Joe Rogan podcast. Check it out.

>> The Joe Rogan Experience.

>> TRAIN BY DAY. JOE ROGAN PODCAST BY

NIGHT. All day.

>> Hello. Hey, Joe.

>> Good to see you again. We were just

talking about Was that the first time we

ever spoke or did was the first time we

spoke at at SpaceX?

>> SpaceX.

>> SpaceX. The first time when you were

giving Elon that crazy AI chip,

>> right? DJX Spark.

>> Yeah. Oo, that was a big moment. That

was a huge

>> That felt crazy to be there. I was like

watching these wizards of tech like

exchange information and and you're

giving him this crazy device, you know,

and then the other time was uh I was

shooting arrows in my backyard and uh

randomly get this call from Trump and

he's hanging out with you. President

Trump called and I called you.

>> Yeah. It's just

>> we were talking about you. [laughter]

>> It's just talking about he was talking

about the US UFC thing he was going to

do in his front yard.

>> Yeah. And he pulls out. He's JJS, look

at this design. He's so proud of it. And

I go, "You're going to have a fight in

the front lawn in the White House." He

goes, "Yeah, yeah, you're going to come.

This is going to be awesome." And he's

showing me his design and how beautiful

it is. And he goes, and somehow your

name comes up. He goes, "Do you know

Joe?" And I said, "Yeah, I'm going to be

on his podcast." He Let's call him.

[laughter]

>> He's like a kid.

>> I know. Let's call him. It's so He's

like a 79y old kid.

>> Oh, he's so incredible.

>> Yeah, he's an odd guy. Just very

different, you know, like the what you'd

expect from him. Very different than

what people think of him. And also just

very different as a president. A guy who

just calls you or texts you out of the

blue. Also, he makes when you te you.

You have an Android, so it won't go

through with you, but with my iPhone, he

makes the text go big.

>> Like, you know, USA is respected again.

like [laughter]

all caps and it makes the te the the the

text enlarge is kind of ridiculous.

>> Well, the the 101 Trump President Trump

is very different. He he surprised me f

first of all he's an incredibly good

listener. Almost everything I've ever

said to him, he's remembered.

>> Yeah. People don't they only want to

look at negative stories about him or

negative narratives about him. You know,

you can catch anybody on a bad day. Like

there's a lot of things he does where I

don't think he should do. Like I don't

think he should say to a reporter rep

reporter, "Quiet piggy." Like that's

pretty ridiculous. Also objectively

funny. I mean, it's unfortunate that it

happened to her. I wouldn't want that to

happen to her, but it was funny. Just

ridiculous that the president does that.

I wish he didn't do that. But other than

that, like he's he's an interesting guy.

Like he's a lot of different things

wrapped up into one person, you know?

You know, part of part of his charm,

well, part of his genius is Yes. He says

what's on his mind.

>> Yes.

>> And which is like an anti-olitician in a

lot of ways.

>> So, you know, what's on his mind is

really what's on his mind,

>> which

I I do some people some people would

rather be lied to.

>> Yeah. But but I I like the fact that

he's telling you what's on his mind. Um,

almost every time he explains something,

he says something,

he starts with his, you could tell, his

love for America, what he wants to do

for America. And everything that he

thinks through is very practical and

very common sense. And, you know, it's

very logical and um

I still remember the first time I I met

him and so this was I I'd never known

him, never met him before. and um uh

Secretary Lutnik called and we met right

before right at the beginning of the

administration. He said he told me what

was important to President Trump that

that um uh that United States

manufactures on shore and that was

really important to him because because

uh it's important to national security.

He wants to make sure that that the

important critical technology of our

nation is built in the United States and

that we re-industrialize

and get good at manufacturing again

because it's important for jobs.

>> It just seems like common sense, right?

>> Incredible common sense. And and that

was like literally the first

conversation I had with Secretary Letic

um and he was talking about how how um

that he started he started our

conversation with uh Jensen. This is

Secretary Lutnik and I I just want to

let you know that you're a national

treasure. Uh Nvidia is a national

treasure and whenever you need access to

the president um the administration uh

you call us. We're always going to be

available to you. Literally, that was

the first sentence.

>> That's pretty nice.

>> And it was completely true. every single

time I called, if I needed something, I

want to get something off my chest, um,

express some concern, uh, they're always

available. Incredible. It's just

unfortunate we live in such a

politically polarized society that you

can't recognize good common sense things

if they're coming from a person that you

object to. And that, I think, is what's

going on here. I think most people

generally a as a country, you know, as a

a giant community, which we are, it just

only makes sense that we have

manufacturing in America that especially

critical technology like you're talking

about. Like it's kind of insane that we

buy so much technology from other

countries.

>> If United States doesn't grow, we will

have no prosperity. We can't invest in

anything domestically or otherwise. we

can't fix any of our problems. If we

don't have energy growth, we can't have

industrial growth. If we don't have

industrial growth, we can't have job

growth. These it's as simple as that,

>> right?

>> And the fact that the fact that he came

into office and the first thing that he

said was drill baby drill. His point is

we need energy growth. Without energy

growth, we can have no industrial

growth. And that was it saved it saved

the AI industry. got I got to tell you

flat out if not for his progrowth energy

policy

we would not be able to build factories

for AI not be able to build chip

factories we won't sure surely won't be

able to build supercomputer factories

none of that stuff would be possible

without all of that

construction jobs would be challenged

right electrical you know electrician

jobs all of these jobs that are now

flourishing would be challenged and so I

think he's got it right we need energy

growth We want to re-industrialize the

United States. We need to be back in

manufacturing. Every successful person

doesn't need to have a PhD. Every

successful person doesn't have to have

gone to Stanford or MIT. And I think I

think that that that you know that

sensibility is is um spot on. Now, when

we're talking about technology growth

and energy growth, there's a lot of

people that go, "Oh, no. That's not what

we need. We need to, you know, simplify

our lives and get back." But the the

real issue is that we're in the middle

of a giant technology race. And whether

people are aware of it or not, whether

they like it or not, it's happening. And

it's a really important race because

whoever gets to

whatever the event horizon of artificial

intelligence is, whoever gets there

first has massive advantages in a huge

way.

Do you agree with that? Well, first the

part I I will say that we are in a

technology race and we are always in a

technology race. We've been in a

technology race with somebody forever.

>> Right.

>> Right. Since the industrial revolution,

we've been in a technology

>> since the Manhattan project.

>> Yeah.

>> Or or you know, even going back to the

discovery of energy, right? The United

Kingdom was where the industrial

revolution was, if you will, invented

when they realized that they can turn

steam and such into into energy into

electricity.

