YouTube Transcript:
Top Minds in AI Explain What’s Coming After GPT-4o | EP #130

Skip watching entire videos - get the full transcript, search for keywords, and copy with one click.

AutoDub

Understand YouTube Foreign Videos

Immersive YouTube Voice Translation

Break language barriers, embrace global quality content

Solve Foreign Video Barriers Instantly

Video Transcript

welcome to a another conversation on AI

I don't think we get enough of this

conversation going I do want to thank

Richard AAS and the FI team for really

increasing the conversation this year on

AI because I think there is no greater

uh topic of import on the financial side

on the leadership side education Side

Medical side it's transforming

everything we have uh three incredible

CEOs here um who are representing uh

variety of different parts of the AI

emergence um I'm going to start by

asking each of them to take just one

minute introduce themselves and what

they're doing and then we're going to

jump into where is this going how fast

is it going how big is it going to get

you know we'll ask the question what is

after chat GPT Prem let's begin with

yourself awesome thank you you thank you

Peter um I'm pre maraju I'm the CEO of

stability AI we are one of the leading

open- Source image video and 3D um

models in in the world and past GPT

pictures are worth a thousand words and

we're making quite a few of them and in

fact 80% of all the images that were

generated by AI last year in 2023 were

driven by our model stable diffusion

amazing Richard hi everyone really

excited to be here my name is Richard

soer I'm the CEO and founder of u.com

y.com it's a productivity engine which

is the next Generation after a search

and an answer engine so we really make

people more productive across a whole

host of different kinds of organizations

from hedge funds to universities to

companies insurance companies and so on

Publishers news agencies and uh almost

everyone else in between who has sales

service marketing research analysis uh

and so on I also run a venture fund

called aiex Ventures that invests in

early stage uh preed seed AI companies

and startups been very fortunate that

when I was a professor at Stanford I had

two students who created this cute

company called hugging face invested in

that a 5 million valuation worth four

and a half billion now so Fun's doing

that's that's bragging that's just

straight up bragging I wish I could brag

like that Dr Kaiu

Lee hi uh I've been working on AI for

about 43 years I was two at no at the uh

in college when I started Ai and um I

think that may have started before my

colleagues were born but um I actually

worked on machine learning and um and I

have a PhD carnegi melon and I have

worked at uh Apple Microsoft Google uh

some of you may know me as with my books

AI superpowers and AI

2041 uh my part-time job is I I run

sinovation Ventures which invests

globally and then my full-time job is I

run 0 one. it's a uh gener generative AI

company uh we build a large language

model we're currently ranked as the

third company with the highest

performance only next to the best models

from open and Google um and you can find

it online uh we're also building uh

consumer and Enterprise Products um

we're based in China uh but our products

are accessible globally and also we

extensively uh do open source as well so

incredible and and first of all Kaiu is

a legend and one of the greatest leaders

globally in this field so very honored

to have him on here um Prem I want to

start with you um uh you very famously

were able to recruit James Cameron onto

your board uh and since stability is

creating video and is creating sort of

the future of

Hollywood um

I am curious about two things one did uh

did Jim get it right with the

Terminator um and and secondly uh you

know there's been a lot of conversation

about the disruption of Hollywood um

that we're going to have AIS creating

the future of all movies all content and

so forth so you said beyond you know GPT

models were you know images worth of

th000 words talk to us about what this

what this future is what is going to

happen in sort of the visualization

world of of TV and Hollywood love it so

did Jim get it right with Terminator uh

let's hope not I guess but the um but

what a great movie it was and I love

when he actually he jokes about it he

says I told you guys like you know this

is coming and now it absolutely is here

um and why did why would someone like

him get involved in stability yeah great

question so you I I had the great

Fortune of of working on Avatar 2 with

him when I was the CEO of wether digital

before I joined as CEO of stability and

that movie took over four years to make

and that's because it was fully rendered

and I think if you fast forward to 5 to

10 years from now the vast majority of

film and television and visual media as

we know it today is not going to be

render is going to be generated and in

fact in Avatar there were certain shots

there were certain uh that took 6,000

7,000 hours of compute time to render

