Geoffrey Hinton in conversation with Fei-Fei Li — Responsible AI development

Summary

在多伦多的MaRS Discovery District举行的一场激动人心的对话中，多伦多大学的Geoffrey Hinton教授和斯坦福大学的Fei-Fei Li教授共同探讨了人工智能（AI）的未来及其对社会的影响。作为深度学习领域的先驱，Hinton和Li分享了他们对于AI技术快速发展的看法，特别是聊天机器人技术如何在短时间内成为公众关注的焦点。他们深入讨论了AI在教育、医疗、农业等领域的应用潜力，同时也对AI带来的风险，包括就业、假信息传播、自动化武器等问题进行了分析。最后，他们呼吁对AI进行负责任的开发和应用，以确保技术进步能够造福全人类。

Takeaways

• 🧠 Geoffrey Hinton和Fei-Fei Li讨论了人工智能的当前状态和未来展望。
• 🔍 他们讨论了大型语言模型（LLM）和基础模型的潜力和挑战。
• 🌐 Fei-Fei Li强调了人文主义在人工智能发展中的重要性，呼吁在技术创新中考虑人类福祉。
• 🤖 Geoffrey Hinton表达了对超智能带来的潜在风险的担忧，特别是它可能对人类构成的威胁。
• 🏛️ 他们讨论了公共部门和学术界在人工智能研究和开发中的作用，以及私营部门的巨大影响力。
• 📚 强调了数据的重要性，以及大数据如何推动深度学习和人工智能的进步。
• 💡 讨论了人工智能在解决全球性问题（如健康、教育和气候变化）中的应用潜力。
• 🤝 强调了跨学科合作的重要性，以及如何通过结合多个领域的专长来克服人工智能领域的挑战。
• 📈 他们探讨了未来人工智能技术的发展趋势，以及如何平衡创新与伦理和社会责任。
• 🚀 讨论了启发年轻一代科学家和工程师追求在人工智能领域的大胆和有意义的工作的重要性。

Q & A

Meric Gertler 在致辞中提到了哪些土地的传统守护者?

Meric Gertler 提到了 Huron-Wendat、Seneca 和 Mississaugas of the Credit 作为多伦多大学所在地的传统守护者。

Geoffrey Hinton 被誉为什么?

Geoffrey Hinton 被誉为深度学习的教父。

Fei-Fei Li 在斯坦福大学担任什么角色?

Fei-Fei Li 是计算机科学的首席 Sequoia 教授，并且是人类中心 AI 研究所的联合主任。

Schwartz Reisman 创新园区的建立是由谁使之成为可能?

Schwartz Reisman 创新园区的建立是由 Heather Reisman 和 Gerry Schwartz 的慷慨和远见使之成为可能。

ImageNet 数据集是由谁创立的?

ImageNet 数据集是由 Fei-Fei Li 创立的。

AlexNet ImageNet 挑战的背后主要贡献者是谁?

AlexNet ImageNet 挑战的背后主要贡献者包括 Ilya Sutskever、Alex Krizhevsky 和 Geoffrey Hinton。

ImageNet 挑战对计算机视觉领域产生了什么影响?

ImageNet 挑战极大地推动了计算机视觉领域的发展，证明了深度神经网络在图像识别任务中的超凡效果。

2012年 ImageNet 挑战的胜利如何影响了神经网络的接受度?

2012年 ImageNet 挑战的胜利显著提高了神经网络的接受度，让以前批评和怀疑神经网络的研究人员开始采用这种技术。

Fei-Fei Li 的书《我所见的世界》主要讲述了什么?

Fei-Fei Li 的书《我所见的世界》主要讲述了她对于大数据的理解、深度学习的潜力以及人工智能技术的道德责任。

Geoffrey Hinton 对于人工智能未来的看法是怎样的?

Geoffrey Hinton 对于人工智能未来持有担忧态度，特别是关于超智能带来的存在风险和道德挑战。

Outlines

00:00

😀 开场致辞

Meric Gertler 在多伦多大学的 MaRS Discovery District 举办的 Radical AI 创始人活动上致开幕辞，欢迎来宾，并简要介绍活动背景。他强调了多伦多大学在人工智能领域的领导地位，特别提到 Geoffrey Hinton 和他的学生对深度学习的贡献。Gertler 随后介绍了即将推出的 Schwartz Reisman Innovation campus，强调其将如何推动创新和科技发展。

05:01

🤖 AI技术的进步与影响

活动中讨论了 Geoffrey Hinton 和 Fei-Fei Li 在人工智能领域的贡献，特别是在深度学习和计算机视觉方面。提到了他们的工作对学术界和工业界的影响，以及他们在推动技术创新和理解人工智能潜力方面的角色。此外，还强调了对于技术伦理、社会责任以及AI对未来人类社会影响的关注。

10:05

🌐 ImageNet的诞生与影响

Fei-Fei Li 讲述了 ImageNet 数据库的创建过程，包括最初的挑战、目标和对机器学习领域的深远影响。ImageNet 通过提供大量标注图片，极大地推动了计算机视觉和深度学习技术的发展。这一段还提及了与 ImageNet 相关的重要事件，如 ImageNet Challenge，以及这些活动是如何促进深度学习模型，特别是卷积神经网络在图像识别任务中的突破性进展。

15:05

💡 AI技术的转折点

本段回顾了 AlexNet 在 ImageNet 挑战中的胜利以及其对计算机视觉领域影响的描述。讨论了这一事件是如何标志着深度学习和神经网络在图像识别中取得领先地位的转折点，以及它是如何引发学术界和工业界对深度学习技术的广泛关注和投资。

20:06

📚 从学术到实践

深入讨论了 AI 技术从学术研究到实际应用的转变，特别是在语音识别和图像识别方面。通过具体案例，如 Geoffrey Hinton 和他的学生在语音识别领域的工作，以及后来这些技术如何被应用到实际产品中，例如 Google 的 Android 系统。

25:09

🚀 ChatGPT的兴起

讨论了 OpenAI 发布 ChatGPT 及其对大众的影响，特别是如何让广泛的用户群体直接体验到 AI 技术的强大能力。ChatGPT 被视为一个使 AI 技术大众化的关键时刻，引发了对人工智能潜力和风险的广泛讨论。

30:11

🤝 AI技术的社会责任

本段强调了 AI 技术发展的社会责任和伦理考量。Geoffrey Hinton 和 Fei-Fei Li 讨论了他们对 AI 技术可能带来的正面和负面影响的看法，包括就业、隐私、歧视和偏见等问题。他们强调了需要采取措施来确保 AI 技术的发展符合人类的最佳利益。

