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the white paper from audiostack.ai. Hello,

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everyone, and welcome to Talk Nerdy. Today is

0:44

Monday, June 10, 2024, and

0:49

I'm the host of the show, Dr. Cara

0:51

Santa Maria. And as always,

0:53

before we dive into this week's episode, I

0:55

do want to thank those of you who

0:57

make Talk Nerdy possible. Remember,

1:00

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top patrons include,

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let's see, Anu

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Wilkinson, Pasquale Gelati, and

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Ulrika Hagman. Thank you

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all so much. So

2:28

let's get into it. This week

2:30

I had the opportunity to speak

2:32

with Dr. Leila Takayama. She

2:34

is a human-robot interaction specialist

2:37

with a long

2:39

history of training and

2:41

publication in social

2:43

science and design across robotic

2:46

and actually AI systems as

2:48

well. So she earned her

2:51

PhD from

2:53

Stanford University and

2:56

she has worked in a lot

2:58

of different multidisciplinary organizations. Currently, she

3:00

is the vice president of design

3:03

and human-robot interaction for robust AI

3:05

and she's going to tell us

3:08

a little bit more during the

3:10

show about the really cool work

3:12

that robust AI is doing. So

3:15

without any further ado, here she is,

3:17

Dr. Leila

3:20

Takayama. Well,

3:22

Leila, thank you so much for joining me

3:24

today. Sure. Thanks for having me. So

3:28

I am excited to talk

3:30

all about your interesting work

3:32

in AI and robotics and

3:35

kind of human-machine relationships

3:38

and design. But before we get into

3:40

the work that you're actively doing, I'm

3:43

super curious. I think

3:45

my listeners probably know this by now because I tend

3:47

to follow a bit of a formula, but

3:49

I'm super curious how you got to where

3:51

you are. So I'd love to learn a

3:54

little bit more about your background and your

3:56

education. Sure. So my

3:59

background. is actually closer to

4:01

yours. So I have a background in

4:03

psychology. And I started

4:05

out in cognitive science, which

4:07

is this interdisciplinary field where

4:09

you study neuroscience and computer

4:11

science and psychology, philosophy, linguistics.

4:15

And so I kind of stumbled

4:17

into looking at

4:19

human-computer interaction first because

4:21

computers are so poorly designed to

4:23

work for people. And

4:26

then I stumbled into human-robot interaction next

4:28

because they're even worse, which

4:32

is kind of a sorry state of

4:34

the world. But it felt to me like

4:37

robotics has all this promise, supposedly, right?

4:39

Sci-fi tells us that it's gonna be

4:41

amazing. And then you actually

4:43

interact with robots and it's just awful. And

4:46

so I feel like there's a big disconnect

4:49

there that could actually be addressed by folks

4:51

who understand what people can

4:53

do and what they want to do. And

4:56

how do we amplify human skills as opposed

4:58

to try to just do exactly what humans

5:00

do already? So it's

5:03

interesting thinking that coming from cognitive science,

5:06

the study of the human mind and brain and

5:08

how it relates to all of these things is

5:12

so fundamental for whether we're

5:14

talking about computing or robots,

5:18

we are the users and we

5:20

are those who engage, yet at

5:22

the same time, it's not

5:24

always the case that that, I

5:27

guess, layer is included from

5:29

the onset, is

5:32

it? Like oftentimes we're talking like

5:34

engineers, physicists, you know, people

5:37

who aren't thinking about the human aspect. Absolutely.

5:42

As much as I love my engineering friends, they

5:45

often design things for themselves or

5:48

for their best friends, right? And so I think

5:51

when you do that, you're limited in what you're

5:53

gonna actually come up with and you're limited in

5:55

who's gonna be able to use your stuff. And

5:58

so, A lot

6:00

of the folks that I work with today are more folks

6:03

who have had that perspective and may have

6:05

fallen flat on their faces and been frustrated

6:07

that no one was smart enough to use

6:10

their robot or use their computer. And

6:13

then they want more. The ones

6:15

who want their wonderful tools

6:18

to get put in the hands

6:20

of a broader population of people

6:22

are the ones who want to

6:24

engage more with human-centered design. And

6:26

so they tend to be the ones that I'm gravitated towards

6:29

because they care about people who are

6:31

not exactly like themselves. Right.

6:34

And that's such an important point

6:36

is when we talk about human-centered design, there is

6:38

no one way to be human. It's

6:41

so variable. So we have to sort of

6:43

be creative in making sure that

6:45

we reach the human

6:48

experience as the kind of royal

6:50

we. Yes.

6:52

I think for me, one

6:54

of the biggest red flags is

6:57

when somebody tells me, I made this

6:59

awesome robot. And I asked them, cool,

7:01

who's going to use it? And their

7:03

answer is, everyone. That's

7:06

when I know, OK, we got some work to

7:08

do here. Let's

7:10

get a little more specific. Yeah.

7:14

When I worked for quite

7:16

some time in television, and I used

7:18

to struggle with that as well when I would

7:21

read for a new show or we'd be talking

7:23

about how we're going to host this thing or

7:25

how we're going to approach it. And yeah, when

7:27

the network or the production company would be like,

7:29

well, OK, so who's the audience? They're everyone. It's

7:31

like, I can't talk to everyone. I

7:33

have to know who I'm talking to. I

7:36

don't talk to children the same way I talk

7:38

to adults. And I don't talk to experts the

7:40

same way I talk to people who don't have

7:42

expertise in these topics. Absolutely. Yeah. And it's the

7:45

same problem when you're designing a computational system. You

7:48

got to know, who are these people? What are

7:50

they trying to do? What do they know? What

7:52

are they capable of? And then you can figure

7:54

out, is this going to work for them or

7:56

not? And I think maybe

7:58

we. The

8:01

individual listener right now who doesn't

8:04

work in a tech field

8:08

may not, I'm speaking

8:11

for myself here, but may

8:13

not realize or often think

8:15

about how many different ways

8:18

that humans interact with machinery,

8:20

with computational machines, with robots.

8:23

I think we're all thinking about our phones and

8:25

our laptops. My

8:27

everyday interaction, I'm

8:30

the user and my phone needs to be

8:32

user friendly for everybody. There are

8:34

so many different industries and so many

8:36

different applications, aren't there, where you

8:38

have to think really specifically? Yeah,

8:41

that's totally true. I think

8:43

maybe another, if we want to

8:45

go the robot direction, probably the most popular robot

8:49

in the world would be the Roomba.

8:52

The little vacuum cleaning robots that look like a

8:54

big hockey puck. Most

8:58

people who have pets that have fur tend

9:00

to have one of these robots too. I

9:03

think that's another common example of

9:05

a hardware software system that's

9:08

in our everyday lives and sometimes we don't

9:10

call it robot, you just call it

9:12

the vacuum cleaner because it vacuums and

9:14

it's really great that it does that for

9:17

me. As

9:19

you're saying, not everyone orients towards

9:21

these the same way, not everyone

9:23

uses them the same way.

9:25

I think one of the beautiful things about

9:28

robots in particular is that

9:31

they tend to come with

9:33

some gender norms. A

9:36

really good friend of mine, Jodi Forlisi,

9:38

who's at Carnegie Mellon, did this awesome

9:40

study where they gave

9:43

randomly assigned half of these families

9:46

a Roomba and

9:48

they randomly assigned the other half an

9:50

equally valuable vacuum cleaning stick that you

9:52

just hold in your

9:55

hand and push around. They

9:58

let them just use them or not. And

10:00

they came back and interviewed them a few times to see

10:02

how it was going. And,

10:05

you know, cleaning the house tends to

10:07

be gendered or female, right?

10:10

And so women tend to be

10:12

expected to take on that work.

