Interview with Dr. Ilya Sutskever, co-founder of OPEN AI - at the Open University studios - English
Summary
TLDR本次访谈中,OpenAI联合创始人兼首席科学家Ilya Sutskever分享了他的个人旅程和对人工智能、机器学习和开放AI的看法。他强调了开放AI的目标是确保人工智能技术的发展能够造福全人类,并讨论了大规模神经网络的发展历程,以及AI对未来教育的潜在影响。此外,他还提到了人工智能领域未来可能的发展方向,包括AI在经济中的进一步整合,以及面对超级智能时的挑战和机遇。
Takeaways
- 🎓 Shai Solomon 是美国以色列开放大学之友的董事会成员,同时也是checkpoint软件技术公司的全球网络安全人才发展总监。
- 👩🏫 Dr Ellie Shai 是开放大学以色列分校的助理教授,也是神经和生物形态工程实验室的主要调查员。
- 🤖 Ilya Sutskever 是机器学习领域的著名科学家,OpenAI的联合创始人兼首席科学家。
- 🏫 采访讨论了与以色列技术、世界以及开放大学相关的问题,强调了开放对话和听取世界领导者的第一手观点的重要性。
- 📚 Ilya Sutskever 在开放大学的学习经历,以及他对人工智能领域的兴趣起源。
- 🌐 OpenAI 的成立初衷是为了让AI技术更好地造福人类,以及对通用人工智能(AGI)的认真对待。
- 🔍 Ilya Sutskever 认为,深度学习技术的发展和信念是OpenAI成立的关键因素。
- 🧠 神经网络的发展历程,从早期的人工神经元概念到现在的大规模神经网络,如GPT-3和GPT-4。
- 📈 AI领域的未来发展趋势,包括短期内AI的能力和应用的增强,以及长期面对超智能AI的挑战。
- 🏫 教育领域的未来,特别是高等教育和AI工具在教育中的应用,以及它们对课程和教育生态系统的影响。
- 🌟 开放大学对以色列社会和创新经济的贡献,以及它在提高以色列人口技能和创新能力方面的关键作用。
Q & A
Shai Solomon 是哪个组织的董事会成员?
-Shai Solomon 是美国以色列开放大学之友(American Friends of the Open University of Israel)的董事会成员。
Ilya Sutskever 在 OpenAI 中担任什么职位?
-Ilya Sutskever 是 OpenAI 的联合创始人兼首席科学家。
Ilya Sutskever 为何对开放大学心怀感激?
-Ilya Sutskever 对开放大学心怀感激,因为他在没有高中文凭的情况下,从八年级开始就能在开放大学上课,这为他提供了学习的机会。
Ilya Sutskever 认为人工智能的未来会如何发展?
-Ilya Sutskever 认为人工智能的未来将涉及更大规模、更复杂的工程项目,需要更多的工程师合作完成,而且他预见到人工智能将朝着通用人工智能(AGI)的方向发展。
OpenAI 成立的主要目的是什么?
-OpenAI 成立的主要目的是推动人工智能的发展,并确保这项技术能够尽可能地惠及全人类。
Ilya Sutskever 是如何描述深度学习技术的发展的?
-Ilya Sutskever 描述深度学习技术的发展是一个长期的过程,从1940年代的人工神经元概念开始,经历了多次重要的技术突破,包括反向传播算法的发明,以及后来的大规模神经网络的成功应用。
Ilya Sutskever 认为未来教育会如何变化?
-Ilya Sutskever 认为未来教育将会出现非常优秀的人工智能辅导老师,他们会帮助学生解答问题并提供个性化的教学,这将对教育体系产生深远的影响。
Ilya Sutskever 对于人工智能的长期发展有何预测?
-Ilya Sutskever 预测,在长期发展中,我们将面临人工智能变得比人类更聪明的挑战,这将需要全球范围内的规则、标准和协调来确保我们能够安全地享受人工智能带来的未来。
开放大学在以色列社会中扮演着怎样的角色?