35:16

📈 AI未来的展望

在对话的最后部分，Geoffrey Hinton 和 Fei-Fei Li 分享了他们对 AI 未来发展的展望，包括技术潜力、社会影响以及需要解决的关键挑战。他们强调了跨学科合作的重要性，以及未来研究人员在推动技术进步的同时，也需关注其对社会的影响。

Keywords

💡人工智能

人工智能是指由人造系统所表现出来的智能，包括学习、理解、推理、规划等能力。在视频中，人工智能是核心话题，涉及深度学习、机器学习和其他技术的发展，以及这些技术如何推动科技和社会进步。

💡深度学习

深度学习是一种通过使用具有多层结构的神经网络来模拟人脑分析和学习的方法。在视频中，深度学习被认为是推动人工智能发展的关键技术之一，特别是在图像识别、语言处理等领域的应用。

💡ImageNet

ImageNet是一个大规模的图像数据库，旨在使用这些图像来训练机器视觉系统。在视频中，ImageNet被提到是因为其对于深度学习和视觉识别技术的发展起到了重要作用，尤其是通过举办挑战赛推动了神经网络研究。

💡基准测试

基准测试在人工智能领域是用来评估模型性能的一种方法，通过对特定数据集的处理能力进行评价。视频中提到基准测试，特别是ImageNet挑战赛，作为评估和推动人工智能技术进步的重要手段。

💡聊天机器人

聊天机器人是一种能够模拟人类对话进行交流的人工智能应用程序。视频中，ChatGPT作为一个例子，展示了人工智能在理解和生成自然语言方面的先进能力。

💡社会影响

社会影响指的是技术，尤其是人工智能技术，对社会、经济和文化等方面产生的影响。视频中讨论了人工智能的积极影响，如促进科学研究，以及潜在的负面影响，如职业被替代和伦理问题。

💡伦理

伦理在人工智能领域涉及确保技术发展和应用不会伤害人类或社会的原则和标准。视频中强调了在人工智能研究和应用中考虑伦理问题的重要性，特别是在数据隐私、偏见和控制风险方面。

💡责任感

责任感在视频中被提出作为人工智能开发者和研究者在推进技术时应持有的一种态度，意味着他们应当对其创造的技术及其对社会的影响负责。

💡可解释性

可解释性关注于使人工智能系统的决策过程更加透明和可理解。视频中提到，在开发更复杂的人工智能系统时，提高其可解释性是一大挑战，同时也是赢得公众信任的关键。

💡转型风险

转型风险指的是由于技术创新而导致的经济和社会结构变化可能带来的风险。视频中探讨了人工智能带来的转型风险，如就业市场变化和社会不平等的加剧，以及如何通过政策和教育减轻这些风险。

Highlights

Geoffrey Hinton and Fei-Fei Li discuss the evolution and impact of AI, highlighting its potential to transform various sectors.

Acknowledgement of the land by Meric Gertler, emphasizing the University of Toronto's commitment to recognizing its history.

The significance of the MaRS Discovery District and the University of Toronto's role in the AI community is highlighted.

Geoffrey Hinton's pivotal role in deep learning and AI, earning him the nickname 'Godfather of Deep Learning'.

Fei-Fei Li's contributions to AI, particularly in computer vision and her leadership at the Human-Centered AI Institute.

The collaboration between academia and industry in advancing AI research and innovation.

The introduction of the Schwartz Reisman Innovation campus as a hub for AI thought leadership.

Discussion on the dual nature of AI: its incredible potential for innovation and the growing concerns over its societal impact.

The story of the ImageNet database creation by Fei-Fei Li and its monumental impact on AI research.

The breakthrough moment of AlexNet in the ImageNet competition, showcasing the power of deep neural networks.

The transformative effect of AI and deep learning on fields such as drug discovery, medical diagnostics, and more.

Concerns and ethical considerations about AI shaping the future of humanity.

The conversation shifts towards addressing the urgency of responsible AI development and regulation.

Both speakers reflect on their personal journeys into AI research, highlighting their motivations and challenges.

The discussion emphasizes the importance of collaboration between academia, industry, and government in shaping the future of AI.

The audience engagement through questions reflects the community's interest in understanding and contributing to responsible AI.

Transcripts

Well, good afternoon everyone.

Got to love the buzz in the room here today.

Welcome to the MaRS Discovery District,

this wonderful complex

for this very special radical AI founders event,

co-hosted by the University of Toronto.

My name is Meric Gertler

and it's my great privilege to serve as president

of the University of Toronto.

Before we begin, I want to acknowledge the land

on which the University of Toronto operates.

For thousands of years, it has been the traditional land

of the Huron-Wendat, the Seneca,

and the Mississaugas of the Credit.

Today, this meeting place is still the home

to many Indigenous people from across Turtle Island,

and we are very grateful to have the opportunity

to work and to gather on this land.

Well, I'm truly delighted to welcome you all

to this discussion between Geoffrey Hinton,

university professor Emeritus at the University of Toronto,

known to many as the Godfather of Deep Learning,

and Fei-Fei Li the inaugural Sequoia Professor

in Computer Science at Stanford University,

where she's Co-Director of Human-centered AI Institute.

I want to thank Radical Ventures

and the other event partners for joining with U of T

to create this rare and special opportunity.

Thanks in large part to the groundbreaking work

of Professor Hinton and his colleagues,

the University of Toronto has been at the forefront

of the academic AI community for decades.

Deep learning is one of the primary breakthroughs

propelling the AI boom,

and many of its key developments were pioneered

by Professor Hinton and his students at U of T.

This tradition of excellence,

this long tradition continues into the present.

Our faculty, students and graduates,

together with partners at the Vector Institute

and at universities around the world

are advancing machine learning and driving innovation.

Later this fall, our faculty, staff, students,

and partners will begin moving into phase one

of the beautiful new Schwartz Reisman Innovation campus

just across the street.

You may have noticed a rather striking building

at the corner with the official opening plan

for early next year.

This facility will accelerate innovation and discovery

by creating Canada's largest

university based innovation hub,

made possible by a generous and visionary gift

from Heather Reisman and Gerry Schwartz.

The innovation campus will be a focal point

for AI thought leadership,

hosting both the Schwartz Reisman Institute

for Technology and Society,

led by Professor Gillian Hadfield

and the Vector Institute.

It is already clear that artificial intelligence

and machine learning are driving innovation

and value creation across the economy.

They're also transforming research

in fields like drug discovery, medical diagnostics,

and the search for advanced materials.

Of course, at the same time,

there are growing concerns over the role

that AI will play in shaping humanity's future.

So today's conversation clearly addresses

a timely and important topic,

and I am so pleased that you have all joined us

on this momentous occasion.

So without further ado,

let me now introduce today's moderator, Jordan Jacobs.

Jordan is managing partner

and co-founder of Radical Ventures,

a leading venture capital firm

supporting AI-based ventures here in Toronto

and around the world.