10:14

But when you have a power

10:16

tool that's called a robot in

10:18

your house, guess who helps out more

10:21

with cleaning the house? Which is just

10:23

great, right? Like that wasn't an intended

10:26

outcome, but the men participated more

10:28

in cleaning the house because

10:31

it was a robot. But they didn't when it

10:33

was just a handheld vacuum stick, right?

10:36

That's so fascinating. I

10:39

love that. And it's not

10:41

just sorry, stay. I mean,

10:43

and that must be also

10:45

kind of sometimes frustrating when

10:48

you're wanting to design or

10:50

to approach these

10:53

problems based

10:55

on usability. And

10:57

at the same time, you probably

10:59

don't want to reinforce gender norms

11:01

or racist or sexist approaches. And

11:03

so what a difficult thing to

11:05

say. We want to overcome, but

11:07

we also want to make sure

11:09

that it fits within the zeitgeist.

11:11

Yeah. Yeah. And it seemed to

11:14

have come up with a design that kind

11:16

of works across

11:18

the genders, right? Which is

11:20

kind of nice to see. It

11:23

doesn't always work that way, right? Right.

11:26

And it's interesting that you did mention

11:28

the kind of gendered nature because you're

11:30

right. I do think that

11:32

we often think of robots as being

11:36

male or as somehow relating

11:38

to male tasks more. Where

11:41

do you think that that comes from? If

11:43

I have to watch another robot deliver beer

11:45

to someone sitting on the couch, I'm going

11:48

to scream. For

11:50

some reason, everyone thinks that this is a

11:53

new idea, but grad students in research labs

11:55

have been doing this for decades now. And

11:58

you can guess what the majority of... of those

12:00

grad students are in terms of gender. Right.

12:03

And is that why you

12:05

think? Because they are, like,

12:08

because historically the field was dominated

12:10

by men, so the perspectives were

12:12

very, it was very male gaze.

12:15

That would be my guess, yes,

12:17

that just participation in imagining a

12:19

future with robots, right, has largely

12:22

been dominated by men. It

12:25

doesn't, that's not true everywhere. I

12:28

will say, like, in my fields, right, I

12:30

study human-robot interaction, we have

12:33

better representation of women in the field.

12:36

It's not 50-50, I don't think. But,

12:38

you know, when I go to the human-robot interaction

12:41

design conferences and you go to

12:43

the bathroom, there's actually a line in

12:46

the women's restroom. And we celebrate that, because

12:48

that's not true at the

12:50

straight up robotics conferences. Right.

12:53

And so there is, it's interesting that

12:55

the minute you add the human component,

12:57

more women feel comfortable entering

12:59

the field. Or we're super frustrated

13:02

with what they look like right now. And

13:04

so we're rolling up our sleeves, and we

13:06

want to fix it, right? Design

13:09

for inclusivity. Absolutely.

13:11

And I think, and this extends

13:13

sort of beyond hardware,

13:15

right, because we often think about

13:18

robots as a form of hardware.

13:20

But we're also talking about the

13:22

software, the why of it all,

13:27

the how of it all, and

13:29

actually what these different things are

13:31

doing, whether they have a physicality

13:33

or like in with regards to

13:35

something like artificial intelligence,

13:39

that this is sort of this

13:41

thing that's happening in the background to

13:46

accomplish some sort of task or

13:48

some sort of goal. But those

13:50

gender concerns exist regardless

13:52

of whether we're talking hardware or not, right? Yeah, that's

13:54

right. I mean, if you look at, I don't know,

13:56

like a voice agent, right? So there's been a lot

13:58

of talk lately about that. her,

14:01

the film, and using

14:03

voice casting. Who are you going to

14:05

voice cast and why? What

14:08

is the character that you're trying to show

14:11

or present to your end users? That's

14:13

a very big and important design decision.

14:17

Whether we like it or

14:19

not, we do use stereotypes

14:21

because they're shortcuts. They're heuristics

14:23

that people use cognitively because

14:25

we're cognitive misers. We're kind

14:27

of lazy when it comes

14:29

to thinking through things. If

14:31

you, for example, presented a

14:33

bunch of information about products

14:35

that are stereotypically feminine, so

14:38

it's cleaning products or fashion, people

14:41

will actually believe you more and buy more stuff

14:43

if you use a female voice agent than a

14:45

male one. Whereas if you're selling

14:47

things that are stereotypically male, say, I

14:49

don't know, power tools or sports equipment,

14:51

people who hear it from a male

14:54

voice agent are more likely to

14:56

believe them and buy more stuff

14:58

from them than if it's female.

15:00

We will deny that we're doing

15:02

that because we know that that's

15:05

not right. But

15:07

then if you look at consumer behavior, it follows

15:10

those patterns. We're falling back on those social

15:12

norms that are sort of deeply ingrained in

15:15

us because that's the world that we live in and

15:17

that's the experiences that we've had and those patterns that

15:19

we see. I think one of the

15:23

frustrating things, but also a

15:25

good thing to know about because it could drive

15:28

the way that we choose to design

15:30

things, is picking the perceived gender or

15:33

the perceived age or the perceived geographic

15:35

origin of even just

15:37

voice agents, not robots. Because

15:40

we use those stereotypes to make

15:42

sense of these agents that we're interacting

15:44

with today. It's

15:47

interesting the example that you use there. It's

15:49

such a, I think, clean example

15:52

of, you know, I'm thinking about

15:54

my own consumer behavior and I'm like, yeah, totally. I

15:56

don't want like a man trying to sell me like

15:58

makeup or something like that. But then,

16:01

right, exactly. But then

16:03

you move into this, what

16:06

I find to be a bit more pernicious,

16:09

utilization of the

16:11

female voice

16:14

to encapsulate helper roles. So like,

16:16

oh, my calendar, my assistant, because

16:18

it's the man who does the

16:20

real work, and then it's the

16:22

woman who supports the man. I

16:24

think that it's so

16:26

sad because we

16:28

were supposed to have moved past that. I know

16:30

we're not past it. Of course, we're not past

16:32

it. But we're not in the era of the

16:35

madmen executives in their offices and

16:38

the all the female secretaries sitting out

16:40

in the bullpen. We're not

16:43

in the era anymore. I remember going to Caltech

16:45

and being like, why

16:47

are there only men's restrooms

16:49

on it? Why are the restrooms

16:51

on separate floors? They're like, oh,

16:53

because back in the day, this

16:55

is where the scientists were, and

16:57

all the women were answering phones.

16:59

Like Jesus. But now we're just

17:01

reinforcing that again. Yeah. As

17:03

designers of these systems, we don't have to. I

17:06

think the beauty of technology is you can

17:08

decouple these things that used to be coupled.

17:11

I think that's what my old grad school

17:13

advisor, Cliff Nass, used to say, wouldn't it

17:15

be cool if the

17:17

physics to teachers that are virtual

17:20

were all female? So kids

17:22

grew up expecting their physics instructors to be

17:24

female. That's fine.

17:26

That's super doable. And

17:29

it's just a matter of voice casting. And

17:31

so we could actually change what people

17:33

are exposed to in

17:36

order to start to change those

17:38

patterns that people are noticing in society, even if

17:40

they face different ones later. Yeah.

17:43

And I think that's such an important

17:45

point. There's nothing essentialist about this. There's

17:47

no fundamental difference. These are

17:49

all norms that have been shaped over

17:52

millennia of very often people in

17:54

power maintaining that power. Yeah. And

17:56

we can shake them up. Right.

