2 Ex-AI CEOs Debate the Future of AI w/ Emad Mostaque & Nat Friedman | EP #98
Summary
TLDR在这段视频脚本中,讨论了人工智能(AI)的快速发展及其对社会的深远影响。提到了5G投资和AI的比较,强调AI在多个领域的能力已超越人类,并预测了AI的未来发展。讨论了AI模型的内部工作原理,尽管对其工作原理尚未完全理解,但AI的“成长”方式类似于人类大脑的思考过程。强调了开放模型与专有模型的不同,以及它们在促进创新和适应性方面的重要性。还提到了AI在医疗、教育和科学领域的应用潜力,以及AI如何帮助解决复杂问题,如疾病诊断和药物设计。最后,强调了AI作为人类智能的延伸,将如何增强我们的能力和创造力,以及如何通过AI实现个人和组织的目标。
Takeaways
- 🚀 过去一年AI领域的快速发展,特别是大型语言模型的应用,已经开始对各行各业产生深远影响。
- 🌐 5G投资巨大,但AI的影响力可能更大,因为它关乎智能和决策能力的提升。
- 🤖 尽管AI模型的能力日益增强,我们对其内部工作机制的理解仍然有限,需要进一步的研究和解释性领域的探索。
- 📈 AI技术的快速发展带来了对标准化的需求,以及对数据透明度和模型可解释性的重视。
- 💡 AI的快速发展为医疗、教育和科学研究等领域带来了新的机遇,有望解决以往难以攻克的难题。
- 🌟 AI的“开放模型”与“专有模型”之间的比较,强调了开放模型在创新和适应性方面的优势。
- 🔍 AI在图像和视频生成方面的进步,展示了从静态图像到动态视频的转变,以及实时编辑和控制的可能性。
- 📊 AI的商业化进程正在加速,从初创公司到大型企业都在积极探索AI的商业应用。
- ⚙️ AI的自主性和代理性是未来发展的关键,能够执行复杂任务的AI代理将极大提高效率和创新能力。
- 🌱 AI作为人类集体智慧的扩展,有潜力解决全球性问题,并推动社会向更加智能和自动化的方向发展。
- ⛓ 随着AI技术的进步,需要考虑其对就业市场的影响,以及如何培养未来所需的新技能和职业。
Q & A
过去一年中,AI领域有哪些重大的发展?
-过去一年中,AI领域的发展非常迅速,特别是大型语言模型(LLM)的商业化。例如,GitHub Copilot的发布,以及随后的Chat GPT、Stable Diffusion和GPT-4的推出,都展示了AI在理解和生成文本、图像等方面的新水平能力。
5G和AI相比,哪个对现代社会的影响更大?
-虽然5G技术非常重要,但AI对现代社会的影响更大。AI不仅改变了数据处理和分析的方式,还推动了自动化、个性化服务和智能决策的发展,其影响力远超5G。
AI模型的内部工作原理是什么,我们真的了解它们吗?
-AI模型,特别是大型神经网络,是通过大量数据训练而非直接设计得到的。尽管我们可以测量模型的每一个权重和连接,但对于模型内部的具体工作机制,我们的理解仍然有限。这类似于我们对大脑工作机制的理解,尽管可以观察到神经元的活动,但对其深层次的认知过程了解不足。
AI在医疗领域的应用有哪些潜力?
-AI在医疗领域的应用潜力巨大,包括提高疾病诊断的准确性、个性化治疗计划、药物设计、以及通过分析大量医学文献来发现新的治疗方法。AI还可以帮助患者和医生更好地理解疾病,提供定制化的健康建议。
AI如何帮助解决科学问题,特别是在生物学和化学领域?
-AI可以通过分析和学习大量的科学数据,帮助科学家发现新的科学规律和原理。在生物学领域,AI可以帮助设计和合成蛋白质,加速药物开发过程。在化学领域,AI可以预测化学反应的结果,帮助设计新材料。
AI技术的发展速度有多快,我们如何适应这种快速变化?
-AI技术的发展速度非常快,每年都有新的突破和应用出现。为了适应这种快速变化,个人和组织需要不断学习和更新知识,同时积极寻找将AI技术应用到实际问题中的方法。
AI技术的进步对社会和经济有哪些潜在的影响?
-AI技术的进步可能会极大地提高生产效率,改变劳动力市场,创造新的就业机会,并可能导致某些职业的消失。同时,AI也可能加剧社会不平等,因此需要政策和教育的配合来确保技术进步惠及社会各个阶层。
AI在教育领域的应用有哪些可能性?
-AI可以在教育领域提供个性化学习计划,辅助学生理解复杂概念,自动评估学生作业,以及提供虚拟助教来解答学生问题。此外,AI还可以帮助教师更好地理解学生的学习进度和需求,从而提高教学质量。
AI的可解释性是什么,为什么它很重要?
-AI的可解释性指的是理解AI模型的决策过程和内部机制的能力。这对于建立对AI系统的信任、确保AI的公正性和透明度、以及优化和改进AI模型都非常重要。
如何平衡使用开放模型和专有模型的优缺点?
-开放模型允许用户访问和修改底层代码,适合需要定制化和隐私保护的应用场景。专有模型则提供服务,但用户无法看到底层代码。两者的平衡取决于具体的应用需求、隐私和安全性要求,以及对定制化程度的需求。
AI在艺术创作中的应用有哪些?