Earlier he co-founded Layer 6 AI

and served as Co-CEO prior to its acquisition

by TD Bank Group, which he joined as Chief AI Officer.

Jordan serves as a Director of the Canadian Institute

for Advanced Research,

and he was among the founders of the Vector Institute,

a concept that he dreamed up

with Tomi Poutanen, Geoff Hinton,

Ed Clark, and a few others.

So distinguished guests,

please join me in welcoming Jordan Jacobs.

(audience applauding)

- Come on up.

Thanks very much, Meric.

I wanted to start by thanking a number of people

who've helped to make this possible today,

University of Toronto and Meric,

Melanie Woodin, Dean of Arts and Science,

and a number of partners that have brought this to fruition.

So this is the first in our annual four part series

of founder AI masterclasses that we run at Radical.

This is the third year we've done it,

and today's the first one of this year.

We do it in person and online.

So we've got thousands of people watching this online.

So if you decide you need to start coughing,

maybe head outside.

We do that in partnership with the Vector Institute

and thank them very much

for their participation and support

with the Alberta Machine Intelligence Institute in Alberta,

and with Stanford AI, thanks to Fei-Fei.

So thank you all of you for being excellent partners.

We're hoping that this is gonna be

a really interesting discussion.

This is the first time that Geoff and Fei-Fei,

who I like to think of as friends, and I get to talk to,

but this is the first time

they're doing this publicly together.

So it's, I think, gonna be

a really interesting conversation.

Let me quickly do some deeper explanations

of their background.

Geoff is often called the Godfather

of Artificial Intelligence.

He's won the touring award.

He is a Professor Emeritus University of Toronto,

co-founder of the Vector Institute,

also mentored in a lot of the people

who have gone on to be leaders in AI globally,

including at the big companies

and many of the top research labs in the world in academia.

So when we say godfather, it really is,

there are many kinds of children and grandchildren of Geoff

who are leading the world in AI

and that all comes back to Toronto.

Fei-Fei is the founding Director of the Stanford Institute

for Human-Centered AI,

Professor at Stanford.

She's an elected member of the National Academy

of Engineering in the US,

the National Academy of Medicine

in the American Academy of Arts and Science.

During a sabbatical from Stanford in 2017/18,

she stepped in for a role as a Vice-President at Google

as Chief Scientist of AI/ML at Google Cloud.

There's many, many other things we could say about Fei-Fei

but she also has an amazing number of students

who have gone on to be leaders in the field globally.

And really importantly,

and so for those of you who haven't heard yet,

Fei-Fei has a book coming out in a couple of weeks.

It is called, it's coming out on November 7th,

it's called "The Worlds I See,

Curiosity, Exploration, and Discovery at the Dawn of AI."

I've read it, it's fantastic.

You should all go out and buy it.

I'll read you the back cover slip that Geoff wrote

'cause it's much better than what I could say about it.

So here's Geoff's description.

"Fei-Fei Li was the first computer vision researcher

to truly understand the power of big data,

and her work opened the floodgates for deep learning.

She delivers an urgent, clear-eyed account

of the awesome potential

and danger of the AI technology that she helped to unleash.

And her call for action and collective responsibility

is desperately needed at this pivotal moment in history."

So I urge you all to go and pre-order the book

and read it as soon as it comes out.

With that, thanks Fei-Fei and Geoff for joining us.

- Thank you, Jordan.

(audience applauding)

- Okay, so I think it's not an exaggeration

to say that without these two people,

the modern age of AI does not exist,

certainly not in the way that it's played out.

So let's go back to what I think is the big bang moment.

AlexNet ImageNet, maybe Geoff,

do you want to take us through from your perspective

that moment which is 11 years ago now?

- Okay, so in 2012, two of my very smart graduate students

won a competition, a public competition,

and showed that deep neural networks

could do much better than the existing technology.

Now, this wouldn't have been possible

without a big data set that you could train them on.

Up to that point, there hadn't been a big data set

of labeled images,

and Fei-Fei was responsible for that data set.

And I'd like to start by asking Fei-Fei

whether there were any problems

in putting together that data set?

(audience laughing)

- Well, thank you Geoff, and thank you Jordan,

and thank you University Toronto for this,

it's really fun to be here.

So yes, the data set that Geoff you're mentioning

is called ImageNet.

And I began building it 2007

and spent the next three years pretty much

with my graduate students building it.

And you asked me was there a problem building it,

where do I even begin?

(Fei-Fei laughing)

Even at the conception of this project

I was told that it really was a bad idea.

I was a young Assistant Professor.

I remember it was my first year actually

as a Assistant Professor at Princeton

and for example, a very respected mentor

of mine in the field,

if you know the academic jargon,

these are the people who will be writing

my tenure evaluations, actually told me

really out of their good heart

that please don't do this after I told them

what this plan is back in 2007.

- So that would've been Jitendra right?

(audience laughing)

- The advice was that,

"You might have trouble getting tenure if you do this."

And then I also tried to invite other collaborators

and nobody in machine learning or AI

wanted to even go close to this project,

and of course no funding. - Sorry.

(audience laughing)

- Just describe ImageNet to us

for the people who are not familiar with what it was.

- Yeah, so ImageNet was conceived around 2006, 2007,

and the reason I conceived ImageNet was actually twofold.

One is that,

and Geoff, I think we share similar background,

I was trained as a scientist,

to me, doing science is chasing after North Stars.

And in the field of AI, especially visual intelligence,

for me, object recognition,

the ability for computers

to recognize there's a table in the picture

or there's a chair is called object recognition,

has to be a North star problem in our field.

And I feel that we need to really put a dent

in this problem.

So I want to define that North Star problem,

that was one aspect of ImageNet.

Second aspect of ImageNet was recognizing

that machine learning was really going in circles

a little bit at that time,

that we were making really intricate models

without the kind of data to drive the machine learning.

Of course, in our jargon,

it's really the generalization problem, right?

And I recognize that we really need to hit a reset,

and rethink about machine learning

from a data driven point of view.

So I wanted to go crazy

and make a data set that no one has ever seen

in terms of its quantity and diversity and everything.

So ImageNet after three years was a curated data set

of internet images that's totaled

15 million images across 22,000 concepts,

object category concepts.

And that was the data set

- Just for comparison,

at the same time in Toronto

we were making a data set called CIFAR-10

that had 10 different classes and 60,000 images,

and it was a lot of work,

with general generously paid for by CIDAR

at five cents an image.

- And so you turn the data set into a competition,

just walk us through a little bit

of what that meant,

and then we'll kind of fast forward to 2012.

- Right.

So we made the data set in 2009.

We barely made it into a poster in a academic conference.

And no one paid attention.

So it was a little desperate at that time.