17:58

I love that. It

18:00

takes me to this other hot topic

18:04

that I see a lot when discussing things

18:07

like AI, especially large

18:09

language models. This idea

18:11

of I

18:15

don't want the robots or I don't want the

18:17

AI to be making art and

18:20

music and poetry. I want the AI to

18:22

be doing my dishes. Yes. I saw that

18:24

tweet and I love it. I

18:27

think this is an important conversation that's

18:29

been happening quite a bit. I'm curious

18:31

your take on that because I'm sure

18:33

that that's front and center for you and

18:35

your work. Oh, absolutely. I think we've talked

18:37

earlier about who is this for. I think

18:39

the very next question and what is it

18:41

for? What's it going to do? Why do

18:44

we want that to do that thing? A

18:48

nice rule of thumb that is

18:50

from robotics is that robots should do things

18:52

that are dirty, dangerous and dull. I

18:56

would add to that things that could be damaging to

18:58

people, but we call them the

19:00

3D's. I think we don't have

19:02

that yet for AI. We have people

19:04

working on AI safety and thinking about

19:07

ethics and policy, but

19:10

I think we don't have a nice

19:12

framework yet for thinking about how are

19:15

you really going to decide which tasks

19:17

are worth tackling first. Coming

19:20

from a human centered design perspective, usually what

19:23

I do is figure out, who

19:25

are we working with? What do they

19:27

care about? What do they hate about? What are their

19:29

pain points and how do we work on those first?

19:33

Because if you go and take away the thing

19:35

that they love about their job first, guess what's

19:37

going to happen? That robot's going to get hijacked.

19:41

Someone's going to hit that big red button and

19:43

shove it aside. But if you

19:45

take on the task that they really wish weren't part of

19:47

their job and you give them more

19:49

time to do the things that they do love, I

19:51

think that can make a really big difference in terms

19:53

of adoption and long term use. Absolutely.

19:57

It's funny, I'm starting to see it in

19:59

the medical... fields more and more where at

20:02

least I'm being served ads right now. I

20:04

love spending time with my patients. I don't

20:07

love charting. I don't want to write notes.

20:10

That is a perfect example. I don't

20:12

want an AI to replace me in

20:15

face-to-face with my patients, but I also

20:17

do want technology

20:19

to help me with the menial and massive

20:21

time suck parts of my job. Yeah. There's

20:23

super tedious parts of our jobs that everyone

20:26

would love to get rid of. Or maybe

20:28

the backbreaking part of your job. Maybe you

20:30

want to work on the part that actually

20:32

makes use of your special

20:34

skills, like having

20:37

a good bedside manner with

20:39

the patient. Those are

20:41

things that I think in my most

20:45

optimistic future of these systems, we

20:48

would be building AI systems or

20:50

robotic systems that are complementary to

20:52

us because we already know

20:54

how to make things that are just like us. Making

20:57

babies. I

20:59

think if we can build things that complement

21:01

our skills so that we can do better,

21:04

that's a more powerful way forward than

21:06

just trying to replicate what we've already

21:08

got. I find you know this. There's

21:12

a lot of people talking about artificial

21:14

general intelligence or AGI. My

21:17

question is, why do we

21:19

want to make it smart the same way

21:21

we are? We have so many limitations. What

21:25

I want is something that's better than me in other ways.

21:29

My memory is not great, but

21:32

computer memory can be pretty awesome compared

21:34

to that. Why aren't we excited about

21:37

leveraging that and working together better? It's

21:41

interesting. You mentioned something there

21:43

that struck me. One

21:47

of the topics that I remember speaking

21:49

about on the other podcast

21:51

that I work on, on the Skeptic's Guide

21:53

to the Universe, and the host, Stephen Novella,

21:56

talking about this tendency of human beings. There

21:58

may even be a new one. name

22:00

for it, like the cognitive bias or maybe a name for it.

22:02

But this tendency of human beings, when

22:05

they are first innovating to try to

22:07

do exactly what you mentioned, it's like

22:09

reinvent the thing that we already have.

22:11

So when you look at the first

22:13

cars, they look like

22:16

horse carriages. Yeah. Like

22:18

they look at it because that's how you would

22:20

get around. Of course, the car is going to

22:22

look like that. It took time to iterate and

22:24

go, oh wait, that's actually not the most ideal

22:26

shape of a car. We

22:29

can make it more streamlined. Do

22:31

you think we're in that era right now

22:33

with robotics and AI, or do you think

22:35

we're still making horse buggies? I

22:38

think we're still making horse buggies, or at least

22:40

a lot of folks are still making horse buggies.

22:43

Yeah, that's just the stage of technological

22:45

development. I think we've still got

22:47

a lot of learning to do, and I don't mean machine

22:49

learning, I mean human learning. Once

22:52

you actually deploy these things and put them in

22:54

the hands of end users and see what really

22:57

happens, that's where I think

22:59

we can start to learn. Maybe having better,

23:07

more streamlined designs of those cars can

23:09

help because they're moving faster. What

23:11

does that look like for a chatbot?

23:13

What does that look like for a floor

23:16

cleaning robot? We're still

23:18

in the very early days of

23:20

trying to stumble our way there. Can

23:23

I ask, as somebody who's done a

23:25

lot of academic work, who works with

23:28

students, who experiences also maybe from a

23:31

consulting perspective, the

23:34

inner workings of corporate America and

23:36

how they think. Bear

23:38

with me on this question, hopefully I can get

23:40

to the root of it. I think about the

23:42

capitalist ethos of grow,

23:48

grow, grow, improve, improve, improve, progress,

23:51

progress, progress, right? I

23:53

think about how those

23:55

pressures on industries often

23:57

induce a need

24:00

and urge a compulsion almost

24:03

to solve problems that don't

24:05

even exist. How

24:07

often is that something that you've grappled

24:10

with in your work? Seeing

24:12

people coming up with these newfangled

24:14

ideas where you're like, what solution does

24:16

that? What problem are

24:18

you solving with that? We don't need

24:21

that just for the sake of making it.

24:24

Oh, I see that a lot. I

24:27

would call that the culture of the demo or

24:29

die culture. You got to do

24:32

something flashy because the executives are coming or

24:34

because the investors are coming. That

24:38

cycle of just demoing and demoing

24:40

and demoing and doing whatever is

24:42

shiny will get you somewhere, but

24:46

it's not necessarily going to get you to

24:48

the point of providing real value

24:52

to a set of customers who might

24:54

actually want that thing in their homes

24:56

or want that thing in their workplace.

24:59

The equivalent of a one-hit wonder product

25:01

or something. Oh, totally. Yeah,

25:03

it's on the shelf and then people get

25:05

tired of it really fast because it's not

25:07

actually solving the problem.

25:10

Yeah. You may have

25:12

seen, especially in hospitals, there's been quite a

25:14

few people trying to build robots for hospitals.

25:16

There are robots in the hospital where I

25:18

work 100%. They come in

25:21

and out of the elevator sometimes. Yes. Do

25:23

you ever get in the elevator with them? Yeah. I

25:26

think the ones that I've seen

25:28

so far are usually with a

25:30

handler still. Oh, okay. That's smart.

25:33

They look like ET. They're

25:35

like this size of ET, I would say.

25:38

Diminutive humans. They

25:41

have some of those

25:43

human-like faces with the

25:45

eyes and stuff, I think, to make them

25:47

more palatable and easier to interact with. I

25:50

don't know yet what they do, but I

25:52

do see them going in and out. Usually

25:55

there's somebody following them with a clipboard. Yeah.

