Inside OpenAI [Entire Talk]
Summary
TLDR本次访谈中,OpenAI联合创始人兼首席科学家Ilya Sutskever讨论了人工智能的发展,特别是大型语言模型GPT-3的创新及其对社会的影响。Ilya分享了他对AI未来的看法,包括对深度学习、专业训练与通用训练的见解,以及OpenAI从非营利组织转变为有限利润公司的背后逻辑。他还谈到了AI的伦理问题,以及如何平衡技术进步与全球公民责任。Ilya鼓励对AI和创业感兴趣的学生,要信任直觉并利用自己独特的才能和视角。
Takeaways
- 🌟 伊利亚·苏茨凯弗是OpenAI的联合创始人兼首席科学家,对大型语言模型GPT-3及其前身产品ChatGPT的发展做出了重要贡献。
- 🎓 伊利亚出生于俄罗斯,成长于以色列,并在多伦多大学完成了数学学士和计算机科学硕士及博士学位。
- 🧠 伊利亚认为,人工智能的发展受到人类大脑神经元工作原理的启发,人工神经网络通过学习数据来提高预测能力。
- 📈 伊利亚讨论了人工智能的潜力,包括在特定领域如法律或医学中通过专业数据集训练来提高性能。
- 🚀 伊利亚提到,尽管目前人工智能在学习和适应方面与人类存在差异,但未来可能会达到或超越人类的学习速度。
- 🌐 OpenAI的使命是确保人工智能的发展惠及全人类,公司从非营利组织转变为有上限利润的公司,并与微软建立了合作关系。
- 💡 伊利亚强调,随着人工智能能力的增长,未来可能会出于安全考虑而不再开源这些模型。
- 📊 伊利亚认为,衡量OpenAI成功的关键是技术进步、研究执行情况以及对模型的控制和引导。
- 🔍 伊利亚提到,尽管OpenAI的某些方面是封闭的,但公司仍为学术研究提供了不同形式的模型访问权限。
- 🎭 伊利亚个人非常喜欢GPT-3的诗歌创作能力,他认为这是人工智能一个有趣且引人注目的特点。
- 🤖 伊利亚讨论了人工智能在团队中的集成如何提高生产力,尽管目前尚未对团队动态产生显著影响。
- 🌍 伊利亚表达了对全球人工智能监管和合理进步的看法,认为未来可能会有更多的政府监管来确保人工智能的安全和负责任的使用。
Q & A
Ilya的背景和教育经历是怎样的?
-Ilya出生于俄罗斯,五岁时移居以色列,在以色列度过了他的成长岁月。他在以色列完成了大学前半部分的学习,之后转学到多伦多大学,获得了数学学士学位。之后,他在多伦多大学继续深造,获得了计算机科学的硕士和博士学位。
Ilya在OpenAI的角色是什么?
-Ilya是OpenAI的联合创始人和首席科学家,致力于构建对全人类有益的人工通用智能。
Ilya对于大型语言模型的看法是什么?
-Ilya认为大型语言模型是一种强大的技术,通过训练大型神经网络来预测文本中的下一个词,从而实现对人类语言的深入理解和生成。
Ilya如何看待人工智能的未来,特别是在学习速度和适应性方面?
-Ilya认为虽然目前人工智能在学习和适应方面与人类有很大不同,但他不会押注反对人工智能在未来某个时刻达到或超过人类的学习速度和适应性。
Ilya对于意识或自我意识的看法是什么?
-Ilya对意识问题持开放态度,他认为意识可能是一个程度问题,并且对于未来的人工智能系统,如果它们能够展现出与人类类似的意识特征,那将是非常值得思考的。
OpenAI的使命和转变是怎样的?
-OpenAI的使命是确保人工通用智能的利益普惠全人类。它最初是一个非营利和开源组织,后来转变为有限责任公司,并与微软建立了紧密的合作关系。
Ilya如何看待OpenAI的开源与闭源的转变?
-Ilya认为在人工智能的能力较低时,开源是有益的,但随着能力的提升,出于安全考虑,闭源可能变得必要。OpenAI的闭源决定是出于对人工智能能力增长的预期和对安全性的考虑。
Ilya对于OpenAI的盈利模式有何看法?
-Ilya解释说OpenAI是一个有利润上限的公司,这意味着一旦对投资者的义务得到履行,OpenAI将再次成为一个非营利组织。他认为这种结构是独特的,并且有助于平衡公司的使命和经济需求。
Ilya对于人工智能在全球范围内的监管和伦理问题有何看法?
-Ilya认为未来的世界需要明智的政府监管来确保人工智能的发展是可控和负责任的。同时,他也认为每个国家都有其在全球人工智能发展中的角色和责任。
Ilya对于想要进入AI领域的斯坦福学生有什么建议?
-Ilya建议学生们应该发挥自己的独特优势和直觉,深入研究自己感兴趣的领域。他认为在研究和创业方面,独特的视角和创新思维都是非常重要的。
Ilya如何看待深度学习在未来5到10年的发展?
