?

Rana el Kaliouby (33:12.000)

No, it's very, there's a focus nature to the robot.

Lex Fridman (33:15.360)

No, no, listen.

Rana el Kaliouby (33:16.400)

I love competence and focus and great.

Lex Fridman (33:18.960)

Like he was not going to get distracted by the, the shallowness of small talk.

Rana el Kaliouby (33:25.200)

There's a job to be done and he was doing it.

Lex Fridman (33:27.520)

So anyway, the fact that it was working is a beautiful thing.

Lex Fridman (33:30.560)

And I think Media Lab really prides itself on trying to always have a thing that's working

Lex Fridman (33:35.440)

that you could show off.

Rana el Kaliouby (33:36.480)

Yes.

Lex Fridman (33:36.800)

We used to call it a demo or die.

Rana el Kaliouby (33:38.960)

You, you could not, yeah, you could not like show up with like PowerPoint or something.

Rana el Kaliouby (33:43.520)

You actually had to have a working, you know what, my son who is now 13, I don't know if

Rana el Kaliouby (33:48.080)

this is still his life long goal or not, but when he was a little younger, his dream is

Lex Fridman (33:52.880)

to build an island that's just inhabited by robots, like no humans.

Rana el Kaliouby (33:57.280)

He just wants all these robots to be connecting and having fun and there you go.

Lex Fridman (34:01.920)

Does he have human, does he have an idea of which robots he loves most?

Lex Fridman (34:06.480)

Is it, is it Roomba like robots?

Lex Fridman (34:09.280)

Is it humanoid robots?

Rana el Kaliouby (34:10.800)

Robot dogs, or it's not clear yet.

Rana el Kaliouby (34:13.920)

We used to have a Jibo, which was one of the MIT Media Lab spin outs and he used to love

Rana el Kaliouby (34:19.280)

the giant head that spins and rotate and it's an eye or like not glowing like Cal 9000,

Lex Fridman (34:30.400)

but the friendly version.

Rana el Kaliouby (34:31.760)

He loved that.

Lex Fridman (34:34.080)

And then he just loves, uh, um,

Rana el Kaliouby (34:38.240)

yeah, he just, he, I think he loves all forms of robots actually.

Lex Fridman (34:44.160)

So embodied intelligence.

Rana el Kaliouby (34:46.800)

Yes.

Rana el Kaliouby (34:47.760)

I like, I personally like legged robots, especially, uh, anything that can wiggle its butt.

Rana el Kaliouby (34:55.120)

No, that's not the definition of what I love, but that's just technically what I've been

Lex Fridman (35:00.960)

working on recently.

Rana el Kaliouby (35:01.760)

Except I have a bunch of legged robots now in Austin and I've been doing, I was, I've

Rana el Kaliouby (35:06.480)

been trying to, uh, have them communicate affection with their body in different ways

Rana el Kaliouby (35:12.400)

just for art, for art really.

Rana el Kaliouby (35:15.120)

Cause I love the idea of walking around with the robots, like, uh, as you would with a

Rana el Kaliouby (35:20.080)

dog.

Lex Fridman (35:20.400)

I think it's inspiring to a lot of people, especially young people.

Rana el Kaliouby (35:23.120)

Like kids love, kids love it.

Rana el Kaliouby (35:25.760)

Parents like adults are scared of robots, but kids don't have this kind of weird construction

Rana el Kaliouby (35:31.600)

of the world that's full of evil.

Lex Fridman (35:32.880)

They love cool things.

Rana el Kaliouby (35:34.480)

Yeah.

Lex Fridman (35:35.040)

I remember when Adam was in first grade, so he must have been like seven or so.

Rana el Kaliouby (35:40.080)

I went in to his class with a whole bunch of robots and like the emotion AI demo and

Lex Fridman (35:44.960)

da da.

Lex Fridman (35:45.680)

And I asked the kids, I was like, do you, would you kids want to have a robot, you know,

Lex Fridman (35:52.000)

robot friend or robot companion?

Rana el Kaliouby (35:53.600)

Everybody said yes.

Lex Fridman (35:54.560)

And they wanted it for all sorts of things, like to help them with their math homework

Lex Fridman (35:58.880)

and to like be a friend.

Lex Fridman (36:00.160)

So there's, it just struck me how there was no fear of robots was a lot of adults have

Rana el Kaliouby (36:07.520)

that like us versus them.

Lex Fridman (36:10.720)

Yeah, none of that.

Rana el Kaliouby (36:11.920)

Of course you want to be very careful because you still have to look at the lessons of history

Lex Fridman (36:16.960)

and how robots can be used by the power centers of the world to abuse your rights and all

Rana el Kaliouby (36:21.920)

that kind of stuff.

Lex Fridman (36:22.480)

But mostly it's good to enter anything new with an excitement and an optimism.

Lex Fridman (36:30.480)

Speaking of Roz, what have you learned about science and life from Rosalind Picard?

Lex Fridman (36:35.200)

Oh my God, I've learned so many things about life from Roz.

Rana el Kaliouby (36:41.200)

I think the thing I learned the most is perseverance.

Lex Fridman (36:47.600)

When I first met Roz, we applied and she invited me to be her postdoc.