25:58

They're in the learning stage. We're

26:00

trying to figure out what it's for. So that's

26:03

good. That's a good first

26:05

sign. Oh my goodness. Yeah, I mean,

26:07

you've also probably seen, right, there's often

26:09

places, including hospitals, where they just sort

26:11

of have robots on display. My

26:15

friend Matt Bean down at UC Santa Barbara

26:17

has done a ton of work on this

26:20

space where, you know, there is marketing value

26:22

for a hospital to show like, we have

26:24

robots. We are from the future, right? And

26:26

sometimes patients will, you know, ask for the

26:29

robot for their surgery, right, instead of

26:31

having human hands in their body. And

26:34

so there's certainly marketing value

26:36

that some companies are leaning into. I

26:40

think, you know, that's

26:42

one step forward, maybe. But

26:44

it makes me sad when I see robots that

26:47

were designed to perform a task instead

26:49

just being used for advertising. Yeah,

26:53

that is a bummer. Yeah. Yeah,

26:55

I think about like, it's funny because I'm

26:58

sure there is a lot of value

27:01

in a robot-assisted laparoscopy. But when I

27:03

had my, and, you know,

27:05

fans of the show know about it because I talked

27:07

about it on air, but when I had cancer now

27:09

almost two years ago and I had a laparoscopic

27:14

hysterectomy, it was comforting to know that my

27:16

surgeon, who had been my gynecologist for years

27:18

and I knew very well, was going to

27:20

be the one doing the surgery. And that's

27:23

not to say that a robot-assisted surgery, it's

27:25

like there's no people in the room. Like,

27:28

of course there's still surgeons doing the

27:30

surgery. But I

27:32

think we're at the stage where having it be

27:34

a robot is not comforting to me yet. Yeah,

27:38

and if you get to see

27:40

those surgical robots right now, they're a little scary, right?

27:44

And so there, you know, you got to look at

27:46

the data, right? Like what are the patient outcomes? Like,

27:48

is it actually better? Is it helping with shortening

27:50

the recovery time? At

27:53

the end of the day, you're right.

27:55

Like especially in the U.S., right, those

27:57

robots for surgeries are teleoperated by a

27:59

surgeon, right? And

28:01

there's a lot of controls to make sure that

28:03

they don't do the wrong thing. Yeah. And

28:06

this is a great thing if it

28:09

means, you know, increasing equity.

28:11

If it means more people being able

28:13

to have the service, you

28:15

know, available to them or to do it

28:18

cheaper or having a surgeon who's, you know,

28:20

not local, but who's very good at this

28:22

thing, now maybe they can perform it across

28:24

the country. I mean, that's amazing. But

28:27

I'm still really wary. Yeah, especially

28:30

with, you know, the internet being as

28:32

reliable as it is. Seriously, yeah. The

28:34

internet just hiccuped while I was on

28:36

with a client earlier. I was

28:38

doing therapy and the internet first. Like, oh, you're

28:40

frozen. Oh, God. You know, like, this is, we're

28:43

not living in a

28:45

world yet where the

28:47

infrastructure always supports these

28:49

things. Yeah, absolutely. We

28:51

have a long way to go there. And, you

28:53

know, it's like setting up audio visual connections,

28:56

as you know well, right? Like, it's hard to

28:59

get it to work. It's hard to get

29:01

the HVAC system into building the work too.

29:03

So, you know, everyone's comfortable at their temperature.

29:06

Right. So reliability is,

29:09

and I think reliability of a machine

29:12

of a, you know, whether

29:14

we're talking about a robot or like

29:16

an AI, reliability is directly linked to

29:19

trust. And

29:22

so how do you, how do you, you

29:25

know, kind of figure trust into the

29:27

work that you do? Oh my gosh.

29:29

So much, right? There are so many

29:31

ways that robots can break our trust,

29:33

right? And I think one of the

29:36

very common ways that they do that

29:38

is that we over promise on what

29:40

they can do. People

29:43

have watched those, you know, fancy demo

29:45

or die videos, and they get really

29:47

excited about them because they think the

29:49

robots can actually do more than they

29:51

actually can. And so I

29:53

think one of the big challenges that we face with

29:55

the AI systems too, right? You know, they're, they're trained

29:57

on a certain data set. And then

29:59

and they're deployed, it may be a different kind of

30:02

setting. And you cross your fingers and just hope that

30:04

it's going to work OK. Sometimes it

30:06

does, and sometimes it doesn't. And

30:09

so I think setting expectations with

30:11

end users is super important for

30:14

earning the trust and

30:16

being honest about what is possible and what

30:18

we're not so sure about yet, letting

30:20

them know when they're getting into the territory that

30:24

maybe you should be ready to catch it in case it

30:26

falls if you're going to use it over

30:28

there. But if you use it over here, it's going to be fine. And

30:32

it's not just education. It's

30:34

also just figuring out how to be

30:36

more transparent at

30:38

the right time, at the right place with those

30:40

end users to let them know when they're at

30:42

the edges of what we

30:44

know works. Right.

30:47

I think there is sometimes a struggle. I

30:50

don't know if this is a Western thing. I don't know

30:52

if this is an American thing. There's

30:54

this struggle with

30:56

reconciling reality

30:58

with sort of, I mean, I live

31:01

in Los Angeles, right?

31:03

And the

31:05

streets of Hollywood don't look like what it

31:07

looks like in the movies. And

31:09

I think we often. I think that's not a set. Exactly.

31:13

But we have been fed a visual

31:17

story on a set for so

31:20

long that we expect that. Yeah,

31:22

pristine sidewalks. Yeah. And

31:24

so when it comes to AI, when it comes to

31:26

robotics, I think that

31:28

maybe there is some cynicism that we see.

31:31

But I think overwhelmingly,

31:34

there's also

31:36

this frustration

31:38

that you often see with individuals

31:40

that things don't always work

31:43

the way that they expect them to, or that

31:45

we haven't sort of made more progress without really

31:47

taking the time to appreciate

31:49

the incredible amount of

31:51

progress that we have made. Yeah.

31:54

I've actually been talking with more

31:56

than a few academic friends about this because there

31:58

is. Just

32:01

like there's the pressure to demo or

32:03

die, right, in the corporate culture. There's

32:05

also in academia, there's this pressure to

32:07

publish or perish. We

32:09

love the alliteration. And with publishing,

32:12

you better have a result, right? And

32:16

if you don't have a result, or your system

32:18

doesn't perform as well as the last one, right?

32:20

You don't get to tell anyone, which

32:22

is kind of a dump. Right, yeah. The

32:24

negative results, just like they die. Yeah, right. And there are

32:26

folks who are fighting that trend. But

32:29

it's going to be a long and uphill battle.

32:32

The idea that we've been floating around for

32:34

robotics that is starting to get some traction

32:37

is that when you go to a robotics

32:39

conference, there's usually all these videos of the

32:41

cool thing that this lab got the robot

32:43

to do. We want to

32:45

open another track in which we do the blooper

32:48

reels, where we show up all

32:50

the failed attempts. Yeah.

32:52

Because we can learn from that, right? And

32:55

also, it'll help people understand robotics

32:57

is hard, AI is hard, right?

33:00

Then in order to see the progress, you got to

33:02

see the failures too. And I

33:04

think making more of a

33:06

fuss about how hard it is and

33:09

when the failures happen would help with

33:12

being more transparent with the rest of

33:14

the world about where the technology actually

33:16

stands. Yeah, and I think

33:18

that that even spreads out. It's funny,

33:20

but I'm always trying to make these

33:23

parallels. Sometimes they don't, they're

33:25

not as parallel as they are in my mind.

33:27

But I think that's the same thing when we

33:29

talk about the scientific method. And really, in a

33:31

lot of ways, it's the same thing when we

33:33

talk about a humanistic approach

33:35

to politics and governance. It's

33:37

like if we can normalize

33:40

making mistakes, learning from mistakes, and improving

33:42

based on the knowledge we got when

33:45

we made that mistake, I think that

33:47

we will be less of a culture

33:49

of, you changed your mind, therefore I

33:51

don't trust you. It's

33:55

like this weird thing where we want our

33:57

leaders to be unwavering. It's

34:00

like, why is that a good thing?

34:03

Don't we want people who grow? Right.