-Ilya预计深度学习将继续取得进步,尽管通过扩大模型规模来获得进步的时代可能已经结束,但深度学习在许多层面上仍有改进的空间,这些改进将共同推动领域的发展。
Outlines
🌟 欢迎与介绍
本段介绍了斯坦福大学与YouTube社区合作的创业思想领袖研讨会,特别邀请了OpenAI的联合创始人兼首席科学家Ilia Sutskever。Ilia在人工智能领域有着杰出的贡献,是GPT-3等大型语言模型的基础思想者。他的个人背景和职业经历也被简要介绍,包括他如何从俄罗斯移居以色列,并在多伦多大学完成学业,最终成为OpenAI的关键人物。
🤖 大型语言模型技术
Ilia解释了大型语言模型的工作原理,包括人工神经网络的学习过程和反向传播算法。他强调了这些模型如何通过预测文本中的下一个词来训练,并指出这些模型的成功依赖于它们对“接下来会发生什么”的预测能力。他还提到了人类与机器学习方式的差异,以及如何通过大量数据训练来提高模型的性能。
🧠 人工智能与意识
Ilia探讨了人工智能与意识之间的关系,分享了他个人对意识的好奇和探索。他提出了一个未来可能进行的实验,即在不提及意识的情况下训练AI,然后观察AI是否能自行理解和表达意识这一概念。他还讨论了意识的度量问题,认为意识可能是一个程度问题,而不是简单的二元对立。
📈 OpenAI的使命与伦理
Ilia讨论了OpenAI的使命,即确保人工智能的利益普惠全人类,以及公司从非营利组织转变为有限责任公司的过程。他解释了与微软的合作关系,以及如何平衡公司的商业目标与对AI安全性的考虑。他还谈到了自己作为首席科学官的角色,以及他对公司发展方向的看法。
🌐 全球视角与AI的传播
Ilia讨论了AI技术在全球范围内传播的重要性,以及他作为世界公民的责任。他提到了与其他国家和地区的合作,以及如何在全球范围内推动AI技术的合理使用和监管。他还分享了自己对于AI技术未来发展的看法,包括深度学习的演进和专业领域内的应用。
🎯 OpenAI的未来方向
Ilia对OpenAI的未来方向提供了见解,包括公司可能成为人们直接使用的平台,或者作为其他应用背后的支持技术。他还讨论了ChatGPT在团队工作中的应用,以及它如何提高生产力和效率。最后,他分享了一些关于ChatGPT的有趣用途,如写诗和说唱,以及它如何被整合到OpenAI的团队工作中。
Mindmap
Keywords
💡人工智能
💡深度学习
💡神经网络
💡GPT-3
💡OpenAI
💡技术创新
💡意识
💡伦理
💡创业
💡数据集
Highlights
Ilya Sutskever 是 OpenAI 的联合创始人兼首席科学家,致力于构建人工通用智能(AGI)以造福全人类。
Ilya 在俄罗斯出生,五岁时移居以色列,并在多伦多大学完成数学学士学位。
Ilya 认为人工神经网络与生物神经网络在某种程度上是相似的,这种假设推动了深度学习的发展。
Ilya 强调了反向传播算法的重要性,这是一种让人工神经网络通过经验学习的数学方程。
大型语言模型(如GPT-3)通过训练神经网络猜测文本中的下一个单词,从而提高其预测能力。
Ilya 认为,尽管人工智能在某些方面超越了人类,但在学习方式上,人类和机器仍然存在显著差异。
OpenAI 的使命是确保人工通用智能(AGI)造福全人类,从非营利和开源开始,现已转变为有利润和闭源的公司。
Ilya 讨论了 OpenAI 与微软的合作关系,以及如何确保投资者的财务责任与 OpenAI 使命的一致性。
Ilya 认为,随着 AI 能力的增长,未来可能会有一天出于安全考虑而不再开源这些模型。
Ilya 讨论了意识问题,以及 AI 是否可能发展出意识,他认为这是一个复杂且难以定义的概念。
Ilya 认为深度学习将继续取得进展,不仅仅是通过扩大模型规模,还包括在多个层面上改进深度学习堆栈。
Ilya 强调了在 AI 研究中,信任自己的直觉和独特见解的重要性。
OpenAI 的成功关键绩效指标(KPI)包括技术进步、研究执行情况以及对 AI 系统的控制和引导。
Ilya 认为,尽管 AI 技术已经整合到团队工作中,提高了生产力,但目前对团队动态的影响并不显著。
Ilya 讨论了 OpenAI 企业结构的独特性,它是一种有利润上限的公司,最终将转变为非营利组织。
Ilya 认为,未来的 AI 系统将在多个小的改进和一些大的改进的基础上,变得更加强大和复杂。
Ilya 讨论了在 AI 领域中,通用训练与专门训练的优劣,以及如何结合两者以取得最佳效果。
Ilya 认为,尽管 AI 模型如 GPT-3 在诗歌创作方面表现出色,但在团队工作中,它们目前还没有显著改变团队动态。
Transcripts
who you are defines how you build
welcome YouTube and Stanford communities
to the entrepreneurial thought leaders
seminar
um brought to you by stvp the
entrepreneurship Center in the School of
Engineering at Stanford and basis The
Business Association of Stanford
entrepreneurial students today we are so
honored to have Ilia suitskiver here at
ETL Ilya is the co-founder and chief
scientist of open AI which aims to build
artificial general intelligence for the
benefit of all Humanity Elon Musk and
others have cited that Ilya is the
foundational mind behind the large
language model generative pre-trained
Transformer 3 or gpt3 and its
public-facing product chat gbt a few
product releases have created as much
excitement Intrigue and fear as the
release of chat gbt in November of 2022.
