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joinmiddy.com. Hello,

1:12

everyone, and welcome to Talk Nerdy.

1:14

Today is Monday, March 25th, 2024,

1:19

and I'm the host of the show, Cara Santa

1:22

Maria. And as always, before we

1:24

dive into this week's episode, I do want

1:26

to thank those of you who make Talk

1:28

Nerdy possible. Remember, Talk Nerdy

1:30

is and will always be 100%

1:33

free to download, and that's because I rely

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1:40

top patrons for the show, that's

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those who pledge the top

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F. Jaramillo, Joe Wilkinson, Pasquale

1:57

Gelati, Riva Keith, and Ulrika

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H. The man. If.

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You would like to pledge your

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support on an episodic and basis.

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All you have to do is

2:08

visit petrionand.com/talk Nerdy where you can

2:10

learn more about how to do

2:13

so. All. right? Let's dive

2:15

into this week's episode. So.

2:18

I. Had the opportunity to speak

2:20

with a doctor din Yellow. Ruth

2:22

See is a roboticist and professor

2:25

of Electrical Engineering and Computer Science

2:27

at Mit. She is the Director

2:29

of Computer Science and Artificial Intelligence

2:32

Laboratory there, and she's also a

2:34

member of the National Academy of

2:36

Engineering, the American Academy of Arts

2:39

and Sciences. and she's a Mutt

2:41

Macarthur Genius Fellow. See.

2:43

Coal wrote with Gregory

2:46

Mon a brand new

2:48

book called of the

2:50

Heart and That ship

2:52

our bright future with

2:54

robots So without any

2:57

further ado here see

2:59

his his officer then

3:01

jealous. Will Daniela? thank you

3:03

so much for joining me today. Thank.

3:06

You car! I'm so excited to spend the

3:08

time with you. As a

3:10

you've got a Bahram new book called.

3:13

The. Hearts and That ship

3:15

our bright future with robots

3:18

and you of course our

3:20

of roboticist and an electrical

3:23

engineer and a computer science

3:25

etc etc. I'm. Talk

3:28

to me a little bit before we

3:30

get into the book and sort of

3:32

our relationship with robots and robotics. Touch

3:35

me a little bit about your. Past

3:37

and your journey. How does

3:39

one become? A roboticists, a

3:42

computer scientist, an engineer. All

3:44

three together. Well

3:46

tara. I grew up in Romania.

3:49

At the time of great austerity,

3:52

And in my spare time I read a

3:54

lot of books. And. I reflected

3:56

on my fantasize about superpowers

3:58

sense far away the places.

4:01

But. I never imagined back then that

4:03

I would be one day working at

4:05

M I T. Building

4:08

robots and thinking about. A

4:10

bright future with machines. My

4:12

family emigrated to the United

4:14

States around that time I

4:17

was finishing high school. And

4:19

I was an undergraduate at the University of

4:21

Iowa. Where. I focused on

4:24

computer science, mathematics, and astronomy.

4:26

And. I often two trips to Chicago

4:29

where I enjoyed the lake, the cigar

4:31

same glass at the museum of our

4:33

it's the Music Life. At

4:36

the foods that it was really

4:38

exciting. Not toward

4:40

the end of my studies I had

4:42

an encounter that change the course of

4:44

my professional life. One

4:47

of the great minds in

4:49

computer science. At Computer

4:51

Scientists John Hop crossed. That

4:53

visited the university and gave

4:56

a distinguished talk. Now.

5:00

He's talk was I was

5:02

extraordinary in his talk. She

5:04

basically explains that computer science

5:06

will solved and he was

5:09

time for the grand applications.

5:11

You. Can imagine as an undergraduate

5:13

student studying computer science. I.

5:15

Found that a little bit unsettling.

5:19

Read a chance to speak with

5:21

him afterwards and what he what

5:23

he said was that a lot

5:25

of the problems that the founders

5:27

of the Fields post. Initially,

5:30

Was. Solve. They have solutions.

5:33

But that was a time of great opportunity.

5:36

And the great opportunity was

5:39

to build computing that interacted

5:41

with a physical world and

5:43

with people. To.

5:45

Think that for granted now, but

5:48

in the late nineteen eighties when

5:50

this conversation took place. Computation.

5:53

Was primarily for the digital world.

5:57

Oh I was very page. And.

6:00

I was excited to join

6:02

the john at Cornell University

6:04

to work on one of

6:06

these grand applications which was

6:09

robotics. And. Job It

6:11

was an extraordinary time. Because.

6:14

I'm we had. We.

6:16

Had the aspiration to solve these

6:18

these big problems will have to.

6:20

Didn't have the machines that could.

6:23

Translate. Our ideas are

6:25

into into solutions into fact.

6:29

And. And so. Simply.

6:32

Developing computational. Techniques.

6:35

Didn't work because the rogue robots

6:37

we had back then was a

6:39

bit big and bulky and am

6:42

had a very limited the repertoire.

6:44

Of doing things. And

6:46

so that's why am I? I sort of

6:49

realize that in order for us to make

6:51

real progress in the field of robotics, We.

6:53

Have to think about two aspects of the

6:56

field. We have to think about the body

6:58

of the machine and the brain of a

7:00

machine. Because. The machine. Will.

7:02

Only be able to do what the

7:05

bodies capable of doing. But.

7:07

Then the most local needs a

7:09

brain because without the brain without

7:11

the controllers and the algorithms without.

7:13

At the A I brain at the

7:15

machine will be an inert mechanism. And

7:18

so know that for robotics to

7:21

achieve it's potential, we really needed

7:23

to think about. The.

7:26

Body of the machine. And the

7:28

brain of the machine. And that's what I've been

7:30

doing ever since. You

7:32

know, I love that I think about so I'm

7:35

I'm a child of the eighties, so I just

7:37

turned forty this past. Year. Was born in

7:39

Nineteen Eighty Three. And when I

7:41

think about my childhood here in the

7:43

United States, I am sort of one

7:46

of the i'm an elder Milenio. You

7:48

could say I'm one of the sort

7:50

of last of the generation. Where are

7:52

my two boys growing up? Were definitely

7:54

not all mechanical. There were a lot

7:57

of sort of electric toy things that

7:59

will bow. Eerie powered. But

8:01

I did not really have computer

8:04

chips in my toys and I

8:06

have always. I grew up in

8:08

a world where there was this

8:11

distinction between the mechanical. Or

8:13

the electrified m, the

8:15

computational or the. You

8:17

know, like the I phone in

8:19

your pocket or the laptop sitting

8:21

in front of you. I always

8:24

saw like a big distinction between

8:26

those two, but children growing up

8:28

now probably don't even have that.

8:30

That. View

8:32

or that. understanding. Because computational

8:34

power is in. So.