34:05

Yeah. I think this idea of having

34:07

a learning culture is super

34:10

important. In Silicon Valley where I am right

34:12

now, there's this called people say that there's

34:14

a culture of failing fast and early. But

34:18

we forget the step of like, and then you learn. You

34:21

learn from the mistake. Ideally, you

34:23

share the lessons that you learned with others who

34:25

are working in a similar space so that you

34:27

save them from bashing their head against that same

34:30

wall later. You

34:33

prevent the Elizabeth Holmes of

34:35

the world because it's the

34:37

culture that creates this compulsion

34:40

to cheat and lie your

34:42

way into success. Because

34:46

of course, like if you're- All the pressure is

34:48

to lie. Yeah. Yeah.

34:52

I mean, obviously, when

34:55

somebody does something like that and commits fraud,

34:57

it is their fault that they committed fraud.

34:59

But they're not doing it in a vacuum.

35:01

They're doing it in a culture that fully

35:03

was asking them to do that. Totally. Totally.

35:05

First of those decisions. Yeah. You

35:07

got to walk that fine line as someone

35:10

who's raising money for your startup. You're going

35:12

to tell your potential investors about things that

35:14

you're excited about for the future. But

35:17

in practice, it's really hard to promise that you're going

35:19

to get it done. Because you're doing something new. You're

35:22

actually doing research, quite

35:24

frankly. We don't know

35:26

what's going to happen and that's why I it's exciting and

35:29

you want to break them along for the ride. But when

35:32

you're in a culture where you need to make

35:34

promises so that investors think they're going to get

35:36

a return on their investment, you

35:38

might make those claims too strongly. Yeah.

35:42

It's so much to navigate. I

35:45

think we're living in a society now

35:47

where, I mean, thinking about you

35:50

and your role and your position,

35:53

we can no longer just do one thing

35:55

or be one thing. I think gone are

35:57

the days of our parents or our grandparents

35:59

who. They made widgets in the factory

36:01

and they put the left side

36:03

of the widget on all day every day

36:06

and they were left expert and then they

36:08

retired. Now

36:10

you have to know something about the marketing and you

36:12

have to know something about the sales and

36:15

you have to – it's very,

36:17

very difficult to not be interdisciplinary.

36:20

Yeah, I remember actually majoring in cognitive science.

36:22

My parents would often ask me like, what

36:25

is that? Is that really a thing? I

36:27

don't know, maybe you should pick a real major. You're

36:30

like, it's all the things. It's really

36:32

a thing, I swear. But

36:35

I do feel like maybe that was good practice

36:37

for this time when

36:39

like, yeah, you have to know more

36:41

than one thing. We don't get to only

36:43

be deep in one area because that just don't work. And

36:47

we can't put all our eggs in one basket in

36:49

that way either. I even think about

36:51

– I worked as a science journalist for years and years and

36:53

years. I finally decided to go back to school. I just finished

36:55

my PhD last year at 39 years old. And

37:00

when people – oh, sorry, what?

37:02

Oh, congrats. That's awesome. Oh, thanks.

37:04

That is quite the hurdle at

37:06

any age. I'm going to

37:09

stop and appreciate that. Yes,

37:11

you should. You know what it's like because you did it too.

37:14

And so, when

37:17

people go, why PhD and not MFT or

37:19

why PhD and not ID or why did

37:21

you choose the path that

37:23

you chose? And it's like because I want options.

37:26

I don't really know what I want to be when I grow

37:28

up. And so, if I want to do private practice, I can.

37:30

If I want to work in the hospital, I can. If I

37:32

want to get an academic position, I can. And

37:34

I think that it's the same sort of reasoning

37:38

with having a broad

37:40

skill set, also having

37:44

a focused area of expertise, but knowing that

37:46

you need to be somewhat light

37:48

on your feet, I think allows

37:51

you to do what it

37:54

is that you're doing in this field,

37:56

which is maybe –

37:59

I almost want to say it's like – bringing empathy

38:01

to the table. Yeah.

38:04

Is that a weird way to put it? That is a great way to

38:06

put it. You bring empathy to this field. Yeah,

38:08

I've actually, so I used to

38:10

teach human-computer interaction at the university.

38:13

And I remember I had one student one day

38:15

who came out to me and we were gathering feedback

38:18

on how's the class going for you. And the student

38:20

wrote to me and said, so

38:22

you're telling me that we need to have

38:24

empathy for our end users in order to

38:27

pursue this career path of user-centered design? And

38:29

I was like, yeah. And they're

38:31

like, I don't do that. So are you

38:33

telling me that I can't do this job? And I was like,

38:37

yeah, maybe not. You're probably

38:39

not well. Yeah. You might want

38:41

to explore other career options, honestly.

38:43

Interesting thing to say. Yeah,

38:46

and I think if you're that self-aware, good for

38:48

you. But

38:51

you might not want to pursue a job

38:53

where it really, really helps to care about

38:55

what it's like to be another person and

38:57

try to understand their world and their worldview.

39:00

Wow. Yeah, it

39:02

matters a lot, right? Yeah. I

39:04

think I choose to work with

39:06

people who do have a lot of

39:08

empathy and are curious about other people

39:11

because I think that's how we do a better job of designing

39:13

things for other people. And

39:16

also, to be clear, I feel like it needs

39:18

to be said. Empathy is not a

39:20

gift. It's not something you are endowed with. It's

39:22

a skill. You learn it. You can be more

39:24

empathetic. You just got to practice. Yeah, you choose

39:26

to learn it. You choose to practice it. Yeah.

39:30

Oh, I love that. And I love that you not

39:33

just do it, but I think there's

39:36

a big difference between doing and making

39:38

the thing that you're doing very explicit.

39:41

So obviously, in your work,

39:43

you are bringing empathy to the table. But also,

39:46

when you are talking about your work,

39:48

when you are teaching, when you are

39:51

marketing, that is an explicit part of

39:53

the conversation, which I think

39:55

is necessary because I don't

39:57

think it's implied. I don't think there's people think about

39:59

that. Yeah, I think that's absolutely right. And

40:01

often, empathy gets poo pooed. It's like, oh, that's a

40:04

soft skill. You know, it'd be nice to have. I

40:06

don't know if you have to have it. But you have

40:08

to have it if you're in this career path. Otherwise,

40:11

you're not gonna make it. You're

40:13

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40:15

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41:21

I think we can all think of examples

41:23

in our lives when we've experienced that with

41:25

technology. I'm facing one

41:27

right now in the setting where

41:29

I work, where they issued me

41:31

a computer. And I don't

41:34

like this computer because I was trained on

41:36

a different type of computer. And

41:39

the type of computer I was trained on

41:41

is very user friendly. And every day I'm

41:44

having to task switch between the two

41:46

and I want to throw it out the window. And

41:48

I don't want to start a war, so I'm not going to

41:50

name any brands. But I can guess. But

41:52

you can guess. And

41:55

that's such an interesting thing that

41:57

there's almost like a tribalism that

41:59

comes. from certain user experiences because

42:01

this is easy and that's complicated

42:03

and why would I do that

42:06

versus this? Relative, right. Right. It

42:08

depends on what you've got practice with, right? Mm-hmm.

42:12

Yeah. That is a

42:14

complicated one. That's fascinating. And also, why is

42:16

it in this field, and you mentioned it

42:18

at the beginning, that sometimes

42:21

you'll see, sometimes with engineers, but I

42:23

think you see this across the board,

42:25

you see it in all different aspects

42:27

of academic science as well, or even

42:29

just academics, that there's

42:31

a certain percentage of individuals

42:34

who relish the

42:37

idea of broadening the message and

42:39

saying, I want to make this

42:41

deeply accessible. And then there's other

42:43

people who feel somehow special, the

42:45

less accessible their experience is. Yes.