-开放大学是以色列最大的高等教育机构,它通过教育大量高技能人才,对以色列的创新经济产生了巨大的正面影响,并且通过为来自社会边缘和地理偏远地区的学生提供教育机会,帮助解决了以色列的一些人口和社会挑战。
Ilya Sutskever 认为未来的工作会有什么变化?
-Ilya Sutskever 认为未来的工作将会发生变化,因为人工智能的发展将改变许多任务和活动的性质。他认为成为一个能够快速学习新事物、具有多面性和能够舒适使用人工智能工具的通才将变得非常重要。
Ilya Sutskever 为何认为深度学习的研究在过去十年主要是关于信念而非发明新事物?
-Ilya Sutskever 认为,深度学习的研究在过去十年主要是基于对这项技术能够实现预期目标的信念,而不仅仅是发明新事物。这是因为深度学习技术已经展示出了巨大的潜力,研究者们相信通过持续的研究和开发,这项技术能够带来突破性的进展。
Outlines
🌟 开场介绍与背景
视频开头介绍了Shai Solomon,他是美国以色列开放大学之友的董事会成员,同时也是checkpoint软件技术公司的全球网络安全人才发展总监。他邀请了Dr Ellie Shai作为嘉宾,后者不仅是神经和生物形态工程实验室的主要调查员,也是以色列开放大学的助理教授。他们讨论了机器学习和OpenAI的共同创始人及首席科学家Ilia Sutskever的成就,并强调了开放对话和听取世界领导人对以色列技术及世界问题的看法的重要性。
📚 Ilya Sutskever的学术旅程
Ilya Sutskever分享了他的学术旅程,他感激开放大学给他学习的机会,尽管他当时没有高中文凭。他从八年级开始就在开放大学上课,通过邮寄方式学习,这让他能够自主安排学习进度。他发现自己对计算机科学、数学和人工智能特别感兴趣,并在开放大学深入学习这些领域。
🤖 OpenAI的创立初衷
Ilya解释了他参与创立OpenAI的原因。在2014和2015年,他在谷歌从事深度学习研究,但他预感到AI的未来将需要更大规模和更有组织的工程项目。他意识到,随着神经网络和GPU的发展,AI的研究将变得更加复杂,需要团队合作。在与Altman、Brockman和Musk的一次晚餐中,他们讨论了如何创建一个新的AI实验室,以与谷歌和DeepMind竞争。最终,Ilya决定离开谷歌,加入并创建OpenAI,旨在推动通用人工智能(AGI)的发展,并确保其对人类有益。
🧠 神经网络的通用性和能力
Ilya讨论了神经网络的通用性和能力。他指出,尽管过去AI研究主要集中在学术界,但未来AI的发展将需要更大规模的工程项目。他强调,重要的不仅是神经网络能否处理多种任务,而是它们是否具备足够的能力。OpenAI的成立是基于对深度学习潜力的信念,即它能够实现更智能的现实。
🚀 神经网络的发展历程
Ilya回顾了神经网络从20世纪40年代的初步想法,到21世纪初的成功应用,再到OpenAI的成立和GPT-3、GPT-4的开发。他提到了人工神经元的发明、反向传播算法的发现,以及大型神经网络在解决实际问题上的成功。他强调了从监督学习到无监督学习的进步,以及OpenAI在推动这一领域发展中的关键项目。
📈 AI的未来和教育的影响
Ilya预测了AI领域的未来发展,他认为短期内AI将变得更加智能和有用,并将更深入地融入经济中。长期来看,他提到了超级智能的概念,这将带来新的挑战和机遇。他还讨论了AI对教育的影响,特别是高等教育,预计AI将成为优秀的私人教师,帮助学生解决学习中的难题。他强调了在AI时代成为通用主义者的重要性。
🎓 教育的未来和开放大学的作用
视频最后强调了以色列开放大学在教育和创新方面的重要作用。作为以色列最大的高等教育机构,开放大学为以色列创新经济培养了大量高技能人才,特别是STEM领域的学生。视频中提到,开放大学通过教育投资,帮助以色列保持全球创新领导者的地位,并解决了社会和人口挑战。
Mindmap
Keywords
💡人工智能
💡深度学习
💡神经网络
💡OpenAI
💡教育
💡机器学习
💡通用人工智能
💡反向传播算法
💡以色列开放大学
💡技术创新
Highlights
Shai Solomon担任美国以色列开放大学之友的董事会成员以及checkpoint software Technologies的全球网络安全人才发展总监。
Dr Ellie Shai是神经和生物形态工程实验室的主要调查员,也是以色列开放大学的助理教授。
Ilia Sutskever是机器学习领域的著名科学家,OpenAI的联合创始人兼首席科学家。
Ilia的学术之旅始于以色列开放大学,他从八年级开始就在那里学习。
Ilia在OpenAI的建立初衷是为了让人工智能(AI)的发展更加有组织、更大规模,以应对AI技术的未来挑战。
Ilia认为深度学习是AI发展的关键,他相信深度学习能够实现通用智能。
OpenAI的目标是开发技术,并找到方法使AI尽可能有益,从而使全人类受益。
Ilia讨论了神经网络的发展历程,从20世纪40年代的人工神经元概念到现代的深度学习。
Ilia提到了他在多伦多工作时,机器学习的成功并不明朗,但随后的发展证明了神经网络的有效性。
Ilia解释了无监督学习的概念,以及如何通过预测下一个词来训练神经网络。
GPT-3和GPT-4的发展展示了神经网络在理解和生成语言方面的巨大潜力。
Ilia预测AI将在中短期内变得更加智能,并在经济中发挥更大的作用。
长期来看,Ilia认为我们将面临超级智能的挑战,并需要制定规则和标准来管理这些智能系统。
Ilia讨论了AI对教育的影响,特别是AI作为个人辅导工具的潜力。
Ilia强调了在教育中学习如何使用AI工具的重要性,以及这将如何改变未来的工作性质。
Ilia对以色列开放大学的赞赏,认为它是以色列创新经济中高技能人才的重要教育基地。
Ilia提到了OpenAI的成立过程,包括与Elon Musk等人的会面和决定离开Google的经历。
Ilia分享了他对于AI未来可能带来的挑战的看法,以及我们如何共同应对这些挑战。
Transcripts
foreign
my name is Shai Solomon and I'm honored
to serve as the board member for the
American Friends of the open University
of Israel as well as the global director
of cyber security Workforce Development
a checkpoint software Technologies
joining me today is Dr Ellie Shai
ezatsuo who is not only the principal
investigator of the neuro and biomorphic
Engineering Labs but also hold the
position of assistant professor at the
open University of Israel we are
delighted to have the opportunity to
interview Elia suitskiver a renowned
scientist in the field of machine
learning and co-founder and chief
scientist at openai
as a sponsor of discussion on issues
related to Israel technology and the
world we are proud to support the open
University of Israel a non-partisan