one single frame thousands of hours that

literally can be reduced down to minutes

now so I think Jim just wants a whole

lot of life back and when you think

about like the creative process we all

watch films we watch movies we love them

from the time we've born to our last

memory it's some it's a commodity we

never get sick of um we never we never

not want to watch it

um and so there's this insatiable

appetite out there in the world to

consume stories and to create stories

and I think that we should just

accelerate that the problem with the

film production process is time and

money so what he really wanted to do is

rip those things out so we can move from

a render to a generated model are we

going to see a situation where we're

ever going to have ai generating entire

movies because it knows my preferences

what I love and it's like the perfect

movie for me

you know personally I kind of hope not

um I don't think that

actually uh the creative process I think

needs to start with a human and I think

that human needs to dictate these tools

in separate agents to actually make that

story and so I'm hoping that you'll

probably want to hear stories that other

people want to tell you all right well

let's take a different direction then

sure am I going to see uh Marilyn Monroe

and you know all stars of the Past

coming back

is there a need for human actors if you

can generate absolutely lifelike uh

actors and actresses perfectly I mean I

can't see a situation where they're

still around yeah I think that it's

actually quite it's faster when you're

talking about the Film Production it's

actually easier to just shoot plates on

an actor just shoot real photography and

get their performance I think there's

that's the visible layer of of

production people gravitate toward it a

lot I think that AI will enhance those

prod those um performances I think the

physicality of a director with a camera

and an actor in front of it is a very

important part of the creative process

and I don't think that that's going to

go away too soon and in fact I think

about the things that aren't going to

change just as much as I think is going

to change but I do think after they take

one take the director is going to say I

got it because they're going to be able

to do what you're talking about which is

manipulate that performance may ask one

more question to you before I move on

what is the most dramatic change we're

going to see in film and TV 10 years

years from now as we see digital super

intelligence we like what's what's the

craziest vision of what we're going to

see in entertainment I think we're going

to see on the magnitude of 5 to 10 to

20x more content being created I think

we're going to see a variation of time

where it's going to be a two-minute like

you said you may want to have 20 minutes

before you go to bed you want to see a

movie that that's you'll have different

type of time signatures and I think that

you're going to have an explosion of

content creation an explosion of number

of artists in the world

I'm going to come back in 10 years and

see if you're right about that

okay uh Richard

um a lot of your work was instrumental

in the early days of bringing neural

Nets to natural language

processing um so what do you see as the

next Frontier Beyond NLP so just explain

if you would what NLP is and where is it

going next yeah natural language

processing NLP used to be a a sub area

of AI and it has I think influenced

pretty much every other area of AI and

uh there lots of different algorithms

you could train and 2010 I had this

crazy idea to train a single neural

network for all of NLP and 2018 we

finally really built the first model uh

that invented prompt engineering where

you can just ask one model all the

different questions you have and over

time of course you can ask questions not

just over text but also over images and

so I think next one of the answers to

the the panel's main topic of what's

after chat gbt is that we have many more

multimodal models you'll be able to have

conversations over images you have

seamless inputs and outputs in not just

the modality of text but also

programming which is a huge unlock uh

visual videos images voice sound but one

really interesting modality that not

many people have quite realized yet is

that of proteins proteins are

essentially the basic Lego blocks of all

of biology everything in our body is

governed by prot proteins and you can

create a protein just like you can ask a

large language model to write AET for

you or a poem for your wife you can ask

an llm to create a specific kind of

protein it will only bind to SARS Cove 2

or only bind to a specific type of

cancer in your brain and what that means

is that we will unlock a lot of

different aspects in medicine so I'm

extremely excited about the future of

LMS going into different modalities and

we're seeing that with Deep Mind

products in you in Alpha proteo and and

such so we had a conversation in back

but I didn't hear the answer and the

question is basically is there an upper