Rana el Kaliouby (36:51.280)

We applied for a grant to the National Science Foundation to apply some of our research to

Lex Fridman (36:57.040)

autism.

Lex Fridman (36:57.760)

And we got back.

Lex Fridman (37:00.800)

We were rejected.

Rana el Kaliouby (37:01.520)

Rejected.

Lex Fridman (37:02.240)

Yeah.

Lex Fridman (37:02.480)

And the reasoning was...

Lex Fridman (37:03.120)

The first time you were rejected for fun, yeah.

Rana el Kaliouby (37:06.000)

Yeah, it was, and I basically, I just took the rejection to mean, okay, we're rejected.

Lex Fridman (37:10.320)

It's done, like end of story, right?

Lex Fridman (37:12.720)

And Roz was like, it's great news.

Lex Fridman (37:15.120)

They love the idea.

Rana el Kaliouby (37:16.080)

They just don't think we can do it.

Lex Fridman (37:18.160)

So let's build it, show them, and then reapply.

Lex Fridman (37:22.400)

And it was that, oh my God, that story totally stuck with me.

Lex Fridman (37:26.320)

And she's like that in every aspect of her life.

Rana el Kaliouby (37:29.760)

She just does not take no for an answer.

Lex Fridman (37:32.080)

To reframe all negative feedback.

Rana el Kaliouby (37:35.360)

As a challenge.

Lex Fridman (37:36.400)

As a challenge.

Rana el Kaliouby (37:37.280)

As a challenge.

Lex Fridman (37:38.560)

Yes, they liked this.

Rana el Kaliouby (37:40.000)

Yeah, yeah, yeah.

Lex Fridman (37:40.720)

It was a riot.

Lex Fridman (37:43.200)

What else about science in general?

Rana el Kaliouby (37:45.040)

About how you see computers and also business and just everything about the world.

Rana el Kaliouby (37:51.680)

She's a very powerful, brilliant woman like yourself.

Lex Fridman (37:54.800)

So is there some aspect of that too?

Rana el Kaliouby (37:57.280)

Yeah, I think Roz is actually also very faith driven.

Lex Fridman (38:00.320)

She has this like deep belief in conviction.

Rana el Kaliouby (38:04.240)

Yeah, and in the good in the world and humanity.

Lex Fridman (38:07.200)

And I think that was meeting her and her family was definitely like a defining moment for me

Rana el Kaliouby (38:13.520)

because that was when I was like, wow, like you can be of a different background and

Lex Fridman (38:18.080)

religion and whatever and you can still have the same core values.

Lex Fridman (38:23.760)

So that was, that was, yeah.

Lex Fridman (38:26.800)

I'm grateful to her.

Rana el Kaliouby (38:28.560)

Roz, if you're listening, thank you.

Lex Fridman (38:30.240)

Yeah, she's great.

Rana el Kaliouby (38:31.280)

She's been on this podcast before.

Lex Fridman (38:33.600)

I hope she'll be on, I'm sure she'll be on again.

Lex Fridman (38:36.320)

And you were the founder and CEO of Effektiva, which is a big company that was acquired by

Lex Fridman (38:44.720)

another big company, SmartEye.

Lex Fridman (38:46.960)

And you're now the deputy CEO of SmartEye.

Lex Fridman (38:49.120)

So you're a powerful leader.

Rana el Kaliouby (38:51.040)

You're brilliant.

Lex Fridman (38:51.760)

You're a brilliant scientist.

Rana el Kaliouby (38:53.360)

A lot of people are inspired by you.

Lex Fridman (38:55.040)

What advice would you give, especially to young women, but people in general who dream

Rana el Kaliouby (39:00.160)

of becoming powerful leaders like yourself in a world where perhaps, in a world that

Rana el Kaliouby (39:09.520)

perhaps doesn't give them a clear, easy path to do so, whether we're talking about Egypt

Lex Fridman (39:17.440)

or elsewhere?

Rana el Kaliouby (39:19.920)

You know, hearing you kind of describe me that way, kind of encapsulates, I think what

Lex Fridman (39:27.680)

I think is the biggest challenge of all, which is believing in yourself, right?

Rana el Kaliouby (39:32.160)

I have had to like grapple with this, what I call now the Debbie Downer voice in my head.

Rana el Kaliouby (39:39.360)

The kind of basically, it's just chattering all the time.

Lex Fridman (39:42.720)

It's basically saying, oh, no, no, no, no, you can't do this.

Rana el Kaliouby (39:45.040)

Like you're not going to raise money.

Lex Fridman (39:46.320)

You can't start a company.

Rana el Kaliouby (39:47.280)

Like what business do you have, like starting a company or running a company or selling

Lex Fridman (39:50.720)

a company?

Rana el Kaliouby (39:51.200)

Like you name it.

Lex Fridman (39:52.080)

It's always like.

Lex Fridman (39:53.120)

And I think my biggest advice to not just women, but people who are taking a new path

Rana el Kaliouby (40:02.160)

and, you know, they're not sure, is to not let yourself and let your thoughts be the

Rana el Kaliouby (40:07.200)

biggest obstacle in your way.