8:37

Many things and I use toys

8:40

as an example because you can

8:42

very much see the evolution of

8:44

toys through these different era and

8:47

see that there quite a. Quite.

8:51

A kind of time capsule of

8:53

the technologies that were that we

8:55

were working within a commercial way.

8:58

and so I'm super curious. You.

9:00

Know from from your perspective.

9:02

Is do you think that that is

9:05

a good way to to look at

9:07

the these changes you know it or

9:09

our our children's toys. Even a good

9:11

sort of metric for that. It's

9:14

a very interesting point of view

9:16

param so as I think about

9:18

it. We observe that

9:20

when I was a kid I

9:22

was his by the porridge. By.

9:25

How that because magically. Abroad

9:28

colors in photos to life right

9:30

this before your eyes. And.

9:33

Now my daughter's live in the

9:35

world where. They. Have in

9:37

some sense the equivalent of

9:39

a digital polarizing the form

9:41

of a small smartphone and

9:43

they touch the smartphone. And

9:45

they get images on screens. To.

9:48

Respond to the pipe. it's a very

9:50

different world. When. I wanna do is

9:52

I want. That. Image to come

9:54

out of the phone and grow

9:56

into a physical structure. physical thing

9:59

that can. Interact with us

10:01

and help us and really transform how

10:03

we view the world. Yeah.

10:07

Sort of this. it's almost like

10:09

this by mode or this this

10:11

from know how to put it

10:14

into words. But we went from

10:16

the. Chemicals the mechanical

10:18

into this a booming era of

10:20

the computational. And now we're in

10:23

this world where there's a deep

10:25

synthesis between the two, where the

10:28

computational is no longer in the

10:30

ether. It's no longer us, just

10:32

on the internet. It's no longer

10:35

just behind the screen, but it's

10:37

becoming an everyday part. Of our

10:39

experience it's becoming integrated into places

10:41

we didn't ever had of imagine

10:43

it would be. Computation.

10:46

Is indeed everywhere is

10:48

in so many devices

10:51

and objects we interact

10:53

with Bots am. There.

10:56

Is a really interesting phenomenon.

10:58

Ah, that is happening right

11:00

under our eyes today. And.

11:03

This is. This. Is the

11:05

Ai phenomenon? So.

11:07

I is is growing

11:09

in capabilities right under

11:11

our eyes. We. We

11:14

are watching these capabilities

11:16

expand. And we

11:18

know a lot about Ai today. But.

11:21

You see most of the the

11:23

extraordinary Ai solutions we have today.

11:26

That. Exhibit: So much intelligence, so

11:28

much computational power are still

11:30

in the digital world. We.

11:33

Also have a physical world

11:35

where we have. Robots.

11:38

On the manufacturing sore. And

11:40

these robots are masterpieces of engineering

11:42

that kinda so much more than

11:45

people can. And. Yet

11:47

am the robots on the

11:49

manufacturing with floor or not

11:51

that intelligence? In. Fact: They

11:54

have to operate in a. In.

11:56

A rigid environments they perform

11:58

sake stop and they have

12:00

to be isolated some people

12:03

because they are. Big.

12:05

And bulky and dangerous to be

12:07

around. What? I would

12:09

like to see ah is emerging of

12:12

the two worlds. Emerging of

12:14

the the extraordinarily intelligence

12:16

digital A I world.

12:19

With. A. Potentially. Magical

12:21

World of physical machines.

12:24

And. I want all of this to

12:26

be intelligence. I wanted to create physical

12:29

intelligence. And. This is actually coming

12:31

to life today. And

12:33

some of the capabilities we

12:35

have are highlighted in the

12:37

book. Or. Maybe you

12:40

can help us sort of go

12:42

back to a more foundational conversation

12:44

because I think we we want

12:46

to make sure we're always working

12:48

from the same operational definitions. And

12:50

I think sometimes when we have

12:52

conversations we take we take things

12:54

for granted and we use words

12:56

without fully understanding what they mean.

12:59

So when we talked about of

13:01

robots, what are we actually talking

13:03

about? Is it just a machine?

13:05

How do we define what a

13:07

robot is as opposed to. You

13:09

know, is my vacuum a robot?

13:12

Or is my vacuum. A machine

13:14

or. Are they won in the same. Is.

13:16

It's a roomba it's the robots. If.

13:18

It's a manual vacuum cleaner. It's

13:21

not a robot. Basically, you can

13:23

think of robotics as putting computation

13:25

in motion. And you can think

13:27

of a robot as a programmable

13:30

mechanical device. That. Can. Exert.

13:33

Forces. By. Taking

13:35

input from the world, reasoning about

13:37

that, inputs to computation, and then

13:39

deciding what to do next. Okay

13:42

so when I think about going back to

13:45

sort of the children's toy example when I

13:47

grew up by I had a toy that

13:49

I loved called had Iraq Spend. So this

13:51

was a a teddy bear that had a

13:53

tape tape player and it's belly and it's

13:55

mouse. Would move along with the with the music

13:57

or with the. Stories of A Selling This was a

13:59

purely. Mechanical device like

14:01

kids today might have a

14:04

similar toy that reacts to

14:06

their voice or that you

14:09

know can take inputs. From

14:11

the real world. And make decisions

14:13

about what to. Do next.

14:15

So that's more of a of

14:18

a robot. Like there are robots

14:20

as children's toys. Yes, There.

14:22

Are some robots? Ah, as

14:25

two boys, Ah, you have

14:27

to think about how much

14:30

autonomy and how much computation

14:32

is processed by that toy.

14:35

And. Co op your toy from

14:38

your childhood. Was a

14:40

sophisticated automaton. Because the

14:42

same thing, no matter what. A

14:46

robot. Would. Have

14:48

to adapt to the situation

14:50

or that it finds itself

14:53

in. Like

14:55

imagine imagine walking into the

14:58

dentist's office. And. Imagine

15:00

being greeted by. And.

15:02

Little humanoids at toy.

15:05

A fad. Recognized as you

15:07

walk through the door. Greets

15:09

you and then asks you. Have

15:12

you lost your teeth recently? That.

15:15

Would be a kind of a robotic

15:17

toy. Because that's.

15:20

That. Instance that devise. Is.

15:22

Able to identify when you

15:24

come in. Is. Able to

15:26

come to you. It's able

15:28

to engage in an interaction

15:31

with you. And is

15:33

able to adapt as interaction.