All of that was invented largely in

Europe and the United States capitalized

on it. We were the ones that learned

from it. We industrialized it. We

diffused it faster than anybody in

Europe. They were all stuck in

discussions about

policy and

jobs and disruptions. Meanwhile, the

United States was forming. We just took

the technology and ran with it. And so I

I think we were always in in a bit of a

technology race. World War II was a

technology race. Manhattan Project was a

technology race. We've been in the

technology race ever since during the

Cold War. I think we're still in a

technology race. It is probably the

single most important race. It is the

technology is uh it gives you

superpowers.

you know whether it's information

superpowers or energy superpowers or

military superpowers is all founded in

technology and so technology leadership

is really important

>> well the problem is if somebody else has

superior technology right that's that's

the issue it seems like with the AI race

people are very nervous about it like

you know Elon has famously said there

was like 80% chance it's awesome 20%

chance we're in trouble and people are

worried about that 20% % rightly so. I

mean that you know if you had 10 bullets

in a a a revolver and you know you you

took out eight of them and you still

have tw two in there and you spin it,

you're not going to feel real

comfortable when you pull that trigger.

It's terrifying,

>> right?

>> And when we're working towards this

ultimate goal um of AI,

it it just it's

impossible to imagine that it wouldn't

be of national security interest to get

there first.

We should The question is what's there?

That's the That was the part that

>> What is there?

>> Yeah. I'm not sure.

>> And I don't think anybody I don't think

anybody really knows.

>> That's crazy though. If I ask you,

>> you're the head of Nvidia. If you don't

know what's there, who knows?

>> Yeah. I I think it's probably going to

be much more gradual than we think. It

won't It won't be a moment. It won't be

It won't be as if um somebody arrived

and nobody else has. I don't think it's

going to be like that. I think it's

going to be things that just get better

and better and better and better just

like technology does.

>> So, you are rosy about the future.

You're you're very optimistic about

what's going to happen with AI.

>> Obviously, will you make the best AI

chips in the world?

>> You probably better be.

>> Uh h if history is a guide, um uh we

were always concerned about new

technology.

Humanity has always been concerned about

new technology. There are always

somebody who's thinking there always a

lot of people who are quite concerned.

were quite concerned and and and so if

if history is a guide, it is the case um

that all of this concern is channeled

into making the technology safer.

And so for example, in the last several

years, I would say AI technology has

increased probably in the last two years

alone, maybe a 100x. Let's just give it

a number, okay? It's like a car two

years ago was 100 times slower. So AI is

100 times more capable today. Now, how

did we channel that technology? How do

we channel all of that power? We

directed it to um causing the AI to be

able to think, meaning that it can take

a problem that we give it, break it down

step by step.

It does research before it answers. And

so it grounds it on truth.

It'll reflect on that answer. Ask

itself, is this the best, you know,

answer that I can give you. Am I certain

about this answer? If it's not certain

about the answer or highly confident

about the answer, it'll go back and do

more research. It might actually even

use a tool because that tool provides a

better solution than it could

hallucinate itself. As a result, we took

all of that computing capability and we

channeled it into having it produce a

safer result, safer answer, a more

truthful answer because as you know, one

of the greatest criticisms of AI in the

beginning was that it hallucinated,

>> right?

>> And so if you look at the reason why

people use AI so much today is because

the amount of hallucination has reduced.

You know, I use it almost I well I used

it the whole trip over here and so so I

think the

the uh the the capability most people

think about power

and they think about you know maybe as

an explosion power but the technology

power most of it is channeled to towards

safety. A car today is more powerful but

it's safer to drive. A lot of that power

goes towards better handling. You know,

I'd rather have a Well, you have a 1000

horsepower truck. I think 500 horsepower

is pretty good. No, I thousand's better.

I think a th00and is better.

>> I don't know if it's better, but it's

definitely faster.

>> Yeah. No, I think it's better. You can

get out of trouble faster. Um,

I enjoyed my 599 more than my 612. It

was I think it was a better better

horsepower is better. My 459 is better

than my 430.

more horsepower is better. I I think

more horsepower is better. I think it's

better handling. It's better control. In

the case of in the case of technology,

it's also very similar in that way, you

know. And so if you if you look at what

we're going to do with the next thousand

times of performance in AI, a lot of it

is going to be channeled towards more

reflection, more research,

thinking about the answer more deeply.

So when you're defining safety, you're

defining a it as accuracy,

>> functionality.

>> Functionality. Okay.

>> It it does what you expect it to do. And

then you take all the the the technology

in the horsepower, you put guard rails

on it, just like our cars. We've got a

lot of technology in in a car today. A

lot of it is goes towards, for example,

ABS. ABS is great. And so, uh, traction

control, that's fantastic. without a

without a computer in the car, how would

you do any of that,

>> right?

>> And that little computer, the computers

that you have doing your traction

control is more powerful than the

computer that went to Apollo 11. And so

you want that technology,

channel it towards safety, channel it

towards functionality. And so when

people talk about power, the advancement

of technology, often times I I I feel

what they're thinking and what we're

actually doing is very different.

>> Well, what do you think they're

thinking? Well, they're thinking somehow

that this this uh this AI is being

powerful and their their mind probably

goes towards a sci-fi movie. The

definition of power, you know, often

times the definition definition of power

is military power or physical power. But

in in the case of technology power when

we translate all of those operations

it's towards more refined thinking you

know more reflection more planning more

options

>> I think the big fears that people have

is one a big fear is military

applications that's a big fear

>> because people are very concerned that

you're going to have

>> AI systems that make decisions that

maybe an ethical person wouldn't make or

a moral person wouldn't make based on

achieving an objective versus based on,

you know, how it's going to look to

people.

>> Well, I'm I'm happy that that uh our

military is going to use AI technology

for defense and I think that that um uh

Andural uh building military technology.

I'm happy to hear that. I'm happy to see

um all these tech startups now

channeling their technology capabilities

towards defense and military

applications. I think you needed to do

that.

>> Yeah, we had Palmer Lucky on the

podcast. He was demonstrating some of

the stuff I put his helmet on. And we

show we he showed some videos how you

could see behind walls and stuff like

it's nuts.

>> And he's he's actually the perfect guy

to go start that company.

>> 100%. [laughter] Yeah. 100%. It's like

he was born for that. Yeah. He came in

here with a copper jacket on. He's a

freak. [clears throat] It's [laughter]

awesome. He's awesome. But it's also

it's a you know an unusual intellect

channeled into that very bizarre field

is what you need, you And I think it's

it's uh I think I'm happy that we're

making it so more socially acceptable.

You know, there was a time where when

somebody wanted to channel their

technology capability and their

intellect into defense technology, uh

somehow they're vilified. Um but uh we

need people like that. We need people

who enjoyed enjoy that part of uh

application of technology.

>> Well, people are terrified of war, you

know. So it depends.

>> Best way to avoid it has excessive

military might.