Ilia was Ilia is another example of how
the U.S and the world has been the
beneficiary of amazing talent from
Israel and Russia is Elia was born in
Russia and then when he was five he
moved to Israel where he grew up and he
spent um at the first half of undergrad
even in Israel and then he transferred
and went to the University of Toronto to
complete his bachelor's degree in
mathematics he went on to get a master's
in PhD in computer science from the
University of Toronto and then came over
here to the farm and did a short stint
with Andrew ing before returning back
to Toronto to work under his advisor
Jeffrey Hintz research company DNN
research Google then acquired DNN
research shortly thereafter in 2013 and
Ilya became a research scientist as part
of Google brain and in 2015 he left
Google to become a director of the then
newly formed open AI it's hard to
overestimate the impact that chat gbt
has had on the world since its release
in November of last year and while it
feels like chat gbt came out of nowhere
to turn the world on its head the truth
is there's a deep history of innovation
that has led to that moment and as
profound as chat gbt is Ilia is No
Stranger in uttering in discontinuous
leaps of innovation and AI Jeff Hinton
has said that Ilya was the main impetus
for Alex net which was the convolutional
neural network in 2012 that is
attributed to setting off the deep
learning Revolution that has led to the
moment that we are now in and of course
it was seven years since the founding of
open AI that chat GB T was finally
Unleashed to the world Ilyas was elected
a fellow of the Royal Society in 2022
he's been named to the MIT tech review
35 under 35 list in 2015. he's received
the University of Toronto's innovator of
the Year award in 2014 and the Google
graduate Fellowship from 2010 to 2012.
so with that everybody please give a
virtual warm Round of Applause and
welcome for Ilia to the entrepreneurial
thought leader seminar so Ilya imagine
lots of Applause and you're always
invited back onto the farm physically
whenever you are able so Ilya there's so
much to discuss and I know we're gonna
have solo time and we have quite a broad
range of fluency around the audience in
terms of chat gbt and lot large language
models I wanted to start off with just a
quick question on the technology which
is just the key technology underlying
open Ai and generative AI more broadly
is large language models can you
describe the technology in simple terms
and now that you're at the Forefront of
the tech can you share would have
surprised you the most about what the
tech can do that you didn't anticipate
yeah
I I can't explain
well this technology is and why it works
I think the explanation for why it works
is both simple and extremely beautiful
and it works for the following reason
so you know how
the human brain is our best example of
intelligence
in in the world
and we know that the human brain is made
out of a large number of neurons a very
very large number of neurons
neuroscientists have studied neurons for
many decades to try to understand how
they work precisely and while the
operation of our biological neurons are
still mysterious
there's been a pretty bold conjecture
made by the earliest deep learning
researchers in the 40s
the idea that an artificial neuron
the ones that we have in our artificial
neural networks kind of sort of similar
to a biological neuron if you squint so
that's there's an assumption there
and we can just run with this assumption
now one of the nice things about these
artificial neurons is that you can
they are much simpler and you can study
them mathematically
and a very important breakthrough that
was done by the very very early deep
learning Pioneers before it was known as
deep learning was the discovery of the
back propagation algorithm
which is a mathematical equation for how
these artificial neural networks should
learn
it
provides us with a way of
taking a large computer
and implementing this neural network in
code and then there would be there is an
equation that we can code up that tells
us how this neural network should adapt
its connections to learn from experience
now a lot of additional further progress
had to do with understanding just how
good and how capable this learning
procedure is
and what are the exact conditions under
which this learning procedure works well
it's although this is although we do
with computers it was a little bit of an
experimental science a little bit like
biology where you have something that is
you know like like like a local
biological experiment a little bit
and
so then a lot of the progress with deep
learning basically boils down to this
we can build these neural networks in
our large computers and we can train
them on some data we can train those
large neural networks to do whatever it
is that the data asks them to do
now the idea of a large language model
is that if you have a very large neural
network
perhaps one that's now not that far from
like these neural networks are pretty
large
and we train them on the task
to guess the next word from a bunch of
previous words
in text
so this is the idea of a large language
model you train a big neural network to
guess the next word from a previous from
the previous words in text and you want
the neural network to guess the next
word as accurately as possible
now the thing that happens here is we
need to come back to our original
assumption that maybe biological neurons
aren't that different from artificial
neurons and so if you have a large
neural network like this that guesses
the next word really well maybe it will
be not that different from what people
do when they speak and that's what you
get so now when you talk to
a neural network like this it's because
it has such a great such an excellent
sense of what comes next what word comes
next it can narrow down it can't see the
future but it can narrow down the
possibilities correctly from its
understanding
being able to guess what comes next very
very accurately requires prediction
which is the way you operationalize
understanding
what does it mean for a neural network
to understand it's hard to come up with
a clean answer but it is very easy to
measure and optimize the Network's
prediction error of the next word
so we say we want understanding but we
can optimize prediction and that's what
we do
and that's how you get this current
large language models these are neural
networks which are large they are
trained with the back propagation
algorithm which is very capable and if
you allow yourself to imagine that an
artificial neuron is not that different
from a biological neuron then yeah like
our brains are doing are capable of