15:36

To. You or to me or

15:38

anybody else. Walking. In the

15:40

in the space. Yeah,

15:43

you know, I think it's interesting

15:45

as a sort of lay consumer

15:47

to think about. where is that

15:50

dividing line? Like where is that

15:52

thresholds you know a lot of

15:54

us are use do the science

15:56

fiction. Versions or the Boston

15:59

Dynamics the versions of these humanoid

16:01

or these dog like or bear

16:03

like robots. We can instantly identify

16:06

the thats of robots or you

16:08

know, like you mentioned the roomba

16:10

We kind of all know that's

16:13

a robot vacuum or these large.

16:15

Manufacturing robots, That are

16:17

working on the line in let's

16:19

say, a car manufacturers. But where

16:22

is that sort of minimum threshold

16:24

for something to go from being.

16:26

Mechanical. Or purely

16:29

computational to into d kind of

16:31

definition of a robot. For.

16:34

The elements that we need.

16:36

Our perception. The. Census

16:38

of Robots. The brain.

16:41

The computation that the robots.

16:45

Executes based on perception

16:47

values. And then the

16:49

actual areas that have the ability

16:51

to do something in this in

16:53

the world. So these other three

16:55

important components that define a robot.

16:57

But I want to make a

17:00

comment about. About

17:02

what you said with respect

17:04

to humanoids and robot arms

17:06

and I want to observe

17:08

that. Most. Of the robots

17:10

that we have today. Are

17:12

inspired by the human form.

17:15

Would. Have been manipulators. That

17:17

look like arms. We have

17:19

humanoids. We also have robots.

17:22

That. Looks like boxes on wheels

17:24

like cars. And

17:26

will I think this is a very

17:28

limited view of what a robot could

17:30

be in our future. Our.

17:33

Bills, Environment. And.

17:35

Our. Natural. Environments

17:37

time in. So many more.

17:39

Safe. We. Have

17:41

fish said we have total said

17:44

we have cheaper saves. We have

17:46

to say we have table sage.

17:49

Imagine if we have the ability.

17:51

To. To take. Any

17:54

kind of shape in in the physical

17:56

world, in the built environment, or in

17:58

the natural environment. And create

18:01

a machine. Inspired.

18:03

By that shape. A. Machine that

18:05

could be optimized. To. Help

18:07

you. Do certain tests. To.

18:09

Help you may be and fits

18:12

right when you have to pay

18:14

attention or help you relax. When.

18:17

You can take it easier. So

18:19

this is part of what I

18:21

want to do out with my

18:23

work and this is an idea

18:26

I tried to convey in the

18:28

book. That's why we are. Really?

18:32

Am. Constrains:

18:34

In what we typically

18:36

associates with a robotic

18:38

devised. I think it's

18:40

important to expand our horizons and

18:42

think about all kinds of things

18:45

in our world. that could be

18:47

robots that could do things for

18:49

us and week. And and also

18:51

we should think about all the

18:53

materials that we used in order

18:55

to build this machine. Traditional.

18:59

Robots are built out

19:01

of metals, them and

19:03

has a plastics and

19:05

this leads to quite

19:07

as rigid. And

19:09

dangerous. Man. Mechanisms to

19:12

be around. But. In the

19:14

metal, the natural world, we have

19:16

so many more material, so why

19:18

not consider making machines out of

19:20

anything? And. In our lab

19:22

we have made robots out of paper

19:25

and plastic and silicone. We even made

19:27

a robot out the foods. And

19:30

so it's sad it's It's

19:32

really a almost liberating to

19:34

think about. All. These

19:36

different kinds of tools that we

19:38

can we can bring into our

19:40

lives in order to support us

19:42

with physical work. Yeah,

19:45

sort of this evolution that

19:47

we've we've been seeing unfolds

19:49

between. like you mentioned these

19:51

these rigid hard parts and

19:53

these more like soft robots

19:55

using these really innovative i'm

19:57

materials from from different mature

19:59

the scientists and also this

20:01

idea of. Bio.

20:04

Mimicry of of sort of looking. At

20:06

the natural world and seeing

20:08

how things move and change.

20:10

And and borrowing from. That it

20:12

reminds me of the. When

20:15

you look at sort of the history

20:17

of technology, you often see that we

20:19

are constrained by our own imaginations and

20:21

I think about the earliest cars right?

20:24

Like I think about like the Model

20:26

T and some of the very early

20:28

I'm experimentation with cars or you watch

20:30

a television show like Picky Blinders and

20:32

you're seeing them driving these these coaches

20:34

and they just look like a carriages

20:37

because that's what we always used to

20:39

before they basically took the horse away

20:41

and put it in the engine. but

20:43

that the shape and the functionality. Of

20:46

these early cars with based on what

20:48

we knew before and I think technology

20:50

often evolve that way we're so constrained

20:52

by well, that's how it worked for.

20:54

I feel the way we can make

20:56

it work instead of thinking well, no,

20:59

now we're using a radically new approach.

21:01

We don't have to be. Constrained by

21:03

the way it's always looked and acted to

21:05

us. What

21:07

exactly? Exactly. We don't have to

21:09

be constrained. Ah, we

21:11

can be very imaginative.

21:14

And. We can imagine. Turning.

21:16

Anything in so robot. Weekend.

21:19

For an orphanage. Signs, robots. Are

21:21

we can Now we can make robots

21:23

that flynn like fish. Are

21:26

we can make robots that seemed

21:28

like turtles? We can make robots

21:30

that run. Ah, Like cheats

21:32

us. And are there are

21:34

many. Extraordinary uses

21:37

of these specialized machines.

21:39

Me in some sense.

21:42

The. Idea of building a machine

21:44

in our own image. That.

21:46

Is Mars and obedience?

21:49

That. Really goes back to

21:51

antiquity. And. We have built.

21:54

So. Many technological marvels.

21:56

Throughout. History. Or

21:58

what makes this machine. Today.

22:02

Is the fact that they have. These bones.

22:04

That. Allow these machines to

22:06

understand the world. To.

22:09

Respond to it's to understand the

22:11

people around them. And. Adapt

22:13

to what they need doing.

22:16

And the idea of making a

22:18

robot as a humanoid. Has.

22:21

An additional and. In.

22:24

An additional aspect to it. Namely.

22:27

That's. A. Robot

22:29

that's built in our own image. Can.

22:32

Be seen as a kind of a

22:34

universal machine. That would be

22:36

able to build a lot of

22:39

different types of tasks or unfortunately,

22:42

Our hardware. Is not

22:44

quite good enough to deliver

22:46

these universal robots yet? And

22:49

our software is not quite good

22:51

enough to have these machines. And.

22:54

Executes tasked with grace and agility

22:56

that we are out we we

22:59

are you so that we seen

23:01

the natural world that the robots

23:03

the humanoid robots at look a

23:06

little bit mechanistic in how they

23:08

move in the world. We

23:10

would like to have machines that

23:13

are much much more. Ads are

23:15

much more graceful in how they

23:17

interact with the world and with

23:20

people. And so if

23:22

you have seen the Boston Dynamics

23:24

machines those muffins sense of these

23:26

are ads as. The

23:28

But Those machines are pre programmed to do

23:30

the tasks that they do. A

23:33

don't have quite an image.