-AI可以在艺术创作中提供灵感,生成新颖的设计图案,辅助音乐作曲,甚至创作视觉艺术作品。AI还可以帮助艺术家探索新的创作风格和技巧,但AI艺术作品的创意和表达仍然需要人类的参与和指导。
Outlines
🚀 5G与AI的快速发展及其影响力
讨论了5G投资和AI的快速发展,强调AI的潜力和影响力远超5G。提到了AI模型的内部工作原理不完全透明,尽管我们可以测量一切,但仍不完全理解其内部机制。强调了AI比人类更有能力,并且这种能力将不断增强。讨论了AI在商业化和产品开发中的应用,以及需要围绕AI制定标准和法规。
🌟 开放与封闭AI模型的对比及未来
解释了开放模型与封闭(专有)模型的区别,开放模型允许用户自行调整和使用,而封闭模型则作为服务提供。讨论了开放模型如何促进创新,并且已经有数百万次的下载。同时指出,尽管专有模型在技术上可能始终领先,但开放模型允许用户针对自己的数据进行优化,因此两者都需要存在。
🎨 AI在图像和视频生成中的进步
讨论了AI在图像生成方面的巨大进步,包括速度的提升和对生成图像的控制能力。提到了AI模型如何结合Transformer架构和扩散模型来生成图像,并且展望了未来视频游戏和视频内容的实时生成能力。
💰 AI的商业化和资金流动
讨论了AI在商业化方面的进展,包括收入和盈利能力。提到了Fountain Life公司,它提供先进的诊断服务,使用AI和医生团队来发现疾病。强调了AI在医疗保健领域的潜力,以及它如何帮助人们延长健康寿命。
🤖 AI作为企业和个人的合作伙伴
探讨了AI在未来企业中的角色,预测AI将如何成为企业运营的核心部分,以及AI原生公司的发展。讨论了AI在特定任务中的超人能力,以及如何将AI集成到业务流程中,提高效率和创新。
🌐 AI的全球影响和治理
讨论了AI对全球经济的潜在影响,以及如何通过AI创造财富和税收基础。提到了一些地区如何通过制定新法规来吸引AI公司。探讨了AI在长文本处理和上下文理解方面的能力,以及这些能力如何帮助创建业务和内容。
🚀 AI的未来发展和投资
讨论了AI的未来发展,特别是其在教育和健康领域的应用。强调了AI作为下一代人类操作系统的潜力,以及它如何增强我们的能力。提到了AI投资的增长,以及它可能对全球GDP的影响。
🌟 AI在医学和科学发现中的潜力
讨论了AI在医学诊断和科学研究中的潜力,以及它如何帮助发现新的治疗方法和科学原理。强调了数据集透明度和模型可解释性的重要性,以及AI如何帮助我们理解和应用现有的科学知识。
📚 AI对教育和知识获取的影响
讨论了AI如何改变我们获取和应用知识的方式,特别是在教育和科学研究领域。强调了AI在提供个性化指导和连接患者信息方面的潜力,以及它如何帮助我们解决健康和科学问题。
Mindmap
Keywords
💡5G
💡人工智能(AI)
💡机器学习模型
💡标准化
💡投资
💡深度学习
💡商业化
💡GitHub Copilot
💡开放模型与专有模型
💡模型的可解释性
💡AI的未来发展
Highlights
在过去一年中,5G投资达到万亿美元,但AI的影响力被认为更大。
AI模型的内部工作原理尚不完全清楚,尽管可以测量一切,但仍缺乏机械性理解。
AI的能力已超越人类,预计未来会变得更好,但需要围绕AI制定标准并扩大其应用。
过去一年中,AI领域发生了巨大变化,从Chat GPT到Stable Diffusion和GPT 4的发布,展示了AI能力的飞跃。
AI的商业化进程正在加速,例如GitHub Copilot的推出,预示着大型语言模型商业产品的浪潮。
Chat GPT的收入在一年内从几乎为零增长到20亿美元,显示出AI技术的商业潜力。
Google的Gemini模型展示了AI在处理大量数据和复杂任务方面的能力,远超人类。
图像生成技术的进步使得在几毫秒内生成图像成为可能,预示着实时视频生成的未来。
开放模型与专有模型的对比,开放模型允许用户根据自己的数据进行调整和优化。
Hugging Face平台的模型下载量达到3.3亿次,显示了AI技术的广泛采用。
AI的未来发展将更多关注于如何利用现有模型,而不仅仅是模型本身的进步。
资本正在迅速流入AI领域,预示着未来几年可能会有更大规模的投资和增长。
AI在医学领域的应用,如提高诊断准确性和患者护理,展现了AI在改善人类生活方面的潜力。
AI的长期愿景是成为人类集体智能的扩展,解决我们面临的所有问题,而不是被个别实体控制。
AI的快速发展可能导致社会结构和工作性质的巨大变化,需要思考新的就业形式和人类的角色。
AI在科学发现方面的潜力,包括新药物设计和未知生物学领域的探索,预示着科学领域的未来突破。
AI作为新大陆的发现,为人类提供了巨大的机会,建议人们积极拥抱AI并探索其可能性。
Transcripts
what I think happened in the last year
really is the starting gun was
fired we spent a trillion dollars on 5G
is AI more impactful than 5G of course
it is do we actually know what's going
on inside the models we can measure
everything and still we don't really
quite understand mechanically what's
happening inside them they're not
engineered and designed they're kind of
grown AI is clearly more capable than
humans now we know this is going to get
better
when does it slow down right now it's a
few companies doing this but we need
standards around it and we need the
expansion and again every country will
invest in this every company will invest
in
this we're here to talk about what
happened I almost to say WTF just
happened in the past year because a lot
has happened imod you were on stage with
me a year ago and things were amazing
then but a lot has happened so I want to
split this into a couple of Parts like
what just happened the last year and
then I want to talk about how how far
forward can you see what do you expect
is going to happen uh you know I'd love
to talk about the companies the models
the dramas what did Ilia
see I think that's going to become a
meme um Nat do you want to kick us off
what what what's the big things that
happened in the last calendar year in
your mind um well yeah it's it's been
kind of an amazing year I I do think for
people who just started paying attention
to the sort of new Ai and deep learning
revolution in November of 22 when chat
GPT came out uh things probably seemed
very fast because we had um stable
diffusion come out over the summer
previously in 22 we had chat GPT in
November and then just a few months
later GPT 4 came out sort of
demonstrated a new level of capabilities
that you know were shockingly improved
over what was there previously and so I
do think there's a set of people who
sort of um extrapolated from those three
data points in terms of the progress
that was going to happen from there and
it's incredible actually how quickly
people can adapt to these new things and
there was a point at which kind of chat
gbt blew everyone's minds and then a few
months later uh it was sort of just
something we accepted as part of the
world as part of reality and there and
we can very quickly enter this kind of
slump where's the new model gpt3 is four
is boring now when do we get GPT 5 come
on Sam put it out already what are you
waiting for you know this kind of thing
so I think we had a little bit of that
this year and then uh over the course of
the year though what we started to see
and and um I'd been waiting this for a
while for this for a while but we
started to see real adoption taking off
and so um you know back in 2020 gpt3 had
come out in 21 when I was running GitHub
we put out GitHub
co-pilot and uh was I guess one of the
first llm commercial products and I
thought that immediately after that
there would be this rapid wave of
commercialization of large language
models because developers would have
seen how capable they were what they
could do and start building products
with them but it really took this kind
of chat gbt moment before entrepreneurs
developers and product people started to
do this and so what I think happened in
the last year really is the starting gun
was fired on the actual um exploitation
of these ra capabilities the building of
products the working of them into
companies and organizations and now what
that's what we see the adoptions been
incredible chat gbt is rumored to have
gone from basically Z to2 billion doar
in Revenue in just about a year um
that's amazing there's uh you know
organizations like stability that put
out models that have you know hundreds
of millions of users um you have you
have mid Journey you have now Google in
the game finally with Gemini I thought
it was exciting to see them not only
release Gemini but very quickly follow
up with Gemini 15 and so there's a way
in which Google is clearly internalized
the pace here and the need to iterate
and ship and so that I don't think a lot
of people realize how far ahead Google
was for a decade yeah right so Google
had really developed a lot of the the
early Tech here and said it's not ready
for release right they were being
responsible uh to a great degree and
then and then how do you not release
after chat p goes gets released they're
shipping pilt now they're going to ship
they're going to going to ship yes um
Iman um the open model uh story that you
told us last year has really blossomed
and in fact it's become sort of a an
ethos in the organization right it's a
Elon is like really twisting the knife
in with uh with Sam and then says but
you know grock's going to be open now
can you talk about what's happened in
last year in in open will open Win do
you think and I'm curious for both you
guys and and describe what open is
versus closed for the audience here yeah
so proprietary AI is you don't see the
code the weights anything and it's
provided as a service whereas open