doing a pretty good job at guessing the
next word if you pay if you pay very
close attention
so so if I let I love that and I just
want to make this more concrete so just
to push that analogy further between the
biological brain and these neural um uh
analog physical networks digital
networks
um if the human if if we consider you
know before it was considered untenable
for these machines to learn now it's a
given that they can learn or do this
um uh do predictive outcomes of what's
going to come next if a human is at 1X
learning and you have the visibility
into the most recent chat gbt models
what would you put the most recent chat
gbt model as a ratio of where the humans
are at so if humans are at 1X what's
chat gpdn
you know it's a bit hard to make direct
comparisons between our artificial
neural networks and people because at
present
people
are able
to learn more from a lot less data
this is why these neural networks like
Chad GPT are trained on so much data to
compensate for their initial slow
learning ability
you know as we train these neural
networks and we make them better
faster learning abilities start to
emerge
but overall
overall it is the case that we are we
are quite different the way people learn
is quite different from the way these
neural networks learn like one example
might be you know these neural networks
they are
you know solidly good at math or
programming
but like the amount of math books they
needed to get let's say good at
something like
calculus is very high or as a person
would need a fairly you know two
textbooks and maybe 200 exercises and
you're pretty pretty much good to go
so there is just to get an order of
magnitude sense if you relax the data
constraint so if you let the machine
consume as much data as it needs do you
think it's operating at like one-tenth
of a human right now or
you know it's quite hard to answer that
question still and let me tell you why I
hesitate to like I think that any figure
like this will be misleading and I want
to explain why like because right now
any such neural network is obviously
very superhuman when it comes to the
breadth of its knowledge and to the very
large number of skills that these neural
networks have for example they're very
good at poetry and they're very good you
know like they can talk eloquently about
any topic pretty much
and they can talk about historical
events and lots of things like this
on the other hand on the other hand
people
can go deep and they do go deep so you
may have an expert like someone who
understands something very deeply
despite having read only a small amount
of documents let's say on the topic so
because of this difference I really
hesitated to answer the question in
terms of oh yeah it's like some some
number between zero do you think there
is a singularity point where the
machines will surpass the humans in
terms of the pace of learning and
adaption yeah and when do you think that
point will occur
I don't know I don't know when it will
occur I think some additional advances
will need to do will happen but
you know I absolutely would not bet
against this point occurring at some at
some point can you give me a range is it
at some point next month is it next year
you know
I think it's like the the uncertainty on
this thing is quite High because these
advances I can imagine it can take in
quite a while I can imagine it can take
any disappoint in a long time I can also
imagine it's taking
you know
some
number of years but it's just very it's
very hard to give a Cali braided answer
and I I know there's lots of push
forward so I'm going to ask one more
question then move on to some of the
other issues but um I know I read that
when you were a child you were disturbed
by the notion of Consciousness and I
wasn't sure what that what that word
meant Disturbed but I'm curious do you
view Consciousness or sentience or
self-awareness as an extenuation of
learning do you think that that is
something that also is an inevitability
that will happen or not
yeah I mean on the Consciousness
questions
like yeah I was as a child that would
like you know look into my in my hand
and I would be like how can it be that
this is my hand that I get to see like I
something of this nature I don't know
how to explain it much better so that's
been something I was curious about
you know it's
It's Tricky with Consciousness because
how do you define it it's something that
the looted definition for a long time
and how can you test it in a system
maybe there is a system which acts
perfectly right but um perfectly the way
you'd expect
um
a conscious system would act yet maybe
it won't be conscious for some reason I
do think there is a very simple way to
there's there is an experiment which we
could run on an AI system which we can't
run on which we can't run just yet but
maybe
in like the Future Point when the AI
learns very very quickly from less from
less data we could do the following
experiment
very carefully with very carefully
curate the data
such that we never ever mention anything
about consciousness it would only say
you know here is
here's a ball and here's a castle and
here is like a little toy like you would
imagine imagine you'd have data of this
sort it would be very controlled maybe
we'd have some number of years worth of
this kind of training data
maybe it would be maybe such an AI
system would be interacting with a lot
of different teachers learning from them
but all very carefully you never ever
mentioned Consciousness you don't talk
about
people don't talk about
anything except for the most surface
level Notions of their experience and
then at some point you sit down this Ai
and you say Okay I want to tell you
about Consciousness it's the stain
that's a little bit not well understood
people disagree about it but that's how
they describe it and imagine if the AI
then goes and says oh my god I've been
feeling the same thing but I didn't know
how to articulate it that would be okay
that would be definitely something to
think about it's like if the AI was just
trained on very mundane data around
objects and going from place to place or
maybe you know
something like this from a very narrow
set of Concepts we would never ever
mention that and if it could somehow
eloquently correctly talk about it in a
way that we would recognize that would
be convincing
and do you think of it as a some as
Consciousness as something of degree or
is it something more binary uh
I think it's something that's more a
matter of degree
I think that
I think that like you know
let's say if a person is very tired
extremely tired and maybe drunk then
perhaps if that's when when someone is
in that state and maybe their
