Hang tight while we fetch the video data and transcripts. This only takes a moment.
Connecting to YouTube player…
Fetching transcript data…
We’ll display the transcript, summary, and all view options as soon as everything loads.
Next steps
Loading transcript tools…
The Minds of Modern AI: Jensen Huang, Yann LeCun, Fei-Fei Li & the AI Vision of the Future | FT Live | FT Live | YouTubeToText
YouTube Transcript: The Minds of Modern AI: Jensen Huang, Yann LeCun, Fei-Fei Li & the AI Vision of the Future | FT Live
Skip watching entire videos - get the full transcript, search for keywords, and copy with one click.
Share:
Video Transcript
Video Summary
Summary
Core Theme
This discussion brings together winners of the Queen Elizabeth Prize for Engineering to reflect on their pivotal "aha" moments in the development of Artificial Intelligence, tracing the evolution of AI from foundational principles to its current transformative impact and future potential.
Mind Map
Click to expand
Click to explore the full interactive mind map • Zoom, pan, and navigate
Hello everybody. Good afternoon, good
morning. And I am delighted to be the
one chosen to introduce to you this
really distinguished group of people
that we've got here sitting around the
table. Six I think of the most
brilliant, most consequential people on
the planet today. And I don't think
that's an overstatement.
So these are the winners of the 2025
Queen Elizabeth Prize for Engineering.
and it honors the laureates here that we
see today for their singular impact on
today's artificial intelligence technology.
technology.
Given your pioneering achievements in
advanced machine learning and AI and how
the innovations that you've helped build
are shaping our lives today, I think
it's clear to everyone why this is a
really rare and exciting opportunity to
have you together around the table. For
me personally, I'm I'm really excited to
hear you reflect on this present moment
that we're in, the one that everybody's
trying to get ahead of and understand
and your journey, the journey that
brought you here today. Um, but also to
understand how your work and you as
individuals have influenced and impacted
one another and the companies and the
technologies that you've built. And
finally, I'd love to hear from you to
look ahead um and to help us all see a
bit more clearly what is to come, which
you are in the best position to do. So,
I'm so um pleased to have you all with
us today and and looking forward to this
to the discussion. So, I'm going to
start going from the zooming out to the
very personal. I want to hear from each
of you your a personal kind of aha
moment in your career that you've had
that you think has sort of impacted the
work that you've done or was a turning
point for you that brought you on this
path to why you're sitting here today
whether it was kind of early in your
career in your research or or much more
recently what you know what was your
personal moment of awakening um that
that has impacted the technology do we
should we start here with you Yeshua
>> thank you yes uh with pleasure. I would
um go to two moments. One when I was a
grad student and I was looking for
something interesting to research on and
I read some of Jeff Hinton's early
papers and I thought wow this is so
exciting. Maybe there are a few simple
principles like the laws of physics that
could help us understand human
intelligence and help us build
intelligent machines. And the second
moment I want to talk about is two and a
half years ago after chat GPT came out
and I realized uhoh what are we doing?
Uh what will happen if we build machines
that understand language uh have goals
and we don't control those goals? What
happens if they are smarter than us? Uh
what happens if people abuse that power?
So that's why I decided to completely
shift my research agenda and my career
to try to do whatever I could about it.
>> That's that that's two kind of very you
know diverging things very interesting
build what tell us about your moment of
like kind of building the infrastructure
that's that's fueling what we have.
>> I'll give you two moments as well. So
the first was you know in in the late
90s I was at Stanford trying to figure
out how to overcome what was at the time
called the memory wall. fact that
accessing data from memory is far more
costly in energy and time than doing
arithmetic on it. And it sort of you
know struck me to organize computations
into these kernels connected by streams.