education institution and the largest of
Israeli 10 accredited universities we
believe that foresting open dialogue and
hearing their first perspective from
world leader on issues related to
Israeli and the world is essential and
we are confident that our audience will
greatly benefit from hearing Ilya unique
perspective of the open University and
his professional career
Ilya is an honor and a pleasure to have
you here with us
thank you for joining us given your
expertise we would like to discuss a
wide range of topics related to your
personal Journey machine learning open
Ai and your thoughts on the future of
Education we will be asking a number of
questions over the next 40 minutes or so
so let's jump in hi Elia can you please
share with us your initial academic
Journey at the open University of Israel
and how we became interested in the
field of artificial intelligence
you know I I owe I feel I feel a lot of
gratitude to the open University
what happened was that
I was in school
and I was doing quite well
and together with my parents we were
looking for
some ways in which I could learn more
and it was so it was the case that
the open University
accepts
anyone regardless of whether they have a
high school degree or not
and so for this reason I was able to
start taking classes in the open
University
starting from eighth grade
and
that was that was that was really great
and I really liked those classes it was
you know how it works you get books by
mail and you send the problem sets you
mailed back the problem sets and you go
write the exam and you can study
whatever you want and I I really like
that
and
it was possible only because the open
University
took me even though I was
a young student without the credentials
to study in a regular University
but then
the question of computer science and
math and AI so I would say that
so I think I think in my case it was
pretty clear
that these are
the subjects that I was most drawn on
even as an early child as a young child
and so that's why I studied them at the
open University it was still
a little bit a few years before I really
set my eyes on AI
that's great I mean sounds like great
experience and did you leverage like
remote learning I mean like sending over
your work or did you did you go to a
physical uh classes there were physical
classes but they would be very
infrequent so I would go maybe once a
week or twice a week yeah so the great
majority of the
of the learning was remote and at my at
my at my own schedule
and I found that it happened to be a
good fit for me
I found that I could just and the books
are very well written too so it made it
very
you could you didn't it was
you know if the books were less good
it would have been harder yeah but I
thought the books were very good and but
for that reason it was very possible to
just read it slowly
do the exercises and that's that's all
you needed
yeah
okay so moving from the past to the
present
uh let's talk about open AI so what were
the main reasons for you to establish
open AI
so the time it's the time maybe a year
before
we started openai
I was a researcher at Google
and I was working on deep learning
and I was having a lot of fun I was
really enjoying my time at Google
doing the research there and working
with the people
with my colleagues at Google
but
the thing which I felt
already then in 2014 and 2015.