Consciousness is already reduced to some
degree
I can imagine that animals have a more
reduced form of Consciousness if you
imagine going from
you know large primates maybe dogs cats
and then eventually you get mice you
might get an insect like
feels like I would say it's pretty
continuous yeah
okay I want to move on even though I
could I would love to keep asking more
questions along the lines of the
technology but I want to move on to
talking about the mission of openai and
how you perceive or any issues around
ethics and your role as Chief science
officer how ethics informs if at all how
you think about your role and so let me
just lay a couple Foundation points out
and then have you speak
um as you know open ai's mission is to
ensure the art of that artificial
general intelligence benefits all of
humanity and it started off as a
non-profit and open source and it is now
a for-profit and closed-sourced and with
a close relationship with Microsoft and
Elon Musk who I believe recruited you to
originally join open Ai and gave 100
million dollars when it was a non-profit
has says that the original Vision was to
create a counterweight to Google and the
corporate world and he didn't want to
have a world in which AI which is has
which he perceives and others can have
an existential threat to humanity to be
solely in the holds of of corporate of a
for-profit
um and now
open AI is neither open nor exclusively
a non-profit it's also a for-profit with
close ties to Microsoft and it looks
like the world may be headed towards
um a private duopoly between Microsoft
and Google
can you shed light on the calculus to
shift from a for-profit to a non-profit
and did you weigh in the ethics of that
decision and do ethics play a role in
how you conceive of your role as the
chief science officer or do you view it
more as something that somebody else
should handle and you are mainly just
tasked with pushing the technology
forward
yeah so this question is many parts let
me yeah let me think about the best way
to to approach it
so there are several parts there is the
there is the question around open source
versus closed source
there is a question around non-profit
versus for-profit
and the connection with Microsoft
and how to see that in the context of
Elon musk's recent comments
and then the question about how I see my
role in this maybe I'll start with that
because I think that's easier Okay so
I feel
yeah
the way I see my role
I feel a lot I I feel
direct responsibility for whatever open
AI does even though
I my role is primarily around advancing
the science
it is still the case I'm one of the
founders of the company
and
ultimately I care a lot
about open ai's overall impact
now I want to go so with this context I
want to go and talk about the open
source versus closed source
and the non-profit versus for-profit
and I want to start with open source
which is closed source
because I think that
you know the challenge with AI
is that AI is so all encompassing
encompassing
and it comes with many different
challenges it comes with many many
different dentures
which come into conflict with each other
and I think the open source versus
closed source is a great example of that
why is it desirable well let me put it
this way
what are some reasons
for which it is desirable to open source
AI
the answer there would be
to
prevent
concentration of power
in the hands of those who are building
the AI so if you are in a world where
let's say there is only a small number
of companies you might that control this
very powerful technology
you might say this is an undesirable
world and that AI should be open and
that anyone could use the AI this is the
argument for open source
but this argument
you know of course you know to State the
obvious there are near-term commercial
incentives against open source but there
is another longer term argument against
open sourcing as well which is
if we believe
if one believes that eventually AI is
going to be unbelievably powerful
if we get to a point where your AI is so
powerful where you can just tell it hey
can you autonomously
create a biological I don't know a
biological research lab
autonomously
do all the paperwork render space hire
the technicians aggregate experiments do
all this autonomously
like that starts to get
incredible that starts to get like
mind-bandingly powerful should this be
open sourced also
so my position on the open source
question is that I think that
I think that there is a maybe
a level of capability you can think
about these neural networks in terms of
capability
how capable they are how smart they are
how much how many how much how much can
they do
when the capability is on the lower end
I think open sourcing is a great thing
but at some point and you know there can
be debate about where the pointer is
but I would say that at some point the
capability will become so vast that it
will be obviously irresponsible to open
source models and was that the driver
Behind Closed sourcing it or was it
driven by a a devil's compact or
business necessity to get cash in uh
from Microsoft or others to support the
viability of the business was the
decision making to close it down
actually driven by that line of
reasoning or was it driven by more so
it's so so the way I'd articulate it you
know my view is that the current level
of capability is still not that high
where it will be the safety
consideration it will drive the close
closed Source in the model this kind of
this kind of research
so in other words a claim that it goes
in phases right now it is indeed the
competitive phase but I claim that as
the capabilities of these models keep
increasing
there will come a day where it will be
the safety consideration that will be
the obvious and immediate driver to not
open source these models
so this is the open source versus closed
Source but your question had enough but
your question in another part which is
non-profit versus for-profit
and we can talk about that also
you know indeed it would be preferable
in a certain meaningful sense if open AI
could just be a for a non-profit from
now until the mission of open AI is
complete
however one of the things that's worth
pointing out is the very significant
cost of these data centers I'm sure
you're reading about various AI startups
and the amount of money they are raising
the great majority of which goes to the
cloud providers
why is that
well the reason so much money is needed
is because
this is
the nature of these large neural
networks they need the compute end of
story you can see something like this
that's all you can see a divide that's
now happening between Academia
and the AI companies
so for a long time for many decades