models and code are ones that you can
adapt yourself you can take bring to
your own data and you own and that's
important CU these models are something
a bit different they're like extensions
of our mind and so the best analogy I've
said is that these are actually like
graduates so they can code they can
write they can say they sometimes go a
bit crazy when they try a bit too hard
you know we'll give them better
education and so open models are like
graduates that you hire and then
proprietary models are like Consultants
you bring in interesting and in the
early stages we all needed Consultants
now people saying well I want this for
my own data I want this for that and
open allows for Innovation to happen so
we've had now 330 million downloads of
our models on hugging face wow um so we
just and what is hugging face for folks
it's GitHub but for AI models so it's
where you go as a developer download the
models and so people millions and
millions of developers are using this
technology but then you look at the
language model side people are taking it
adapting it and optimizing it um to have
innovations that now catching up with
even proprietary guys but they're
complimentary you will have your own
team and then you'll bring in the
Specialists and I think that's the best
way to think about this because you
can't Outsource your internal
intelligence from a personal company or
even country level to models that you
don't know what the provenant are and
this is one of the debates that we saw
over the last year the first step was oh
my God exponential extrapolation you
know as Nat said kind of actually the
foundations for this were dug like maybe
a decade ago and now we've been filling
in the cement and now we're all building
houses and we're like well the houses
will become skyscrapers so it was like
well they could kill us all and those
and those are valid things discussed
then it became about sovereignty and
well gp4 is amazing but I want my own
version as well for my own private data
and so I think as we advance proprietary
models will always beat open models
proprietary will always be what open
models open okay because you can take
the open model and bring your private
data to it and optimize it CU open
models are like generalized graduates as
it were but both of them need to exist
and I think we'll see both of them
continue to take off do we actually know
what's going on inside the models I was
listening to a conversation with the
chief scientist at uh uh anthropic who
will be on stage with us next year um
and he's saying we actually don't
understand really how these models work
is that is that a fair statement yeah we
I I mean I just just so you understand
all right it's like they're amazing but
we actually don't understand yeah how
how the weights and connections and all
are really working I mean we we
understand it at this like very micro
level you know we can see the
multiplications happening and we can see
the signal moving to the next layer in
the neural network but um it's actually
sort of similar although not the same
mechanisms to the way we don't have a
perfect understanding the way thinking
happens in our brains um we don't even
though in the case of our brains when we
try to you know do the neuroscience and
understand what's happening at the
neuron level and the organel level um
we're limited by our measurement right
we can't measure the state of every
single neuron in your brain while you're
thinking that would that's not something
we have the sensing technology to do now
that's not a problem we have with these
AI Minds that we're building we can
measure everything and still we don't
really quite understand mechanically
What's happen happening inside them
they're not engineered and designed
they're kind of grown you know they're
the products of us actually because we
have the internet which digitized the
world and put the sort of all the data
we've produced online and there's a way
in which that process of building the
internet was like a bootstrapping
process for building AI because it was a
precondition to making AI we digitized
the world we put all the contents online
and then we could use all of that to
sort of grow and train these AI models
but we don't quite know how they work
now there's a new field um new not in
time but but relatively small field uh
called interpretability which is about
trying to understand what's going on in
these things what are they doing how do
they make the decisions that they make
obviously there's benefits if you can do
that to making them better in all sorts
of ways you can make them smarter maybe
and make make better decisions and you
can also hope to make sure they do the
thing you want them to do and not not
something else um but it's new Elon put
a tweet out which I read during my
opening remarks that said by 20 yeah
we're going to have human level AI next
year and by 2029 AI will be equivalent
to all 8 billion people uh do you agree
with that emod um do you think it's
going to move that fast I think we had a
big discussion about generalized AI that
can do everything and that was the focus
of Deep Mind and open AI but for
specific tasks AI is clearly more
capable than humans now so for example
uh Google's Gemini Ultra model has a
million 10 million token context window
what does that mean it means that
someone can upload themselves debugging
a piece of code in the code base and
it'll correct it and no human could ever
do that you know Ram isn't big enough
you know on image now we can generate
images faster than anything songs in
seconds and other things so let's talk
let's talk about that a second um Sora
was I mean pretty like holy moment
right um and
someone is doing it right at open AI
with chat GPT and then Sora uh but I
remember last year you told us it used
to take like 30 seconds to generate on
stability a single image and then it was
down to a second and you've Advanced the
technology orders magnitude since then
yeah so I think um if we can put the
thing up but yeah put up the slide um
stable image yeah and then if we
actually go to the next slide talk about
speed here
um I'll should I click this let's see oh
we can click this to give an idea of
where it's gone so image was kind of one
of the things that kicked us off in 2022
all of these images can be generated on
a MacBook on a MacBook just from
description and now we've perfected text
and other things but the next step after
you can generate all these beautiful
images is that you want to move to
control so the models are just the first
step you have to have chat GPT and other
things to make them useful but then you
want to be able to take that guy and say
upscale and that's all we said and it
upscaled him you want to say replace a
lion with a cat and we can now do that
real time you know or tiger with a
unicorn replace background with a forest
forest so all of this you can do pretty
much real time because if you look at
the next slide from
this okay I'll I said it was 20 seconds