is that the future of AI
is going to be
much
is going to have that so maybe for a
little bit of context
AI research has strong academic groups
yeah it means that all of the AI was
done in University departments it was
done by professors with their grad
students almost entirely there's also
been some AI being done in companies but
I would say that for the most part the
majority of the most exciting work came
from universities
and then back in the day that was the
the only successful model
and that was also the model that Google
has adopted where you have as an
environment that is similar to the
university environment where you have
small groups of researchers working
together on a project
and already then I felt that that's not
the future I felt that the future would
be much
more
much larger and much more organized
engineering projects
because it was clear that AI was going
larger with larger neural networks and
larger but more gpus which in turn means
more engineer the stack gets very
complex it becomes very difficult for a
small group of people to do to do to do
something like a very small group of
people to
complete a big project like this
teamwork is required
and that was one of the reasons and so I
was kind of sitting at Google and
feeling a little bit Restless
but I didn't know what to do about it
so I was
feeling a bit
like it wasn't quite right and then one
day
basically
like some kind of picture this here I am
Daydream like it was daydreaming that
maybe I could start an AI company but it
really wasn't clear how I would do it
how would you possibly get the money for
such a thing those things would be
expensive
there was there was a daydreaming
element to it but I didn't really think
very seriously about it because it was
obviously impossible
and then one day I received an
invitation to get dinner with some
Altman and Greg Brockman and Elon Musk
and here here I am sitting getting
dinner with these amazing people in mind
you it was a cold email it's reached out
to me say hey let's let's hang out
essentially
how did they reach out to you
email email like uh
just just an email you received the name
and say hey like you know do you want to
join yeah it sounds like in that context
it sounds like a you know uh fishing or
some uh malicious email because it's so
extreme
no I mean you know it looks it looks but
it's it was
that it was definitely not that it was
very clearly authentic but it was a
little bit for me it was a small moment
of wow that is so amazing so of course I
went and here I was at the dinner and
they were discussing how could you start
a new AI lab which would be a competitor
to
Google into deepmind which back then had
absolute dominance
and that was the initial conversation
you know then it was of course for me to
leave Google it was quite uh
difficult decision because Google was
very good to me it was very very a very
good place to be
but eventually I decided to leave Google
and to join and create open Ai and
ultimately the pre the idea of open air
is to take the idea of AGI seriously
it's the idea is to take like you know
because when you are a researcher you
know researchers
are somehow I would say train to think
small
I think researchers
due to the nature of the work small
thinking gets rewarded because you have
these problems and you're trying to
solve them all the time and it's quite
hard to make even small steps so you're
just focused on what's coming at you the
next step and it's harder to see the
bigger pitch
but at open AI we took the liberty to
take to look at the big picture
we ask ourselves okay what's the where
is AI going towards
and the answer is AI is going towards
AGR towards an AI which eventually is as
smart or smarter than a human in every
way
and you think about that and you go wow
this is a really profound
that is a very profound thing
and so with open AI
we thought it
we thought that it made the most sense
to give it the explicit goal
to make AI benefit make AGI benefit of
humanity because this technology is just
going to be so transformative it's going
to turn everything upside down on its
head
Whenever there is such a big change who
knows what's going to happen
so for this reason the goal of open AI
is not only to develop the technology
but also
to find a way to make it
as beneficial as possible to make it
benefit of humanity and so the
combination of those big ideas and those
incredible people that were at that
dinner it just I I just
despite despite all the difficulties
that Google has put in in front of me to
leave I still decided to go for it
and yeah it's been now more than seven
and a half years and
it's been a very
exciting and gratifying Journey
thank you for being so honest and open
with us we really appreciate it so you
know back in the days when people talked
about machine learning it was more about
finding you know small patterns and
maybe find some statistical