23:35

They look amazing how they

23:37

do such extraordinary. Our

23:40

behaviors and pass. I love

23:42

how they dance that to

23:44

on the Rolling Stones. A

23:48

sister. My favorites are i'm bad

23:50

at but still those. It

23:52

took a lot of engineers to fine

23:54

tune all the parameters and get those

23:56

robots to do what they're doing. If.

23:59

You take. A robot that has been

24:01

pre programmed like that and you put

24:04

the so but in a different environment

24:06

and asked his robots to do a

24:08

decent task. And the robots

24:10

will have trouble. Or the

24:12

engineers will have to come in and and start

24:14

all over. With. Tasking and programming

24:16

the robots. And so

24:18

an alternative to this idea

24:20

of a universal robot is

24:22

to think about designing machines

24:24

that. Are. Specialized and

24:27

optimized for a small a set

24:29

of tasks. Then you

24:31

get a smart device. That.

24:34

Is able to do really well.

24:36

A smaller set of tasks. Not

24:39

all the tasks. And

24:41

so that's an alternative view for why

24:44

these machines might be helpful to us.

24:47

Because Why we don't quite

24:49

have this universal humanoids? Rosie.

24:53

Ah, we actually can have. A

24:55

lot of other specialized machines that

24:58

already do so much work for

25:00

us. We. Have robots

25:02

that help with backing him factory

25:05

side by side with people. Ah,

25:07

we have robots that monitor facilities.

25:10

We. Have robots that assessed. Ah,

25:12

Doctors and Hospitals. We have

25:15

robots that are exploring Mars

25:17

or exploring the depths of

25:19

the ocean. We.

25:21

Have even robots that know

25:23

courthouse. Is a

25:25

specialized machines that will do so much more

25:28

for us in the future. Yeah,

25:30

you know, and I think that

25:32

is the you make. Such An

25:34

important points that I've often grappled

25:37

with is this idea that somehow.

25:39

A superior robot is one that's

25:42

made in our image Or that

25:44

somehow because quote unquote, we're at

25:46

the top of the food chain

25:48

or weeks you know. it's such

25:51

an anthropocentric view that v the

25:53

best or the most attuned or

25:55

the most effective robots would be

25:57

those that look like us. That

26:00

assumes that we are the most

26:02

effective A we are the best

26:04

at certain task, but there's a

26:06

hubris and that that ignores exactly

26:08

the point that you made that

26:10

maybe the human form is not

26:12

the most adapted to milking a

26:14

cat our M C V, You

26:16

know there can be a much

26:18

more efficient and effective and even

26:20

towel friendly robot that milk the

26:22

cow that feel safer and more

26:24

comfortable and more welcoming them than

26:26

human hands for example, Exactly.

26:30

And. These. Specialized.

26:32

Robots These robots designs with

26:34

specific tasks in mind. Can.

26:37

Do so much for all. They

26:40

can take on physical tasks

26:42

and so that they can

26:45

save us invaluable time. But.

26:47

They can also enhance

26:49

our ability to perform

26:52

words and to get

26:54

entertains was unparalleled accuracy

26:56

for instance, Robots.

26:59

Can magnify our vision. They.

27:01

Can allow us to see with

27:03

clarity beyond the national capacity of

27:05

the human eye. Robots.

27:08

Can also. Help us

27:10

extend our reach. Try

27:13

to fight the throne of that

27:16

would go and investigate. And.

27:18

Or inspect the far away bridge or

27:20

the robots on Mars that are advancing

27:22

science. They. Can amplify

27:25

our strength like the

27:27

wearable exoskeleton. That. Are

27:29

beginning to get deployed. In.

27:32

Construction and for

27:34

physical rehabilitation. They.

27:36

Can refine our precision.

27:39

And so you see robots

27:41

You can imagine. robots. Taking.

27:43

On so many shapes the clothes you were

27:46

could be robots. The

27:48

I'm. Ah the drones

27:51

are are in these robots that

27:53

you can imagine them as kind

27:55

of mobile eyes that we throw

27:57

in the distance. To. Give us.

28:00

I saw. An. Extra

28:02

length in how we can perceive the

28:05

world. Out we have all

28:07

these the sense. Possibilities.

28:10

And am What I I'm

28:12

excited about and inspired about

28:15

is this idea that with

28:17

machines we can amplify a

28:19

human. Capabilities we

28:22

can transcend. Our

28:24

human limitations. And

28:26

we we can do so by

28:28

using machines to expand our reach

28:31

to give us strength and precision.

28:34

To. Help us defy gravity

28:36

like Ironman, By. Lifting up

28:38

in the third dimension. And.

28:40

To see really small things. And.

28:43

Even. Even to achieve the

28:45

kind of magical. Attributes.

28:48

That we typically associate with

28:50

story books and Six Sun

28:52

and Movies. And

28:55

and co the these these. Remarkable.

28:58

a machine. We.

29:01

Are not just augmenting our

29:03

physical capabilities were also redefining

29:05

the very boundaries of the

29:07

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lived to see what's the weirdest

29:41

voice you've gotten Lucky Lucky. In

29:44

line as a deli against ah, my

29:46

dentist's office more than once actually do

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I have to say. Yes, you do. In

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the car before my kids C t

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A meeting really has. Excuse me What's

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What he anywhere playing at Lucky Landslides

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know I com play for free right

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now. Are you feeling lucky? Know businesses

30:06

are not easy clusters was. You.

30:11

Know you think about if we were

30:13

to survey a hundred. Years ago.

30:16

Citizens of our planet About.

30:18

The Future of robots. And

30:21

you might see a lot of.

30:24

Anxiety. A lot of fear.

30:26

You might also see a

30:28

lot of hope and a

30:30

lot of excitement. I think

30:32

that said day, we're living

30:34

in this really transitional period

30:36

where there's still so much

30:38

yet to discover and to

30:41

achieve. But there's also be

30:43

com a normalization of the

30:45

concept of robotics that people

30:47

are sort of familiar and

30:49

comfortable. Living among machines is

30:51

in a even Intelligent Machines.

30:53

Because we all have computers in

30:56

our back pocket, because we have

30:58

smart versions of things in our

31:00

homes, I think that there's a

31:03

comfort that has evolved yet. There's.