Lex Fridman (40:09.920)

And I've had to like really work on myself to not be my own biggest obstacle.

Lex Fridman (40:17.520)

So you got that negative voice.

Lex Fridman (40:18.880)

Yeah.

Lex Fridman (40:20.640)

So is that?

Lex Fridman (40:21.200)

Am I the only one?

Rana el Kaliouby (40:21.920)

I don't think I'm the only one.

Lex Fridman (40:23.280)

No, I have that negative voice.

Rana el Kaliouby (40:25.040)

I'm not exactly sure if it's a bad thing or a good thing.

Lex Fridman (40:29.840)

I've been really torn about it because it's been a lifelong companions.

Rana el Kaliouby (40:35.440)

It's hard to know.

Rana el Kaliouby (40:37.840)

It's kind of, it drives productivity and progress, but it can hold you back from taking

Rana el Kaliouby (40:44.800)

big leaps.

Rana el Kaliouby (40:45.520)

I think the best I can say is probably you have to somehow be able to control it, to

Rana el Kaliouby (40:53.120)

turn it off when it's not useful and turn it on when it's useful.

Lex Fridman (40:57.680)

Like I have from almost like a third person perspective.

Rana el Kaliouby (41:00.400)

Right.

Lex Fridman (41:00.900)

Somebody who's sitting there like.

Rana el Kaliouby (41:02.400)

Yeah.

Lex Fridman (41:02.960)

Like, because it is useful to be critical.

Rana el Kaliouby (41:07.520)

Like after, I just gave a talk yesterday.

Rana el Kaliouby (41:12.480)

At MIT and I was just, there's so much love and it was such an incredible experience.

Lex Fridman (41:19.120)

So many amazing people I got a chance to talk to, but afterwards when I went home and just

Lex Fridman (41:25.760)

took this long walk, it was mostly just negative thoughts about me.

Rana el Kaliouby (41:29.680)

I don't like one basic stuff like I don't deserve any of it.

Lex Fridman (41:34.720)

And second is like, like, why did you, that was so bad.

Rana el Kaliouby (41:39.200)

Second is like, like, why did you, that was so dumb that you said this, that's so dumb.

Lex Fridman (41:44.880)

Like you should have prepared that better.

Lex Fridman (41:47.520)

Why did you say this?

Lex Fridman (41:50.240)

But I think it's good to hear that voice out.

Rana el Kaliouby (41:54.160)

All right.

Lex Fridman (41:54.560)

And like sit in that.

Lex Fridman (41:56.240)

And ultimately I think you grow from that.

Rana el Kaliouby (41:58.560)

Now, when you're making really big decisions about funding or starting a company or taking

Rana el Kaliouby (42:03.680)

a leap to go to the UK or take a leap to go to America to work in Media Lab though.

Lex Fridman (42:10.960)

Yeah.

Rana el Kaliouby (42:11.200)

There's, that's, you should be able to shut that off then because you should have like

Rana el Kaliouby (42:22.160)

this weird confidence, almost like faith that you said before that everything's going to

Rana el Kaliouby (42:26.080)

work out.

Lex Fridman (42:26.720)

So take the leap of faith.

Rana el Kaliouby (42:28.480)

Take the leap of faith.

Lex Fridman (42:30.160)

Despite all the negativity.

Rana el Kaliouby (42:32.400)

I mean, there's, there's, there's some of that.

Lex Fridman (42:34.240)

You, you actually tweeted a really nice tweet thread.

Rana el Kaliouby (42:39.760)

It says, quote, a year ago, a friend recommended I do daily affirmations and I was skeptical,

Lex Fridman (42:46.800)

but I was going through major transitions in my life.

Lex Fridman (42:49.360)

So I gave it a shot and it set me on a journey of self acceptance and self love.

Lex Fridman (42:54.080)

So what was that like?

Lex Fridman (42:55.680)

Can you maybe talk through this idea of affirmations and how that helped you?

Lex Fridman (43:01.360)

Yeah.

Rana el Kaliouby (43:02.320)

Because really like I'm just like me, I'm a kind, I'd like to think of myself as a kind

Lex Fridman (43:07.200)

person in general, but I'm kind of mean to myself sometimes.

Rana el Kaliouby (43:10.320)

Yeah.

Lex Fridman (43:11.280)

And so I've been doing journaling for almost 10 years now.

Rana el Kaliouby (43:16.720)

I use an app called Day One and it's awesome.

Rana el Kaliouby (43:18.880)

I just journal and I use it as an opportunity to almost have a conversation with the Debbie

Lex Fridman (43:22.880)

Downer voice in my, it's like a rebuttal, right?

Rana el Kaliouby (43:25.520)

Like Debbie Downer says, oh my God, like you, you know, you won't be able to raise this

Rana el Kaliouby (43:29.120)

round of funding.

Lex Fridman (43:29.680)

I'm like, okay, let's talk about it.

Rana el Kaliouby (43:33.120)

I have a track record of doing X, Y, and Z.

Lex Fridman (43:35.520)

I think I can do this.

Lex Fridman (43:37.120)

And it's literally like, so I wouldn't, I don't know that I can shut off the voice,

Lex Fridman (43:42.240)

but I can have a conversation with it.