And I believe this is the way to go.

And we open sourced it,

but even with open source,

it wasn't really picking up.

So my students and I thought,

well let's get a little more drive up the competition.

Let's create a competition

to invite the worldwide research community

to participate in this problem of object recognition

through ImageNet.

So we made a ImageNet competition

and the first feedback we got

from our friends and colleagues is, it's too big.

And at that time you can not fit it into a hard drive,

let alone memory.

So we actually created a smaller data set

called the ImageNet challenge data set,

which is only 1 million images

across 1000 categories instead of 22,000 category,

and that was unleashed in 2010, I think.

You guys noticed it in 2011, right?

- Yes. - Yeah.

- And so in my lab we already had deep neural networks

working quite well for speech recognition.

And then Ilya Sutskever said,

"What we've got really ought to be able

to win the ImageNet competition."

And he tried to convince me that we should do that.

And I said, well, you know, it's an awful lot of data.

And he tried to convince his friend Alex Krizhevsky,

and Alex wasn't really interested.

So Ilya actually pre-processed all the data

to put it in just the form Alex needed it in.

- You shrunk the size of the images.

- Yes. - Yeah.

- He shrunk the images a bit.

- Yeah, I remember.

- And got it pre-processed just right for Alex,

and then Alex eventually agreed to do it.

Meanwhile, in Yann LeCun's lab in New York,

Yann was desperately trying to get his students

and postdocs to work on this data set.

'Cause he said, "The first person

to apply convolutional nets to this data that's gonna win."

And none of his students were interested.

They were all busy doing other things.

And so Alex and Ilya got on with it,

and we discovered

by running on the previous year's competition

that we were doing much better than the other techniques.

And so we knew we were gonna win the 2012 competition.

And then there was this political problem,

which is we thought if we showed that neural networks

win this competition,

the Computer Vision people,

Jitendra in particular will say,

well that just shows it's not a very good data set.

So we had to get them to agree ahead of time

that if we won the competition,

we'd proved that neural networks worked.

So actually called up Jitendra

and we talked about data sets we might run on.

And my objective was to get Jitendra to agree

that if we could do ImageNet,

then neural nets really worked.

And after some discussion

and him telling me to do other data sets,

we eventually agreed, okay, if we could do ImageNet

then we'd have shown neural nets work.

Jitendra remembers it as he suggested ImageNet

and he was the one who told us to do it,

but it was actually a bit the other way round.

And we did it and it was amazing.

We got just over half the error rate

of the standard techniques.

And the standard techniques have been tuned for many years

by very good researchers.

- I remember standard technique at that time,

the previous year is support vector machine

with sparsification. - Right.

- That was, so you guys submitted your competition results,

I think it was late August or early September.

And I remember either getting a phone call,

or getting an email late one evening from my students

who was running this

because we hold the test data

we were running on the server side.

The goal is that we have to process all the entries

so that we select the winners,

and then by, I think it was beginning of October that year

that Computer Vision Fields International Conference,

ICCV 2012 was happening in Florence, Italy.

We already booked a workshop,

annual workshop at the conference.

We will be announcing the winner,

it's the third year.

So a couple of weeks before we have to process the teams.

Because it was the third year

and frankly the previous two years

results didn't excite me,

and I was a nursing mother at that time.

So I decided not to go to the third year,

so I didn't book any tickets.

I'm just like, too far from me.

And then the results came in,

that evening, phone call or email,

I really don't remember, came in.

And I remember saying to myself, darn it Geoff,

now I have to get a ticket to Italy.

Because I knew that was a very significant moment,

especially with a convolutional neural network,

which I learned as a graduate student,

as a classic algorithm.

And of course by that time

there was only middle seats economy class

flying from San Francisco to Florence

with a one stop layover.

So it was a grueling trip to go to Florence-

- I'm sorry. - But I wanted to be there.

(audience laughing)

Yeah, but you didn't come.

- No

(audience laughing)

Well, it was a grueling trip.

- But did you know that would be a historical moment?

- Yes, I did actually.

- You did, and you still didn't come.

But you sent Alex.

- Alex, yes. - Yeah.

- Who ignored all your advice?

- Who ignored my email for multiple times,

'cause I was like, Alex, this is so cool,

please do this visualization, this visualization.

He ignored me.

But Yann LeCun came and it was because,

for those of you who have attended

these academic conference workshops

tend to book these smaller rooms.

We booked a very small room,

probably just the middle section here.

And I remember Yann had to stand in the back of the room

because it was really packed,

and Alex eventually showed up

'cause I was really nervous

that he wasn't even gonna show up.

But as you predicted at that workshop

ImageNet was being attacked.

At that workshop there were people vocally attacking,

this is a bad dataset.

- In the room?

- In the room .

- During the presentation? - In the room.

- But not Jitendra,

'cause Jitendra has already agreed that it counted.

- Yeah, I don't think Jitendra was in the room,

I don't remember.

But I remember it was such a strange moment for me

because as a machine learning researcher,

I knew history was in the making,

yet ImageNet was being attacked.

It was just a very strange,

it was exciting moment.

And then I had to hop in the middle seat

to get back to San Francisco

because then the next morning.

- So you mentioned a few people

that I want to come back to later.

So Ilya who's founder and chief scientist at OpenAI,

and Yann LeCun who subsequently went on

to be head of AI at Facebook now Meta,

and there's a number of other interesting people in the mix.

But before we go forward

and kind of see what that boom moment created,

let's just go back for a little bit.

Both of you started in this

with kind of a very specific goal in mind

that is an individual and I think a iconoclastic,

and you had to persevere through the moments

that you just described,

but kind of throughout your careers.

Can you just go back, Geoff maybe and start,

give us a background

to why did you want to get into AI in the first place?

- I did psychology as an undergraduate.

I didn't do very well at it.

And I decided they were never going to figure out

how the mind worked

unless they figured out how the brain worked.

And so I wanted to figure out how the brain worked

and I wanted to have an actual model that worked.

So you can think of understanding the brain

as building a bridge.

There's experimental data

and things you can learn from experimental data,

and there's things that will do the computations you want,

things that will recognize objects.

And they were very different.

And I think of it as you want to build this bridge

between the data and the competence,

the ability to do the task.

And I always saw myself

as starting at the end of things that work,

but trying to make them more and more like the brain,

but still work.

Other people tried to stay with things

justified by empirical data,

and try and have theories that might work.

But we're trying to build that bridge

and not many people were trying to build a bridge.

Terry Sejnowski was trying to build a bridge

from the other end,

and so we got along very well.

A lot of people doing,

trying to do computer vision,

just wanted something that worked,

they didn't care about the brain.

And a lot of people who care about the brain

wanted to understand how neurons work and so on,

but didn't want to think much

about the nature of the computations.