So you could do a lot of arithmetic
without having to do very much memory
access. That basically led the way to
what became called stream processing and
ultimately GPU computing. Um and we we
originally built that thinking we could
apply GPUs not just for graphics but to
general scientific computations. So the
second moment was I was having breakfast
with my colleague Andrew Ing at Stanford
and at the time he was working at Google
finding cats on the internet you using
16,000 CPUs in this technology called
neural networks
>> which fay had something to do with those
>> and uh um he uh he he basically
convinced me this is a great technology
so I with Brian Kenzo repeated the
experiment on 48 GPUs in Nvidia and when
I saw the results of that I was
absolutely convinced that this is what
Nvidia should be doing. we should be
building our GPUs to do deep learning
because this has, you know, huge
applications in all sorts of fields
beyond finding cats. And that was kind
of an aha moment to really start working
very hard on specializing the GPUs for
deep learning and and to make them more effective.
effective.
>> And when was that what year?
>> Um, the breakfast was in 2010 and I
think we repeated the experiment in 2011.
2011. >> Okay.
>> Okay. >> Yeah.
>> Yeah.
>> Jeff, tell us tell us about your work.
One very important moment was when I in
about 1984 I tried using back
propagation to learn the next word in a
sequence of words. So it was a tiny
language model and discovered it would
learn interesting features for the
meanings of words. So just giving it a
string of symbols it just by trying to
predict the next word in a string of
symbols it could learn how to convert
words into sets of features that
captured the meaning of the word and
have interactions between those features
predict the features of the next word.
>> So that was actually a tiny language
model from 1980 late 1984
um that I think of as as a precursor for
these big language models. The basic
principles were the same. It was just
tiny. We had 100 training examples. It
took 40 years to get us here though.
>> And it took 40 years to get here. And
the reason it took 40 years was we
didn't have the compute and we didn't
have the data and we didn't know that at
the time. We couldn't understand why we
weren't just solving everything with
back propagation.
>> Which takes us cleanly to to Jensen. We
didn't have the compute for 40 years and
here now you are building it. Tell tell
us about your moments that of real kind
of clarity.
Well, for my career, um, I was the, uh,
first generation of chip designers that
was able to use higher level representations
representations
and design tools to design chips.
and and uh that that discovery
um uh was helpful when I learned about a
new way of developing software
uh around the 2010 time frame
simultaneously from three different labs
uh what was going on in uh uh University
of Toronto researchers uh reached out
reached out to us at the same time that
uh researchers at the NYU reached out to
um as well as uh in Stanford reached out
to us at the same time and I I I saw the
early indications of what turned out to
have been deep learning around the same
time uh using uh a framework uh and a
structured design to uh create software
and that software turned out to have
been incredibly effective.
Uh and that second that second observation
observation
uh is seen again using frameworks rep
higher level representations
structured types of uh structures like
the deep learning networks. I uh was
able to develop software uh w was very
similar to designing chips for me and
the patterns were very similar and I
realized at that time maybe we could
develop software uh and capabilities
that that scale very nicely as we've
scaled uh chip design over the years and
so that was that was a quite a quite a
moment for me
>> and when do you think was the moment
when the chips really started to help
scale up today's sort of the the LLMs
that we have today because you you said
2010 that's still a 15 year.
>> Yeah. The the thing about about Nvidia's
architecture is is once you're able to
get something to run well on a GPU
because it became parallel, you could
get it to run well on multiple GPUs.
that same sensibility of scaling uh the
algorithm to run on many processors on
one GPU. This is the same logic and the
same reasoning that you could do it on
multiple GPUs and then now multiple
systems and in fact you know multiple
data centers and so that once we
realized we could do that effectively
then then the rest of it is about about
uh imagining how far you could
extrapolate this capability. you know,
how much data do we have? How large can
the networks be? How much dimensionality
can it capture? What kind of problems
can it solve? Uh the all of all of that
is is really engineering at that point.
You know, the the observation that that
uh the deep learn deep learning models
are so effective uh is is really quite
the the the spark. The rest of it is
really engineering extrapolation.
Fei, tell us about your your moment.
>> Yeah, I also have two moments to share.
