Interview with Dr. Ilya Sutskever, co-founder of OPEN AI - at the Open University studios - English

Summary

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

00:00

🌟 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。

05:01

📚 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。

10:01

🤖 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。

15:04

🧠 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。

20:05

🚀 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。

25:09

📈 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。

30:11

🎓 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。

Keywords

💡人工智能

人工智能是指由人造系统所表现出来的智能行为，它能够通过学习和经验来执行复杂任务。在视频中，Ilya Sutskever讨论了人工智能的发展历程，特别是深度学习和神经网络在AI领域的重要性。

💡深度学习

深度学习是机器学习的一个子领域，它通过模拟人脑神经网络的结构和功能来学习数据的表示和模式识别。视频中提到，深度学习是AI发展的关键驱动力，特别是在图像识别和自然语言处理等领域。

💡神经网络

神经网络是一种模仿生物神经网络行为的计算模型，由大量简单的处理单元（类似于神经元）组成，并通过连接权重来传递和处理信息。在视频中，神经网络是讨论AI发展的核心概念之一。

💡OpenAI

OpenAI是一个致力于确保人工智能（AI）对全人类友好并能够安全应用的研究机构。视频中提到，Ilya Sutskever是OpenAI的联合创始人和首席科学家，他分享了OpenAI的成立初衷和目标。

💡教育

教育是指系统地获取知识、技能和习惯的过程，通常通过学校或其他教育机构进行。视频中讨论了AI技术对教育领域的潜在影响，包括个性化学习工具的发展。

💡机器学习

机器学习是人工智能的一个分支，它使计算机系统能够通过经验自动改进性能。在视频中，Ilya Sutskever讨论了机器学习的历史和发展，特别是深度学习在机器学习中的作用。

💡通用人工智能

通用人工智能（AGI）是指能够执行任何智能任务的人工智能系统，与人类智能相当或超越。视频中，Ilya Sutskever讨论了OpenAI的目标之一是认真对待AGI的可能性，并确保其对人类有益。

💡反向传播算法

反向传播算法是一种在神经网络中用于有效训练的数学算法，它通过计算误差梯度并反向传播这些梯度来调整网络权重。视频中提到，反向传播算法是由Ilya Sutskever的博士导师Jeff Hinton在1986年提出的。

💡以色列开放大学

以色列开放大学是一所非营利性的教育机构，提供远程教育和学位课程，旨在为所有希望接受高等教育的人提供学习机会。视频中提到，以色列开放大学是以色列最大的教育机构之一，对以色列的创新经济有着重要贡献。

💡技术创新

技术创新指的是创造或改进产品、服务或流程的新颖方法。视频中强调了技术创新在推动社会进步和经济增长中的关键作用，特别是在人工智能和机器学习领域。

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

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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