Cutting Edge research in AI took place
in academic departments in universities
that cap being the case up until the
mid-2010s
but at some point when the complexity
and the cost of this project started to
get very large it no longer remained
possible for universities to be
competitive and now universities need a
University Research in AI needs to find
some other way in which to contribute
those ways exist they're just different
from the way they're used to and
different from the way the companies are
contributing right now
now
with this context you're saying okay
the thing about non-profit a non-profit
is the people who give money to a
non-profit never get to see any any of
it back it is a real donation
and believe it or not it is quite a bit
harder to convince people to give money
to a non-profit and so we so so we think
what's what's the solution there or what
is a good course of action
so we came up with an idea
that to my knowledge is unique
in all corporate structures in the world
the open air corporate structure is
absolutely unique
open AI is not a for-profit company it
is a capped profit company
and I'd like to explain what that means
what that means
is that
equity in open AI can be better seen as
Bond rather than equity in a normal
company the main feature of a bond is
that once it's paid out it's gone
so in other words
open AI has a finite obligation to its
investors as opposed to an infinite
obligation to that normal companies have
and does that include the founders do
the founders have equity in open AI
so Sam Altman does not have equity
but the other Founders do and is it
capped or is it unlimited it is capped
and how does that cap is that capped at
because the the founders I presume
didn't buy in unless it's capped at the
nominal
Share value
um
I'm not sure I understand the question
precisely but what I can say like what
what I can answer the part which I do
understand which is like
there is certainly like it isn't there
are it is a different it is different
from normal startup Equity but there are
some similarities as well where the
earlier you join the company
the higher the cap is because then
the larger cap is needed to attract the
initial investors as the company
continues to succeed the cap decreases
and why is that important it's important
because it means that the company
one once when once all the obligation to
investors and employees are paid out
open AI becomes a non-profit again
and you can say this is totally crazy
what are you talking about like it's not
going to change anything
but it's worth considering what we
expect like it's worth
looking at what we think AI will be
I mean we can look at what AI is today
and I think it is not at all
inconceivable
for open AI tool achieve its
to pay out its obligation to the
investors and employees become a
non-profit at around the time when
perhaps the computers will become so
capable where the economic destruction
will be very big where this transition
will be very beneficial
so this is the answer on the cap profit
versus
non-profit
there was a last part to your question I
know I'm speaking for a while but the
question had many parts the last part of
your question is the Microsoft
relationship
and
so here
the thing that's very fortunate
is that Microsoft is a
there thinking about these questions the
right way they understand
the potential and the gravity of AGI and
so for example on the on all the
investor documents that any investor in
open AI has signed and by the way
Microsoft is an investor into open AI
which is a very different relationship
from the
deepmind
any anyone who signed any document any
investment document there is a a purple
rectangle at the top of the investment
document which says that the fiduciary
duty of open AI is to the open AI
mission
which means that you run the risk of
potentially losing all your money if the
mission comes in conflict
so this is something that all the
investors have signed
and let me just make this clear for
everybody because Google Google acquired
deepmind so deepmind was just an asset
inside of Google but beholden to Google
you're making the distinction that with
openai Microsoft is an investor and so
beholden to this fiduciary duty for the
mission of openai which is held by the
non-profit which is a is is a a GP or an
LP in the
um in in the for-profit
um okay understood yeah so it's not
something like this you know I am
you know there are people
I can't tell you the precise details
yeah but so but this is the general
picture
and you know some have claimed though
now especially it uh um Steve Wozniak
the co-founder of apple and Elon Muska
famously signed this very public
petition saying that the point of no
return is already passed or we're
approaching it where it's going to be
impossible to reign in Ai and it's and
it's it's repercussions if we don't halt
it now and they've called for halting AI
um
I'm curious
on you are a world citizen Ilia you were
born in Russia you were raised in Israel
you're Canadian
um and I'm and it's open ai's response
to that public petition was
um I know Sam basically said that you
know this wasn't the right way to go
about doing that but also in parallel
Sam is on a world tour with many
countries that also can be antagonistic
towards the West
are there any citizen obligations
ethical obligations that you think also
overweigh your your technological
obligations when it comes to spreading
the technology around the world right
now through open AI do you think that
should be beholden to a regulation or
some oversight
let me think
once again the question had a number of
Parts did I apologize I'm trying to give
you the so you can respond however you
want to on that I know we're going to
come out of off of time so I just want
to give you the mic and just share
everything that's on my mind and you can
decide how you want to handle it yeah
thank you
I mean
you know
it is true
that AI is going to become truly
extremely powerful and truly extremely
transformative
and I do think
that we will want to move to a world
with sensible government regulations and
there you know there are several
Dimensions to it
we want to be in a world where there are
clear rules about for example training
more powerful neural networks
we want there to be some kind of careful
evaluation careful prediction of these
of what we expect these neural networks
of what they can do today and on what we
expect them to be able to do let's say
in a year from now or by the time they
finish training I think all these things
will be very necessary in order to
like rational like rationally
I wouldn't use the word slow down the
progress I would use the term you want
to make it so that the progress is