31:05

Still, this. Ah,

31:07

I'm. Disquiet. This

31:10

sort of. This

31:13

discomfort around this idea

31:15

of have. Too many

31:17

machines or machines becoming too intelligent or

31:19

being able to band together. this, you

31:22

know? Let's call it that,

31:24

The Terminator Fear and I hear

31:26

so much optimism in your voice

31:28

and in your writing when we

31:30

speak. Tell me a little bit

31:32

about some of your ethical concerns

31:35

or the places where you do

31:37

find caution in your optimism. So.

31:41

Let me say, I had usually.

31:44

When I tell people what I do,

31:46

I get two types of the accent.

31:49

Assessor. Some. People get

31:52

nervous and they miracle of

31:54

kindness. And the app in

31:56

the robots will take over their jobs.

31:59

And yeah, The group gets. Was excited

32:01

and asks me how much more

32:03

they can offload to machine. Can.

32:06

The machine for my laundry. When.

32:08

Will my car be totally self driving?

32:11

From. I personally believe that everyone

32:13

stands to benefit from a I,

32:16

but it's important to understand the

32:18

fears of that first group at

32:20

the Fears About Robots. And.

32:22

Coming to get us. And

32:25

it's also important to provide

32:27

solutions and perspective on how

32:29

to see things differently. And.

32:31

This threat to the understanding that robots

32:34

are tools. They. Are

32:36

incredibly powerful, Tools. But.

32:39

Like any other tool. They're.

32:41

Not inherently good or bad.

32:44

They. Are what we choose to do

32:46

with them and I believe we can

32:49

choose to do incredible things. And

32:51

this is. This is my

32:54

vision. This is my aspiration

32:56

for a future with robots.

32:58

Mine is a vision of

33:00

a world in which bespoke

33:02

machines assists people with both

33:04

basic and complex tasks, and

33:06

that honestly, the future is

33:09

beginning to mature in my

33:11

lab and in the lives

33:13

of my friends and. And

33:16

colleagues at other universities

33:18

and institutions. Around

33:21

the world and add some for

33:23

for sinking companies. That

33:26

crime. That. Alongside

33:29

the optimistic vision, we also

33:31

have to consider scenarios where

33:34

the integration of machines into

33:36

society introduces complex, technical, social

33:39

and ethical dilemma and add

33:41

the substance. Now.

33:44

One. Reason I

33:46

am are extraordinarily optimistic.

33:49

ah is because knowing

33:51

how. Machines. Words

33:53

I can tell you. That. There

33:55

are limits to what these machines can do. And

33:58

these limits or defy. And by the body

34:01

of the machine and the brain of the

34:03

machine. A robot

34:05

is limited by it's says the

34:07

core body which consists of the

34:10

save the sensor, the actuators, the

34:12

computers, data sources, storage power, And

34:15

the robot is also eliminate that

34:18

limited by it's been by the

34:20

software part that consists of all

34:22

the low level controllers, the algorithms,

34:25

and the ai systems. That.

34:27

Guy's it's most. Let

34:29

me give you an example. A self

34:31

driving car will not come in

34:34

your home so vacuum your carpet

34:36

and a robotic vacuum cleaner will

34:38

not be to access. So

34:40

we already have limits. To

34:44

watch these machines can do and

34:46

these limits are consolable by people.

34:49

Because. We designed them. We

34:51

build them as tools now.

34:53

of course we introduce these

34:55

machines to help us with

34:58

works. One. That's a

35:00

concern is. Are we putting

35:02

people out of them? Words. Are

35:04

the machine taking over the

35:06

jobs? And

35:09

the way I think about it. Is.

35:11

That. Jobs. Are very

35:13

complex, And. Jobs usually

35:16

consists of multiple tasks. In

35:19

a typical job, we. Am

35:21

we do predictable physical

35:24

work with the unpredictable

35:26

physical work with a

35:28

data tasks we apply

35:30

expertise. We. Interact with

35:32

with others, with stakeholders

35:34

and colleagues. Into

35:36

this, machines are primarily

35:39

useful for. Predictable.

35:41

Physical Work. They're. Not

35:43

so good at unpredictable physical work.

35:45

This is why it's much easier

35:47

to send a robot to Mars

35:50

then to get a robot to

35:52

clear your dining table after your

35:54

former. Squirrel Am

35:57

so you'll see it. There are

35:59

limits. To walk machines can do.

36:02

Birch. Are, but the

36:04

capabilities are gradually getting

36:06

better. And as the

36:09

capabilities are getting better, I believe

36:11

that. They. Can take on

36:13

that the kind of the low level

36:15

or tasks that we have on our

36:17

plates no matter what we do. And

36:21

that means that we can focus

36:23

more time on the more interesting,

36:26

more can't cognitive tasks. The.

36:29

Other aspects to jobs is that

36:31

there are many job for which

36:33

we do not have enough people.

36:36

To carry on those jobs. It.

36:39

Means that now we can fulfill the

36:41

past That. Otherwise would be in

36:43

a queue would be waiting a long

36:45

time for these tasks. And let me

36:48

give you an example. And.

36:50

I collaborate with and.

36:53

Who is a very young. Forwards.

36:55

That and they're thinking

36:57

doctor who specializes in

36:59

physical therapy. And

37:02

in working with a physical therapist.

37:05

I have learned that in fact,

37:08

The physical therapists, Spends.

37:11

A lot of time not applying

37:13

their expertise. They spent about a

37:15

third of that time. Going

37:18

to the hospital room. To. Pick

37:20

as a patient and say the patience

37:22

to the gym. They spend about a

37:25

third of the time. Working.

37:27

with a patient in the gym. And

37:29

then they spend another third of the time.

37:32

Taking. The pace and back to the

37:34

hospital room. And. For

37:36

now, imagine. If we had

37:38

a robotic wilshere. That. The

37:40

patients could use to travel autonomously

37:43

to the gym. That patients

37:45

could spend. Three times as much time.

37:48

With a doctor. And I would

37:50

be very beneficial for the patience. And

37:52

the doctor would have. Much. More

37:55

interesting ways. Of

37:57

applying expertise for longer period.

38:00

The time. You. Know

38:02

it's interesting because I think that

38:04

one of the things that. I.

38:06

Struggle with for coming

38:08

from a psychology perspective

38:11

is the integration of

38:13

these. Ideas. In

38:15

a global sort of

38:18

capitalist. Climate So you

38:20

know you mentioned before at that

38:22

these are tools, right? And I

38:25

think it's such an important point

38:27

to make that. I. Think

38:29

many people's fears about

38:32

the capabilities of robots

38:34

or less. I.

38:36

Would say they're They're not so

38:39

much fears about the robots themselves,

38:41

on their fears about the darker

38:43

side of humanity and how humanity

38:45

might choose to use these tools.