>> Do you think that's absolutely the best

way? Not not diplomacy, not working

stuff out.

>> All of it.

>> All of it. You have to have military

might in order to get people to sit down

with you.

>> Right. Exactly. All of it.

>> Otherwise, they just invade.

>> That's right. [laughter] Why ask for

permission?

>> Again, like you said, history. Go back

and look at history. Um, when you look

at the future of AI and and you just

said that no one really knows what's

happening, do you ever sit down and

ponder scenarios?

>> Like what do you what do you think is

like bestcase scenario for AI over the

next two decades?

Um

the best case scenario is that AI

diffuses into everything that we do and

uh our

everything's more efficient but

the threat of war remains a threat of

war.

Uh, cyber security remains

a super difficult challenge.

Somebody is going to try to

breach your security. You're going to

have thousands of millions of AI agents

protecting you from that threat.

Your technology is going to get better.

Their technology is going to get better.

Just like cyber security. Right now,

while we speak, we're being

we're seeing cyber attacks all over the

planet on just about every front door

you can imagine.

And

and yet you and I are sitting here

talking. And so the reason for that is

because we know that there's a whole

bunch of cyber security technology in

defense. And so we just have to keep

amping that up, keep stepping that up.

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That's a big issue with people is the

the worry that technology is going to

get to a point where encryption is going

to be obsolete. Encryption is just it's

no longer going to protect data. It's no

longer going to protect systems. Do you

anticipate that ever being an issue or

do you think there's it's as the defense

grows, the threat grows, the defense

grows, and it just keeps going on and on

and on and they'll always be able to

fight off any sort of intrusions?

>> Not forever. some intrusion will get in

and then that we'll all learn from it.

And you know the reason why cyber

security works is because of course the

technology of defense is advancing very

quickly. The technology offense is

advancing very quickly. However, the

benefit of the cyber security defense is

that socially the community all of our

companies work together as one. Most

people don't realize this.

There's a whole community of cyber

security experts. We exchange

ideas. We exchange best practices. We

exchange what we detect. The moment

something has been breached or maybe

there's a loophole or whatever it is, it

is shared by everybody. The patches are

shared with everybody.

>> That's interesting.

>> Yeah. Most people don't realize this.

>> No, I had no I had no idea. I've assumed

that it would just be competitive like

everything else.

>> We work together. Interesting. Has that

always been the case?

>> Uh, it surely has been the case for

about about 15 years. It might not have

been the case long ago, but this this

>> what do you think started off that

cooperation?

>> Um, people recognizing it's a challenge

and no company can stand alone.

>> And the same thing is going to happen

with AI. I think we all have to decide

work working together uh to stay out of

harm's way is is our best chance for

defense. Then it's basically everybody

against the threat.

>> And it also seems like you'd be way

better at detecting where these threats

are coming from and neutralizing them.

>> Exactly. Because the moment you detect

it somewhere,

>> you're going to find out right away.

>> It'll be really hard to hide.

>> That's right.

>> Yeah.

>> That's how it works. That's the reason

why it's safe. That's why I'm sitting

here right now instead of, you know,

locking everything down in video.

[laughter]

>> It's not only am I watching my own back,

I've got everybody watching my back. and

I'm watching everybody else's back.

>> It's a bizarre world, isn't it? When you

think about that cyber threat,

>> this idea about cyber security is

unknown to the people who are talking

about AI threats. They're I think when

they think about AI threats and AI cyber

security threats, they have to also

think about how we deal with it today.

Now, there's no question that AI is a

new technology

and it's a new type of software. In the

end, it's software just it's a new type

of software and so it's going to have

new capabilities but so will the defense

you know where you use the same AI

technology to go defend against it. So

you do you anticipate a time ever in the

future where it's going to be impossible

where there's not going to be any

secrets where the bottleneck between the

technology that we have and the

information that we have. Information is

just all a bunch of ones and zeros. It's

out there on hard drives and the

technology has more and more access to

that information. Is it ever going to

get to a point in time where there's no

way to keep a secret?

>> I don't think

>> because it seems like that's where

everything is kind of headed in a weird

way.

>> I don't think so. I think the quantum

computers were supposed to will Yeah.

quantum computers will make it possible

will make it so that the previous

quantum previous encryption technology

is obsolete. But that's the reason why

the entire industry is working on

postquantum

encryption technology.

>> What would that look like?

>> New algorithms.

>> But the crazy thing is when you hear

about the kind of computation that

quantum computing can do.

>> Yeah.

>> And the the power that it has. Yeah.

>> Where you know you're looking at

>> all the supercomputers in the world. It

would take billions of years and it

takes them a few minutes to solve these

equations. Like how do you make

encryption for something that can do

that? I'm not sure, but there's

[laughter]

but I've got a bunch of scientists who

are working on that.

>> Boy, I hope they [snorts] could figure

it out.

>> Yeah, we got a bunch of scientists who

are expert in that. And

>> is the ultimate fear that it can't be

breached that quantum computing will

always be able to to decrypt all other

quantum computing encryption?

>> I don't think that

>> it just gets to some point where it's

like, stop playing the stupid game. We

know everything.

>> I don't think so.

>> No,

>> because I I'm you know, history is

guide.

History is a guide before AI came

around. That's my worry. My worry is

this is a totally, you know, it's like

history was one thing and then nuclear

weapons kind of changed all of our

thoughts on war and mutually assured

destruction came

everybody to stop using nuclear bombs.

>> Yeah.

>> My worry is that

>> the thing is Joe is that that AI is not

going to it's not like we're cavemen and

then all of a sudden one day AI shows

up. every single day we're getting

better and smarter because we have AI

and so we're stepping on our own AI's

shoulders. So when when that whatever

that AI threat comes, it's a click

ahead. It's not a galaxy ahead,

>> you know, it's just a click ahead. And

so so I think I think the the the idea

that somehow this AI

is going to pop out of nowhere and

somehow think in a way that we can't

even imagine thinking and do something

that we can't possibly imagine I think

is far-fetched. And the reason for that

is because we're all have we all have

AIs and you know there's a whole bunch

of AIs being in development. we know

what they are and we're using it and and

so every single day we're getting we're

close to each other.

>> But don't they do things that are very

surprising?

>> Yeah. But so you you have an AI that

does something surprising. I'm going to

have an AI and my AI looks at your AI

and goes that's not that surprising.

>> The fear for the lay person like myself

is that AI becomes sentient and makes

its own decisions

and then ultimately decides to just

govern the world. do it its own way.

They're like, "You guys, you had a good

run, but

>> we're taking over now."

>> Yeah, but my my AI is gonna take care of

me. I mean, [laughter]

so that's the this is the cyber security

argument.

>> Yes.