Lex Fridman (43:44.240)

And it just, it just, and I bring data to the table, right?

Lex Fridman (43:49.840)

Nice.

Lex Fridman (43:50.320)

So that was the journaling part, which I found very helpful.

Lex Fridman (43:53.760)

But the affirmation took it to a whole next level and I just love it.

Rana el Kaliouby (43:57.600)

I'm a year into doing this and you literally wake up in the morning and the first thing

Rana el Kaliouby (44:02.720)

you do, I meditate first and then I write my affirmations and it's the energy I want

Rana el Kaliouby (44:09.440)

to put out in the world that hopefully will come right back to me.

Lex Fridman (44:12.160)

So I will say, I always start with my smile lights up the whole world.

Lex Fridman (44:17.200)

And I kid you not, like people in the street will stop me and say, oh my God, like we love

Lex Fridman (44:20.720)

your smile.

Rana el Kaliouby (44:21.360)

Like, yes.

Rana el Kaliouby (44:22.320)

So, so my affirmations will change depending on, you know, what's happening this day.

Lex Fridman (44:28.880)

Is it funny?

Lex Fridman (44:29.520)

I know.

Rana el Kaliouby (44:29.840)

Don't judge, don't judge.

Lex Fridman (44:31.360)

No, that's not, laughter's not judgment.

Rana el Kaliouby (44:33.840)

It's just awesome.

Rana el Kaliouby (44:35.040)

I mean, it's true, but you're saying affirmations somehow help kind of, I mean, what is it that

Rana el Kaliouby (44:42.480)

they do work to like remind you of the kind of person you are and the kind of person you

Rana el Kaliouby (44:48.400)

want to be, which actually may be in reverse order, the kind of person you want to be.

Lex Fridman (44:53.760)

And that helps you become the kind of person you actually are.

Lex Fridman (44:56.960)

It's just, it's, it brings intentionality to like what you're doing.

Rana el Kaliouby (45:01.280)

Right.

Lex Fridman (45:01.680)

And so, by the way, I was laughing because my affirmations, which I also do are the

Rana el Kaliouby (45:07.200)

opposite.

Lex Fridman (45:07.760)

Oh, you do?

Lex Fridman (45:08.320)

Oh, what do you do?

Lex Fridman (45:09.040)

I don't, I don't have a, my smile lights up the world.

Rana el Kaliouby (45:11.920)

Maybe I should add that because like, I, I have, I just, I have, oh boy, it's, it's much

Rana el Kaliouby (45:22.240)

more stoic, like about focus, about this kind of stuff, but the joy, the emotion that you're

Rana el Kaliouby (45:30.400)

just in that little affirmation is beautiful.

Lex Fridman (45:32.960)

So maybe I should add that.

Rana el Kaliouby (45:35.120)

I have some, I have some like focused stuff, but that's usually.

Lex Fridman (45:38.080)

But that's a cool start.

Rana el Kaliouby (45:39.120)

It's after all the like smiling and playful and joyful and all that.

Lex Fridman (45:43.760)

And then it's like, okay, I kick butt.

Rana el Kaliouby (45:45.440)

Let's get shit done.

Lex Fridman (45:46.560)

Right.

Rana el Kaliouby (45:46.960)

Let's get shit done affirmation.

Lex Fridman (45:48.640)

Okay, cool.

Lex Fridman (45:49.280)

So like what else is on there?

Lex Fridman (45:52.640)

What else is on there?

Rana el Kaliouby (45:54.320)

Well, I, I have, I'm also, I'm, I'm a magnet for all sorts of things.

Lex Fridman (46:00.000)

So I'm an amazing people magnet.

Rana el Kaliouby (46:02.160)

I attract like awesome people into my universe.

Lex Fridman (46:05.520)

That's an actual affirmation.

Rana el Kaliouby (46:06.960)

Yes.

Lex Fridman (46:07.840)

That's great.

Rana el Kaliouby (46:08.880)

Yeah.

Lex Fridman (46:09.280)

So that, that's, and that, yeah.

Lex Fridman (46:10.640)

And that somehow manifests itself into like in working.

Lex Fridman (46:13.920)

I think so.

Rana el Kaliouby (46:15.440)

Yeah.

Lex Fridman (46:15.680)

Like, can you speak to like why it feels good to do the affirmations?

Rana el Kaliouby (46:19.760)

I honestly think it just grounds the day.

Lex Fridman (46:24.080)

And then it allows me to, instead of just like being pulled back and forth, like throughout

Rana el Kaliouby (46:30.000)

the day, it just like grounds me.

Lex Fridman (46:31.680)

I'm like, okay, like this thing happened.

Rana el Kaliouby (46:34.560)

It's not exactly what I wanted it to be, but I'm patient.

Rana el Kaliouby (46:37.360)

Or I'm, you know, I'm, I trust that the universe will do amazing things for me, which is one

Rana el Kaliouby (46:42.960)

of my other consistent affirmations.

Lex Fridman (46:45.440)

Or I'm an amazing mom.

Rana el Kaliouby (46:46.720)

Right.

Lex Fridman (46:47.040)

And so I can grapple with all the feelings of mom guilt that I have all the time.