to 1 second this is live real time you
can just type and it automatically
adjusts the cat it gives them a hat but
then if you look at the optimization of
that with the next
slide oh back one we missed it this one
okay well we missed it go back one slide
sorry with this process here we're just
releasing a new distillation model today
we've got it to 200 cats with Hats per
second 200 cats with Hats per second so
that's your speed of of image generation
that's a new unit of image generation 50
milliseconds per image yeah exactly it's
cuz there's not enough cats on the
internet right so we're going to add
more cats to the internet oh 5
milliseconds 5 millisecs 5 milliseconds
yeah but I think with the new chips that
are coming and video is going to
announce another one we'll get up
towards 1 th000 images per second so
that's real live that's live video times
times times 30 yeah and so open ai's
Innovation there were two major things
there was the Transformer architecture
the language models and diffusion that
drive the media models they combine the
two together so our new image model
stable diffusion 3 which is the best
performing image model combines those
together as well so if you go forward a
few slides from
here skip um another one another one
that's the upscaling you know so you're
basically said upscale this image on the
left and you get you get the image on
the right you'll have that real time in
a couple of years so your boxy video
games will look a lot more realistic so
just literally as you're you can take an
old game and play it in real life yes
but if you go to the next slide these
videos with our video model all
generated on a consumer graphics card
with 5 GB of
vram so I mean the the point is it's
this is in everybody's hands it's in
everyone's hands and again we haven't
even optimized the data so in an hour uh
next
slide we're releasing in the world's
most advanced 3D model so all of these
are generated just from descriptions and
so the fastest version of this does it
in 1 second so you can generate that
Dragon but then the next step is you'll
be able to control every element make
its horns bigger make these adjustments
so how long would that take for a normal
creative person in kind of industry a
huge amount of time but now it works on
the edge and it works even faster in the
cloud so a future here where you can be
describing the video game you want
created and it's generating all the
characters and generating the play yeah
and so if you go back a few slides to
that thing with all the nodes I think
this is an important thing sorry I
didn't do the slides
properly I think one of the things that
we're having right now is this is a
system we buil called comfy UI that's
used by just about everyone now so you
take the face the pose the dress and
then you have the output but if I share
that image with you it reconstructs the
entire flow because last year was the
year of creation model that create then
chat GPT com UI allowed you to control
and compose them and the next bit is
bringing that all together because chat
GPT all the knowledge that you build
from writing your speech you don't have
files anymore you have flows with these
models and assets there and I think
that's the next step because you know
when you go beyond just spitting out
ideas to be able to control them like
that that's a huge
deal amazing uh and you're announcing
this in an hour uh the 3D models
releasing in an hour open source to
everyone give it up for emod
here um you really have uh I now don't
know if you're able to say this I mean
um uh you're uh driving revenue and Su
net profitability what's your financial
I can't say that publicly you can't okay
but it's going well we're ahead of
forecasts as can say all right he told
me backstage was good yeah okay do all
right yeah everybody I want to take a
short break from our episode to talk
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listeners all right let's go back to our
episode um I am curious about the idea
of how far are we from having an AI that
I can have a conversation with and say
I'd really like to create a new business
that does this this this and this and
just have that like a brainstorm partner
Ai and it will do the
incorporation um it will write the code
it will generate the marketing
materials um and it will be able to you
know we're all entrepreneurs here that's
all we you know find a problem build a
business find a problem build a business
how far are we from that that reality of
an AI created well I'll come back to the
second part in a minute but an AI that
can that can be your thought partner and
really sta up a company I think it's
it's already happening gradually um and
then maybe suddenly so we have I think
companies will these models are neural
they're neural networks and so I think
the right way to think about it is that
companies in the future will just be
increasingly neural and there will be
more and more of the company and what
used to be the Departments of the
company that are single models or swarms
of models that are doing work I think
it'll you know at some point probably
very soon we'll look back on 2024 and
say God do you remember when companies
had like hundreds of people in the
finance department doing accounts
payable and just like transforming
information from one form of text to
another essentially and doing this
coordination and um it so I think you
will have some form of both existing
companies that just adopt more and more
AI because it gives them advantages it
makes them more efficient maybe it gives
them better customer service people
enjoy the responsiveness intelligence
politeness Clarity Etc of the AI
counterparty and um you'll also have new
companies that'll be AI native started
from scratch neural from the beginning
so we you some people may remember when
Instagram was acquired by Facebook for a
billion dollars everyone was just
marveling at the fact that a a small
company 13 employees 13 people could be
worth a billion dollars how is that
possible and the joke was you know gosh
could you ever get down to a one person
company that's worth a billion dollars
and while like this is clearly going to
happen maybe eventually a zero person
company that's so we talked about this
getting ready and I said when are we
going to have a scenario where uh a
government and we have some governance
in the room here uh says we're going to
create a new regulation that allows an
AI to incorporate on its own in our
jurisdiction I think it's I think it's a
winning scenario because all of a sudden
the AI incorporates you get the tax base
and every AI Incorporated company will
move there I think Wyoming's done that
what's that Wyoming's done that they
have a new structure called aduna for a
decentralized autonomous organization
I'm s who has Wyoming Wyoming Wyoming
all places yeah yeah we have some
Wyoming fans here in the audience yeah
so technically that could happen
today amazing uh I mean I I do think