So around 2006
and 2007, I was transitioning from a
graduate student to an a young assistant
professor and I was among the first
generation of machine learning graduate
students um reading papers from young
Yoshua uh Jeff and I was really obsessed
in trying to solve the problem of ob uh
visual recognition which is the ability
for machines to see meaning in objects
in everyday pictures and uh we were
struggling with this problem in machine
learning called generalizability
which is um after learning from certain
number of examples can we recognize
something a a new example new sample and
I've tried every single algorithm under
the sun from baset support vector
machines to neuronet network and the
missing piece that my student and I
realized is that data is missing that uh
uh you know if you look at the evolution
or development of uh intelligent animals
like humans we were inundated with data
in the early years of development but
our machines were starved with data. So
we um decided to do something crazy at
that time to create a internet scale
data set uh over the course of three
years called imageet that uh uh in uh
included 15 million images handcurated
um by by people around the world across
22,000 categories. So, so for me the aha
moment at that point is big data drives
machine learning
>> and it's now it's it's the limiting
factor the building block of all of the
you know algorithms that we're seeing with
with
>> yeah it's part of the scaling law of
today's AI and the second aha moment is
um 2018
I was the first chief scientist of uh AI
at Google cloud uh part of the the work
we do is serving all vertical industries
under the sun, right? From healthcare to
financial services, from entertainment
to uh manufacturing, from agriculture to energy.
energy.
And that was a few years after the the
what we call the image that Alex moment,
a couple of years after Alph Go, and I realized
realized
>> Alph Go being the algorithm that was
able to beat humans at playing the
Chinese board game Go. Uh yes and as the
chief scientist at Google I realized
this is a civilizational technology
that's going to impact every single
human individual as well as sector of
business and uh if humanity is going to
go enter an AI era what is the guiding
framework so that we not only innovate
but we also bring benevolence
to uh through this powerful technology.
technology to everybody and that's when
I returned to Stanford as a professor to
uh co co-found the human center AI
institute and and uh propose the human-
center AI framework so that we can keep
humanity and human values in the center
of this uh technology.
>> So developing but also looking at the
impact and what's next which is where
the rest of us come in.
>> Um Yan do you want to round us out here?
What's what's been your highlight?
>> Yeah, probably go back a long time. Um,
I realized when I was in undergrad, I
was fascinated by the question of AI and
intelligence more generally and
discovered that people in the 50s and
60s that worked on
training machines instead of programming
them. I was really fascinated by this
idea probably because I thought I was
either too stupid or too lazy to
actually build an intelligent machine
from scratch, right? So it's better to
let itself be um like train itself or
self-organized and that's the way you
know intelligence in in in life uh
builds itself. It's uh it's
selforganized. So I I thought this
concept was really fascinating and I
couldn't find anybody when I graduated
from engineering. I was doing chip
design by the way um wanted to go to
grad school. I couldn't find anybody who
was uh working on this but connected
with some people who kind of were
interested in this and discovered Jeff's
papers for example uh and uh he was the
person in the world I wanted to meet
most in 1983 when I started grad school
and we eventually met two years later um and
and
>> and today you're friends would you say?
>> Yes. Oh, we we we we had lunch together
in 1985 and we could finish each other's
sentences. Basically, he had uh
um I had a a paper written in French at
a conference where he was a keynote
speaker and and managed to actually kind
of decipher the the math. It was kind of
sort of like back propagation a little
bit to train multi-layer nets. It was
known from the 60s that the limitation
of machine learning was due to the fact
that we could not train machine with
multiple layers. So that was really my
obsession and it was his obsession too
and um and so I had a paper that kind of
proposed some some way of doing it and
he kind of managed to read the math. So
that's how we hooked up and
>> and that's what has set you on this path.
path.
>> Right. So and and then after that you
know once you can you can train complex
systems like this you ask yourself
questions. So how do I build them so
they do something useful like
recognizing images or things of that
type? And at at the time Jeff and I had
this debate when I was a postoc with him
in the late 80s. Um I I I thought um the
only machine learning paradigm that was
well formulated was supervised running.
You you show an image to the machine and
you tell it what the answer is, right?