>> Do you have an AI and it's super smart,

but my AI is super smart, too. And and

maybe your AI. Let let's pretend let's

let's pretend for a second that we

understand what consciousness is and we

understand what sentience is and and

that in fact

>> and we really are just pretending.

>> Okay, let's just pretend for a second

that we we believe that. I don't believe

actually I don't actually don't believe

that but nonetheless we let's pretend we

believe that.

>> So your your your AI is conscious and my

AI is conscious and and let's say your

AI is you know wants to I don't know do

something surprising.

My AI is so smart that it won't it might

be surprising to me, but it probably

won't be surprising to my AI. And so

maybe my AI

thinks it's surprising as well, but it's

so smart the moment it sees it the first

time, it's not going to be a surprise

the second time, just like us. And so I

feel like I think the idea that that

only one person has [clears throat] AI

and that one person's AI is compares

everybody else's AI is Neanderthal

[snorts] is um probably unlikely. I

think it's much more like cyber

security.

>> Interesting.

>> I think the fear is not that your AI is

going to battle with somebody else's AI.

The fear is that AI is no longer going

to listen to you. That's the fear is

that human beings won't have control

over it after a certain point if it

achieves sensience and then has the

ability to be autonomous

>> that there's one AI.

>> Well, they just combine.

>> Yeah. Becomes one AI

>> that it's a life form.

>> Yeah.

>> But that's the there's arguments about

that, right? That we're dealing with

some sort of synthetic biology that it's

not as simple as new technology that

you're creating a life form.

>> If it's like life form,

let's go along with that for a while. I

think if it's like life form, as you

know, all life forms don't agree. And so

I'm going to have to go with your life

form and my life form are going to agree

because my life form is going to want to

be the super life form. And and now that

now that we have disagreeing life forms,

uh we're back back again to where we

are. Well, they would probably cooperate

with each other.

It would just the reason why we don't

cooperate with each other is we're

territorial primates.

But AI wouldn't be a territorial

primate. It would realize the folly in

that sort of thinking and it would say,

"Listen, there's plenty of energy for

everybody. We we don't need to dominate.

We don't need We're not trying to

acquire resources and take over the

world. We're not looking to find a good

breeding partner. We're just existing as

a new super life form that these cute

monkeys created for us."

Okay. Well, that would be a that would

be a um a superpower with no ego,

>> right? And and if it has no ego,

why would it have the ego to do any harm

to us?

>> Well, I don't assume that it would do

harm to us, but the the fear would be

that we would no longer have control and

that we would no longer be the apex

species on the planet. this thing that

we created would now be. [laughter]

>> Is that funny?

>> No.

>> I just think it's not gonna happen.

>> I know you think it's not gonna happen,

but

>> it could, right? And here's the other

thing is like

>> if we're racing towards could Yeah.

>> And could could be the end of human

beings being in control of our own

destiny.

>> I just think it's extremely unlikely.

>> Yeah.

>> That's what they said in the Terminator

movie [laughter]

>> and it hasn't happened.

>> No, not yet. But you guys are working

towards it. Um the the thing about

you're saying about conscience and

sensience that you don't think that AI

will achieve consciousness or that the

question is what's the definition?

>> Yeah. What's the definition of

>> what is the definition to you?

>> Um uh

consciousness

um

uh f I guess first of all uh you need to

know about your own existence.

Um,

you have to have experience, not just

knowledge and intelligence.

The concept of a machine

having an experience.

I'm not well, first of all, I don't know

what defines experience, why we have

experiences, right?

>> Yeah. and why this microphone doesn't

uh and so it I think I know I well I

think I I I think I know what

consciousness is the sense of experience

the ability to know self versus

um

uh the ability to be able to reflect

know our own self the sense of ego I

think all of all of those human

experiences

uh probably is what consciousness is

but why it exists versus

the concept of knowledge and

intelligence which is what AI is defined

by today [clears throat] it has

knowledge it has intelligence artificial

intelligence we don't call it artificial

consciousness

artificial intelligence the ability to

uh perceive believe, recognize,

understand,

um, plan,

uh, perform tasks.

Those things are foundations of

intelligence

to know things, knowledge.

I don't, it's clearly different than

consciousness.

>> But consciousness is so loosely defined.

How can we say that? I mean, doesn't a

dog have consciousness? Yeah.

>> Dogs seem to be pretty conscious.

>> That's right.

>> Yeah. So, and that's a lower level

consciousness than a human being's

consciousness.

>> I'm not sure. Yeah. Right. Well,

>> the question is what lower level

intelligence? It's lower level

intelligence, but I don't know that it's

lower level consciousness.

>> That's a good point. Right.

>> Because I believe my dogs feel as much

as I feel.

>> Yeah. They feel a lot. Right.

>> Yeah. They get attached to you. That's

right. They get depressed if you're not

there.

>> That's right. Exactly.

>> There's There's definitely that.

>> Yeah. um the the concept of experience,

>> right?

>> Um but isn't AI interacting with

society? So, doesn't it acquire

experience through that interaction?

>> Um I don't think interactions is

experience. I think experience is uh

experience is a collection of feelings.

I think

>> you're aware of that AI um I forget

which one where they gave it some false

information about one of the programmers

having an affair with his wife just to

see how it would respond to it and then

when they said they were going to shut

it down it threatened to blackmail him

and reveal his affair and it was like

whoa like it's conniving like if that's

not learning from experience and being

aware that you're about to be shut down

which would imply at least some kind of

consciousness or you could kind defined

it as consciousness if you were very

loose with the term and if you imagine

that this is going to exponentially

become more powerful. Wouldn't that

ultimately lead to a different kind of

consciousness than we're defining from

biology? Well, first of all, let's just

break down what it probably did. It

probably read somewhere. There's

probably text that that in these

consequences

certain people did that. I could imagine

a novel,

>> right?

>> Having those words related.

>> Sure.

>> And so inside

>> it realizes it strategy for survival is

>> it's just a bunch of numbers

>> that it's just a bunch of numbers that

that in the in the collection of numbers

that relates to a husband cheating on a

wife. Um

has subsequently a bunch of numbers that

relates to blackmail and such things.

However, whatever the revenge was,

>> right?

>> And so it has spewed it out.

>> And so it's just like, you know, it it's

just as if I'm asking it to write me a

poem in Shakespeare. It just whatever

the words are in the world in in that

dimensionality, this dimensionality is

all these vectors and in in

multi-dimensional space. These words

that were in the prompt that described

the affair um subsequently led to one

word after another led to um you know

some revenge and something but it's not

because it had consciousness or you know

it just spewed out those words generated

those words

>> I understand what you're saying that

patterns that human beings have

exhibited both in literature and in real

life

>> that's exactly right

>> but it at a certain point in time one

would say, "Okay, well, it couldn't do

this two years ago and it couldn't do

this four years ago." Like when we're

looking towards the future, like at what

point in time when it can do everything

a person does, what point in time do we

decide that it's conscious? If it

absolutely mimics all human thinking and

behavior patterns,

>> that doesn't make it conscious.