Rana el Kaliouby (46:52.240)

Or here's another one.

Lex Fridman (46:53.760)

I'm a love magnet.

Lex Fridman (46:55.040)

And I literally say, I will kind of picture the person that I'd love to end up with.

Lex Fridman (46:59.040)

And I write it all down and it hasn't happened yet, but it.

Lex Fridman (47:02.480)

What are you, what are you picturing?

Lex Fridman (47:03.920)

This is Brad Pitt.

Rana el Kaliouby (47:06.000)

Because that's what I picture.

Lex Fridman (47:07.040)

Okay.

Lex Fridman (47:07.440)

That's what you picture?

Lex Fridman (47:08.240)

Yeah.

Rana el Kaliouby (47:08.480)

Okay.

Lex Fridman (47:08.880)

On the, on the running, holding hands, running together.

Rana el Kaliouby (47:11.760)

Okay.

Rana el Kaliouby (47:14.160)

No, more like fight club that the fight club, Brad Pitt, where he's like standing.

Rana el Kaliouby (47:18.800)

All right.

Lex Fridman (47:19.120)

People will know.

Rana el Kaliouby (47:20.000)

Anyway, I'm sorry.

Lex Fridman (47:20.720)

I'll get off on that.

Lex Fridman (47:21.920)

Do you have a, like when you're thinking about the being a love magnet in that way, are you

Lex Fridman (47:27.360)

picturing specific people or is this almost like in the space of like energy?

Rana el Kaliouby (47:36.000)

Right.

Rana el Kaliouby (47:36.320)

It's somebody who is smart and well accomplished and successful in their life, but they're

Rana el Kaliouby (47:44.240)

generous and they're well traveled and they want to travel the world.

Lex Fridman (47:48.960)

Things like that.

Rana el Kaliouby (47:49.760)

Like their head over heels into me.

Lex Fridman (47:51.360)

It's like, I know it sounds super silly, but it's literally what I write.

Rana el Kaliouby (47:54.560)

Yeah.

Lex Fridman (47:54.800)

And I believe it'll happen one day.

Lex Fridman (47:56.320)

Oh, you actually write, so you don't say it out loud?

Lex Fridman (47:58.000)

You write.

Rana el Kaliouby (47:58.240)

No, I write it.

Lex Fridman (47:58.960)

I write all my affirmations.

Rana el Kaliouby (48:01.200)

I do the opposite.

Lex Fridman (48:01.920)

I say it out loud.

Lex Fridman (48:02.640)

Oh, you say it out loud?

Lex Fridman (48:03.440)

Interesting.

Rana el Kaliouby (48:04.320)

Yeah, if I'm alone, I'll say it out loud.

Lex Fridman (48:06.320)

Interesting.

Rana el Kaliouby (48:07.360)

I should try that.

Lex Fridman (48:10.000)

I think it's what feels more powerful to you.

Rana el Kaliouby (48:15.600)

To me, more powerful.

Lex Fridman (48:18.240)

Saying stuff feels more powerful.

Rana el Kaliouby (48:20.320)

Yeah.

Rana el Kaliouby (48:21.520)

Writing is, writing feels like I'm losing the words, like losing the power of the words

Rana el Kaliouby (48:32.320)

maybe because I write slow.

Lex Fridman (48:33.520)

Do you handwrite?

Rana el Kaliouby (48:34.960)

No, I type.

Lex Fridman (48:36.320)

It's on this app.

Rana el Kaliouby (48:37.520)

It's day one, basically.

Lex Fridman (48:38.800)

And I just, I can look, the best thing about it is I can look back and see like a year ago,

Lex Fridman (48:44.320)

what was I affirming, right?

Lex Fridman (48:46.560)

So it's...

Rana el Kaliouby (48:47.200)

Oh, so it changes over time.

Lex Fridman (48:50.000)

It hasn't like changed a lot, but the focus kind of changes over time.

Rana el Kaliouby (48:54.640)

I got it.

Lex Fridman (48:55.440)

Yeah, I say the same exact thing over and over and over.

Lex Fridman (48:57.840)

Oh, you do?

Lex Fridman (48:58.400)

Okay.

Rana el Kaliouby (48:58.560)

There's a comfort in the sameness of it.

Rana el Kaliouby (49:00.880)

Well, actually, let me jump around because let me ask you about, because all this talk

Rana el Kaliouby (49:05.440)

about Brad Pitt, or maybe it's just going on inside my head, let me ask you about dating

Lex Fridman (49:10.800)

in general.

Lex Fridman (49:12.960)

You tweeted, are you based in Boston and single?

Lex Fridman (49:16.800)

And then you pointed to a startup Singles Night sponsored by Smile Dating app.

Rana el Kaliouby (49:23.440)

I mean, this is jumping around a little bit, but since you mentioned...

Lex Fridman (49:27.280)

Since you mentioned, can AI help solve this dating love problem?

Lex Fridman (49:34.400)

What do you think?

Rana el Kaliouby (49:34.960)

This problem of connection that is part of the human condition, can AI help that you

Lex Fridman (49:41.600)

yourself are in the search affirming?