And he said no no no like the only way
we're going to get to make progress is
through unsupervised running. And I was
kind of dismissing this at the time. Um,
and what happened in you know the mid
2000 when he Yosha and I sort of start
getting together and restart the
interest of the of the community in deep
learning. We actually kind of uh made
our bet on unsupervised learning or self
reinforcement loop. Right?
>> This is not reinforcement. So this is
basically discovering the structure in
data without training the machine to do
any particular task which is by the way
the way LLMs are trained. So an LLM is
trained to predict the next word but
it's not really a task. It's just a way
for the system to learn a good kind of
uh representation or capture the
>> is there no reward system there that
sorry to get geeky but is there no
nothing to say this is correct and
therefore keep doing it because
>> well this is correct if you predict the
next word correctly right
>> from the rewards in reinforcement
learning where you say that's good
>> yeah okay
>> um and so in fact uh I'm going to blame
it on you uh it turns out produced this
big data set called imageet and uh which
is which was labeled and so we could use
supervised learning to train the systems
on and that turned out to work actually
much better than we expected and so we
temporarily abandoned the whole program
of working on self-supervised
unsupervised learning because supervised
learning was working so well we figured
out a few tricks
>> Joshua stuck with it
>> I said I didn't
>> no you didn't I didn't either but uh but
it it kind of
refocus the entire industry and and the
research community if you want on sort
of deep deep learning supervised
learning etc. Mhm.
>> And it it it took another few years
maybe around 201617
to uh tell people like this is not going
to tell take us where we want. We need
to do self-s supervised learning now and
that's what LLM really are the best
example of this. >> Okay.
>> Okay.
>> But uh what we're working on now is
applying this to other types of data
like like video sensor data which LLM
are really not very good at at all. Um
and that's a new challenge for the next
few years. So that brings us actually to
the present moment and I think you know
you'll all have seen this crest of the
interest from people who had no idea
what AI was before who had no interest
in it and now everybody's flocking to
this and this has become more than a
technical innovation right that's a huge
business boom it's become a geopolitical
strategy issue um and you know
everybody's trying to get their hands
around what this is so or their heads
around it Jensen I'll come to you here
first to I want you all
to reflect on this moment now here
Nvidia in particular has it's basically
in the news every day hour week you know
and you have become the most valuable
company in the world so there's
something there that people want
>> you'll be to hear that
>> yeah you know tell us about do are you
worried that we are getting to the point
where people don't quite understand and
we're all getting ahead of ourselves and
there's going to be a reckoning that
there's a bubble that's going to burst
and then it will write itself self and
if not what is the kind of biggest
misconception about demand coming from
AI that is different to say the dotcom
era or that people don't understand you
know if if that's not the case
>> uh during the dotcom era during the the
bubble the vast majority of the fiber
deployed were dark
meaning the industry deployed a lot more
fiber than it needed Mhm.
>> Today almost every GPU you could find is
lit up and used.
And so uh the reason why I think it's
important to take a take a step back and
understand and understand what AI is,
you know, for a lot of people AI is Chad
GBT and it's image generation and and it
that's all true. That's one of the
applications of it. Um, and AI has
advanced tremendously in the last
several years. The ability to not just
memorize and generalize, but to reason
and effectively think and ground itself
through research. It's able to produce
answers and do things that are much more
valuable now. It's much more effective.
and the number of companies that are
able to build businesses that are that
are helpful to other businesses. For
example, a software programming company,
an AI software company that that we use
called Cursor, uh they're very
profitable and we use their software
tremendously and it's incredibly useful.
uh or a bridged or open evidence who are
uh serving the healthcare industry doing
very very well producing really good
results and and so so the AI capability
has grown so much and as a result we
were seeing these two exponentials that
are happening at the same time on the
one hand the amount of computation
necessary to produce an answer has grown
tremendously on the other hand the
amount of usage of these AI models are
growing also exponentially these two exponentials
exponentials
are causing a lot of demand on compute.