that that is a so the speed of wealth
creation
um how how should F so let's talk about
what are the wow moments that might be
possible in the next year what are some
crazy wow moments that might you might
see I I think one of the most important
things is uh the accuracy and then these
long context windows so explain what the
long context window is again so you're
absorbing information right now and it's
quite high definition because you can
see everything here and other stuff but
you're still writing it down and the
reason organizations get big is because
text is a lossy trans transmiss format
we lose so much context the final PDF
loses all of that stuff now with the new
Google models Nat has some amazing
companies in this space as well you can
upload hundreds of thousands of words
thousands of documents and the AI can
interpret them all at once there doesn't
need to be trained on it so you can
upload all of your ideas and say build a
business based on this and it will do
that or you can upload like a whole
bunch of movies and then tell it to
write a script that incorporates all of
that and it'll do that in reference time
that again is something super human but
we all have these massive like
repositories of all these ideas we've
had being able to dump that now and then
the AI without having to be trained spit
back answers ideas and things like that
I think is a really huge step along with
that composition step that kind I've
discussed before I think yeah I totally
agree with that I think there will be a
couple things coming probably pretty
soon um it's hard to predict exact
timelines on these things uh sometimes
things happen faster than you expect
effect and sometimes a little slower but
one of the clearly amazing clearly
possible now U products to build is a
voice too model that's indistinguishable
from talking to a human maybe for a
conversation of up to a couple minutes
where what happens after a couple of
minutes well maybe you can kind of just
tell somehow that it's not quite human
after a couple of minutes uh I'm I'm
setting a milestone that I think is
achievable this year maybe when if you
can do 2 minutes you can do 12 minutes I
I don't know um but uh
yeah you know you would it's it's
actually about all the Technologies
there it all just has to be integrated
and so you need the sort of the ability
to recognize speech is there the ability
to interpret it with a language model
and generate responses is there and then
the ability to turn that text into
incredibly realistic voices there and
kind of putting that all together into a
package that has very low latency that's
talking the way we talk where you can
kind of interrupt me and maybe there's
an avatar that's giving you this human
like I mean Aristotle was very
impressive but I knew that that was not
a real person right and so um I think we
could yeah he was a real person yeah
that's right and then I think the other
thing that's a very big deal is this
this idea of autonomy and agents um
there's been a lot of talk of it with AI
over the last year today these things
are not agents they're tools they're
call and response you go to chat gbt you
type something you hit enter you watch
the response kind of scream back and I
think what people don't necessarily
understand is that when these language
models are responding to you that it's
it's almost like a rap battle they have
a fixed amount of time to generate each
word and so they can't sort of sit there
and Ponder for a minute you know what
they're going to say they have to talk
to a metronome and um so that's why when
you see the words that they're writing
out that's not like they've thought
about it a lot and then you see the
words it's actually the thinking is
happening during the output would you
say that's how a human does it too I
often a lot of times sometimes I'm
really pissed off at what came out my
mouth cuz I didn't think about it in
advance yeah yeah I think so I mean
sometimes you have a conversation with a
smart friend and you just forming the
words to respond you have a better idea
and so I think we we definitely do that
too but um the agent the autonomy is
about kind of increasing the unit of
work you can trust the AI to do without
making a mistake so right now you can
ask it one question get a response you
interpret it you figure out the next
thing but what if it could go do 10 or
100 steps you're talking about an AI
business you know do you trust it to
come up with the title of the blog post
that you're going to post or do you do
you trust it to actually come up with
the whole idea of the marketing campaign
right come up with a strategy for how to
execute it all of the content it's going
to generate the partners it will reach
out to and negotiate advertising or or
whatever it's going to do have those
conversations that'll multi-step
successfully all the way to like
measuring its results without
supervision and I think that agency
thing we're starting to see it in
programming there was a very impressive
demo a week or two ago of a company
called cognition that um I think it was
maybe the it was arguably the first
really impressive demo of working agents
in Ai and and they did it with gp4 so
not with a new brand new model they did
it by being very clever about the way
they squeeze and distill the
intelligence out of gp4 by repeatedly
calling it and and analyzing and
evaluating its results and choosing the
best ones and so sort of going from rap
battle to like draft and redraft and
sort of think and ponder about it a
little bit harder mode and uh it can do
hundreds of steps in programming
successfully
so okay I was going to ask you one quick
question then please jump into that how
far out can you imagine right now about
three years three years is a Max yeah
how about
you well you can dream uh you can dream
yeah so um I actually the thing I I I
find easier is to think about the long
term because the the where the ASM toote
of where we're yeah and that's the key
question of our time is the ASM toote
it's like we know this is going to get
better when does it slow down and what's
the shape of the curve to get there you
mind I I cut you off apologies uh no
it's fine uh I think probably the thing
to look forward to is the last couple of
year F the first year was about the
technology and the breakthroughs and the
research last year was about the models
next year going forward no one give a
down about the models it's all about
what you can do with the models bringing
them together because the models have
satisficed they've got good enough fast
enough and cheap enough they will get
even better and there's probably two
orders of magnitude Improvement still in
the speed and reliability of the model
but this will really become about and
the stories of the next year will be
about we used this model to do this and
it was amazing you know and so I think
that's one of the things to look at and
that's what Nat said about tying them
all together you've got the ingredients