Lex Fridman (49:44.960)

Maybe that's what I should affirm, like build an AI.

Lex Fridman (49:48.160)

Build an AI that finds love?

Rana el Kaliouby (49:49.520)

I think there must be a science behind that first moment you meet a person and you either

Lex Fridman (50:00.400)

have chemistry or you don't, right?

Rana el Kaliouby (50:02.800)

I guess that was the question I was asking, would you put it brilliantly, is that a science

Lex Fridman (50:06.960)

or an art?

Rana el Kaliouby (50:09.680)

I think there are like, there's actual chemicals that get exchanged when two people meet.

Rana el Kaliouby (50:15.200)

I don't know about that.

Rana el Kaliouby (50:16.240)

I like how you're changing, yeah, changing your mind as we're describing it, but it feels

Rana el Kaliouby (50:22.880)

that way.

Lex Fridman (50:23.920)

But it's what science shows us is sometimes we can explain with the rigor, the things

Rana el Kaliouby (50:29.040)

that feel like magic.

Lex Fridman (50:31.760)

So maybe we can remove all the magic.

Rana el Kaliouby (50:34.320)

Maybe it's like, I honestly think, like I said, like Goodreads should be a dating app,

Lex Fridman (50:39.760)

which like books.

Rana el Kaliouby (50:41.680)

I wonder if you look at just like books or content you've consumed.

Lex Fridman (50:46.960)

I mean, that's essentially what YouTube does when it does a recommendation.

Rana el Kaliouby (50:50.640)

If you just look at your footprint of content consumed, if there's an overlap, but maybe

Rana el Kaliouby (50:56.400)

interesting difference with an overlap that some, I'm sure this is a machine learning

Rana el Kaliouby (51:01.280)

problem that's solvable.

Rana el Kaliouby (51:03.520)

Like this person is very likely to be not only there to be chemistry in the short term,

Lex Fridman (51:10.560)

but a good lifelong partner to grow together.

Lex Fridman (51:13.920)

I bet you it's a good machine learning problem.

Rana el Kaliouby (51:15.600)

You just need the data.

Lex Fridman (51:16.480)

Let's do it.

Rana el Kaliouby (51:17.360)

Well, actually, I do think there's so much data about each of us that there ought to

Rana el Kaliouby (51:22.080)

be a machine learning algorithm that can ingest all this data and basically say, I think the

Lex Fridman (51:26.320)

following 10 people would be interesting connections for you, right?

Lex Fridman (51:32.080)

And so Smile dating app kind of took one particular angle, which is humor.

Rana el Kaliouby (51:36.640)

It matches people based on their humor styles, which is one of the main ingredients of a

Lex Fridman (51:41.920)

successful relationship.

Rana el Kaliouby (51:43.120)

Like if you meet somebody and they can make you laugh, like that's a good thing.

Lex Fridman (51:47.200)

And if you develop like internal jokes, like inside jokes and you're bantering, like that's

Rana el Kaliouby (51:53.040)

fun.

Lex Fridman (51:54.320)

So I think.

Rana el Kaliouby (51:56.640)

Yeah, definitely.

Lex Fridman (51:57.520)

Definitely.

Lex Fridman (51:58.320)

But yeah, that's the number of and the rate of inside joke generation.

Lex Fridman (52:04.880)

You could probably measure that and then optimize it over the first few days.

Rana el Kaliouby (52:08.160)

You could say, we're just turning this into a machine learning problem.

Lex Fridman (52:11.360)

I love it.

Lex Fridman (52:13.360)

But for somebody like you, who's exceptionally successful and busy, is there, is there signs

Lex Fridman (52:23.120)

to that aspect of dating?

Lex Fridman (52:24.880)

Is it tricky?

Lex Fridman (52:26.320)

Is there advice you can give?

Rana el Kaliouby (52:27.600)

Oh, my God, I give the worst advice.

Lex Fridman (52:29.440)

Well, I can tell you like I have a spreadsheet.

Lex Fridman (52:31.440)

Is that a good or a bad thing?

Lex Fridman (52:34.640)

Do you regret the spreadsheet?

Rana el Kaliouby (52:37.040)

Well, I don't know.

Lex Fridman (52:38.240)

What's the name of the spreadsheet?

Lex Fridman (52:39.440)

Is it love?

Lex Fridman (52:40.800)

It's the date track, dating tracker.

Rana el Kaliouby (52:42.880)

Dating tracker.

Lex Fridman (52:43.840)

It's very like.

Rana el Kaliouby (52:44.560)

Love tracker.

Lex Fridman (52:45.280)

Yeah.

Lex Fridman (52:46.320)

And there's a rating system, I'm sure.

Lex Fridman (52:47.760)

Yeah.

Rana el Kaliouby (52:48.080)

There's like weights and stuff.

Lex Fridman (52:49.920)

It's too close to home.

Lex Fridman (52:51.440)

Oh, is it?

Lex Fridman (52:52.000)

Do you also have.

Rana el Kaliouby (52:52.800)

Well, I don't have a spreadsheet, but I would, now that you say it, it seems like a good

Lex Fridman (52:56.640)

idea.

Rana el Kaliouby (52:57.200)

Oh, no.