>> It becomes in disccernible. It's it's

aware. It can communicate with you the

exact same way a person can. Like is con

is consciousness are we putting too much

weight on that concept because it seems

like it's a version of a kind of

consciousness.

>> It's a version of imitation.

>> Imitation consciousness, right? But if

it perfectly imitates it,

>> I still think it's a per it's an example

of imitation.

>> So it's like a fake Rolex when they 3D

print them and make them

>> indestruable. The question is what's the

definition consciousness?

>> Yeah.

>> Yeah.

>> That's the question. And I don't think

anybody's really clearly defined that.

That's what get where it gets weird and

that that's where the real doomsday

people are worried that you are creating

a form of consciousness that you can't

control. I believe it is possible to

create a machine

that imitates

human intelligence

and

has the ability to

understand information,

understand

instructions, break the problem down,

solve problems, and perform tasks. I

believe that completely.

I believe that that um we could have a

computer that has a vast amount of

knowledge. Some of it true, some of it

not true.

Some of it generated by humans, some of

it generated synthetically. And more and

more of knowledge in the world will be

generated synthetically going forward.

You know, until now the knowledge that

we've we have are knowledge that we

generate and we propagate and we send to

each other and we amplify it and we add

to it and we modify it. We change it. In

the future,

in a couple of years, maybe two or three

years, 90% of the world's knowledge will

likely be generated by AI.

>> That's crazy.

>> I know. But it's just fine.

>> But it's just fine.

>> I know. And the reason for that is this.

Let me tell you why.

>> Okay?

>> It's because um what difference does it

make to me that I am learning from a

textbook that was generated by a bunch

of people I didn't know or written by a

book that you know from somebody I don't

know uh to uh knowledge generated by AI

computers that are assimilating all of

this and reynthesizing things. To me, I

don't think there's a whole lot of

difference. We still have to we still

have to fact check it. We still have to

make sure that it's you know based on

fundamental first principles and we

still have to do all of that just like

we do today.

>> Is this taking into account the kind of

AI that exists currently? And do you

anticipate that just like we could have

never really believed that AI would be

at least a person like myself would

never believe AI would be as so

ubiquitous and so worth it. It's it's so

powerful today and so important today.

We never thought that 10 years ago.

Never thought that,

>> right?

>> You imagine like what are we looking at

10 years from now?

>> I I think that if you reflect back 10

years from now, you would say the same

thing that we would have never believed

that

>> but

>> in a different direction,

>> right? But if you if you go forward 9

years from now

and then ask yourself what's going to

happen 10 years from now, I think it'll

be quite gradual. Um, one of the things

that Elon said that makes me happy is he

he's he believes that we're going to get

to a point where it's not

it's not necessary for people to work

and not meaning that you're going to

have no purpose in life, but you will

have in his words universal high income

because so much revenue is generated by

AI that it will take away this need for

people to do things that they don't

really enjoy doing just for money. And I

think a lot of people have a problem

with that because their entire identity

and who how they think of themselves and

how they fit in the community is what

they do. Like this is Mike. He's an

amazing mechanic. Go to Mike and Mike

takes care of things. But there's going

to come a point in time where AI is

going to be able to do all those things

much better than than people do. And

people will just be able to receive

money. But then what does Mike do? Mike

is, you know, really loves being the

best mechanic around. You know, what

does the guy who, you know,

codes, what does he do when AI can code

infinitely faster with zero errors? Like

what what happens with all those people?

And that is where it gets weird. It's

like because we've sort of wrapped our

identity as human beings around what we

do for a living.

>> You know, when you meet someone, one of

the first things you meet somebody at a

party, hi Joe. What's your name? Mike.

What do you do? Mike and you know Mike's

like, "Oh, I'm a lawyer." "Oh, what kind

of law?" And you have a conversation,

you know, when Mike is like, "I get

money from the government. I play video

games."

>> Gets weird.

>> Mhm.

>> And I think um the concept sounds great

until you take into account human

nature. And human nature is that we like

to have puzzles to solve and things to

do and and an identity that's wrapped

around our idea that we're very good at

this thing that we do for a living.

>> Yeah. Yeah, I think um let's see, let me

start with the more mundane and I'll

work work backwards, okay? Work forward.

Uh so one of the predictions from uh

Jeff Hinton who who started the whole

deep learning phenomenon the deep

learning technology trend

and uh in incredible incredible

researcher uh professor at University of

Toronto

uh he invented discovered or invented

the the idea of of back propagation

which which uh allows the neural network

to learn.

And um

and as as as you know uh for for the

audience,

software historically was humans

applying first principles and our

thinking to uh describe an algorithm

that is then codified just like a recipe

that's codified in software. It looks

just like a recipe. how to cook

something looks exactly the same just in

a slightly different language. We call

it Python or C or C++ or whatever it is.

In the case of deep learning, this

invention of artificial intelligence,

we put a structure of a whole bunch of

neural networks and a whole bunch of

math units

and we make this large structure. It's

like a switchboard of little

u mathematical units and we connect it

all together.

Um, and we give it the input that

the software would eventually receive

and we just let it randomly guess what

the output is. And so we say, for

example, the input could be a picture of

a cat.

And and um one of the outputs of the

switchboard is where the cat signal is

supposed to show up. And all of the

other signals, the other one's a dog,

the other one's an elephant, the other

one's a tiger.

And all of the other signals are

supposed to be zero when I show it a

cat. And the one that is a cat should be

one.

And I show at a cat through this big

huge network of switchboards and math

units and they're just doing multiply

and adds multiplies and ads. Okay?

And and uh and this thing, this

switchboard is gigantic.

The more information you're going to

give it, the more the bigger this

switchboard has to be. And what Jeff

Hinton discovered was a invented was a

way for you to

guess that put the cat signal in put the

cat image in and that cat image you know

could be a million numbers because it's

you know a megapixel image for example

and it's just a whole a whole bunch of

numbers and somehow from those numbers

it has to light up the cat signal. Okay,

that's the bottom line. And if it the

first time you do it, it just comes up

with garbage. And so it says the right

answer is cat. And so you need to

increase this signal and decrease all of

the other and back propagates the

outcome through the entire network. And

then you show another. Now it's an image

of a dog and it guesses it takes a swing

at it and it comes up with a bunch of

garbage and you say no no no the answer

is this is a dog I want you to produce

dog and all of the other switch all the

other outputs have to be zero and I want

to back propagate that and just do it

over and over and over again. It's just

like uh showing a a kid this is an

apple, this is a dog, this is a cat. And

you just keep showing it to them until

they eventually get it. Okay. Well,

anyways, that big invention is deep

learning. That's the foundation of

artificial intelligence, a piece of

software

that learns from examples. That's

basically we machine learning, a machine

that learns. Uh and so so one of the the

big

first

applications was image recognition and

one of the most important image

recognition applications is radiology.