limit to

intelligence and you know we've talked

about and we just did a conclave on

digital

superintelligence and how fast we're

going to get there and what does it mean

um as we think about AI becoming more

and more intelligent yes I want speak to

Elon he said okay 2029 2030 equal to

intelligence to the entire human

race is it just you know a million times

more and then a billion times more and

then a trillion times more is there an

upper limit to intelligence yeah so

really interesting question so just to

talk about Alpha fold and Google for a

second as you mentioned it like that was

really interesting uh to understand how

proteins fold because that will help you

understand how they are likely to

function interact in your body what we

did in 2020 is create the first LM that

generates a completely new kind of

protein and it was 40% different uh to

any naturally occurring protein and it

actually we synthesized it in the wet

lab this was at Salesforce research did

scientist there and it was an

antibacterial Lio type of protein that

is basically has antibacterial

properties and just to put that into

perspective was really close to covid-19

so make sure you weren't um got to be

careful what you say online sometimes um

but what was interesting is that

multiple startups have now started from

this line of research and and I think

it's hard for people to Fathom like how

much that can change medicine in terms

of upper bounds of intelligence it's a

really interesting question can it just

keep going and going going I think you

have to basically look at the different

dimensions of intelligence right there's

language intelligence visual perception

intelligence reasoning knowledge

extraction uh and a few others physical

manipulation and just I'll show you just

one example I don't want to talk talk

about this for hours but visual

intelligence right there are you know

for a long time people have looked at

just the electromagnetic uh frequency

spectrum of human vision and there you

know classifying every object on the

planet is actually not that hard and the

upper limit is classifying all the

objects um on the planet and we're

probably going to reach that and we're

not too far away from it but that's just

human Vision AI could eventually see all

the way down to gamma frequencies and

see and try to perceive atoms right and

there you actually start to hit limits

uh of physics like Quantum limits of

like what can actually be observable and

you can go all the way into like seeing

uh massively larger scale things at the

universe level and how many uh different

sensors do you have in that then you can

process all of that information and AI

could have billions of uh sensors that

go out and then you get into really

interesting limits of like the speed of

light cone of like so I can talk about

for hours it's a really tough subject

but in some cases we are astronomically

far away from those upper bounds and in

some cases we already got pretty close

fasc you talk about work productivity as

U.C com's objective what does that mean

and uh I guess the question is the same

is there any limitation on work

productivity that we're going to

see given the fact that I can command AI

agents and robots to just do anything

and everything and just and self-improve

along the way it seems like we're going

to hit sort of an infinite GDP at some

point yeah there there's some areas of

AI where AI can actually get into a

self-training loop if there's a

simulation of something that and

anything that can be simulated AI can

solve everything in that areas for

instance chess the game of Go you can

perfectly simulate it hence the I can

train and play with itself billions and

billions of times cre almost infinite

amounts of training data and hence solve

every problem in that domain what are

other domains that we can perfectly

simulate is programming if you can

programming languages can be run and

then you can simulate the outputs

obviously in the computer and then the

AI can get better and better and

eventually get super human uh in terms

of programming but where I can't

simulate things uh infinitely many times

is in like customer service right you

can have billions and billions of

customers kind of ask about all the

different things that uh can go wrong

with a product that you're sending and

so in those kinds of areas the limits

are going to be on data collection can

you actually fully digitize a process I

often joke like plumbers are probably

the safest from AI disruption because no

one's even collecting data on how to do

plumbing right you like crawl somewhere

get different pipes no one's having

GoPro and 3D sensors and robotic arms

and so on collecting data for that so

that will take much much longer um I

think in terms of work productivity a

lot of us are going to become managers a

lot of current employees that are

individual contributors are going to

have to learn to manage an AI to do the

kinds of work that they do and it turns

out managing is also a skill not

everyone is a good manager from day one