sensible so that with each step we've
done the homework and indeed we can make
a credible story that okay
the neural network the system that we've
trained it has we are doing this and
here all the steps and it's been
verified or certified I think that is
the world that we are headed to which I
think is correct
and as for the citizen obligation I feel
like
I mean
15 what
I'll answer it like this
like I think I think like there are
there are two answers to it so obviously
you know I live I live in the United
States and I really like it here and I
want and I want this place to flourish
as much as possible
I care about that
I think that of course there will be
lots of but the world is much more than
just the US
and I think that these are the kind of
questions which I feel a little bit
let's say outside of my expertise how
these
between country relationships work out
but I'm sure there will be lots of
discussions there as well
yeah
um Julia can I turn a little bit towards
strategy
um I'm curious for you guys internally
what metrics do you track as your North
Star what are the most sacred kpis that
you use to measure open ai's success
right now
the most sacred kpis
you know I think this is also the kind
of question where maybe different people
will give you different answers
but I would say I would say that there
are
if I were to really narrow it down
I would say that there are
there is a couple of really important
kpi of a really important dimensions of
progress
one is undeniably the technical progress
are we doing good research
do we understand our systems better are
we able to train them better can we
control them better I is our
is ours is our research plan being
executed well is our safety plan being
executed well how happy are we with it I
would say this would be my description
of the primary kpi which is do a good
job of the technology then there is of
course stuff around the product but
which I think is cool but I would say
that it is really the core technology
which is the heart of openai the
technology its development
and on end
its control
it's steering
and and do you view um right now chat
gbt is a destination
do you view open AI in the future being
a destination that people go to like
Google or will it be powering other
applications and be the back end or be
be you know used as part of the back end
infrastructure
um is it a destination or is it going to
be more behind the scenes
um in in five to ten years
yeah well I mean things change so fast I
I cannot make any claims about
five to ten years in terms of the
correct shape of the product I imagine a
little bit of both perhaps but this kind
of question
I mean I think it remains to be seen I
think there are I think this stuff is
still so new
okay
I'm gonna ask one more question I'm
gonna jump to the student questions if
you're a student at Stanford today
interested in AI if you were you know
somebody who wants to be Ilia um what
would you focus your time and another
second question on this if you're also
interested in entrepreneurship
um where would you what would you what
advice would you give for a Stanford
undergrad engineer that's interested in
Ai and Entrepreneurship
so
I think on the first one
it's always hard to give generic advice
like this
but
I can still provide some generic advice
nonetheless
and I think it's something like
it it is generally a good idea to lean
into
one's unique predispositions
you know every you know why if you think
if you look if you think about the set
of let's say inclinations or skills or
talents that the person might have the
combination is pretty rare so leaning
into that is a very good idea no matter
which direction you choose to go look to
going and then on the AI research
like I would say
I would say that there
you know
I could say something but even but there
especially you want to lean into your
own ideas and really ask yourself what
can you is is there something
that's totally obvious to you that makes
you go why is everyone else not getting
it if you feel like this that's a good
sign it means that you might be able
that that you you want to lean into that
and explore it and see if your instinct
is true or not it may not be true
but you know my my advisor Jeff Hinton
says this thing which I really like he
says you should trust your intuition
because if your intuition is good you go
really far and if it's not good then
there's nothing you can do
hmm
and as far as entrepreneurship is
concerned
I feel like
this is a place where the unique
perspective is even more valuable or
maybe equally it's because it's maybe
maybe I'll I'll explain why I think it's
more valuable than in research well in
research it's very valuable too but in
entrepreneurship like you need to like
almost pull from your unique life
experience where you say okay I see this
thing I see this technology I see
something like take a very very Broad
View and see if you can hone in on
something and then actually just go for
it
so that would that would be the
conclusion of my generic advice okay
which is great that's also great I'm
going to move on to the student question
so one of the most upvoted question is
how do you see the field of deep
learning evolving in the next five to
ten years
let's see
you know I expect deep learning to
continue to make progress I
I expect that
you know there was a period of time
where
a lot of progress came from scaling
and you you saw that most in the most
pronounced way in going from GPT 1 to
gpd3
but
things will change a little bit the
reason the reason that the reason that
progress in scaling was so rapid is
because people had all these data
centers which they weren't using for a
single training run
so by simply reallocating existing
resources you could make a lot of
progress
and it doesn't take that long
necessarily to reallocate existing
resources you just need to you know
someone just needs to decide to do so
it is different now because the training
runs are very big and the scaling is not
going to be progressing as fast as it
used to be because building data center
takes time
but at the same time I expect deep
learning to continue to make progress in
uh from other places the Deep learning
stack is quite deep and I expect that
there will be improvements in many
layers of the stack and together they
will still lead to progress being very
robust
and so
if I had to guess I'd imagine that there
would be maybe
I'm certain we will discover new
properties which are currently unknown
of deep learning and those properties
will be utilized and I fully expect that
the systems of five to ten years from
now will be much much better than once
they are we have right now
but exactly how it's going to look like