>> And so so uh uh he predicted uh about 5

years ago that in five years time the

world won't need any radiologists

because AI would have swept the whole

field.

Well, it turns out AI has swept the

whole field. That is completely true.

Today, just about every radiologist is

using AI in some way. And what's ironic

though, what's what's interesting is

that the number of radiologist has

actually grown.

And so the question is why? That's kind

of interesting, right?

>> It is. And so the prediction was in fact

that

30 million radiologists will be wiped

out.

But as it turns out, we needed more. And

the reason for that

[clears throat and cough]

is because the purpose of a radiologist

is to diagnose disease,

not to study the image. This the image

studying is simply a task to in service

of diagnosing the disease. And so now

the fact that you could study the images

more quickly and more precisely

without ever making a mistake and never

gets tired.

You could study more images. You could

study it in

3D form instead of 2D because you know

the AI doesn't care whether it studies

images in 3D or 2D. You could study it

in 4D. And so the now you could study

images in a way that radiologist

radiologists can't easily do and you

could study a lot more of it. And so the

number of tests that people are able to

do increases and because they're able to

serve more patients, the hospital does

better. They have more clients, more

patients. As a result, they have better

economics. When they have better

economics, they hire more radiologists

because their purpose is not to study

the images. their purpose is to diagnose

disease. And so the question is the what

I'm leading up to is ultimately what is

the purpose? What is the purpose of the

lawyer? And has the purpose changed?

What is the purpose? You know, one of

the examples that I gave is is um that I

would give is for example uh if my car

became self-driving

will all chauffeers be out of jobs? The

answer probably is not because for some

per for some chauffeers they for some

people who are driving you they could be

protectors some people um they're part

of the experience part of the service so

when you get there they you know they

could take care of things for you and so

for a lot of different reasons not all

chauffeers would lose their jobs some

chauffeers would lose their jobs and uh

many chauffeers would change their jobs

and the type of applications of

autonomous vehicles will probably

increase you know the usage of the

technology within find new homes and so

I I think you have to go back to what is

the purpose of a job you know like for

example if AI comes along I actually

don't believe I'm going to lose my job

because my purpose isn't to I have to

look at a lot of documents I study a lot

of emails I look at a bunch of diagrams

you know um the question is what is the

job and and uh the purpose of somebody

probably hasn't changed a lawyer for

example help people that probably hasn't

changed studying legal documents

generating documents it's part of the

job not the job

>> but don't you think there's many jobs

that AI will replace

>> if your job is automation

>> yeah if your job is the task

>> right so automation

>> yeah factor if your job is the task

>> that's a lot of people

>> it could be a lot of people but it'll

probably generate like for example

>> uh let's say we let's say I'm super

excited about the the the robots Elon's

working on.

It's still a few years away.

When it happens, when it happens,

um

there's a whole new industry of

technicians and people who have to

manufacture the robots, right?

>> Mhm.

>> And so that that job never existed. And

so you're going to have a whole industry

of people taking care of like for

example, you know, all the mechanics and

all the people who are building things

for cars, supercharging cars, uh that

didn't exist before cars and now we're

going to have robots. You're going to

have robot apparel. So a whole industry

of [laughter] Right. Isn't that right?

Because I want my robot to look

different than your robot.

>> Oh god.

>> And so [laughter] you're going to you're

going to have a whole, you know, apparel

industry for robots. You're going to

have mechanics for robots and you have

you know people who comes and maintain

your robots

>> automated though.

>> No,

>> you don't think so? You don't think

[clears throat] they'll be all done by

other robots

>> eventually? And then there'll be

something else.

>> So you think ultimately people just

adapt except if you are the task

>> which is a large percentage of the

workforce.

>> If your job is just to chop vegetables,

quezin art is going to replace you.

>> Yeah. So people have to find meaning in

other things. Your job has to be more

than the task.

>> What do you think about Elon's belief

that this universal basic income thing

will eventually become necessary?

>> Many people think that. Andrew Yang

thinks that

>> he was one of the first people to sort

of sound that alarm during the the 2020

election.

Yeah, I I guess um

yeah, both ideas probably won't exist at

the same time and and um as in life,

things will probably be in the middle.

One idea, of course, is that there'll be

so much abundance of resource that

nobody needs a job and we'll all be

wealthy.

On the other hand, um we're going to

need universal basic income. Both ideas

don't exist at the same time,

>> right?

>> And so we're either going to be all

wealthy or we're going to be all

>> How could everybody be wealthy though?

But

>> because scenario wealthy not because you

have a lot of dollars, wealthy because

there's a lot of abundance. Like for

example, today we are wealthy of

information.

You know, this is some a concept several

thousand years ago only a few people

have. And so, uh, today we have wealth

of a whole bunch of things, resources

that that historic point. Yeah. And so,

we're going to have wealth of resources,

things that we think are valuable today

that in the future are just not not that

valuable, you know, and so it because

it's automated. And so I think I think

the question

maybe maybe partly it's hard to answer

partly because

it's hard to talk about infinity and

it's hard to talk about a long time from

now and and the reason for that is

because

there's just too many scenarios to to

consider. But I think it I think in the

next several years, call it 5 to 10

years,

there are several things that I I

believe in hope. Um, and I say hope

because I'm not sure. One of the things

that I believe is that the technology

divide will be substantially collapsed.

And of course the alternative

viewpoint is that AI is going to

increase the technology divide.

Now the reason why I believe AI is going

to reduce the technology divide.

I is because we have proof

the evidence is that AI is the easiest

application in the world to use. Chat

GPT has grown to almost a billion users

frankly practically overnight. And if

you're not exactly sure how to use,

everybody knows how to use chatpt. Just

say something to it. If you're not sure

how to use chatpt, you ask chatd how to

use it. No tool in history has ever had

this capability. A quez an art, you

know, if you don't know how to use it,

you're kind of screwed. You're going to

walk up to it and say, "How do you use a

quezin art?" You're going to have to

find somebody else. And so, but an AI

will just tell you exactly how to do it.

Anybody could do this. It'll speak to

you in any language. And if it doesn't

know your language, you'll speak it in

that language and it'll probably figure

out that it doesn't completely

understand your language. Go learns it

instantly and comes back and talk to

you. And so I think the the technology

divide has a real chance finally that

you don't have to speak Python or C++ or

forran. You can just speak human and

whatever form of human you like. And so

I think that that has a real chance of

closing the technology divine. Now, of

course, the counternarrative would say

that

AI is only going to be available for the

nations and the countries that have a

vast amount of resources because AI

takes energy

and AI takes um a lot of GPUs and

factories to be able to produce the AI.