Now when you take a step back, you ask
yourself fundamentally what's different
between AI today and the software
industry of the past. Well, software in
the past was pre-ompiled
and the amount of computation necessary
for the software is not very high.
>> But in order for AI to be effective, it
has to be contextually aware. It has to
it can only produce the intelligence at
the moment. You can't produce it in
advance and retrieve it. That's you know
that's called content. AI intelligence
has to be produced and generated in real
time. And so as a result we now have an
industry where the computation necessary
to produce something that's really
valuable in high demand is quite
substantial. We have created an an
industry that requires factories. That's
why I I remind ourselves that AI needs
factories to produce these tokens to
produce the intelligence and this is
this is a a once you know once in a it's
never happened before where the computer
is actually part of a factory and and so
we need hundreds of billions of dollars
of these factories in order to serve the
trillions of dollars of industries that
sits on top of intelligence. You know,
you go come back and take a look at at
software in the past. Software in the
past is they're software tools. They're
used by people. For the first time, AI
is intelligence that augments people.
And so, it addresses labor. It addresses
work. It does work.
>> So, you're saying no, this is not a bubble.
bubble.
>> I think this we're we're well in the
beginning of the buildout of
intelligence. And and the fact of the
matter is most people still don't use AI
today. And someday in the near future,
almost everything we do, you know, every
moment of the day, you're going to be
engaging AI somehow. And so between
where we are today where the usage is
quite low to where we will be someday
where the usage is basically continuous,
that buildout is is you know what
>> and if even if the LLM runway runs out,
you think GPUs and the infrastructure
you're building can still be of use in a
different paradigm and then I want to
open up to others to talk. LLM is a is a
piece of the AI technology. You know,
AIS are systems of models, not just LLMs
and LLM are big part of it, but there
are systems of models and and uh the the
technology necessary for for AI to be
much more productive from where where it
is today irrespective of what we call
it. Um we still have a lot of technology
to develop yet.
>> Can who wants to jump in on on this?
>> Um I don't think
>> especially if you disagree. I don't
think we should call them LLMs anymore.
Um they're not language models anymore.
They they >> right
>> right
>> start as language models at least that's
the pre-training but but more recently
there's been a lot of advances in making
them agents. In other words, uh go
through a sequence of steps in order to
achieve something interactively with an
environment with people right now
through a dialogue but more and more
with a computing infrastructure.
And the technology is changing. It's not
at all the same thing as what it was
three years ago. I don't think we can
predict where the technology will be in
two years, 5 years, 10 years. U but we
can see the trend. So one of the things
I'm doing is trying to uh bring together
a group of international experts to keep
track of what's happening with AI where
it is going um what are the risks how
are they being mitigated and and and and
the trends are very clear across so many
benchmarks now you know because we've
had so much success in improving the technology
technology
uh in the past it doesn't mean that's
going to be the same in the future. So
then then there would be financial uh
consequences uh if the expectations are
not met but in the long run I completely
agree. Um
>> but currently what about the rest of
you? Do you think that the valuations
are justified in terms of what you know
about the technology the applications?
>> So I think there are three trends that
sort of explain what's going on. The
first is the models are getting more
efficient. If you look just at attention
for example, going from straight
attention to GQA to MLA, you get the
same or better results with far less
computation. And so that then drives
demand in ways where things that may
have been too expensive before become
inexpensive of now. You can do more with
AI. At the same time, the models are
getting better and you know, maybe
they'll continue to get better with
transformers or maybe a new architecture
will come along, but we will we won't go
backwards. We're going to continue to
have better models that also
>> they still need GPUs even if
>> absolutely transformer based
>> um in fact it makes it makes them much
more valuable compared to more
specialized things because they're more
flexible and they can evolve with the
models better but then the final thing
is I think we've just begun to scratch
the surface on applications so almost
every aspect of human life can be made
better by having AI you know assist
somebody in their profession help them
in their daily lives and you know I
think we've you know started to reach
maybe 1% of the ultimate demand for
this. So as that expands, you know, the,
you know, number of uses of this are
going to go up. So I don't think there's
any bubble here. I think we're, like
Jensen said, we're riding a multiple
exponential and we're at the very
beginning of it and it's going to just
keep going.