42:48

Like, I have my friends who are like, I only use Linux

42:51

machines. I'm like, okay. I have

42:53

heard those people, yes. Right. And

42:55

it's like, I'm very proud of you. Like, I don't... Right.

42:58

But cool, good for you. Like, this does not impress me. You

43:01

know? Yeah. Like, where does that

43:03

come from? I mean, I think tribalism is a great

43:05

way to describe that, right? You're trying to identify with

43:08

a group of people that you feel are special and

43:10

that you want to be a member of. And

43:13

part of tribalism is like, there's in-group and there's out-group,

43:15

so you got to have an out-group, right? And

43:18

it might as well be them, because they can't handle command

43:20

lines, right? Or they don't

43:22

like Ubuntu, my version, right? So

43:26

it makes us feel special, right? When we're

43:28

members of a club, whatever

43:30

lines you want to use to draw

43:32

them. Yeah. How

43:34

do you overcome that in your work, though? Because I

43:36

can imagine that sometimes you're actually facing

43:39

that in a corporate setting or you're

43:41

facing that in an engineering setting. And

43:44

like, it's your role, I would

43:47

guess, to try and convince individuals. Like,

43:49

that is not going to give us

43:51

the best bang for our buck. Like,

43:54

that's not going to reach people. Ah,

43:56

yeah. That's happened more than

43:59

a few times. I think,

44:01

so the best example I have of

44:03

where I think we made a little

44:05

bit of progress towards changing their minds

44:08

was back when I

44:10

was working at Willow Garage, we were working

44:12

on the robot operating system, which is ROS.

44:16

And there was this, in

44:19

open source software culture, there is

44:21

often the sense of like, well, if

44:23

you can't use it, then you're just too dumb and you

44:25

don't get to use it, right? It's

44:27

a little, you know, it's the tribe, right? And

44:30

you either belong to the tribe or you don't. And if

44:32

you can't, then we don't care, right? But

44:34

there's also some people on our team who

44:36

are very interested in, you know,

44:39

what they would call, say, democratizing robotics, right?

44:41

And like putting these capabilities in the hands

44:43

of more people, those two

44:45

things are in conflict. And

44:47

so I started, you know, just talking to folks

44:49

walking around the parking lot, trying to understand where they're

44:52

coming from. And I

44:54

kept asking them like, okay, so who's this

44:56

for? Right? Just like we were talking about

44:58

earlier. And they, they would come around to

45:00

like, well, it'd be really cool if, you

45:03

know, the top PhD students, the top robotics

45:05

programs would use our stuff. But

45:08

they don't. And it's like, oh, that's interesting.

45:10

So it just so happens that

45:12

the university right next door to where we were with Stanford,

45:15

and they had one of the top, you know, PhD programs

45:17

in robotics. So I grabbed a bunch of friends I had

45:19

in that program was like, hey, you want to come over

45:21

and try some new software? We have

45:23

cookies. And so they came over

45:25

for the cookies and stuck around

45:27

for the code too. And

45:30

we actually ran some user studies with them,

45:32

where we told them, okay, go ahead and

45:34

like, try to build a little robot model,

45:36

right and try to run it in the

45:38

simulator. And oh, my

45:40

gosh, it was so frustrating.

45:43

And our team who was working on the software

45:45

development for Ross were like, why can't they use

45:48

it? Like these guys are smart, these women are

45:50

smart, like, but they can't use our tool. And

45:52

so I think it, it

45:54

helped a little say like, okay, you're trying to get these people

45:57

to be able to do this thing. And no,

45:59

you don't get to stand next to them and show them how to do

46:01

it because you can't do that at scale. And

46:04

I think it helped us to build this practice

46:06

of like, let's bring in those people who we

46:08

think should be able to use it, let them

46:10

try it, get some feedback from them, and then

46:12

make it better for them over time, right, and

46:15

iterate. And that's just like basic,

46:17

you know, user centered design 101.

46:21

But I think it was kind of newer for the

46:23

folks who were used to developing code for themselves, right,

46:25

and their friends. Yeah, I

46:27

just bringing that to the table, I think

46:29

can start to make some headway in a

46:32

good direction. Absolutely. I mean, I'm reminded, and

46:34

I think I might have even mentioned this

46:36

on the last episode, but I just recently

46:38

watched this documentary about reading Rainbow, which

46:42

I loved reading. I grew up on that.

46:44

And it's such

46:46

a lovely, like, you know, just,

46:48

yeah, it's a good documentary to

46:50

watch if you need a boost.

46:53

But there was a part in it where

46:55

the executive producers were talking about how like,

46:58

we're a bunch of adults trying to write

47:00

a show for children, and try and

47:02

come up with what children

47:04

would want, or what's too much for them, or

47:06

what's not enough for them. Like, you can't do

47:09

that unless you sit with the kids and you

47:11

focus group the show. Like,

47:14

we are adults writing for children,

47:16

we need the children to tell

47:18

us what's working. Totally play testing

47:20

it, right? It's such an

47:23

easy concept. But I think it

47:25

kind of goes back to this weird thing

47:27

where like, when you've busted your ass, and

47:29

you've got the expertise

47:32

and the accolades, then somehow you need to

47:34

prove to everybody else that like, well, I'm

47:36

smart enough to do this thing. And so

47:39

I want it to only be like, I'm the

47:41

key to this lock, like, only

47:43

smart enough. But the truth of

47:45

the matter is, I don't want everything in

47:47

my daily life to require that I use

47:50

that level of cognition. I want most of

47:52

the things in my life to be really

47:54

easy. Right. You've got enough things to worry

47:56

about and think about and spend your cognitive

47:58

effort on. So it doesn't need to be.

48:00

also driving your car home shouldn't feel like

48:02

a massive effort at the end of the day. Right. Yeah.

48:05

Right. Why does my electronic

48:08

medical record software

48:12

make me feel like I'm taking an

48:14

exam every day? You are not alone

48:16

in that feeling. That is when almost

48:19

every doctor I know complains to me

48:21

about too. Yes,

48:23

there's a massive pain point in that industry. There

48:26

is. These are the

48:28

things, this kind of takes us, I love

48:30

this, like here towards the end of our

48:33

conversation, these are the

48:35

things that are fundamental to the work that

48:37

you do. I would love to hear

48:39

about what is robust

48:41

AI and what is it that you do

48:43

there? Sure. Robust AI started

48:45

as an AI company and we

48:48

kind of got forced into becoming

48:50

a robotics company because

48:53

we chose a problem to work on and

48:55

we chose a user group to care about.

48:57

And so those people are warehouse workers. When

49:00

you order stuff online, they're the people who

49:02

actually are physically going and finding the stuff

49:04

that you ordered, putting it on a cart,

49:06

loading it into a box and shipping it

49:08

out to you. And there

49:10

is a super high demand for that.

49:13

It's increasing, but nobody wants those jobs

49:15

and they're quitting and they're quitting at

49:17

ridiculous rates. This is not just a

49:20

really, really hard jobs. It's rough and

49:22

it can be rough on your body.

49:24

And to make it financially feasible, they're

49:26

probably not getting paid nearly enough. Right.

49:28

Especially when you've got supposedly free shipping.