now what are the recipes we're going to
make very quickly capital and regulation
uh I was in a conversation with the CEO
of one of the major um AI companies and
uh just he was saying he's raising three
billion and I said you know have a
venture fund I said great and and I said
what's your minimum and he said probably
100 million I said okay well that's out
of my ballpark um but uh and and how
Quil you think I'm you're going to raise
it he goes next month I mean there is a
lot of capital flowing in yeah I mean
have you ever seen a capital Rush faster
than
this um no no I mean I think there are a
couple of comparisons you can look at um
I think the railways at when Railway
infrastructure was being laid in the UK
I think it was some double digit percent
of GDP investment um I think the solar
explosion that's happening right now is
actually pretty amazing it's something
like half a point of global GDP is being
invested in solar so those are pretty
big um we're nowhere near that yet with
AI and so I I actually think if AI keeps
working which it seems like it will um
you should expect the future to look
more like that Railway or solar
situation where there's you measuring
the investment in intelligence because
it's so valuable intelligence AI is
intelligence intelligence is power
power is valuable it's power over nature
it's power over others and so you'll
probably measure the amount of
investment in it in points of global GDP
and whether that happens in two years or
10 years I'm I'm not sure but it seems
likely I mean to put this in context
less money has been spent on private AI
companies than the Los Angeles San
Francisco Railway that hasn't started
yet there you go wow right but that's
almost done I heard so really fantastic
they haven't even started we wrote down
on it the ai ai right now isn't
infrastructure but it should be which
talk about your vision there because I
find it very powerful um your your your
vision there in education in health talk
to me this is the next Generation human
operating system because these AIS
extend our capabilities and again you'll
need the AIS that are open and the ones
that are proprietary to have the best
outcome for everyone cuz all of our
companies here are all information
systems and we've seen how better it can
be so the total amount of spending in
this will be trillions of dollars we
spent a trillion dollars on 5G is AI
more impactful than 5G of course it is
should it be infrastructure of course it
should be should the data be transparent
of course it should be because you need
to know how our Railways are made the
information knowledge Super Highway of
the future right now it's a few
companies doing this but we need
standards around it and we need the
expansion and again every country will
invest in this every company will invest
in this I mean is anyone here who runs a
company not investing in this in some
way at least your time right yeah
multiplied by every company in the world
why where are we percentage wise at the
investment in AI are we at a fraction of
1% still I would say so yes how about
you Nate seems likely so a lot of upside
still opportunity uh it is crazy though
I saw a tweet the other day I mean I
live through do and um all these you
know little attack Bubbles and um uh I
saw I saw a tweet the other day from
someone that said if you don't secure
equity in an AI startup now then your
children will be chattles slaves for the
machine God for all eternity and I
remember thinking I don't remember
anyone saying that
during like then you switched around and
bought some more G right this is somehow
a little bit more extreme than the prior
bubbles
so I mean look I think web 3 kind of
received a lot without any results
whereas now you're seeing impact here
yes but again you multiply this by the
number of people it effects the value
created it's insane because it will go
all abstraction you've had the base the
found found ations now the base now
you're building the houses you're
building a whole ecosystem around this
and the transformation that that has and
the amount of capital is bigger than
anything we've seen let me ask the
question we're going to be debating
today on the ASM toote How concerned are
you about digital super intelligence I'm
defining this for a purpose of
conversation as AI a billionfold more
advanced than the human being
um how do you think about that what's
your position in that
debate Pro con anywh I can kick off my
belief is that humans can break the atom
or we can go to Mars and so my vision is
every single nation person company
country culture has their own AI data
sets and self- Sovereign needs to figure
out the governance of this because it's
important and then that AI is our
collective intelligence it's the human
Colossus so it isn't controlled by
anyone individual it isn't embodied like
that but again it's amplifying all of us
and it's solving every single problem
that we can have I think that's a
positive version of the
future I think it seems really likely
that I mean it seems undoubtable to me
actually that intelligence is just a
material process like muscle strength we
have organs called muscles we use them
to to move we have an organ called the
brain and we use it to think and so if
you look at if you just sort of ask what
that means well we've managed to exceed
muscle strength with artificial
artificial machines with with machines
uh hundreds of years ago and um you know
we're going to do that with brains too
we're going to have artificial Minds
that are much more powerful than ours
it's um and you it's almost like you
just have to be an AI doubter not to
believe that or you have to not believe
in human Ingenuity all the most
brilliant people in the world are now
working on this and there's a huge
amount of capital going into to it in a
way we've just started and so the idea
that it wouldn't improve seems hard to
believe did you know that your
microbiome is composed of trillions of
bacteria viruses and microbes and that
they play a critical role in your health
you know research has increasingly shown
that microbiomes impact not just
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diabetes autoimmune disease like
rheumatoid arthritis and multiple
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depression and anxiety and
cardiovascular disease you know viome
has a product I've been using for years
called full body intelligence which
collects just a few drops of your blood
saliva and stool and can tell you so
much about your health they've tested
over 700,000 individuals and used their
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what supplements or probiotics to take
as well as your biological age and other
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Stellar as reported in the American
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listen I've been using viome for 3 years
I know that my oral and gut health is