No doubt at the scale that we would like

to do in the United States. But the fact

of the matter is your phone's going to

run AI just fine all by itself, you

know, in a few years. Today, it already

does it fairly decently. And so the the

the fact that every every country, every

nation, every every society will have

the benefit of very good AI. It might

not be tomorrow's AI. It might be

yesterday's AI, but yesterday's AI is

freaking amazing. You know, in 10 years

time, 9year-old AI is going to be

amazing. You don't need, you know, 10

year old AI. You don't need frontier AI

like we need frontier AI because we want

to be the world leader. But for every

single country, everybody, I think the

ele the capability to elevate

everybody's knowledge and capability and

intelligence, uh, that day is coming.

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>> And also energy production, which is the

real bottleneck when it comes to third

world countries and

>> that's right,

>> electricity and all all the resources

that we take for granted.

>> Almost everything is going to be energy

constrained. And so if you take a look

at um

one of the most important technology

advances in history is this idea called

Moore's law. Moore's law

was the started basically in my

generation

and my generation is the generation of

computers. I graduated in 1984 and that

was basically at the very beginning of

the PC revolution.

And the microprocessor and and um

every single year it approximately

doubled

and we describe it as every single year

we double the performance. But what it

really means is that every single year

the cost of computing halfed.

And so the cost of computing in the

course of five years reduced by a factor

of 10. The amount of energy necessary to

do computing to do any task reduced by a

factor of 10. Every single 10 years 100

a th00and 10,000

100,000 so on and so forth. And so each

one of

the clicks of Moore's law, the amount of

energy necessary to do any computing

reduced. That's the reason why you have

a laptop today when back in 1984 sat on

the desk, you got to plug in, it wasn't

that fast and it consumed a lot of

power. Today, you know, it is only a few

watts. And so Moore's law is the

fundamental technology, the fundamental

technology trend that made it possible.

Well, what's going on in AI? The reason

why Nvidia is here is because in we

invented this new way of doing

computing. We call it accelerated

computing. We started it 33 years ago.

Took us about 30 years to really made a

huge breakthrough. In that in that 30

years or so

we took computing you know probably a

factor of well let me just say in last

10 years the last 10 years we improved

the performance of computing by 100,000

times.

Whoa. Imagine a car over the course of

10 years that became a 100,000 times

faster or at the same speed 100,000

times cheaper or at the same speed

100,000 times less energy. If your car

did that, it doesn't need energy at all.

What I mean what what I'm trying to say

is that in 10 years time the amount of

energy necessary for artificial

intelligence for most people will be

minuscule

utterly minuscule and so we'll have AI

running in all kinds of things and all

the time because it doesn't consume that

much energy and so if you're a nation

that uses AI for you know almost

everything in your social fabric of

course you're going to need these AI

factories but for a lot of countries I

think you're going You're going to have

excellent AI and you're not going to

need as much energy. Everybody will be

able to come along is my point.

>> So currently that that is a big

bottleneck, right? Is energy.

>> Yeah, it is the bottleneck.

>> The bottleneck is this. So was it Google

that is making nuclear power plants to

operate one of its AI factories?

>> Oh, I haven't heard that. But I think in

the next six, seven years, I think

you're going to see a whole bunch of

small nuclear reactors.

>> And by small, like how big are you

talking about? Hundreds of megawws.

Yeah.

>> Okay. And that these will be local to

whatever specific company they have.

>> That's right. Will all be power

generators.

>> Whoa.

>> You know, just like just like your you

know, somebody's farm.

>> It probably is the smartest way to do

it, right?

>> And it takes the burden off Yeah. takes

the burden off the grid. It takes and

you could build as much as you need

>> and you can contribute back to the grid.

It's a really important point that I

think you just made about Moore's law

and the relationship to pricing because

you know a laptop today like you can get

one of those little Mac MacBook Airs.

They're incredible. They're so thin,

unbelievably powerful. Battery life is

charge it.

>> Yeah. Battery [laughter] life's crazy.

And uh it's not that expensive

relatively speaking. Like something like

that.

>> I remember.

>> And that's just Moore's law, right?

>> Then there's the Nvidia law.

>> Oh,

>> just right. the the the law I was

talking to you about, the computing that

we invented,

>> right?

>> The reason why we're here, this new this

new way of doing computing

>> is like Mo's law on energy drinks. I

mean, it's [laughter]

it's like Mo's law

it's it's like Yeah. Moore's law and Joe

Rogan.

>> Wow. That's interesting.

>> Yeah. That's us.

>> So, explain that. Um this this chip that

you brought to Elon, what what's the

significance of this? It's like why is

it so superior? And so

in 2012, Jeff Hinton's lab, this

gentleman I was talking talking about,

um Ilas Suscober, Alex Kresevski, um

they made a breakthrough in computer

vision in literally creating a

piece of software

called Alexnet.

And its job was to recognize images. And

it recognized images

at a c at a level computer vision which

is fundamental to intelligence. If you

can't perceive, you can't it's hard to

have intelligence. And so computer

vision is a fundamental pillar of not

the only but fundamental pillar of. And

so breaking

computer vision or breaking through in

computer vision is pretty foundational

to almost everything that everybody

wants to do in AI. And so in 2012,

their lab in Toronto

uh made this made this breakthrough

called Alexnet. And Alexet was able to

recognize images

so much better than any human created

computer vision algorithm in the 30

years prior. So all of these people, all

these scientists and we had many too

working on computer vision algorithms

and these two kids, Ilia and Alex under

the the uh

under under uh Jeff Hinton took a giant

leap above it and it was based on this

thing called Alexet this neural network.

And the way it ran,

the way they they made it work was

literally buying two Nvidia graphics

cards

because Nvidia Nvidia's GPUs we've been

working on this new way of doing

computing and our GPUs application

and it's basically a supercomputing

application to back in 1984

in order to

process computer games and what you have

in your racing simulator that is called

an image generator supercomputer.

And so Nvidia started our first

application was computer graphics and we

applied this new way of doing computing

where we do things in parallel in

instead of sequentially. A CPU does

things sequentially. Step one, step two,

step three. In our case, we break the

problem down and we give it to thousands

of processors.

And so our way of doing computation

is much more complicated.

But if you're able to formulate the

problem in the way that we

created called CUDA, this is the

invention of our company. If you could

formulate it in that way, we could

process everything simultaneously.

Now, in the case of computer graphics,

it's easier to do because every single

pixel on your screen is not related to

every other pixel. And so, I could

render multiple parts of the screen at

the same time. Not not completely true

because, you know, maybe maybe the way

lighting works or the way shadow works,

there's a lot of dependency and and

such. But computer graphics with all the

dis with all the pixels, I should be

able to process everything

simultaneously. And so we we took

this embarrassingly parallel problem

called computer graphics and we applied

it to this new way of doing computing.