49:32

So those margins are tiny, but

49:35

I think there's a really cool opportunity

49:37

there to make their lives better. And

49:40

the nice thing is that now their bosses

49:42

are very incentivized to make their lives better

49:45

because they're having such a hard time recruiting

49:47

people and they're having an even harder time

49:49

retaining them. And so

49:51

there's this beautiful alignment between like, well, the

49:53

bosses want people to be there and they

49:56

want them to stay. And

49:58

the users are really frustrated, but I think think we can

50:00

actually make their lives better. And so at Robust,

50:03

we are working on a pushcart that

50:06

happens to have a robot inside. And

50:10

so this pushcart can drive itself around. It doesn't have

50:12

to be pushed around by a person. So you can

50:14

sort of, you know, it'll valet park itself if you

50:16

want it to. You can

50:18

call it over when you want to use it. But when

50:21

you actually want to show it what to do,

50:23

you grab it by its handlebars and push it

50:25

around. Right. And so it's a very familiar form

50:27

factor. And now,

50:30

you know, after thinking about chatting with you, now

50:32

I'm wondering, like, is this like the horse drawn

50:34

cart? Maybe

50:37

we're at that stage, maybe we have more learning to do.

50:39

I'm sure we have more learning to do. Of course, right?

50:41

You can't skip ahead to the cybertruck. Because then you're like,

50:43

what is that? I don't want that. Yeah,

50:46

yeah, that thing is sharp and pointy, man. Also, I

50:48

don't want to give it that much credit. That is

50:50

not skipping ahead. But yes. That

50:53

is a vision of the future. Exactly.

50:55

You take my point. Yes. This is

50:57

actually my vision. Yeah. So it's, I

51:00

mean, it's been super fun for me, like getting

51:02

to hang out with warehouse workers, getting to spend

51:04

time seeing how they actually do the problem solving.

51:06

And the coolest thing is, you

51:09

know, there's this sort of meme out there that's

51:11

like, we don't need no stinking humans. We're just

51:13

going to make a dark warehouse, right? And the

51:15

whole warehouse is a robot. And it turns out

51:17

that doesn't work very well. It's

51:21

the humans in the loop who make the whole stupid

51:23

thing run, right? And they figure out, like, well, actually,

51:25

you have to shim this one machine this way to

51:27

make it do its thing properly, right?

51:30

And those little tricks are

51:32

what makes the whole operation work or not

51:34

work. And so if you take

51:36

the opposite assumption of like, actually, we do need

51:39

those people, and we need to value

51:41

them, and we need to treat them better and

51:43

make more humane working conditions, I

51:45

think that's a better winning way

51:47

forward. And so we're sort of

51:49

betting on that hypothesis right now and trying to make

51:53

more capable push carts that make, you know, make

51:55

the load feel lighter, that make it easier to

51:57

find the thing you're looking for. and

52:00

make you walk fewer steps. Some

52:02

of these warehouse workers can walk like 30,000 steps or

52:04

more per day. And

52:07

like, you know, it's good to get some exercise, but

52:09

that's a lot. That's pretty good. Yeah, I

52:11

think the goal is what, 7,000 a day? Isn't

52:13

that what we're all looking for? Something like this. Yeah,

52:16

you don't need that much of a workout just at

52:18

work. And then it limits

52:20

who can actually participate in those jobs, right?

52:23

And so I think we have a pretty cool

52:26

chance to try to make these jobs better and

52:28

make them more accessible for

52:30

a broader range of people too, which would be

52:32

pretty cool. So the bar is very high for

52:34

design. There

52:37

are many different languages spoken in these warehouses,

52:39

and so we have to support all of

52:41

them, right? And I think, you

52:44

know, it's a, to me it's a

52:46

fun challenge to work on because if we don't

52:48

get the user-centered design right, then we've failed just

52:50

as a company. And yes,

52:53

the machine learning part of it matters.

52:55

Yes, the mechanical design really matters too.

52:58

And so this is a time when we

53:00

get to test our ability to work together

53:02

as an interdisciplinary team to work on a

53:04

real world problem, right? As opposed to, let's

53:07

make a robot and then figure out what it's for

53:09

later, right? Which is

53:12

kind of more typical in this industry.

53:15

You know, I'm curious, thinking

53:18

about the work that you're specifically doing

53:20

right now, I can

53:23

imagine that when you're talking to different

53:25

stakeholders, when you're talking to individuals who

53:27

are curious about this type

53:29

of progress, that a common question that

53:31

comes up is, okay, yes,

53:34

you're trying to make it so that the people

53:36

and the robots can work together seamlessly and blah,

53:38

blah, blah, blah, but isn't the goal ultimately to

53:40

do the dark warehouse? Like maybe we're just not

53:43

doing the dark warehouse because we're not there yet,

53:45

technically. Like how do you respond

53:48

to that question?

53:50

Yeah, I think of it as like,

53:52

you know, they're short term and long term. And right

53:54

now I think there's an obvious direction that

53:57

go for the robot. short

54:00

term because if you look at

54:02

the fully autonomous systems like there's lots of startups

54:05

and other larger companies that are making the

54:07

dark warehouse bet and

54:09

we're seeing how that's panning out and it's

54:11

it's tough because

54:14

you need to be very sure that

54:16

the items that you're shipping are always going to be

54:18

the same items right if you build a big

54:20

warehouse robot and it's got totes that are

54:23

a certain size for carrying stuff and then

54:25

suddenly now you're carrying bigger items

54:27

that don't fit in those totes right what

54:29

you're gonna do and so they

54:31

tend to be very brittle solutions and

54:34

then they end up having to like build yet another warehouse

54:36

where they handle all the other stuff that doesn't fit. Right

54:39

so they're too narrow oh that's

54:41

interesting. Yeah like it can work

54:43

if you're very sure that you're never going to change

54:45

what you're shipping and you're pretty sure

54:47

you're never going to change your order profile

54:49

so you know if you're if you're e-commerce

54:51

like holiday time is insane right they call

54:54

it peak season and

54:56

being ready for that is really hard right

54:58

you got to flex up and down

55:00

very quickly in response to whatever customers

55:02

are ordering and that can

55:04

be hard so I think it's really the

55:06

dynamic nature of that industry that makes it

55:08

super hard to have very

55:10

like brittle solutions so

55:13

I would bet on actually like

55:15

what happens now is people get it done right

55:19

and we just get it done better and

55:21

those are they can be really great entry-level

55:23

jobs for folks who are entering a new

55:25

space right that they they haven't worked in

55:27

before or a country that they haven't worked

55:30

in before and make opportunities for those people

55:32

yeah yeah yeah we need that to have

55:34

a functioning economy and to have a functioning

55:36

social you know net you know

55:38

it's it's I

55:40

guess here sort of at the

55:42

end of of the hour this this

55:44

then leads us to the big question

55:46

that's often kind of on the tip

55:48

of everybody's tongues and not to get too

55:51

dystopian and not to get too dark but you

55:53

know yes you said that there

55:55

are those startups that are working on the dark warehouse and

55:57

of course that's just a synecdoche right that's like an existing

56:00

of something larger. But often when

56:02

we talk about AI and robotics,

56:05

we go to that

56:07

extreme place of like

56:10

general intelligence or of

56:12

sort of the robots taking over,

56:14

you know, this like what happens

56:16

if and when. And you

56:19

have a realistic handle on

56:21

where we are now, kind of the,

56:23

I guess we

56:26

could say the way that the

56:28

future is progressing. I work

56:31

with a lot of people who either

56:33

consider themselves futurists or who

56:35

are just very like interested

56:37

and enamored with the

56:40

world of robotics and AI. And

56:42

I find a lot of them

56:44

to be like massive techno optimists.

56:48

I sometimes worry that I'm like the

56:50

wet blanket, like I'm a bit of

56:52

a cynic. And I want to really

56:54

net out in a place that

56:57

is authentic and realistic. And so I guess this

56:59

is my long way to ask kind of where

57:03

do you find your sensibilities

57:05

when it comes to what

57:07

a long picture might look like

57:09

and what the future, maybe

57:12

there's a difference between could hold versus

57:14

where you actually think the future is

57:16

headed. Yeah, I

57:19

think that's got pounded into me in grad

57:21

school. But there's this notion of like, the

57:24

future isn't just this thing that's

57:26

coming at us, right? Especially with

57:28

technology, like we are literally deciding

57:31

what we want that future to

57:33

look like, because we're actively

57:35

participating in it. And

57:37

so I feel like, you

57:39

know, if that dystopian future comes to pass,

57:41

that's our fault, right? Because we're

57:44

choosing to take that path. And

57:46

I agree with you, there's a ton of techno

57:48

optimists out there. There's also techno pessimists, right? There's

57:51

lots of sci fi that shows us that, you know,

57:53

if we push on this one dimension in the wrong

57:55

way, the world's going to end, right? And

57:58

I think those are good warnings. they're

58:01

good possible futures to consider.