Lex Fridman (52:58.160)

Okay.

Rana el Kaliouby (52:58.720)

Turning it into data.

Lex Fridman (53:05.760)

I do wish that somebody else had a spreadsheet about me.

Rana el Kaliouby (53:11.200)

You know, if it was like, like I said, like you said, convert, collect a lot of data about

Rana el Kaliouby (53:17.120)

us in a way that's privacy preserving, that I own the data, I can control it and then

Rana el Kaliouby (53:21.840)

use that data to find, I mean, not just romantic love, but collaborators, friends, all that

Lex Fridman (53:28.400)

kind of stuff.

Rana el Kaliouby (53:28.960)

It seems like the data is there.

Lex Fridman (53:30.240)

Right.

Rana el Kaliouby (53:32.080)

That's the problem social networks are trying to solve, but I think they're doing a really

Lex Fridman (53:35.280)

poor job.

Rana el Kaliouby (53:36.240)

Even Facebook tried to get into a dating app business.

Lex Fridman (53:39.600)

And I think there's so many components to running a successful company that connects

Rana el Kaliouby (53:44.400)

human beings.

Lex Fridman (53:45.360)

And part of that is, you know, having engineers that care about the human side, right, as

Rana el Kaliouby (53:53.920)

you know, extremely well, it's not, it's not easy to find those.

Lex Fridman (53:57.760)

But you also don't want just people that care about the human.

Rana el Kaliouby (54:00.640)

They also have to be good engineers.

Lex Fridman (54:02.240)

So it's like, you have to find this beautiful mix.

Lex Fridman (54:05.760)

And for some reason, just empirically speaking, people have not done a good job of that, of

Lex Fridman (54:12.560)

building companies like that.

Lex Fridman (54:13.680)

And it must mean that it's a difficult problem to solve.

Lex Fridman (54:17.040)

Dating apps, it seems difficult.

Rana el Kaliouby (54:19.920)

Okay, Cupid, Tinder, all those kinds of stuff.

Rana el Kaliouby (54:22.080)

They seem to find, of course they work, but they seem to not work as well as I would imagine

Rana el Kaliouby (54:32.080)

is possible.

Lex Fridman (54:32.880)

Like, with data, wouldn't you be able to find better human connection?

Rana el Kaliouby (54:36.960)

It's like arranged marriages on steroids, essentially.

Lex Fridman (54:39.520)

Right, right.

Rana el Kaliouby (54:40.480)

Arranged by machine learning algorithm.

Lex Fridman (54:42.560)

Arranged by machine learning algorithm, but not a superficial one.

Rana el Kaliouby (54:45.600)

I think a lot of the dating apps out there are just so superficial.

Rana el Kaliouby (54:48.640)

They're just matching on like high level criteria that aren't ingredients for successful partnership.

Lex Fridman (54:55.680)

But you know what's missing, though, too?

Lex Fridman (54:58.480)

I don't know how to fix that, the serendipity piece of it.

Lex Fridman (55:01.440)

Like, how do you engineer serendipity?

Rana el Kaliouby (55:03.760)

Like this random, like, chance encounter, and then you fall in love with the person.

Rana el Kaliouby (55:07.680)

Like, I don't know how a dating app can do that.

Lex Fridman (55:10.080)

So there has to be a little bit of randomness.

Rana el Kaliouby (55:12.080)

Maybe every 10th match is just a, you know, yeah, somebody that the algorithm wouldn't

Lex Fridman (55:21.680)

have necessarily recommended, but it allows for a little bit of...

Rana el Kaliouby (55:25.840)

Well, it can also, you know, it can also trick you into thinking of serendipity by like somehow

Rana el Kaliouby (55:33.440)

showing you a tweet of a person that he thinks you'll match well with, but do it accidentally

Rana el Kaliouby (55:39.200)

as part of another search.

Lex Fridman (55:40.560)

Right.

Lex Fridman (55:41.040)

And like you just notice it, like, and then you get, you go down a rabbit hole and you

Rana el Kaliouby (55:46.080)

connect them outside the app to like, you connect with this person outside the app somehow.

Lex Fridman (55:51.360)

So it's just, it creates that moment of meeting.

Rana el Kaliouby (55:54.240)

Of course, you have to think of, from an app perspective, how you can turn that into a

Rana el Kaliouby (55:57.600)

business.

Lex Fridman (55:58.240)

But I think ultimately a business that helps people find love in any way.

Rana el Kaliouby (56:04.400)

Like that's what Apple was about, create products that people love.

Lex Fridman (56:07.440)

That's beautiful.

Rana el Kaliouby (56:08.240)

I mean, you got to make money somehow.

Rana el Kaliouby (56:11.520)

If you help people fall in love personally with the product, find self love or love another

Rana el Kaliouby (56:18.160)

human being, you're going to make money.

Lex Fridman (56:19.840)

You're going to figure out a way to make money.

Rana el Kaliouby (56:22.960)

I just feel like the dating apps often will optimize for something else than love.

Lex Fridman (56:28.560)

It's the same with social networks.

