Is There a Difference Between Generative AI and the Zuckerberg Model?

Watch: https://www.youtube.com/watch?v=UQkeRxeh34I

Transcript

eric schmidt is a business leader and

software engineer that served as

google's chief executive officer from

2001 to 2011.

under his leadership google grew from an

early silicon valley startup to arguably

the most important technology company on

the planet

schmidt is currently co-founder of

schmidt futures and sits on the board of

many public and private institutions

he is still involved with technology

consults with the us department of

defense

also talks about ai in his latest book

the age of ai and our human future

written alongside former u.s secretary

of state henry kissinger and computer

scientist daniel huddenlocker

schmidt was a guest at the milken global

conference and here he anticipates some

of the ai innovations that we will

certainly see in five years

he also predicts what we might see in 20

years

here are the details

recently in the last couple of years

there have been

extraordinary gains so for example a

team at google and at the baker lab

separately

figured out a way to actually understand

if you take dna what proteins are

generated and what their structure is

that's an extraordinary achievement in

my opinion worth a nobel prize

there are drugs being designed now that

could not be possibly

designed by humans

in any way because of their complexity

there's evidence that ai can be used in

biology ai is mapped to biology the way

math is to physics in other words

biology is so complicated that ai will

be used to interpret biology and predict

its outcome

over and over again ai will arrive in

your life

another example is the hottest area in

my industry right now are large language

models uh recently a set of startups

have been funded between 100 and a

billion 100 million and a billion

dollars

they have no current product or revenue

plans um

the the belief of the power of this

technology these large language models

are interesting because you suck all the

information in

like you read all the web which

computers can do but we can't and then

they discover things they appear to

discover a structure of language and an

example of recent google product last

week can actually translate from one

computer language to another and we

didn't give it any examples of one to

translate to the other it discovered a

structure and it can predict it

these are the beginning of general

intelligence

the the current um excitement stems from

a technology called transformers that

was invented three or four years ago and

what transformers do is it can predict

the next word after a set of words so if

you give it a sentence it can predict

what the word will be and it's done

using a complicated mathematical

technique it turns out predicting the

next word is mathematically the same

thing as predicting the next sound the

next video the next image

all of that and so you have a

unification a multi multi-modal

unification of video text and speech so

these systems sound and look like

they're intelligent

a good example is gpt3 which came out

last year

which kicked the current revolution off

you asked it

do you think like a human and it says no

i do not

because i am a large language model and

you are a i think a human who has been

taught to think in this way

now is that

it thinking about you or is it pattern

matching we can't tell and the truth is

and i'm as part of philanthropic work

i'm funding projects to try to

understand this we don't actually

understand why this works we don't

mathematically understand why it works

and we also don't understand its failure

modes

so you wouldn't want to use this as a

replacement for something that's live

critical because we can't say when it

fails when does it just crash

the current large language models for

example have trouble with the notion of

gravity so if you say to them i moved it

i moved the object from here to here and

then i put it up here and i put it down

there and so forth now everyone just

followed what i did the large language

model gets confused because it doesn't

understand gravity so the computer

scientists say we're going to now add

concepts

right

so with concepts and then with planning

maybe you get to the point where it

looks like a human-like intelligence

which has all sorts of issues

if i were 24 today this is exactly what

i'd be working on this is where the

hardest and most challenging computer

science systems problems are with the

greatest payoff

now remember that the system can predict

patterns

and if you can predict a pattern you can

also generate an artifact there's a

duality in these systems where they can

generate things

so part of the issues that we face now

is that these systems can generate

speech i'll give you an example

within five years

the following will be true

you'll be able to take a system

um take one of these language models

which would be infinitely expensive to

make but you didn't pay for it

it shows up in your doorstep and it fine

tunes the technical term is literally

fine-tuning it you fine-tune it to you

who are you what do you care about it

sort of watches you and learns from you

it learns your voice

right all of a sudden it can generate

videos with you in it

now you could think of this as a second

ai right

now the interesting thing is imagine

five years from now i install this thing

and i use it for a few years and

eventually we all die unfortunately well

it lives on

right

as a pretty good impersonation of me

and what happens when i'm dead and it's

still learning is that me

is that an artifact of me or is it just

a stupid artifact of history that you'll

keep in a box and some future will say

oh eric was so stupid back then but it's

entertaining to watch him right because

he didn't keep learning we don't know we

have no way of discussing these things

this stuff is incredibly powerful it

will be the basis of enormous gains in

human health

language translation communication

summary and education

all the things that milken represents

will be affected in an almost always

positive way having said that there's

terrifying consequences as well so the

first question has to do with jobs

does this fundamentally mean there are

more jobs or less jobs i spent my whole

life people saying computers will

replace humans humans won't have

anything to do so far that narrative has

been false notice that there's a huge

surplus of jobs and not people to fill

it certainly in the united states the

second one has to do with national

security something i've worked on for

almost a decade now

and in our in the kissinger book we talk

a lot about this

what happens when the...