58:03

But at the end of the

58:05

day, I feel like it's our

58:07

responsibility to be involved in inventing

58:10

the future that we actually want to live in. That's

58:13

my old mentor, Stu

58:16

Card is the one who I blame for saying

58:18

it because it's stuck in my head forever. I

58:21

feel like if we don't take

58:24

action now to participate in the

58:26

design of that future, then

58:29

we only have ourselves to blame. Figuring

58:32

out how to make that participation more inclusive

58:34

of all the people who this is

58:36

going to impact is also

58:38

critical to a success

58:40

that is good for more

58:42

people than just the ones who invented

58:45

it at the beginning. Yeah. I think

58:47

that that's

58:49

an important question in and of itself,

58:51

is that this future that is what

58:53

it is because of how we shape

58:56

it, there

58:59

are specifics here and obviously I'm

59:02

not looking for some exhaustive list

59:04

here at the close of the

59:06

show. But obviously having more viewpoints,

59:08

more seats at the table, people

59:11

with different life experiences, really

59:14

having a lot of power in

59:16

the conversations is one fundamentally important

59:18

part of shaping that future. But

59:20

what are some other examples

59:23

of things that you think

59:25

are important to help shape

59:28

that future in a way

59:30

that is responsible and safe,

59:32

and I guess more humanistic?

59:35

Yeah. There is a

59:37

beautiful framework that's one of

59:39

my favorite textbooks on human

59:41

factors and the design

59:43

of technologies. It's updated

59:45

thankfully. The original version of this was

59:48

called Mabamabam, which was stood for men

59:50

are better at and machines are better

59:52

at. Oh God. It's from the

59:55

70s. That has changed. Now, it's

59:57

like people are better at versus machines are

59:59

better at. like the things machines are better

1:00:01

has changed too right in the last few decades. And

1:00:04

I think you know we're talking about like why are

1:00:07

we building things that are exactly like ourselves when we

1:00:09

know that we have so many limitations.

1:00:13

I think that a better

1:00:15

future that could be one in

1:00:17

which we look for the complementary

1:00:19

skills and for the complementary strengths

1:00:22

that can help us to overcome

1:00:24

our limitations right. So you know forgetting

1:00:26

things forgetting can be good sometimes

1:00:29

but when I forget my keys at home

1:00:31

that's not so great right. And

1:00:34

so being able to figure out how

1:00:36

to design tools including computational tools right

1:00:38

that help us to overcome our psychological

1:00:42

limitations our physical limitations right. We're seeing

1:00:44

robots being used for things like exploring

1:00:46

space and exploring the deep sea because

1:00:48

quite frankly our bodies couldn't handle that

1:00:51

right. Exactly yeah. And I think that

1:00:53

future to me is brighter

1:00:56

because it's first of all being self-reflective

1:00:59

about what we're bad at and

1:01:01

what we're good at right and then designing

1:01:03

for the rest of it right and

1:01:05

designing for the complementary skills I

1:01:09

think is a more productive way of

1:01:11

thinking about what's worth building right.

1:01:13

I love that and the future is

1:01:15

now because we are already

1:01:17

doing this and we don't often stop to reflect on it

1:01:20

like I do not know how

1:01:22

to get anywhere without my GPS and I don't

1:01:24

know anybody's phone number because they're saved in my

1:01:26

phone and that's a good thing because yeah that

1:01:28

is real estate I don't need to be taken

1:01:30

up. Yeah you're free to

1:01:32

do other things now. Yeah yeah

1:01:35

I think that's such a such a

1:01:37

wonderful way to kind of close out this

1:01:40

discussion but before we go I guess I should

1:01:42

ask if there's anything that we didn't cover or

1:01:44

if there's anything that you feel like oh god

1:01:47

no we gotta make sure we um

1:01:50

oh my gosh there's so many things that we could talk about

1:01:52

and I'd be happy to chat again anytime. Oh

1:01:56

gosh that is a great question. I

1:02:00

think I would

1:02:02

not recommend that we do this right now. But

1:02:05

there is this question of, again,

1:02:10

from films like her, this

1:02:12

question of our relationship with

1:02:14

these technologies and

1:02:17

how we make sense of them and what that means about

1:02:19

us. Right. And how

1:02:21

can we be humanistic even though that might

1:02:23

not be a human? Because, yeah, I do

1:02:26

worry the sort of genocidal

1:02:28

playbook here, right? Like the

1:02:30

more that we teach something that is quote,

1:02:32

unhuman or quote subhuman a certain

1:02:34

kind of way, the more that we can easily

1:02:37

translate that to our interactions with actual

1:02:39

humans. Oh, totally. A good friend of

1:02:41

mine named his daughter

1:02:44

Alexa just before Amazon

1:02:46

launched Alexa. And now she's in school

1:02:48

and that can be, you

1:02:53

know, it's unfortunate that they had,

1:02:55

they picked the same name, right? Because now there's

1:02:57

all this baggage that comes with that name, right?

1:03:00

Like play music, Alexa, right? Like you're just commanding

1:03:02

and it doesn't need

1:03:04

to be that way. Right. So you're

1:03:06

right. How does that shape our understanding

1:03:08

of our interactions with

1:03:10

each other, right? And how we value

1:03:13

other people versus other kinds of

1:03:15

agents, right? That can feel social even

1:03:17

if deep down we rationally

1:03:19

know that they're not real

1:03:21

people, right? Right. Yeah.

1:03:24

And I guess my take is

1:03:26

I want to always aid on, or aid,

1:03:28

that's not the right word I'm looking

1:03:30

for. I want to err on the

1:03:32

side of kindness. Like even if it's

1:03:34

to a robot, because I feel like it's

1:03:37

like empathy, like we said, is a skill.

1:03:39

You just need to practice it all the

1:03:41

time. If I'm kind to my robots, I

1:03:43

know that I'm reinforcing just

1:03:45

being kind. Right. Right.

1:03:48

Yeah. And when I see people abusing robots, which does

1:03:50

happen pretty often, it kind of makes you wonder, like,

1:03:52

what do they like when they go home? Exactly.

1:03:55

Yeah. Yes.

1:03:58

Fascinating. Gosh. And like said,

1:04:00

of course, there are so many more topics that

1:04:02

we could cover, but hopefully this was a good

1:04:05

kind of smorgasbord

1:04:07

of different things. And I'm hoping that

1:04:09

it could sort of whet some people's

1:04:11

appetites to learn more and to dig

1:04:13

a little bit deeper and just to

1:04:15

be a little bit maybe more mindful

1:04:19

in their engagements with technology.

1:04:21

I just can't thank you

1:04:23

enough, Laila, for A, the work that you do,

1:04:25

but B, for spending the time with us sharing

1:04:28

about it today. Thank you for spending the time

1:04:30

with me. These were amazing questions and

1:04:32

so many juicy topics to work on together. There's

1:04:34

a lot more that we still need to

1:04:36

do. Absolutely. And everyone listening, there's

1:04:38

more we need to do too. So I

1:04:41

just want to thank you for coming back

1:04:43

week after week. I'm really

1:04:45

looking forward to the next time we all get

1:04:47

together to talk nerdy. What

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