and statistical you know
is a statistical pattern within the data
for very specific problems so you had a
model for computer vision you had a
model for language and you had a model
for for this in the middle for that but
here you are talking about general
intelligence
and can you tell can you identify the
moment when you said you know this
technology this this neural networks can
be used for multiple problems for
multimodal sensing they can be something
that can be General enough
because back in the days when we were
limited by you know the hardware
capabilities that we had you know before
the age of the gpus and everything it
was pretty limited to specific domains
but when was the time that you said this
is going to be big this this can get
seriously in the field of general
intelligence to go ahead and
start open AI
it was a bet on deep learning it was a
bet that somehow with deep learning we
will figure out how to make smarter and
smarter realities so in some sense the
creation of open AI was already an
expression of this bet of the idea that
deep learning can do it you just need to
believe and in fact I would argue that a
lot of a lot of you know deep learning
research at least in the past decade
maybe a bit less now has been about
faith about
rather than inventing new things just
believing that the technology that the
Deep learning technology can do it
but now I want to talk about the
question and you said and why I want to
explain just a bit why I think it's not
quite the right question
so
you asked when
do you become clear that you know a
neural network could be General and can
do many tasks which in some sense is
what we are moving towards but I would
argue that this is the less important
dimension
the more important dimension
is that of
capability and act and and competence
rather is the neural network competent
you know you can have a specialized
language neural network where you don't
have a language an image neural network
but is it actually good
if it's not good
then it's not interesting
so the question is not whether
deep learning can be General
but whether it can be competent
and what we are seeing now is the Deep
learning can indeed be competent maybe
you can talk us it take us a little bit
into your journey in the development of
this
large-scale neural network that you
worked in I mean where did you start and
how it was evolved over the years to
become GPT 3 and gpt4
you know it's a it's a long it's a long
story with many
interlocking parts
let's say the evolution has gone
the story of deep learning can be seen
it's quite an old story
maybe a 70 year old story
back in the 40s
researchers have already started to
think about the ideas that were later to
become the Genesis and deep learning
it is the idea of the artificial neural
you see the human brain
is big
in a sense that it has 100 billion
neurons
and the human brain is also
at least until like or arguably steal
the best example of intelligence that
exists in the universe
so then you can start asking yourself
the question of okay so what is it about
the brain that makes it smart
well maybe if you had
a lot of neurons arranged in a certain
correct way
you would get intelligence
and so now you can ask yourself what's a
neuron
so biological neurons have lots of
complicated behaviors but the idea that
the scientists from the 40s have is
maybe you can simplify
those biological neurons down to
something which would be their essential
computation something which is called
the artificially and it is very simple
it's just a simple mathematical formula
and then they started to ask questions
like what can you do with this
artificial neurons how can you arrange
them what kind of little problems they
can run
what kind of functions they can they can
compute
but this was just the first step this
was the first biggest
first big step is to invent the
artificial View
the second big step was
to discover
how these neurons can learn even in
principle
one of the obvious things about human
intelligence and also animal
intelligence
is that we learn
we learn from experience and we learn
and generalize and
this is the basis of us succeeding in
the world
so how does learning work
you know it's not you know right now we
are used to the idea that computers can
learn obviously
but I would say that even in
the year
2003 when I started working on
machine learning in Toronto
it wasn't clear that learning can be
successful they haven't been a really
successful examples
and so
a very big Discovery was an equation of
learning in neural networks a
mathematical equation that tells you how
to change the synapses of the neural
network
so to incorporate the experience
but it was just an idea it wasn't a
proven idea it was an idea that maybe
here is a mathematical mathematical
equation which might have the desirable
properties of learning that was done
that's the back propagation algorithm it
was done in 86.