>> And in some ways, Nvidia is in to that
because even if this paradigm changes
and there's other types of AI and other
architectures, you're still going to
need the the atoms underneath. So that
makes sense for you. Did you want to
jump in Fay? Uh yeah, I do think that um
of course from a market point of view,
it will have its own um dynamics and
sometimes it does adjust itself, but if
you look at the long-term trend, let's
not forget AI by and large is still a
very young field, right? We walk into
this room and on the wall there were
equations of physics. Physics has been a
more than 400 year old uh discipline.
Even if we look at uh modern physics and
AI is less than 70 years old if we go
back to Alan Turing you that's about 75
years so there is a lot more new
frontiers that is to come uh you know
Jensen and Yoshua talk about LLMs and
agents those are more languagebased but
even if you do uh self uh introspection
of human intelligence there's more
intelligent capabilities is beyond
language. I have been working on spatial
intelligence which is really the
combination or the lynchpin between
perception and action where um where uh
you know humans and animals have
incredible ability to perceive reason
interact with and uh and create uh
worlds that goes far beyond language.
And even today's most powerful
language-based uh or LLM based models uh
fail at rudimentary spatial intelligence
uh tests. So from that point of view as
a as a discipline as a science there's
far more frontiers to conquer and to uh
open up and that brings the applications
uh you know opens up more applications.
>> Yeah. and you work at a company and so
you have the kind of dual perspective of
being a researcher and working in a
commercial space. Do you agree? Do you
do you believe that this is all
justified and you can see where this is
all coming from or do you think we're
reaching an end here and we need to find
a new path?
>> So I think there are several point of
views for which uh we're not in a bubble
and at least one point of view
suggesting that we we are in a bubble
but there is but it's a different thing.
So we're not in a bubble in the sense
that um there are a lot of applications
to develop based on LLMs. LLM is the
current dominant paradigm and there's a
lot to uh milk there. This is you know
what Bill was was saying to kind of help
people in the daily lives with current
technology that technology needs to be
pushed and that justifies all the
investment that is done on the software
side and also on the infrastructure
side. uh once we have you know smart
wearable devices um in everybody's hands
assisting them in their daily lives the
amount of computation that would be
required as as Jensen was saying to uh
to serve all those all those people is
going to be enormous so in that sense
the investment is not is not wasted but
there is a sense in which there is a
bubble and it's the idea somehow that
the current paradigm of LLM would be
pushed to the point of having human
level intelligence which I personally
don't believe in and you don't either And
And
we we need kind of a few breakthroughs
before we get to machines that really
have the kind of intelligence we observe
not just in humans but also animals. We
don't have robots that are nearly as
smart as a cat, right? Um and so we're
missing something big still. Which is
why AI progress is not just a question
of more infrastructure, more data, uh
more investment and more development of
the current paradigm. It's actually a
scientific question of how do we make
progress towards the next generation of AI
AI
>> which is why all of you are here right
because you actually sparked the entire
thing off and I feel like you know we're
moving much towards the engineering
application side but what you're saying
is we need to come back to what brought
you here originally um on that question
of human level intelligence we don't
have long left so I just want to do a
quick fire I'm curious can each of you
say how long you think it will take
until we do reach that point where you
believe we're you know equivalent
machine intelligence to a human or even
a clever animal like an octopus or
whatever. How far away are we just just
the years?
>> It's not going to be an event. >> Okay.
>> Okay.
>> Okay. Because the capabilities are going
to expand progressively in various domains.
domains.
>> Over what time periods?
>> Over, you know, maybe we'll make some
significant progress over the next five
to 10 years to come up with a new paradigm.
paradigm.
>> F and then maybe, you know, progress
will come. But it'll it'll take longer
than we think. Okay. Parts of machines
will supersede human intelligence and
part of the machine intelligence will
never be similar um or the same as human
intelligence. They are they are they're
built for different purposes and they will
will
>> when do we get to superseding?