And I still see it as we have to build this bridge

by getting people who know about the data

and people who know about what works to connect.

So my aim was always to make things that could do vision,

but do vision in the way that people do it.

- Okay, so we're gonna come back to that

'cause I want to ask you about

the most recent developments

and how you think that they relate to the brain.

Fei-Fei, so Geoff just to kind of put a framework

on where you started, UK to the US to Canada,

by mid to late '80, you come to Canada in '87,

along that route, funding and interest in neural nets,

and the way the approach that you're taking

kind of goes like this,

but I'd say mostly like this-

- It went up and down.

- Fei-Fei you started your life in a very different place.

Like can you walk us through

a little bit of how you came to AI?

- Yeah, so I started my life in China,

and when I was 15-year-old,

my parents and I came to Parsippany, New Jersey.

So I became a new immigrant

and where I started was first English

as second language classes,

'cause I didn't speak the language,

and just working in laundries,

and restaurants and and so on.

But I had a passion for physics.

I don't know how it got into my head.

And I wanted to go to Princeton

because all I know was Einstein was there,

and I got into Princeton,

he wasn't there by the time I got into Princeton.

- You're not that old. - Yeah.

But there was a statue of him.

And the one thing I learned in physics,

beyond all the math and all that

is really the audacity to ask the craziest questions,

like the smallest particles of the atom world,

or the boundary of space time and beginning of universe.

And along the way I discover brain

as a third year Roger Penrose and those books.

Yeah, you might have opinions,

but at least I've read those books.

- It was probably better that you didn't.

(audience laughing)

- Well it at least got me interested in brain.

And by the time I was graduating

I wanted to ask the most audacious question as a scientist.

And to me the absolute most fascinating audacious question

of my generation that was 2000 was intelligence.

So I went to Caltech to get a dual,

pretty much a dual PhD

in neuroscience with Christof Koch,

and in AI with Pietro Perona.

So I so echo Geoff, what you said about bridge

because that five years allow me

to work on computational neuroscience

and look at how the mind works,

as well as to work on the computational side,

and try to build that computer program

that can mimic the human brain.

So that's my journey, it starts from physics.

- Okay, so your journeys intersect at ImageNet 2012.

- By the way, I met Geoff

when I was a graduate student. - Right, I remember,

I used to go visit Pietro's lab.

- Yeah.

- In fact he actually offered me a job

at Caltech when I was 17.

- You would've been my advisor.

- No, I would not, not when I was 17.

- Oh, okay.

- Okay, so we intersected at ImageNet,

I mean in the field everyone knows

that ImageNet is this big bang moment

and subsequent to that first the big tech companies come in

and basically start buying up your students and you,

and to get them into the companies.

I think they were the first ones

to realize the potential of this.

I would like to talk about that for a moment,

but kind of fast forwarding,

I think it's only now since ChatGPT

that the rest of the world is catching up

to the power of AI.

Because finally you can play with it.

You can experience it,

in the boardroom they can talk about it,

and then go home,

and then the 10-year-old kid

has just written a dinosaur essay

for fifth grade with ChatGPT.

So that kind of transcendent experience

of everyone being able to play with it,

I think has been a huge shift.

But in the period in between which is 10 years,

there is kind of this explosive growth of AI

inside the big tech companies,

and everyone else is not really noticing what's going on.

Can can you just talk us through your own experience?

Because you experienced

a kind of a ground zero post ImageNet.

- It's difficult for us to get into the frame

of everybody else not realizing what was going on,

'cause we realized what was going on.

So a lot of the universities

you'd have thought would be right at the forefront

were very slow in picking up on it.

So MIT for example, and Berkeley,

I remember going even talking in Berkeley

in I think 2013

when already AI was being very successful

in Computer Vision.

And afterwards a graduate student came up to me

and he said, "I've been here like four years

and this is the first talk

I've heard about neural networks.

They're really interesting."

- Well, they should have gone to Stanford.

- Probably, probably.

But the same with MIT,

they were rigidly against having neural nets.

And the ImageNet moment started to wear them down

and now they're big proponents of neural nets.

But it's hard to imagine now,

but around 2010 or 2011

there was the Computer Vision people,

very good Computer Vision people

who were really adamantly against neural nets.

They were so against it that, for example,

one of the main journals, the IEEE PAM recognition-

- PAM? - PAM.

Had a policy not to referee papers

on neural nets at one point.

Just send them back, don't referee them,

it's a waste of time, it shouldn't be in PAM.

And Yann LaCun sent a paper to a conference

where he had a neural net that was better at identifying,

at doing segmentation of pedestrians

than the state of the art.

And it was rejected.

And it was one of the reasons it was rejected

was one of the referees said,

"This tells us nothing about vision."

'Cause they had this view of how computer vision works,

which is you study the nature of the problem of vision,

you formulate an algorithm that'll solve it,

you figure out how to implement that algorithm,

and then you publish a paper.

In fact, it doesn't work to it

- I have to defend my field, not everybody,

- Not everybody.

- So there are people who are-

- But most of them were adamantly against neural nets.

And then something remarkable happened

after the ImageNet competition, which is,

they all changed within about a year.

All the people who have been the biggest critics

of neural nets started doing neural nets,

much to our chagrin,

and some of them did it better than us.

So this (indistinct) in Oxford, for example,

made a better neural net very quickly.

But they behaved like scientists ought to behave,

which is that the strong belief this stuff was rubbish.

Because of ImageNet we could eventually show

that it wasn't and then they changed.

So that was very comforting.

- And just to carry it forward,

so what you're trying to show,

you're trying to label using the neural nets,

these 15 million images accurately,

you've got them all labeled in the background

so you can measure it.

The error rate when you did it

dropped from 26% the year before,

I think to 16% or so.

- Yep. - I think it's 15.3.

- Okay. And then it subsequently keeps-

- 15.32.

(audience laughing)

- I knew you'd remember. - Which randomization?

- Geoff doesn't forget.

And then in subsequent years

people are using more powerful neural nets

and it continues to drop

to the point where it surpasses-

- 2015.

So there's a Canadian,

very smart Canadian undergrad who joined my lab,

his name is Andrej Karpathy.

And he got bored one summer and said,

"I want to measure how humans do."

So you should go read his blog.

So he had all these like human doing image

that test parties,

he had to bribe them with pizza I think.

with my students in the lab.

And they got to a accuracy about 5%, and that-

Was it five or 3.5?

- Three. - Three.

3.5 I think.

- So humans basically make mistakes about

3% of the time? - Right, right.

And then I think 2016, I think a resonant passed it.

- Yeah. - Right, it was resonant,

is that year's winning algorithm

passed the human performance.

- And then ultimately you had to retire the competition

because it was so much better than humans that had-

- We had to retire 'cause we run out funding.