you have to really explain to the AI how

you do a certain kind of job and what

we've seen with for instance uh a really

large cyber security company called

minecast is we've they've had 200 seat

licenses using their product and then we

did a workshop with them and actually

explained to all the different groups

like this is what you can do and someone

from marketing can say well I usually

get this long product description and

then I have to describe it for these

different Industries and an email

campaign and I have to write three

tweets and three LinkedIn messages all

this stuff and we're like well just say

that to this agent and then the I agent

does it for them they're like wow now

it's like six to 20 hours of work every

other week just got automated by

describing this workflow that I used to

do manually to a agent and I think that

will change pretty much all work and

pretty much every industry Kaiu um I can

go in a thousand different directions uh

here uh first of all uh your Venture

fund Innovations which is how many

billions of capital a uh we manage about

$3 billion about3 billion and you've

been one of the most prolific AI

investors I've had the pleasure to visit

you multiple times in China and thank

you for your amazing Hospitality you've

now become an

entrepreneur um and you're running both

uh company in China and a company in the

United States uh why did you do

that well because this this time is for

real right imagine you know this was my

dream practice before well this was my

dream in when I went to college that AI

was nothing no one knew what it was but

I felt this was the thing I needed to do

and then we went through multiple

winters of AI where uh there's

disillusionment and I had to do other

things and about uh you know seven eight

years ago we saw with um you know deep

learning it was became clear it would

create a lot of value so but at the time

I didn't really see it becoming AGI so I

was an investor we actually created 12

AI unicorns in sinovation Ventures but

this time with generative AI uh the

speed at which is growing um is just

phenomenal you could help yourself you

yeah I felt if I just invested I'd be

missing out I I would be in the back

seat I want to be in the in the driver's

seat by the way everybody I hope you

feel the same right I I'm very clear

about saying there are two kinds of

companies at the end of this decade

companies that are fully utilizing Ai

and everyone else is out of business and

I I fundamentally believe that is it is

true um you've written a number of books

uh AI superpowers I commend to all of

you so since that was

published what's the biggest changes in

the global AI race and it is an AI arms

race going on well it isn't isn't

because the companies in China are

largely competing against each other for

the China market and they're generally

not I don't mean Nation to National but

it is between companies around the world

yeah so you mean Chinese companies what

are their characteristics so in my book

a superpowers I described uh the

American companies are generally

speaking more breakthrough Innovative

they come up with new things um and then

the Chinese companies are better at

engineering execution attention to

detail doing the grunt work user

interfaces user interfaces building apps

so um in the case of mobile or deep

learning we saw that Americans invented

pretty much everything but China created

a lot of value arguably more uh given

technologies that were largely invented

in the US so now we're in this

generative AI again invented by

Americans and we're in a in a in a

unique position where where the

technology is disrupting itself very

quickly in the US and elsewhere um so it

arguably is still the age of Discovery

and US ought to win but then the Chinese

companies are able to watch the

Innovations make some themselves and

then do better engineering and deliver

Solutions so the company I'm building

01 is doing exactly that we don't claim

to have invented everything or even most

things we learned a lot from the Giants

and silicon valy open Ai and others but

we think we build a more solidly faster

execute better so an example was I

talked about how 01 now has is the third

best model modeling company in the world

ranking number six in models measured by

lmis and UC Berkeley but the most

amazing thing I think the thing that

shocks my friends in the solic valley is

not just our performance but that we

train the model with $3 million and GPT

4 was trained by 80 to 100 million and

um GPT 5 is rumored to be trained by

about a billion dollars so it is not the

case we believe in scaling law but when

you do excellent detailed engineering it

is not the case you have to spend a

billion dollars to train a great so this

is really important for the audience

here because there's a lot of parts of

the world that don't have access to you

know 100,000 H you know h100 clusters

right and the question is oh my God can

I really build a business or a product

in pick your favorite country with a