38:47

And so when we think about

38:50

something like taking over a simple

38:52

job that sounds great in theory

38:54

I would love it if the

38:56

sort of low skilled. Or like

38:58

low. Paying or like the

39:00

aspects of our jobs that

39:03

are the most tedious were

39:05

taken over by robots, but

39:08

it also requires I think

39:10

a sort of societal level

39:13

change that values. Certain.

39:15

Parts of the workforce. In a

39:17

way that I'm not sure that we

39:19

do as. A society and

39:21

as the governing body and I

39:24

think to even piggyback off of

39:26

that. When we talk

39:28

about things like a i one

39:30

of the big take a ways

39:32

I noticed at the beginning of

39:34

this sort of a majority. Or

39:37

Dolly Revolution. Was that

39:39

we think of a as this incredible

39:41

tool to help us? You. Know

39:43

I don't know. Do lit sources

39:45

to write or dissertation? swords? Or

39:48

help us with the sort of

39:50

tasks that are frustrating. but what

39:52

we end up seeing in social

39:55

media or out in front of

39:57

our eyes is a I making.

40:00

Poetry and Making Art. And

40:02

I don't wanna live in a world where

40:04

the robots are taking over the art and

40:06

poetry. I want to live in a world

40:08

where the robots are cleaning my house so

40:11

i have more times the make art and

40:13

poetry but the only way that gonna work

40:15

is if we value those things. In.

40:17

A society and I can afford to live

40:19

that way. And so it's almost like this

40:22

is one piece of a much larger puzzle

40:24

that I feel we. May

40:26

not be spending enough time talking

40:28

about. I'm curious how you feel

40:30

about that. Yeah. You

40:32

make some very, very important points.

40:34

Am I too want to say

40:37

something about jobs? I do want

40:39

to add something to our ah,

40:41

our discussion on jobs. I want

40:43

to. Remind

40:45

you that economists estimate

40:48

that over sixty percent

40:50

of jobs perform today.

40:53

When. Non Existence The for ninety. Moon.

40:56

Interests And so echoing that I

40:58

see the rise of robots as.

41:01

Promising. To create all kinds of new

41:04

jobs. Robot. Operator.

41:07

Robots and rates. Data analysts.

41:10

User experience Designers You can imagine

41:12

a lot of new jobs. Some.

41:15

Created Jobs. Some. Technical

41:17

jobs. Some. As they

41:19

call jobs. That do not

41:21

exist yet. Son. Not

41:24

worried that aren't humanity is not

41:26

going to figure out what to

41:28

do because we're in the thing.

41:31

These extraordinary tools. And

41:33

the other thing I want to say

41:35

that were much better. At. Thinking

41:37

about what will go away.

41:40

Then. Imagining what will com. Yeah,

41:43

we definitely have a bias there a

41:45

way of, for example, So let me give

41:47

you a concrete example. In.

41:50

Around the year is two thousand,

41:52

which I remember very well. Out

41:55

there was a computer revolution as

41:57

a digital revolution. And

42:00

we were talking. About all sorts of

42:02

things, But. We're not talking

42:04

about social media. We, We're not

42:06

talking about smartphones. We're not talking

42:09

about cloud computing. And.

42:11

These three social. Media, Cloud

42:13

Computing and thus the

42:15

smartphone. App came into

42:17

being in two thousand and seven. And

42:21

these new technologies have enabled

42:23

so many new jobs. For.

42:25

Creators as well as for

42:28

geeks. Right going.

42:30

Back to your question about a

42:32

I. How do

42:35

we think about Ai? Because

42:37

yeah, we can. Now we

42:39

can not imagine a I

42:41

powering the brains of the

42:44

robots, but we can also

42:46

think about all the extraordinary

42:48

capabilities that the generative ai

42:51

and foundational and models have

42:53

unleashed. And. When.

42:55

I think about that. I

42:58

can see multiple the friends.

43:01

At. Suggest Harrys for Humanity and

43:03

I know exactly how it's

43:05

going to all play out.

43:07

But there are many, many

43:09

different ways in which ah,

43:12

we can go. And

43:14

so let me give you some are

43:16

some examples. In.

43:19

Today's world when we work of out

43:21

when we work was a I We

43:23

essentially build larger and larger. Machine.

43:26

Learning models. And we

43:28

put our increasingly more data

43:30

into these models. Out

43:32

so that these models become a

43:35

very capable so that they are

43:37

sounds and amaze us in all

43:39

kinds of. Digital

43:41

tasks. And.

43:44

This is a possible way forward. But.

43:48

There's something really interesting about

43:51

this approach. Is you

43:53

believe that the future is all

43:55

about building these large models and

43:57

putting data ah into these models?

44:01

We. Have. We.

44:03

Have a danger wheat we

44:05

have. We have

44:07

the possibility of creating these huge

44:10

machine so we do not fully

44:12

understand. The. Large

44:14

A models are black boxes

44:16

is is really impossible to

44:19

explain how they reach their

44:21

decisions. So we

44:23

may get to the point.

44:26

Where. We we have

44:28

extraordinary machines. That and

44:30

that's that. develop. An.

44:32

Extraordinary capabilities. But

44:35

the risk here. Is that?

44:38

We. Will not be able to explain

44:41

how these machines work. And

44:43

this is kind of like

44:45

killed pilots flying sophisticated aircraft

44:48

by instruments alone. Without.

44:50

Understanding the principles of flight,

44:52

And. Without knowing how to navigate

44:54

in the absence of the instruments. And

44:58

so to me, working with

45:00

intelligent machines requires that we

45:02

understand what. They do. So

45:06

that's impossible for it.

45:08

And. And this could

45:10

be. this could be true. But.

45:13

What if we're wrong? What

45:16

if we have a different kind

45:18

of future? Where are

45:21

these projects and will prove

45:23

flaws in practice? And.

45:25

Essentially will have become

45:28

increasingly reliant. On

45:30

machines that we do not understand

45:32

and we have no idea how

45:34

to fix when these machines date

45:36

And frankly, this is my nightmare.

45:39

I don't think about.

45:42

The. Hollywood vision of suddenly same

45:44

chin machines and that decides to

45:46

extinguish off because I don't see

45:48

that as a possibility. But I

45:51

do worry that we will end

45:53

up with a massive complex system

45:55

would depend on and we can't

45:57

comprehend. And then we will.

46:00

Have. Enormous. Amounts

46:02

of discarded technologies and

46:04

electronic waste. That

46:06

will not be a good stuff for it. Not.

46:09

What I like to see as we

46:11

move forward. Is. An. Intelligent

46:14

Machines as smart as tools

46:16

for people. I would like

46:18

to see a future in

46:20

which robots and A I

46:22

Solutions. Are. Trustworthy, Their.

46:25

Explainable, they can be soda five.