by my by my PhD advisor Jeff Hinton
but then you so now you have the
artificial neuron and you have the back
propagation algorithm
and it's still an idea it's not proven
so I would argue then the next big step
and that took I would say the two
thousands was to prove that this idea is
actually good
and it is and it culminated this decade
culminated with a few demonstrations of
large neural networks large by the
standards of that decade really really
small by today's standards but a
demonstration that neural networks
trained with the back propagation
algorithm can in fact solve interesting
challenging and meaningful problems much
better than anyone could have imagined
and that was
like one of these demonstrations was the
neural network which beat all other
methods on on imagenet in 2012 which is
a project I was very fortunate to have
contributed to
and
that began
previous decade the 2010s where people
would just say okay well let's just
Tinker with these neural networks and
trying to improve them a little bit more
and progress continuing then continue
then continue
but it was all all of those so now I'm
going to get a little bit technical just
slightly technical for the I apologize
so all the success of deep learning up
until this point was in something which
is called supervised learning
it's a technicality it's very familiar
to those who are
um who have some for experience with
machine learning
or everything was about supervised
learning
in the first half of the 2010s it became
accepted
that if you have a neural network and
you do supervised learning it will
succeed and supervised learning means
that you know exactly what you want the
neural network to do
but then unsupervised learning which is
the much more exciting idea that you can
learn just from General data about the
world and learn everything somehow and
understand how the world Works without
being told without there being like a
like a teacher telling you what you're
supposed to learn that was not done yet
and then at open AI we had a sequence of
projects the first one was
with a sentiment newer and I want to
just explain that because that was an
important project
in our in our thinking
where we've shown that when you train a
neural network to predict the next word
in this case the next character
in Amazon reviews
one of the neurons in the neural network
will will eventually represent whether
this review is positive or negative
represent the sentiment but the
interesting thing here is that the
neural network was not trained to
predict the sentiment it was trained to
predict the next character and so that
project validated the idea that if you
can predict what comes next really well
you actually have to discover everything
there is about
the world or the the data source all the
secrets which are hidden in the data
become exposed to the neural network
as you can guess what comes next better
and better and better
and think about it like there is an
example which I've used a number of
times which I found that people uh like
were like imagine if you're like an
extreme example would be if you were
reading a book and some kind of a
mystery novel and on the last page of
the book The Mystery is revealed and
there is one place where the word or the
name of you know some key person is
revealed
if you can guess that name then wow
you've understood that novel pretty well
and so the neural network is strained to
predict what's going to come next to
guess you can't really you can only
narrow its guesses and have sharper and
sharper predictions
and that led then the scale up of that
led to GPD one and then gpt2 and gpt3
and then
you know with gpd3 in particular
it was a very surprising and a result
because of the really cool emerging
capabilities that showed up and then
further work and improvements and scale
out of led to gbt4
so I would say this is how we got to
where we are right now and obviously the
way everyone thinks about neural
networks is very different from before
if before
it just wasn't clear to
people that this stuff works I think it
is very clear to people now and in fact
right now we are grappling these
questions of well it works too well it's
going to be smarter than other than us
eventually what are we doing about that
right yeah so that's correct yes you
know yeah for sure so thank you for the
historical perspective and and obviously
you've been in you've been in key in
some very interesting key points to the
development of neural networks which was
fascinating to hear from you about it
so maybe you can elaborate a little bit
about how do you think the field of AI
will continue to evolve and many
advances in the in the future
and what do you think should we do in
order to take to ensure it's responsible
development
so my expectation
is that the way the field will evolve
is is as follows
I believe that in the near to medium
term
it will be a little bit like businesses
youth
where I expect
that the various companies that are
working on their AIS will continue to
make their AIS more more competence more
capable smarter more useful
I expect that AI will achieve a greater
a great and greater integration into the
economy more and more
tasks and activities will be assisted by
AI
