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00:00:00

Um, you know, you said recently tens of

00:00:03

billions of robots, but that's decades

00:00:05

away.

00:00:06

>> At least one decade away.

00:00:09

>> It's got to be more than that.

00:00:10

>> It's going to grow very fast.

00:00:12

>> Why do you think that?

00:00:15

>> I think I think uh humanoid robots will

00:00:17

be the biggest product ever. Uh the

00:00:19

demand will be insatiable. Um you've

00:00:21

said that

00:00:22

>> everyone's going to want one. It's like

00:00:23

basically who wouldn't want uh their own

00:00:26

personal C3PO R2-D2.

00:00:29

>> No, right. You've said that and you know

00:00:32

your your goal you've also said is to

00:00:34

produce a million robots I think by

00:00:36

2030. That's what I had you on the

00:00:38

record as saying.

00:00:39

>> I think that's a reasonable target

00:00:40

>> and then and then start towards

00:00:42

sustainable abundance which he can get

00:00:44

into. But, you know, I wonder we we've

00:00:46

been talking about autonomous and how

00:00:47

long it takes it takes to train the

00:00:49

automobile to be able to be the

00:00:51

equivalent of or exceed human

00:00:52

capabilities,

00:00:53

>> but what about these robots to the

00:00:55

extent that how much training are they

00:00:57

going to need to actually be able to do

00:00:59

various different types of tasks? Is

00:01:02

that isn't that something take a long

00:01:05

time? Yeah.

00:01:06

>> Uh yes, it's going to take a lot of

00:01:07

compute resources. Um and it'll take

00:01:10

it'll take time. I think there there's

00:01:12

certain uh threshold breakthroughs that

00:01:15

that uh we think we can achieve uh where

00:01:18

if um if optimists can can watch videos

00:01:22

uh you know YouTube videos or how-to

00:01:24

videos or whatever and based on that

00:01:26

video just like a human can uh learn how

00:01:29

to do that thing then you you you really

00:01:32

have a task extensibility that is

00:01:35

dramatic because then you it can learn

00:01:37

anything very quickly. Um, so I I think

00:01:40

we'll I think we'll get there in the

00:01:42

next >> We're not there yet, though.

00:01:43

>> We're not there yet. >> You're relying on a

00:01:45

>> on a significant uptick in terms of of

00:01:49

learning and training.

00:01:51

>> Yeah. No, that's that's why I'm calling

00:01:53

it a very significant threshold would be

00:01:55

the ability to learn from watching a

00:01:57

video just so

00:01:59

>> right. Uh but at the point

00:02:00

>> as opposed to watching a human, right,

00:02:02

which is or having a human sort of train

00:02:03

it right now with a task

00:02:05

>> I mean right right now we're really

00:02:07

we're training optimists to do like

00:02:09

primitive tasks where a human in a kind

00:02:12

of a what's called a mocap suit uh is uh

00:02:17

and and sort of cameras on the head is

00:02:20

uh moving in the way that the robot

00:02:22

would move to say pick up an object or

00:02:25

uh open a door or basic tasks throw a

00:02:29

um dance

00:02:31

>> um and uh we need to

00:02:36

I think that's needed to to sort of

00:02:38

bootstrap the intelligence so you can

00:02:40

have the basic functions. Then where I

00:02:43

think it gets very interesting um and

00:02:45

and very much like humans is that um you

00:02:49

you want the robot to selfplay.

00:02:52

>> So you say how does a child learn? Well,

00:02:54

a child has toys and a child plays with

00:02:56

the toys, plays with the blocks, you

00:02:58

know, at some point figures how to put

00:03:00

the triangle in the triangle hole and

00:03:02

the circle in the circle hole by by

00:03:04

doing it over and over again. And this

00:03:05

this selfplay once you have a lot of

00:03:07

robots, uh, you can do this selfplay,

00:03:10

which is that you just put the robot in

00:03:12

a room with toys

00:03:14

>> and and have the robot literally have

00:03:16

the robot play with toys

00:03:17

>> and it will learn.

00:03:18

>> Yeah.

00:03:20

And and but you have to have to have a

00:03:21

reward function to say like okay your

00:03:24

the goal of the robot is to put you know

00:03:26

that classic child's toy you put the

00:03:27

circle in the circle hole the square and

00:03:28

square triangle and triangle hole and

00:03:31

keep doing it until it works and the

00:03:33

reward function is succeeding

00:03:35

>> and there are no advances needed to

00:03:36

accomplish that now I mean no advances

00:03:38

in AI or or just compute and things of

00:03:41

that nature that can happen.

00:03:43

>> Uh there are some advances needed but I

00:03:45

don't think these are insurmountable. I

00:03:47

think we I think we can we I think we

00:03:49

can solve these things in the next few

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