46:27

Hour their well understood than

46:29

their capabilities are well understood.

46:32

And are these robots and they

46:35

I solution. Will. Then assist

46:37

us safely. And. Was

46:39

in a way that allows us

46:41

to trust them. And they

46:43

will assist us with a lot of

46:45

physical tasks. A lot of cognitive tasks.

46:48

They will lift humanity as a whole

46:50

to a higher standard of living. And

46:53

perhaps they give out. They will even give

46:55

us superpowers. Like the heroes

46:57

from our childhood books. You.

47:01

Know it's it's it's such

47:04

an interesting concept. This idea

47:06

of cause and effect versus

47:08

correlation. This idea of the

47:10

statistical understanding of sort of

47:13

what comes first and amplify

47:15

something else. And I think

47:17

one of the things that

47:19

I continue to grapple with

47:22

as I am a cautious.

47:24

Techno. Optimists. Is

47:27

that? There. Are

47:29

deep social inequities and there

47:31

are supremacist forces at work

47:34

and authorities authoritarian forces at

47:36

work that really are striving

47:38

to keep people divide. It

47:40

did that are striving to.

47:43

Let's say keep for example,

47:45

poor people, people of color,

47:47

Imprisoned. Women

47:50

in a subservient role. There

47:52

are. These kind of invisible.

47:55

You. Know social forces at play,

47:57

And I think that technology can be

47:59

used as the tools to continue to

48:01

divide, but it can also be used

48:04

as a tool for it. He

48:06

gala terry and purposes to

48:08

unify. And to you know

48:10

that the sort of a rising

48:12

tide lifts all boats. And

48:15

my concern sometimes is. Admin.

48:18

Maybe it's my cynicism or

48:20

my pessimism is that. Advances

48:23

in technology especially in

48:25

a and robotics will

48:27

deep in social divides.

48:30

Whereas what I'm hearing from you

48:32

is that may be. With

48:35

these advances. We will see

48:37

more movement towards any gala

48:39

terry and future. And

48:41

it's hard to know, right? because we don't

48:43

know what it is. It the human activity.

48:46

That. Is. Affecting.

48:48

The. Or

48:50

that the human I guess interests

48:52

that are affecting the technological advancements

48:55

or as the technology itself in

48:57

turn having an effect on our

48:59

humanity and it's probably both the

49:01

you know, it's that kind of

49:03

a chicken and egg scenario. but

49:06

I'm curious about how you grapple

49:08

with those questions about sort of

49:10

the darker side of humanity and

49:12

technology being a tool for our

49:14

purposes that can be used. For

49:17

good or or deep deep evil.

49:19

Corrupt. The spread of intelligent

49:21

machines will make our lives easier.

49:24

But. Many of the roles that he

49:27

can play. Will

49:29

displace work done by humans today.

49:32

So. We really need to

49:34

anticipate and respond to the

49:36

economic inequality this could create.

49:39

At the same time we

49:41

have some moms at technical

49:44

and technological issues because especially

49:46

with I we have a

49:48

lack of interprets ability. And

49:52

dislikes of interprets ability could

49:54

lead to significant issues around.

49:56

Trust and Privacy. And

49:59

it needs. Dress these issues. We

50:01

need to develop an ethics and

50:03

legal framework for how to use

50:05

intelligent machines for the greater good.

50:09

At the same time,

50:11

the spread of misinformation

50:13

and disinformation remains a

50:15

huge concern. And

50:17

am social media platforms are

50:19

struggling with a response. as

50:23

the face get better and more

50:25

widespread. This. Problem becomes

50:27

even more urgent for national

50:30

security. And

50:32

because as we gather more

50:34

and more data feeds into

50:37

our intelligence ai systems and

50:39

not only. Is the

50:41

risks to privacy higher? But.

50:43

So are the opportunities for

50:46

authoritarian governments to leverage these

50:48

tools to curtail freedom and

50:50

democracy around the world. But

50:54

the way I see it is that

50:56

these problems aren't like the pandemic. We.

50:59

Know they're coming. And weekend

51:01

out to find solutions at

51:03

the intersection of policy, technology

51:06

and business in advance. And

51:08

so I can tell you

51:10

that we already have. Solutions.

51:13

For the bias problem, We

51:16

can look at how a machine model

51:18

is. Trains were kind of data. The

51:20

machine is trained on. And

51:23

weekend the biased or beta so that

51:25

the function of them the side of

51:27

the machine learning model is the biased.

51:30

We. Can also

51:32

starts. At lot

51:34

of marking trusted information sources.

51:37

So that when we get a

51:39

piece of information, we know whether

51:41

it comes from a trusted source

51:43

or not. I think

51:45

it's important. For. Us to

51:48

know whether we interact with machines are

51:50

people. When. We interact. And

51:53

so you see, there

51:55

are already beginnings of

51:57

technological solutions that cancelled.

52:00

Make things better. And

52:03

this is. Part. Of the

52:05

reason. Why I'm hopeful other

52:07

solutions will not be entirely.

52:10

In. The realm of technology. We

52:13

really have to think about how

52:15

the solutions in the hands of

52:17

people impact society and that means

52:20

we need to include policy we

52:22

need to include the i'm the

52:24

business side to have comprehensive discuss

52:27

sense for how to deploy these

52:29

tools safely. What needs to be

52:31

done before a tool can be

52:34

safely are put in the hands

52:36

of people. but we are making

52:39

a lot of progress. Every

52:41

day we are in at we

52:43

are understanding more about. The.

52:45

Flaws of our systems and we're

52:47

coming up with solutions to address

52:49

them. If. Anything I

52:52

want to say as

52:54

that you were asking

52:56

about whether these technologies.

52:58

Are. Limited to the elite.

53:01

And I want to highlight that

53:04

the trajectory of. Robotics.

53:07

Much. Like the trajectory of

53:09

the smartphones. Is. Really

53:11

poised to transition from

53:13

rarity and luxury so

53:15

ubiquity and accessibility. And

53:19

I see that initially

53:21

the robotic technology particularly

53:23

out with respect to

53:25

advance the timeless robots

53:27

like building a self

53:29

driving car ah remains

53:31

expensive and somewhat limited

53:33

to industrial applications and

53:35

nice markets. And

53:37

this is akin to the early

53:40

days of smartphones ah which were

53:42

very costly and considered luxury items.

53:45

But as robotic technology

53:47

advances the initial high

53:49

costs associated with with

53:52

research and development and

53:54

the novelty of the

53:56

technology will decrease because

53:58

over time manner. Acting

54:00

processes will improve, the economies

54:02

of scale will be achieved

54:04

and the cost of producing.