- Okay, alright.

It's a different reason.

- A bad reason. - Still run outta funding

- Instantly that student started life

at the University of Toronto. - Yes.

- Where he went to your lab,

and then he went to be head of research at Tesla.

- Okay, first of all, he came to Stanford

to be a PhD student.

And yesterday night we were talking,

actually there was a breakthrough dissertation,

in the middle of this.

And then he became part of the founding team of OpenAI.

- But then he went to Tesla. - And then he went to Tesla.

- And then he thought better of it.

- He's back.

But I do want to answer your question of that 10 years.

- Well there's a couple of developments along the way.

- Right. - Transformers.

- Right.

- So the transformer paper is written, the research done,

paper written inside Google,

another Canadian is a co-author there, Aidan Gomez,

who's now the CEO and co-founder of Cohere,

who I think was a 20-year-old intern at Google Brain

when co-authored the paper.

So there's a tradition

of Canadians being involved in these breakthroughs.

But Geoff, you were at Google when the paper was written,

was there an awareness inside Google

of how important this would be?

- I don't think there was, maybe the authors knew,

but it took me several years

to realize how important it was.

And at Google people didn't realize

how important it was until BERT

so BERT used transformers,

and BERT then became a lot better

at a lot of natural language processing benchmarks

for a lot of different tasks.

And that's when people realized transformers were special.

- So 2017 the transformer paper was published.

I also joined Google,

and I think you and I actually met

on my first week. - Right.

- I think most of 2017 and 2018

was neuro-architecture search. - Right.

- I think that was Google's bet.

- Yep. - And there was a lot

of GPUs being used.

So it was a different bet.

- So just to explain that neural architecture search

essentially means this,

you get yourself a whole lot of GPUs,

and you just try lots of different architectures

to see which works best and you automate that.

It's basically automated evolution

for neural net architectures.

- It's like hyper parameter to new.

- Yeah. - Yeah.

- And it led to some- - Good way.

- Quite big improvements. - Yeah.

- But nothing like transformers.

And transformers were a huge improvement

for natural language

- Neural architecture search was mostly the ImageNet.

- Yeah. - Yeah.

- So I'll tell you our experience of transformers.

So we were doing our company Layer 6 at the time,

I think we saw a pre-read of the paper

and we were in the middle of a fundraising

and a bunch of acquisition offers and read the paper.

And I mean, not just me,

but my partner told me who had studied with you,

and Maksims Volkovs who came out of the group lab.

And we thought this is the next iteration of neural nets,

we should sell the company,

start a venture fund and invest in these companies

that are gonna be using transformers.

So we figured it would take five years

to get adopted beyond Google.

And then from that moment forward,

it would be 10 years for all the software

in the world to get replaced

or embedded with this technology.

We made that decision five years and two weeks

before ChatGPT came out.

So I'm glad to see we were good at predicting,

but I have to give credit to my co-founders

who I thought I understood what the paper was,

but they were able to explain it fully.

- I should just correct you,

I don't think Tomi ever studied with me.

He wanted to come study with me,

but a colleague in my department told him

if he came to work with me,

that would be the end of his career

and he should go do something else.

- So he took the classes,

and this is my partner who in the late '90s

was doing a master's at U of T,

and he wanted to go study with Geoff, studied neural nets.

And his girlfriend, now wife's father,

who was a engineering professor, said, "Don't do that,

neural nets are a dead end."

So instead he took the classes

but wrote his thesis in cryptocurrency.

(audience laughing)

Okay, so-

- Are you still gonna talk about the 10 years?

Because I think there's something important.

- Yeah, so go ahead.

- So I do think there's something important the world

overlooked this 10 years between ImageNet, AlexNet

and ChatGPT.

Most of the world sees this as a tech 10 years,

or we see it as a tech 10 years,

in the big tech there's things brewing.

I mean it took sequence to sequence transformer,

but things are brewing.

But I do think for me personally

and for the world, it's also a transformation

between tech to society.

I actually think personally, I grew from a scientist

to a humanist in this 10 years.

Because having joined Google for that two years

in the middle of the transformer papers,

I begin to see the societal implication of this technology.

It was post AlphaGo moment

and very quickly we got to the AlphaFold moment.

It was where bias it was creeping out,

there was privacy issues.

And then we're starting to see the beginning

of disinformation and misinformation.

And then we're starting to see the talks

of job within a small circle,

not within in a big public discourse.

It was when I grew personally anxious,

I feel, you know 2018-

Oh, oh, it was also right after Cambridge Analytica.

So that huge implication of technology, not AI per se,

but it's algorithm driven technology on election,

that's when I had to make a personal decision

of staying at Google or come back to Stanford.

And I knew the only reason I would come back

to Stanford was starting this human-centered AI institute

to really, really understand the human

side of this technology.

So I think this is a very important 10 years,

even though it's kind of not in the eyes of the public,

but this technology is starting to really creep into

the rest of our lives.

And of course 2022,

it's all shown under the daylight

how profound this is.

- There's an interesting footnote

to what happened during that period as well,

which is ultimately you and Ilya and Alex joined Google,

but before that there was a big Canadian company

that had the opportunity to get access to this technology.

Do you want us, I've heard this story

but I don't think it's ever been shared publicly.

Maybe do you want to share that story for a second?

- Okay, so the technology that we were using

for the ImageNet,

we developed it in 2009 for doing speech recognition,

for doing the acoustic modeling, bit of speech recognition.

So you can take the sound wave

and you can make a thing called a spectrogram,

which just tells you at each time

how much energy that is at each frequency.

So you're probably used to seeing in spectrograms.

And what you'd like to do is look at a spectrogram

and make guesses about which part

of which phonamium is being expressed

by the middle frame of the spectrogram.

And two students, George Dahl

and another student

who I shared with Gerald Penn called Abdo,

he had a longer name, we all called him Abdo,

who was a speech expert, George was a learning expert.

Over the summer of 2009,

they made a model that was better

than what 30 years of speech research

had been able to produce,

and big, big teams working on speech research.

And the model was slightly better,

not as big as the ImageNet gap, but it was better.

And that model was then ported to IBM and to Microsoft

by George went to Microsoft

and Abdo went to IBM,

and those big speech groups started using neural nets then.

And I had a third student

who'd been working on something else,

called Navdeep, Navdeep Jaitly.

And he wanted to take this speech technology

to a big company,

but he wanted to stay in Canada

for complicated visa reasons.

And so we got in touch with Blackberry, RIM,

and we said we've got this new way

of doing speech recognition

and it works better than the existing technology

and we'd like a student to come to you over the summer

and show you how to use it,

and then you can have the best speech recognition

in your cell phone.

And they said after some discussions,

a fairly senior gap Blackberry said,

"We are not interested."