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viome is Affordable which is part of my
mission to democratize healthcare if you
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go to vi.com Peter when it comes to your
health knowledge is power again that's
vom.com SL Peter here's the challenge it
feels like potentially win or take all
scenarios where if all of a sudden my
company is able to utilize the most
advanced Ai and build out the next
generation of systems and I'm doing it
with you know me and my my versions of
Haley and and and Aristotle that there's
you know they're working 24/7 I'm
feeding them as as many gpus as possible
and I have a chance to really
outrun um outrun the competition and it
used to be that in the early days in the
mechanical world if you were using
mechanical systems to outrun the horse
that was a local phenomenon right but
now this is a global phenomenon because
my my bits are reaching around the world
and you know when you say yeah it can do
my marketing do my marketing campaigns
it can do my scientific analysis it can
do everything
and you know I keep on hearing that is
like not 10 years from now not 5 years
from now but that's like a three-year
scenario I don't know how to think about
that I think the the pace of change you
know is going to be very high and um
Global GDP growth has been kind of in
the 2% range and we have a society and
civilization that's able to adapt to
that amount of change per year mostly
not entirely it's been a little bit
higher before um maybe it's going to be
much higher very soon and uh whether
there's a huge spread of outcomes that
come from that I mean even if you don't
have the extermination scenarios the
extinction scenarios in mind um I think
I think you actually should have other
scenarios in mind what does it mean to
be human uh is the economy human human
dominated you know 10 years from now or
15 where are decisions made and and who
makes them um and then just uh you know
this this vision of of sovereign AI you
know it's a new level of potentially
cooperation and competition between
countries and sources of this kind of
power and so um a lot of changes coming
I think you should I think uh you know
we went through this before we had the
sort of five big inventions of like the
1880s to the through the 1920s and that
was a a wild time of transformation it's
like almost unimaginable people who grew
up with horse carts you know and up
riding on jet planes and um we're going
to have at least probably much more than
that amount of change happen this time I
go to uod next but when you're you're
about to go after this to part two of
that tool and I want you to be thinking
about how can AI help you solve the
challenges you listed how will AI
threaten the dominant positions or the
strengths that you have and how will AI
this year help you meet your 20
24 goals right that's that's the goal to
putting it to use you might do you want
to comment on that and I want to also
talk about medicine and you know you're
dedicated to using AI models to solve
autism and cancer and death um or let's
just
say sick sickness yeah
sickness yeah I mean uh I just had a
thought actually you know we had Steve
Jobs qu earlier maybe the AI is jet
planes for the mind right there you go
here we go but the kind of the impact
here there's a sociological impact we
can extrapolate forward but an ASI is
just something we can't think if you've
got super intelligence if you've got too
infinite supply of graduates what do the
existing graduates do if the floor is
raised you don't need to hire as much
you become more efficient what are the
new jobs of the future in a knowledge
based economy we have to think about
that because as Nat alluded to it's like
slowly slowly then all at once like a
turkey you know at Thanksgiving I think
if you look at this though there's the
negative side but then there's a
positive side so I think mentioned kind
of last year Google's medpar model
outperforms human doctors in medical
diagnosis accuracy but also empathy and
we have medical models like that anyone
here who's had someone who's had
multiple sclerosis Alzheimer's autism
something like that you don't have
comprehensive authority to up toate
knowledge and nobody to guide you
through that process and you lose
agency well guess what we will make open
source models and they'll be available
to everyone and you'll never be alone
again through that and then be used for
diagnosis and organizing all lar so
describe that a little bit more so
people can can feel what that feels like
it means again anyone's experienced that
someone comes to you and says you have a
diagnosis of Alzheimer's for your father
or Autism for your child you lose agency
because you're like what now there's no
cure there's no treatment where do I
even go for information a lot of our
stuff is a coordination issue whereas if
we have a specialized medical model for
that topic and retrieval augmentation
and tie it into all these systems you
can have all that knowledge at your
fingertips and it can help guide you
empathetically through that it can
literally talk to you it can connect you
with other people going through the same
process and then the next step is it can
organize all the knowledge in this area
because there's so many promising
treatments but what how does anyone here
find out all the treatments about autism
or cancer you used to be patients like
me but it's hasn't survived but this is
supercharges that and then you do that
once and it's infrastructure for the 50%
of people around the world that will
have a diagnosis of cancer and again
you've transformed it because there will
always be someone with every single
person that can connect them to the
right information at the right time so
this is the positive view of the future
and that creates boundless new potential
in terms of both addressing our health
but science and again most of our
Science is based on files a PDF and we
throw away all the stuff that doesn't
work if you look at that knowledge flow
of that image with a different things
and again if I drag and send you that
image it'll reconstruct the whole flow I
think most of our things will go from
files to CL so we can remix our
knowledge so that we can find out what
works and what doesn't work and that's
how we get breakthroughs and is it true
that most the large language models were
built on top of all the social media
Facebook websites and so but not
crawling science magazines and most of
the scientific databases out there
science was a part of it but again what
we did is Big compute was a substitute
for bad data it was like cooking a steak
for too long now we see that high
quality data is even better but we're
only understanding what high quality
means and this is why we need
specialized models which are transparent
especially for things that affect things
like health education and others so I
think data set transparency will be a
big deal and this goes to Nat's point
about interpretability as well like you