54:07

These machines will decrease. And

54:10

so I think of this

54:12

trend coupled with competitive pressures

54:14

will make robotic technology much

54:17

more affordable and accessible to

54:19

the public. And

54:21

I believe that this mirrors

54:23

how we have experienced a

54:25

smartphone trajectory. Where.

54:28

It was the advancements in

54:30

technology and mass production. That.

54:32

Reduce the cost and made

54:35

smartphones accessible to of the

54:37

ass population on our planet.

54:41

Here you know it's it's, it's I

54:43

feel like this is almost like a

54:45

good, a really good starting point for

54:48

us because it sort of encapsulates a

54:50

lot of the things that we've been

54:52

talking about and leaves us with a

54:55

lot to think about that I have

54:57

death I have to just ask because

54:59

one question they came up, especially when

55:02

you were talking about the sort of

55:04

former issue of ethics and I'm thinking

55:06

about things in advance, but also the

55:09

multi disciplinary aspect of technology research. Right

55:11

that we need to be talking to policy makers,

55:13

That we need to be talking to psychologists and

55:15

we need to be talking to as this is.

55:19

Like you're a professor, right? The

55:21

you have students coming into your

55:23

tier laboratory. I'm wondering. how much

55:25

are they interested in grappling with

55:27

those questions? How much does a

55:29

field bird and some like I

55:31

just want to do good science

55:33

and you know, can't that be

55:35

somebody elses job Like I can't

55:37

do everything versus how based in

55:39

do these considerations need to be

55:41

in the technology itself. Whenever

55:44

we start a new project, We.

55:47

Organize a worksop. On.

55:49

What can go wrong? And

55:52

we. Gather. Students

55:55

and researchers of this and

55:57

backgrounds. And we brain.

56:00

storm, all the different ways in which

56:02

we can imagine using the technology and

56:04

thinking about what could go wrong with

56:06

it. And

56:08

if we start from this point, we

56:12

already are a few steps ahead

56:14

from the point of view of making

56:16

technology that can be used

56:18

for the greater good rather than

56:21

technology that can empower the supervillains.

56:26

We can begin to also

56:28

teach the students that it

56:30

is important to consider the consequences

56:32

of our work. And

56:35

it's important to proceed

56:37

cautiously and remember that

56:39

ultimately we are advancing

56:42

science. We are aiming

56:44

to understand AI. We

56:47

are aiming to understand robotics. We're

56:49

aiming to understand the science and

56:52

engineering of intelligence. And

56:55

doing this work is teaching us right

56:57

now that we have

56:59

so much more to learn about

57:01

these technologies and also

57:03

about life itself. And

57:06

I want to emphasize that

57:10

we are the only species

57:12

on our planet. So

57:15

smart, so aware, so

57:18

capable of building incredible tools.

57:22

But this also comes with the

57:24

responsibility of ensuring that

57:27

our work can be

57:29

deployed for

57:32

good, that we are

57:34

responsible for everything on

57:36

our planet, for

57:38

the plants and the animals, for

57:40

the neighbors we share this planet with, and

57:42

also for the future generation. We

57:45

are responsible and we have to

57:47

proceed with our work in a

57:49

way that ensures the global good.

57:53

And I personally believe that this is possible

57:55

and I'm very excited about all

57:57

the things we can do at this point.

58:00

in our history. And

58:03

of course that is a central

58:05

message to your book and

58:08

it's a central message to this conversation.

58:10

I mean just look at the

58:12

title of your book of The Heart and the

58:14

Chip, Our Bright Future with Robots.

58:17

I have to ask Daniela before we go and I

58:19

know it's a big question, but is there anything? Obviously,

58:22

there's probably a lot that we

58:24

didn't touch on or didn't talk about, but is

58:26

there anything that you feel like you

58:29

know is important to be said before before

58:32

we say goodbye. Is there anything that

58:34

we didn't cover that is important for

58:36

the listeners to know? I

58:39

want to emphasize that while

58:41

I understand that techno-optimism

58:43

isn't very popular right

58:46

now, I believe that with enough

58:49

forethought and planning, we

58:51

can achieve a future in

58:53

which our humanity

58:57

gets elevated. And

58:59

I know that this is not going to

59:01

be easy because the transition

59:04

will demand very careful considerations

59:06

of ethical, privacy and employment

59:09

implications to ensure that the

59:11

benefits of intelligent machines are

59:14

equitably distributed. But

59:19

I do want

59:21

to highlight that the

59:24

conversation around the impact of robots

59:26

and AI on a

59:28

grand or global scale tends

59:31

to focus on doomsday scenarios.

59:34

And these are obviously important

59:36

and absolutely must be accounted

59:38

for. We must

59:40

do everything we can to steer the

59:42

future in a positive direction. But

59:46

we should also think about how

59:48

we can use these new tools,

59:50

these intelligent machines to

59:53

solve some of the largest problems we face

59:55

as a species. Because

59:58

these Intelligent

1:00:00

systems powered by advances

1:00:03

in AI and robotics

1:00:05

offer unprecedented capabilities to

1:00:07

analyze fast data sets, to

1:00:10

identify patterns, to execute tasks

1:00:12

with a precision and efficiency

1:00:15

that far surpasses human limitations.

1:00:18

And the applications can

1:00:21

involve combating climate change

1:00:23

by optimizing energy consumption

1:00:25

and reducing emissions to

1:00:28

revolutionizing healthcare through individualized

1:00:30

medicine and robotic surgery

1:00:34

to creating sustainable solutions. There

1:00:36

are so many different

1:00:39

things that we can do with what

1:00:41

we have today. One

1:00:44

important area is using

1:00:46

intelligent machines to

1:00:48

advance scientific research, to

1:00:51

automate tedious experiments, to

1:00:53

provide hypotheses, to simulate

1:00:55

complex theoretical models, really

1:00:57

to accelerate the pace

1:00:59

of discovery. And

1:01:02

I believe that by effectively

1:01:04

merging the heart and the chip

1:01:07

and guiding the development of

1:01:09

applications with wisdom, with

1:01:11

foresight and with a commitment to the

1:01:13

greater good, we could

1:01:16

solve the problems facing

1:01:18

our people and our

1:01:20

planet. And we

1:01:22

could elevate humanity at

1:01:25

the same time. So well

1:01:27

said. Thank you so much. Well,

1:01:29

everybody, the book is The Heart

1:01:31

and the Chip, Our Bright Future

1:01:33

with Robots by Dr. Daniella Roos

1:01:35

along with Gregory Mohn. Thank you

1:01:37

so much for spending your time

1:01:39

with us today. Thank

1:01:42

you very much, Cara. And

1:01:45

everybody listening, thank you for coming back

1:01:47

week after week. I'm really looking forward

1:01:49

to the next time we all get together. Daily

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