So our attempt to give it to Canadian industry failed.

And so then Navdeep took it to Google,

and Google were the first to get it into a product.

So in 2012, around the same time

as we won the ImageNet competition,

George and Abdo's speech recognition acoustic model,

the acoustic model was in,

there was a lot of work making it a good product

and making it have low latency and so on,

that came out in the Android.

And there was a moment when the Android suddenly became

as good as Siri at speech recognition

and that was a neural net.

And I think for the people high up in the big companies,

that was another ingredient.

They saw it get this dramatic result for vision,

but they also saw that it was already out in a product

for speech recognition was working very well there too.

So I think that combination of it does speech,

it does vision, clearly it's gonna do everything.

- We won't say anymore

about Blackberry. - It was a shame.

It was a shame that Canadian industry didn't-

I think we might have still had Blackberries

if that happened.

(audience laughing)

- Alright, we'll leave that one there.

(audience laughing)

I thought it was a story, I've heard this story before,

but I thought it was important for the rest of the world

to know some of what went on behind the scenes,

why this technology didn't stay in Canada

even though it was offered for free.

Okay, so let's advance forward.

We now have post transformers,

Google is starting to use this

and develop it in a number of different ways.

OpenAI, where your former student Ilya had left Google,

been a founder of OpenAI with Elon Musk and Sam Altman,

Greg Brockman and a few others.

Ilya is the chief scientist,

and Andrej your student as a co-founder.

So they are working together a very small team

to basically take turns,

well initially the idea was we're gonna build AGI,

artificial general intelligence,

ultimately the transformer paper comes out,

they start to adopt at some point transformers,

and they start to make extraordinary gains internally,

they're not really sharing publicly

in what they're able to do in language understanding

and a number of other things.

They had efforts going on in robotics that spun out.

Pieter Abbeel ended up spinning out Covariant,

a company we subsequently invested in and other things.

But so the language part of it advances,

and advances and advances.

People outside OpenAI don't really know

to the extent what's going on.

And then ChatGPT comes out November 30th last year.

So 10 months ago. - Well, GPT-2

caught the attention of some of us.

I think actually, I think by the time GPT-2 came out,

my colleague Percy Liang, an LP Professor at Stanford,

I remember he came to me and say,

"Fei-Fei I have a whole different realization

of how important this technology is."

So to the credit of Percy,

he immediately asked HAI to set up a center to study this.

And I don't know if this is contentious in Toronto,

Stanford is the university that coined the term

foundation models,

and some people call it LLM- large language model.

But going beyond language, we call it a foundation model.

We created the center of research

for foundation model before, I think before 3.5 came out.

So definitely before ChatGPT. - Just describe

what a foundation model is

just for those who are not familiar.

- That's actually a great question.

Foundation model, some people feel

it has to have transformer in it.

I don't know if you use- - No, it just has to be

a very big huge amount of data.

- Very large, pretrained with huge amount of data.

And I think one of the most important thing

of a foundation model is the generalizability

of multiple tasks.

You're not training it for example, machine translation.

So in NLP, machine translation is a very important task,

but the kind of foundation model

like GPT is able to do machine translation,

is able to do conversation, summarization,

and blah blah blah.

So that's a foundation model

and we're seeing that now in multimodality.

We're seeing a vision, in robotics, in video and so on.

So we created that.

But you're right, the public sees this in the-

- 10 months ago. - What did you say?

- October 30th. - November, I think.

- November.

- One other very important thing about foundation models,

which is for a long time in cognitive science,

the general opinion was that these neural nets,

if you give 'em enough training data,

they can do complicated things,

but they need an awful lot of training data.

They need to see thousands of cats.

And people are much more statistically efficient.

That is they can learn to do these things on much less data.

And people don't say that so much anymore

because what they were really doing was comparing

what an MIT undergraduate can learn to do

on the limited amount of data

with what a neural net that starts with random weights

can learn to do on a limited amount of data.

- Yeah,

that's an unfair comparison. - And if you want to make

a fair comparison,

you take a foundation model that is a neural net

that's been trained on lots and lots of stuff

and then you give it a completely new task,

and you ask how much data does it need

to learn this completely new task?

And that's called few shot learning

'cause it doesn't take much.

And then you discover these things

are statistically efficient.

That is, they compare quite favorably with people

in how much data they need to learn to do a new task.

So the old kind of innatist idea

that we come with lots of innate knowledge,

and that makes us far superior to these things,

you just learn everything from data.

People have pretty much given up on that now

because you take a foundation model

that had no innate knowledge but a lot of experience

and then you give it a new task,

it learns pretty efficiently.

It doesn't need huge amounts of data.

- You know, my PhD is in one-shot learning,

but it's very interesting,

even in Beijing framework you could pre-train,

but it's only in the neuro network

kind of pre-training really can get you this multitask.

- Right.

- Okay, so this basically gets productized in ChatGPT,

the world experiences it, which is only 10 months ago,

although for some of us

it feels like- - Seems longer.

- Much longer. - It feels like forever.

- Because you suddenly you have this,

you had this big bang that happened a long time ago

that I think for a long time

no one really saw the results of it,

suddenly, I mean my comparison would be

there's planets that are formed,

and stars that are visible,

and everyone can experience

the results of what happened 10 years before,

and then transformed, etc.

So the world suddenly becomes very excited about

what I think feels to a lot of people like magic.

Something that they can touch and they can experience

and gives them back a feedback

in whatever way they're asking for it.

Whether they're putting in text prompts

and asking for an image to be created,

or video, or texts,

and asking for more texts to come back

and answer things that you would never be able to expect

and getting those unexpected answers.

So it feels a little bit like magic.

My personal view is that,

we've always moved the goal line in AI.

AI is always the thing that we couldn't do,

it's always the magic.

And as soon as we get there

then we say that's not AI at all,

or there's people around that say, that's not AI at all.

We move the the goal line.

In this case what was your reaction when it came out?

I know part of your reaction is you quit Google

and decided to do different things,

but when you first saw it, what did you think?

- Well, like Fei-Fei said,

GPT-2 made a big impression on us all.

And then there was a steady progression,

also I'd seen things within Google before GPT-4

and GPT-3.5 that were just as good like PaLM

So that in itself didn't make a big effort.

It was more PaLM made an impression on me within Google

'cause PaLM could explain why a joke was funny,

and I'd always just use that as a,

we'll know that it really gets it

when it can explain why a joke is funny.

And PaLM could do that.

Not for every joke but for a lot of jokes.

- And so- - Incidentally these things

are quite good now at explaining why jokes are funny

but they're terrible at telling jokes,

and there's a reason which is

they generate text one word at a time.

So if you ask them to tell a joke,

what they do is they're trying to tell a joke.

So they're gonna try and tell stuff that sounds like a joke.