were given that example of ariv earlier
right um science you know one of the
things that I think is amazing is the
potential for these AIS to help us
discover new science
Beyond interpolation or extrapolation
new laws of physics new understandings
of fundamental biology and chemistry uh
do you think that is possible when are
we going to see that and speak one
second to the vvus challenge that you oh
sure yeah um I think definitely they
will um which by the way for me is like
the most exciting thing if you could
unleash these AI models and say go and
create room temperature superconductors
go and cre you know uh life extension
processes so there's uh in biology
especially biology you know there's this
sort of analogy that's been put out
there that uh the language of of you
know physics was was mathematics the
language of biology might be machine
learning because you're dealing with
enormously complex systems machine
learning is great at sort of
understanding those and so the potential
for AI to transform biology is enormous
I think it's all in the very earliest
stages right now and we have not yet had
the kind of gpt3 let alone on the chat
GPT moment for biology and AI I think
it'll come soon since Google is our
sponsor I say the Gemini moment there
you go the Gemini moment hasn't occurred
yet The Bard what is it The Bard moment
maybe I don't know but it's coming and
um you know I'm involved with some
companies that are training enormous
models to help uh synthesize and design
proteins there are a few great efforts
out there to do this and um the
capabilities that are popping out of
these things are incredible the ability
to you describe a Target describe a
structure and just have it produce a
sequence of amino acids that you can
then synthesize and test in the lab for
safety and efficacy it can short circuit
a huge part of this uh sort of cognitive
work and experimental work that's been
necessary historically for drug design
and so I think that's one thing I think
another thing is is um there will be a
surge of new discoveries that happen as
we as AI digests all the existing
scientific literature and so there's
there's a whole area of study which is
metaanalysis where people will study
across papers to find connections and
correlations across existing research
that haven't been noticed I so I mean
I'm bubbling with excitement on that
notion that idea yeah so imagine you
know I I have a friend uh shaa Swan
she's a a scientist at Berkeley and does
a bunch of uh research on environmental
toxins she spent two years running one
metaanalysis that you know I'm now
working with her and trying to support
her and the effort to use AI to automate
this and it'll I mean in theory with
these long context Windows this could
potentially happen in in minutes and so
I think new discoveries will pop out of
that immediately and just because we're
short on time I'm going to say you found
Mount Vesuvius oh yeah after the
eruption buried a number of uh parchment
Scrolls that were if you imagine buried
under all of the ashes and so and so
forth you took some of those Scrolls you
x-rayed them gathered the X-ray d from
those Scrolls and then you ran a order
an order of a million dooll uh vus
challenge like an X prize yeah and your
the winner solved it it worked yeah no
it's true so yeah 2,000 years ago Mount
vuia erupts it buried the town of
herculanum under 65 ft of Ash and mud
and then in the 1700s some Farmers
digging a well found the Villa 65 ft
down and then later during these
tunneling excursions people kept running
into these little chunks of char Co they
didn't know what they were they turned
out to be carbonized Papyrus Scrolls
that were not openable physically they
just sort of turn to dust in your hands
when you open them they've been stored
in a library in Naples for years and we
used a particle accelerator to scan them
at super high resolution but then we
needed to use Ai and machine learning to
to unroll them they're so badly
distorted by how cool is that being a
volcano
[Applause]
yeah um want you close us out here with
what you're most excited about um going
forward what's a vision of the world in
the next 1 to three years that you want
people to take away from here I I think
this AI is augmenting not replacing and
so you see what kind of nat said and I
look how creatives use it AI can't do
art it can do content right now but you
can use it to riff and jam with so
you're in flow more often so the thing
I'm most excited about is its impact
upon education science over the coming
years is because everyone will have
access to all the knowledge that
amplifies them I think things like
creativity are also great because it's
great to create but realistically again
every single person in this room will
just have access this is the worst it
will ever be that's so important this is
the worst AI will ever be and view it
and the lowest Bitcoin will ever be
too yeah and then view it amplifying
yourself and there's no there's nothing
you can't achieve with this I
think um
people are sitting down here with goals
for the year uh objectives they want to
achieve what is your top piece of advice
for the CEO entrepreneur philanthropist
here that has like oh man I I'm trying
to do more and more what should they
what should they think about well I
think um what's happening or what's
about to happen it's sort of like we've
just discovered a new continent with 100
billion people on it and there willing
to work for free for us and you should
probably factor that into your plans
over the next few years
because uh your competitors will and U
because it'll benefit you enormously at
home at work and your family we just
discovered a new content with 100
million brilliant workers willing to
work 100 billion 100 bil they're going
to outnumber us yeah yeah 100 billion
okay and they'll work for a few watts of
power and um so this is the good
scenario and I think it's very very
likely and uh so I would and I think you
know this sort of like being an internet
native you want to be an AI native and
you want to spend time using the stuff
and not looking for the problems but
looking for the looking for the value
and how to what's it good at what's it
not good at how do you interact with it
it's amazing to see people use stable
diffusion for example um you've improved
uh it so much over time but there are
there's a skill to being good at
interacting with these models and
partnering with them and and so I think
we all have an advantage just to get
that hands- on ourselves you know
imagine being a CEO of a company when
electrification was happening and um you
know obviously every company should
Electrify uh and then not doing it in
your own home like that would be
strange amazing imod you're going to be
with us for the next few days so thank
you so much for that uh Nate it's a
pleasure to get to know you thank you
for joining us this morning let's give
it up for Nate and mod
[Music]
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