small number of gpus yeah and I think

the constraint on gpus forced you to

innovate right can you speak to that I

think it's really important we talked

about that on our last podcast together

yeah I think you know as a company in

China first we have limited access to

gpus due to the US regulations and

secondly the Chinese companies are not

valued what American companies are I

mean we're F we're we're valued at a

fraction of the equivalent American

company so when when we have less money

and difficulty to get gpus I truly

believe that necessity is the mother of

in Innovation so when we only have 2,000

gpus well the team has to figure out how

to use it I as the CEO have to figure

out how to prioritize it and then not

only do we have to make training fast we

have make inference fast so our

inference is designed by figuring out

the bottlenecks in the entire process by

trying to turn a computational problem

to a memory problem by building a

multi-layer cache by building a specific

inference engine and so on but the

bottom line is our inference cost is 10

cents per million tokens and that's uh

130th of what the typical comparable

model charges and where's it going

where's the 10 cents going yeah it's

well the 10 cents would lead to building

apps for much lower cost so if you

wanted to build a u.com or perplexity or

some other app you can either pay open

AI $440 per million tokens or if you

have our model it costs you just 10

cents and if you buy our API it just

costs you 14 cents we're very

transparent with our pricing yes Richard

there's there's a really interesting uh

Paradox called jevans Paradox from the

previous industrial re ution a lot of

smart people back then were working on

making more efficient steam engines and

using that use less coal they thought oh

if we make the steam engines more

efficient we're going to need less coal

but instead we needed more steam engines

everywhere and I think that's exactly

what's going to happen we're currently

in the jeevan's Paradox of intelligence

we're just going to use intelligence in

many more places everyone is going to

have their own assistant their own

medical team that like understands

everything about them instead of being

restricted by Intelligence being very

very expensive yeah I totally agree I

want to clarify I'm not saying there's a

fixed workload we're making it cheaper

I'm saying we're enabling a workload

much much larger corre I want to ask one

closing question to all of you we have

people here who have daughters and sons

or nephews or brothers and

sisters what's your advice to someone

who is 20 years old listening to this um

or through this what's your advice to

someone at the beginning of their uh

sort of academic and professional career

given what you know is going on in AI

right now

Prem I think it's don't waste your time

learning how to code because I think the

new language is going to be I think the

new code language is going to be English

and I think that uh absolutely learn as

fast as you humanly possibly can on all

AI in all Ai modalities and I think if

you and then once you find your passion

I think you're going to then find a very

narrow AI to empower you to do what

you're what you're really set out to do

thank you PR Richard I I will disagree I

think you should still learn how to

program uh I think that is how you get

to really understand how this technology

works at the foundational level uh and

how it becomes less magic and more

something that you can yourself modify

and construct with but you need to

combine computer science and programming

with another passion that you can

actually apply all of that intelligence

to and ideally the younger you are the

more you learn the foundations Ma ma

physics The Sciences I think I'm going

to cut you off because I'm being yanked

I want I want to have kyu's final word

here okay I actually agree and this

agree with both of you I think people

should follow their hearts right if you

dream of becoming a fantastic programmer

and you can do it you should do what

Richard says if you think programming is

the way that make the most money no then

you should follow what prime says ladies

and Gentlemen please give it up to these

three amazing

CEOs thank you thank you thank you thank

you

Click on any text or timestamp to jump to that moment in the video

Most transcripts ready in under 5 seconds

One-Click Copy125+ LanguagesSearch ContentJump to Timestamps

Paste YouTube URL

Enter any YouTube video link to get the full transcript

Most transcripts ready in under 5 seconds

Get Our Chrome Extension

Get transcripts instantly without leaving YouTube. Install our Chrome extension for one-click access to any video's transcript directly on the watch page.

Add to Chrome — Free

Works with YouTube, Coursera, Udemy and more educational platforms

Get Instant Transcripts: Just Edit the Domain in Your Address Bar!

YouTube

←

→

↻

https://www.youtube.com/watch?v=UF8uR6Z6KLc

YoutubeToText

←

→

↻

https://youtubetotext.net/watch?v=UF8uR6Z6KLc

YouTube TranscriptPreparing your results…