that's I would say this is the near to
medium term
in the long term eventually we will
start to face the question of AI that is
actually smarter than all of us
with the super intelligence and that
starts to bring you into the domain of
Science Fiction but in reality
rather the idea is that people have
speculated about in the context of
Science Fiction become applicable
so at some point if you imagine a really
really smart AI
that is a scary concept
and as companies that are moving towards
it
will want to
have some kind of rules some kind of
Standards some kind of coordination
around
whatever it is that needs to be done on
the signs
on the
way that we use those AIS and how
they're being deployed on the way that
they are secured
so that we actually get to enjoy this
amazing
future that AI could create for us
if you manage to address all these
challenges so I would maybe phrase it
this way I say I get smarter and smarter
the challenges like the opportunity the
amazing things you could do increases
but the challenges still become
extremely dramatic
the challenges will become very
significant
and I think that everyone who's
developing this will be will somehow
be working together
to Grapple with those challenges to
solve the technical problems and the
human problems
to mitigate and to manage them I expect
that that's
rather I think that's something that
could happen
and I would really like for it to happen
back to education uh
we wanted to ask you how do you see the
future of Education especially higher
education
and uh you know AI tools and education
how it will impact the the the processes
to digest information to make it
accessible for uh students or for you
know the teachers the whole thing is
going through kind of transformation now
and would like to hear your perspective
about you know how it will impact the
curriculum and the whole ecosystem of
Education
yeah
so I mean I can I can you know I can
tell you that my my kids are using the
you know check GTP as an assistant for
their studies but that's you know that's
just a small example if you can take it
for a broader perspective
yeah
so I can talk about the near and medium
term
because I think there you can make
some educated guess is about what will
happen
and I think at this point it's pretty
obvious that we're going to have really
good really excellent AI tutors you may
maybe take
a little bit of time to really iron out
with the various
iron out the I guess issues to make it
really good and really reliable tutor
but it will be possible
so you could just have an amazing
private tutor that could answer detailed
questions about
almost any topic and help
help with any misunderstandings that you
might have and that's going to be that's
going to be pretty dramatic obviously so
like we go from having
being a student
requiring
to interact with one teacher and maybe
wrestle with books on your own to having
a really good teacher that can help you
with the
subject matter
write and answer your questions and
that's very interesting but um
and so I would say that this
all the students obviously going to use
that they'll want to use them
now I think a related question for
higher education or education in general
is what to study
because the nature of
the jobs that we would be having do
change
and I think that
probably being a really good generalist
who can
study new things quickly and be
versatile
and it can it and be very comfortable
with these AI tools I think that will be
very important for the near and medium
term
long term I don't know
but for the near and medium term I can
make that same
so I think now we will switch to Hebrew
if it's okay with you yeah certainly
so divisions what
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for taking time out of your very busy
schedule
to be with us today and speak about your
journey and the involvement of openai
let me add a word
to your comments although Israel now has
some natural gas
its key resource remain its human
capital and it must continue to invest
in it in order for Israeli to remain a
global Innovation leader
higher education in particular is the
critical investment needed to enhance
Israeli skill set and its ability to
innovate
in that regard we see the open
University with 53 000 students by far
the largest of its 10 accredited
University in Israel with nearly 40
percent of students studying stem
the open University is by far the
largest educator of Highly skilled
Talent into the Israeli Innovation
economy
educating press one quarter of all stem
students studying
across all Israeli University and with
80 percent of students at its open
University being first generation in
their family to attend University
including many who came from geographal
and a social periphery of Israeli
Society
it is also broadening the pie of who can
access higher education and thereby in
parallel addressing some of the Israeli
demographic and Social Challenges
among Israeli most vital institutions
that when that tremendous positive
impact of the open University on Israeli
Society is invaluable
I want to thank you our listener for
showing your commitment to Israeli and
the topics discussed here today thank
you all
thank you
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