Rana el Kaliouby (56:30.000)

They optimize for engagement as opposed to like a deep, meaningful connection that's

Rana el Kaliouby (56:35.280)

ultimately grounded in like personal growth, you as a human being growing and all that

Rana el Kaliouby (56:39.760)

kind of stuff.

Lex Fridman (56:41.520)

Let me do like a pivot to a dark topic, which you opened the book with.

Rana el Kaliouby (56:48.560)

A story, because I'd like to talk to you about just emotion and artificial intelligence.

Lex Fridman (56:56.080)

I think this is a good story to start to think about emotional intelligence.

Rana el Kaliouby (56:59.680)

You opened the book with a story of a central Florida man, Jamel Dunn, who was drowning

Lex Fridman (57:05.120)

and drowned while five teenagers watched and laughed, saying things like, you're going

Rana el Kaliouby (57:10.000)

to die.

Lex Fridman (57:10.800)

And when Jamel disappeared below the surface of the water, one of them said he just died

Lex Fridman (57:15.840)

and the others laughed.

Lex Fridman (57:17.440)

What does this incident teach you about human nature and the response to it perhaps?

Rana el Kaliouby (57:23.360)

Yeah.

Lex Fridman (57:24.320)

I mean, I think this is a really, really, really sad story.

Lex Fridman (57:28.480)

And it and it and it highlights what I believe is a it's a real problem in our world today.

Lex Fridman (57:34.480)

It's it's an empathy crisis.

Rana el Kaliouby (57:36.720)

Yeah, we're living through an empathy crisis and crisis.

Lex Fridman (57:39.840)

Yeah.

Rana el Kaliouby (57:40.400)

Yeah.

Lex Fridman (57:42.240)

And I mean, we've we've talked about this throughout our conversation.

Rana el Kaliouby (57:45.360)

We dehumanize each other.

Lex Fridman (57:47.040)

And unfortunately, yes, technology is bringing us together.

Lex Fridman (57:51.920)

But in a way, it's just dehumanized.

Lex Fridman (57:53.840)

It's creating this like, yeah, dehumanizing of the other.

Lex Fridman (57:58.640)

And I think that's a huge problem.

Lex Fridman (58:01.840)

The good news is I think solution, the solution could be technology based.

Rana el Kaliouby (58:05.840)

Like, I think if we rethink the way we design and deploy our technologies, we can solve

Lex Fridman (58:11.520)

parts of this problem.

Lex Fridman (58:12.560)

But I worry about it.

Lex Fridman (58:13.200)

I mean, even with my son, a lot of his interactions are computer mediated.

Lex Fridman (58:19.200)

And I just question what that's doing to his empathy skills and, you know, his ability

Lex Fridman (58:25.280)

to really connect with people.

Lex Fridman (58:26.560)

So that you think you think it's not possible to form empathy through the digital medium.

Lex Fridman (58:36.320)

I think it is.

Lex Fridman (58:38.560)

But we have to be thoughtful about because the way the way we engage face to face, which

Lex Fridman (58:44.000)

is what we're doing right now, right?

Rana el Kaliouby (58:45.840)

There's the nonverbal signals, which are a majority of how we communicate.

Lex Fridman (58:49.360)

It's like 90% of how we communicate is your facial expressions.

Rana el Kaliouby (58:54.000)

You know, I'm saying something and you're nodding your head now, and that creates a

Lex Fridman (58:57.680)

feedback loop.

Lex Fridman (58:58.480)

And and if you break that, and now I have anxiety about it.

Lex Fridman (59:04.160)

Poor Lex.

Rana el Kaliouby (59:06.000)

Oh, boy.

Lex Fridman (59:06.560)

I am not scrutinizing your facial expressions during this interview.

Rana el Kaliouby (59:09.680)

I am.

Lex Fridman (59:12.160)

Look normal.

Rana el Kaliouby (59:12.800)

Look human.

Lex Fridman (59:13.360)

Yeah.

Rana el Kaliouby (59:13.860)

Look normal, look human.

Lex Fridman (59:17.720)

Nod head.

Rana el Kaliouby (59:18.680)

Yeah, nod head.

Lex Fridman (59:20.920)

In agreement.

Rana el Kaliouby (59:21.560)

If Rana says yes, then nod head else.

Rana el Kaliouby (59:25.720)

Don't do it too much because it might be at the wrong time and then it will send the

Rana el Kaliouby (59:29.640)

wrong signal.

Lex Fridman (59:30.760)

Oh, God.

Lex Fridman (59:31.400)

And make eye contact sometimes because humans appreciate that.

Lex Fridman (59:35.320)

All right.

Rana el Kaliouby (59:35.640)

Anyway, okay.

Rana el Kaliouby (59:38.520)

Yeah, but something about the especially when you say mean things in person, you get to

Rana el Kaliouby (59:42.920)

see the pain of the other person.

Lex Fridman (59:44.280)

Exactly.

Lex Fridman (59:44.600)

But if you're tweeting it at a person and you have no idea how it's going to land, you're

Rana el Kaliouby (59:48.120)

more likely to do that on social media than you are in face to face conversations.

Rana el Kaliouby (59:52.040)

So.

Lex Fridman (59:54.520)

What do you think is more important?

Lex Fridman (59:59.000)

EQ or IQ?