I think
I think it's a bit harder to answer it's
a bit like
it's because the improvements that there
is there will be maybe a small number of
big improvements and also a large number
of small improvements all integrated
into a large complex engineering
artifact
and can I ask your you know your
co-founder Sam Altman has said that
we've reached the limits of what we can
achieve by scaling to larger language
models is do you agree
um and if so you know what then what is
the next Innovation Frontier that you're
focusing on if that's the case yeah so
I think maybe
I don't remember I don't know exactly
what he said but maybe he meant
something like that the age of easy
scaling has ended or something like this
like of course of course the larger
neural Nets will be better but it will
be a lot of effort and cost to do them
but I think there will be lots of
different Frontiers and actually into
the question of how can one contribute
in deep learning identifying such a
frontier perhaps one that's been missed
by others is very fruitful
and is it can I go even just deeper on
that because I think there is this
debate about vertical Focus versus
General
um uh General's training you know is it
better do you think there's better
performance that can be achieved in
particular domains such as law or
Medicine by training with special data
sets or is it likely that generalist
training with all available data will be
more beneficial
so
like at some point we should absolutely
expect Specialists training to make a
huge impact
but the reason we do the generalist
training
is just so that we can even reach the
point where
just so that we can reach the point
where the neural network can even
understand the questions that we are
asking and only when it has a very
robust understanding only then we can go
into specialist training and really
benefit from it so yeah I mean I think
all these I think these are all fruitful
directions
but you don't think when do you think
we'll be at that point when specialist
training
is the thing to focus on I mean
you know like if you look at people who
do open source work people who work with
open source models they do a fair bit of
this kind of specialist training because
they have a fairly underpowered model
and they try to get any ounce of
performance they can out of it
so I would say that this is an example
I'd say that this is an example of it
happening like it's already happening to
some degree it's not a binary it's you
might want to think of it as of like a
continual Spectrum but do you think that
the competitor do you think that the
winning Advantage is going to be having
these proprietary data sets or is it
going to be having a much higher
performance large language model when it
comes to these applications of AI into
verticals
so I think it may be productive to think
about about an AI like this as a
combination of multiple factors where
each factor makes a contribution
and
is it better to have a special data
which helps you make
your AI better in a particular set of
tasks of course is it better to have a
more capable base model of course from
the perspective of the task so maybe
this is the the answer it's not an
either or
I'm going to move down to the other
questions
um there's a question on what was the
cost of training and developing GPT T3
slash four
yeah so
you know for for obvious reasons I can't
comment on that
um but there I think there is a you know
I think even from our research Community
there's a strong desire to be able to
get access to
um uh to different aspects of open ai's
technology and are there any plans for
releasing it to researchers or to other
startups to encourage more competition
and Innovation some of the requests that
I've heard are unfettered interactions
without safeguards to understand the
model's performance model specifications
including details on how it was trained
and access to the model itself I.E the
trained parameters do you want to
comment on any of that
yeah I mean I think
like it's related to our earlier
question about open versus closed
I think that there are some
intermediate approaches which can be
very fruitful
for example
model access and various combinations of
that
can be very very productive because
these mineral networks already have such
a large and complicated surface area of
behavior
and
and studying that alone can be extremely
interesting look if you have an academic
access problem we provide various forms
of access to the models and in fact
plenty of academic research Labs do
study them in this way
so
I think this kind of approach
is viable
and it's something that we could that we
are doing
I know we're coming up on time I want to
end with just one final question which
is can you just share any unintuitive
but compelling use cases for how you
love to use chat gbt that others may not
know about
um
so I mean I don't I wouldn't say that
it's unknown but I I really enjoy its
poem writing ability
it can write poems it can rap it can it
can be it can be it can be pretty
amusing
and do you guys use it is it is it an
integrated part of the
um of teamwork at open I assume it is
but I'm curious do you have any insights
on how it changes Dynamics with teams
when you have ai deeply integrated into
you know a human team and how they're
working and any insights into to what we
may not know but that will come
I would say I would say to today the
best way to describe the impact is that
everyone is a little bit more productive
people are a little bit more on top of
things I wouldn't say that right now
there is a dramatic impact on Dynamics
which I can say oh yeah the Dynamics
have shifted in this pronounced way
okay I'm curious if it depersonalizes
conversations because it's the AI bot or
maybe it but maybe we're not at that
point yet where it's specifically
that I definitely I I don't think that's
the case and
I predict that will not be the case but
we'll see
well thank you Ilya for a fascinating
discussion time is always too short
you're always invited back to the farm
um we'd love to have you either
virtually or in person
um so thank you thank you thank you um
to our audience thank you for tuning in
for this session of the entrepreneurial
thought leader series next week where
we're going to be joined by the
executive chairman and co-founder of
OCTA Frederick karist and you can find
that event and other future events in
this ETL series on our Stanford e-corner
YouTube channel and you'll find even
more of the videos podcasts and articles
about entrepreneurship and Innovation at
Stanford e-corner that's
ecorner.stanford.edu and as always thank
you for tuning in to ETL
thank you
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