>> Part of it is already here. How many of
us can recognize 22,000 objects in the
world? So part of
>> do you not think an adult human can
recognize 22,000 objects?
>> Um the kind of granularity and fidelity.
No. How many adult humans can translate
a 100 languages?
>> That's harder. Yeah.
>> So yeah.
>> So I think we should be nuanced and
grounded in scientific facts that uh
just like airplanes fly but they don't
fly like birds. and u machine-based
intelligence will do a lot of powerful
things but there is a profound
um place for human intelligence to to
always be critical in our human society.
Jensen, do you have
>> we have enough general intelligence to
uh translate the technology to an
enormous amount of uh society useful applications
applications
uh in the next coming years and with
respect to >> Yeah.
>> Yeah.
>> Yeah. Yeah. We're doing it today.
>> Yeah. And so I think I think uh one
we're already there
>> and two the the other part of the answer
is it doesn't matter
>> because at this point it's a bit of an
academic question. We're going to apply
the technology to and the technology is
going to keep on getting better and
we're going to apply the technology to
solve a lot of very important things
from this point forward. And so okay
>> I I think the answer is it doesn't matter
matter
>> and and it's now as well.
>> Yeah you decide. Right. If you refine
the question a bit to say how long
before if you have a debate with this
machine it'll always win.
>> I think that's definitely coming within
20 years. We're not there yet but I
think fairly definitely within 20 years
we'll have that. So if you define that as
as
>> AGI it'll always win a debate with you.
>> We're going to get we're going to get
there in less than 20 years probably.
>> Okay. Bill, do you have
>> Yeah. Well, I'm sort of with Jensen that
it's the wrong question, right? Because
our goal is not to build AI to replace
humans or to be better than humans.
>> But it's a scientific question. It's not
that we'll replace humans. The question
is could we as as a society build something?
something?
>> But our goal is to build AI to augment
humans. And so what we want to do is
complement what what humans are good at.
Humans can't recognize 22,000 categories
or most of us can't solve these math
olympiad problems. Um so we build AI to
do that. So humans can do what is
uniquely human, which is be creative and
be empathetic and and understand how to
interact with other people in our world.
And I think that it's not clear to me
that AI will ever do that, but AI can be
huge assistance to humans.
>> So I'll beg to differ on this. Uh I
don't see any reason why at some point
we wouldn't be able to build machines
that can do pretty much everything we
can do. Um, of course, for now on the
spatial and you know, robotic side, it's
lagging, but there's no like uh
conceptual reason why we couldn't. So on
on the timeline, I think there's a lot
of uncertainty and that we should plan
accordingly. Um, but there is some data
that I find interesting where we see um
the capability of AI to plan over
different horizons to grow exponentially
fast in the last six years. And if we
continue that that trend, it would place
roughly the level that an employee has
in their job to uh AI being able to do
it within about five years. Now this is
only one category of engineering tasks
and there are many other things that
matter. For example, uh one thing that
could change the game that is that many
companies are aiming to just to focus on
the ability of AI to do AI research. In
other words, to do engineering, to do
computer science, and to design the next
generation of AI, including maybe
improving robotics and spatial
understanding. So, I'm not saying it
will happen, but the area of ability of
AI to do better and better programming
and understanding of algorithms that is
going very very fast and that could
unlock many other things. We don't know
and we should we should be really
agnostic and not make big claims because
there's a lot of possible futures there.
M so so our consensus is in some ways we
think that future is here today but
there's never going to be one moment and
the job of you all here today has helped
to guide us along this route um until we
get to a point where we're working
alongside these systems. Very excited
personally to see where we're going to
go with this. If we do this again in a
year it'll be a different world. But
thank you so much for joining us for
sharing your stories and for talking us
through this this huge kind of
revolutionary moment. Thank you. Thank you.
you.
Click on any text or timestamp to jump to that moment in the video
Share:
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
Transcript Extraction Form
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.