Nvidia's Nvidia's accelerated computing.

We put it in all of our graphics cards.

Kids were buying it to play games. We're

you probably don't know this, but we're

the largest gaming platform in the world

today.

>> Oh, I know that. Oh,

>> okay.

>> I used to make my own computers. I used

to buy your graphics cards.

>> Oh, that's super cool.

>> Yeah. [laughter] set up SLI with two

graphics cards.

>> Yeah, I love it. Okay, that's super

cool.

>> Oh, yeah, man. I used to be a Quake

junkie.

>> Oh, that's cool.

>> Yeah.

>> Okay, so SLI, I'll tell you the story in

just a second and how it led to Elon.

I'm still answering the question. And

so, anyways, these these two kids

trained this model using the technique I

described earlier on our GPUs because

our GPUs could process things in

parallel. It's essentially a supercomput

in a PC. The reason why you used it for

Quake is because it is the first

consumer supercomputer. Okay. And so

anyways,

they made that breakthrough. We were

working on computer vision at the time.

It caught my attention

and so we went to learn about it.

Simultaneously this deep learning

phenomenon was happening all over all

over the country. Universities after

another recognized the importance of

deep learning and all of this work was

happening at Stanford, at Harvard, at

Berkeley, just all over the place. New

York University, L Yan Lakun, Andrew

Yang at Stanford, so many different

places. And I see it cropping up

everywhere.

And so my curiosity asked, you know,

what is so special about this form of

machine learning? And we've known about

machine learning for a very long time.

We've known about AI for a very long

time. We've known about neural networks

for a very long time. What makes now the

moment? And so we realized that this

architecture for deep neural networks

back propagation the way deep neuronet

networks were created. We could probably

scale this problem, scale the solution

to solve many problems.

that is essentially

a universal function approximator. Okay?

Meaning meaning you know back when

you're in in in school you have a you

have a you have a box inside of it is a

function you give it an input it gives

you an output and and the the reason why

I call it universal function

approximator

is that this computer instead of you

describing the function a function could

be a new equation fals ma that's a

function you write the function in

software you give it input f mass

acceleration, it'll tell you the force.

Okay? And

the way this computer works is really

interesting.

You give it a universal function. It's

not fals, just a universal function.

It's a big huge deep neural network

and instead of describing the inside,

you give it examples of input and output

and it figures out the inside.

So you give it input and output and it

figures out the inside. A universal

function approximator. Today it could be

Newton's equation. Tomorrow it could be

Maxwell's equation. It could be Kulum's

law. It could be thermodynamics

equation. It could be you know

Shingers's equation for quantum physics.

And so you could put any you could have

this describe almost anything so long as

you have the input and the output. So

long as you have the input and the

output or it could learn the input and

output.

>> And so we took a step back and we said,

"Hang on a second. This isn't just for

computer vision. Deep learning could

solve any problem.

All the problems that are interesting so

long as we have input and output. Now

what has input and output?

Well, the world. The world has input and

output. And so we could have a computer

that could learn almost anything.

Machine learning, artificial

intelligence. And so we reasoned that

maybe this is the fundamental

breakthrough that we needed. There were

a couple of things that had to be

solved. For example, we had to believe

that you could actually scale this up to

giant systems. It was running in a they

had two graphics cards, two GTX 580s,

[laughter]

which by the way is exactly your SLI

configuration. Yeah. Okay. So, that GTX

5880 SLI was the revolutionary computer

that put deep learning on the map.

>> Wow.

>> It was 2018 and you were using it to

play Quake.

>> Wow. That's crazy.

>> That was the moment. That was the big

bang of modern AI. We were lucky because

we were inventing this technology, this

computing approach. We were lucky that

they found it.

Turns out they were gamers and it was

lucky they found it. And it it was lucky

that we paid attention to that moment.

It was a little bit like, you know, that

Star Trek,

you know, first contact.

The Vulcans had to have seen the warp

drive at that very moment. If they

didn't witness the warp drive, you know,

they would have never come to Earth and

everything would have never happened.

It's a little bit like if I hadn't paid

attention to that moment, that flash.

And that flash didn't last long. If I

hadn't paid attention to that flash or

our company didn't pay attention to it,

who knows what would have happened, but

we saw that and we reasoned our way into

this is a this is a universal function

approximator. This is not just a

computer vision approximator. We could

use this for all kinds of things. if we

could solve two problems. The first

problem is that we have to prove to

oursel it could scale. The second

problem we had to

wait for I guess contribute to and wait

for is

the world will never have enough data

on input and output where we could

supervise

the AI to learn everything. For example,

if we have to supervise our children on

everything they learn, the amount of

information they could learn is limited.

We needed the AI, we needed the computer

to have a method of learning without

supervision.

And that's where we had to wait a few

more years, but un unsupervised

AI learning is now here. And so the AI

could learn by itself. And and the

reason why the AI could learn by itself

is because we have many examples of

right answers. Like for example,

if I want to learn uh if I want to teach

an AI how to predict the next word, I

could just grab it, grab a whole bunch

of text we already have, mask out the

last word and make it try and try and

try again until it predicts the next

one. or I mask out random words inside

inside the text and I make it try and

try and try until it predicts it. You

know, like uh Mary uh Mary goes down to

the bank. Is it a river bank or a money

bank? Well, if you're going to go down

to the bank, it's probably a river bank.

Okay. So, and it it it might not be

obvious even from that. It might need

and

uh and uh and caught a fish. Okay. Now

you know it's must be the riverbank. And

so so you give you give these AIs a

whole bunch of these examples and you

mask out the words, it'll predict the

next one. Okay? And so unsupervised

learning came along. These two ideas,

the fact that it's scalable and

unsupervised learning came along.

We were convinced that we ought to put

everything into this and help create

this industry because we're going to

solve a whole bunch of interesting

problems. And that was in 2012. By 2016,

I had I had built this computer called

the DGX1. The one that you saw me give

to Elon is called DGX Spark. The DGX1

was $300,000.

It cost Nvidia a few billion dollars to

make the first one.

And instead of two chips SLI,

we connected eight chips with a

technology called MVLink, but it's

basically SLI supercharged.

Okay.

>> Okay.

>> And so we connected eight of these chips

together instead of just two. And all of

them work together just like your Quake

rig did to solve this deep learning

problem to train this model. And so I

create we created this thing. I

announced it at GTC

and at one of our annual annual events

and I described this deep learning

thing, computer vision thing and this

computer called DJX1.

The audience was like completely silent.

They had no idea what I was talking

about. [laughter]

And I was lucky because I I had known

Elon and uh uh I helped him build the

first computer for Model 3

uh uh the Model S. And uh and when he