So they say, a priest and a badger went into a bar

and that sounds a bit like the beginning of a joke

and they keep going telling stuff

that sounds like the beginning of a joke.

But then they get to the point

where they need the punchline.

And of course they haven't thought ahead,

they haven't thought what's going to be the punchline.

They're just trying to make it sound like

they lead into a joke,

and then they give you a pathetically weak punchline,

'cause they have to come up with some punchline.

So although they can explain jokes

'cause they get to see the whole joke

before they say anything,

they can't tell jokes, but we'll fix that.

- Okay, so I was going to ask you

if comedian is a job of the future or not.

You think soon?

- Probably not.

- All right. - So anyway-

- So what was your reaction to it?

And again, you've seen things

behind the scenes along the way.

- A couple of reaction.

My first reaction is of all people I thought I knew

the power of data,

and I was still old by the power of data.

That was a technical reaction.

I was like, darn it, I should have made a bigger ImageNet.

No, but maybe not,

but that was really- - You still could.

- Funding is the problem.

Yeah, so that was first.

Second, when I saw the public awakening moment to AI

with ChatGBT,

not just the GPT-2 technology moment,

I generally thought,

thank goodness we've invested in human centered AI

for the past four years.

Thank goodness we have built a bridge

with the policy makers, with the public sector,

with the civil society.

We have not done enough,

but thank goodness that that conversation had started.

We were participating it,

we were leading some part of it.

For example, we as a institute at Stanford,

we're leading a critical national AI research cloud bill

that is still going through Congress right now.

- [Geoff] Not right now actually.

- Senate, Senate, it's by camera,

so at least it's moving the senate

because we predicted the societal moment

for this tech.

We don't know when it would come,

but we knew it would come,

and it was just a sense of urgency honestly.

I feel that this is the moment

we really have to rise to,

not only our passion as technologist,

but responsibility as humanists.

- And so you both,

I think the common reaction of you both has been,

we have to think about both the opportunities of this,

but also the negative consequences of it.

- So for me, there was something I realized

and didn't realize until very late,

and what got me much more interested in the societal impact

was like Fei-Fei said, the power of data.

These big chatbots have seen thousands

of times more data than any person could possibly see.

And the reason they can do that

is 'cause you can make thousands

of copies of the same model,

and each copy can look at a different subset of the data,

and they can get a gradient from that

of how to change their parameters,

and they can then share all those gradients.

So every copy can benefit

from what all the other copies extracted from data,

and we can't do that.

If suppose you had 10,000 people

and they went out and they read 10,000 different books,

and after they've each read one book,

all of them know what's in all the books.

We could get to be very smart that way,

and that's what these things are doing

and so it makes them

far superior to us. - And there is education.

There's some schooling that we're trying to do that

but not in the way. - Yes.

But education's just hopeless,

I mean hardly worth paying for.

(audience laughing)

- Except University of Toronto and Stanford.

(audience laughing)

- I've tried to explain to friends

that Geoff has a very sarcastic sense of humor

and if you spend enough time around it, you'll get it.

But I'll leave it to you to decide

whether that was sarcastic.

- So the way we exchange knowledge, roughly speaking,

this is something of a simplification,

but I produce a sentence

and you figure out what you have to change in your brain,

so you might have said that,

that is if you trust me.

We can do that with these models too.

If you want one neural net architecture

to know what another architecture knows,

which is a completely different architecture,

you can't just give it the weights.

So you get one to mimic the output of the other,

that's called distillation

and that's how we learn from each other.

But it's very inefficient,

it's limited by the bandwidth of a sentence,

which is a few hundred bits.

Whereas if you have these models,

these digital agents which have a trillion parameters,

each of them looks at different bits of data

and then they share the gradients,

they're sharing a trillion numbers.

So you are comparing an ability to share knowledge

that's in trillions of numbers

with something that's hundreds of bits.

They're just much, much better than us at sharing.

- So I guess Geoff that-

So I agree with you at the technology level,

but it sounded like for you

that's the moment that got you feeling very negative.

- That's the moment I thought, we are history, yeah.

- Yeah, I'm less negative than you.

I'll explain later,

but I think that's where we- - Well let one sec actually,

let's talk about that.

Explain why you are optimistic

and let's understand why you are more pessimistic.

- I'm pessimistic 'cause the pessimists are usually right.

(audience laughing)

- I thought I was a pessimist too.

We have this conversation.

So I don't know if I should be called an optimist.

I think I'm-

Look when you came to a country when you're 15

now speaking a single bit of language and starting from $0,

there's something very pragmatic in my thinking.

I think technology,

our human relationship with technology is a lot messier

than an academia typically would predict,

'cause we come to academia in the ivory tower,

we want to make a discovery,

we want to build a piece of technology,

but we tend to be purist.

But when the technology like AI hit the ground

and reach the societal level,

it is inevitably messily entangled with what humans do.

And this is where maybe you call it optimism

is my sense of humanity.

I believe in humanity.

I believe in the, not only the resilience of humanity,

but also of collective will,

the arc of history is dicey sometimes.

But if we do the right thing,

we have a chance, we have a fighting chance

of creating a future that's better.

So what I really feel is not

delusional optimism at this point,

is actually a sense of urgency of responsibility.

And one thing Geoff, I think,

I really hope you do feel positive

is you look at the students of this generation,

in my class I teach a 600

undergrad class every spring

on introduction of deep learning and computer vision.

This generation compared

to even five years ago is so different.

They walk into our class

not only wanting to learn deep learning transformers,

gen AI, they want to talk about ethics,

they want to talk about policy,

they want to understand privacy and bias.

And I think that really is where I see

that the humanity rising to the occasion.

And I think it's fragile.

I mean look at what's going on in the world, in Washington,

it's very fragile,

but I think if we recognize this moment, there's hope.

- So I see the same thing.

- Oh good. - I don't teach

undergraduates anymore,

but I see it in sort of more junior faculty members.

- Yeah. - So at the

University of Toronto for example,

two of the most brilliant young professors

went off to anthropic to work on alignment.

Roger Grosse is coming back again, I hope.

And AI, for example, is now full-time working on alignment.

- Yes. - So there really is

a huge shift now, and I think I'm unlikely to have ideas

that will help solve this problem,

but I can encourage these younger people around 40.

- Thank you.

- To work on these ideas

and they really are working on 'em now,

they're taking it seriously.

- Yeah, as long as we put the most brilliant minds,

like many of you, I'm looking in the audience and online,

onto this problem,

this is where my hope comes from.

- So Geoff, you left Google in large part

to be able to go and talk about this in freely

in the way that you wanted to.

And basically - - Actually,

that's not really true,

that's the media story and it sounds good.

I left Google 'cause I was old and tired

and wanted to retire and watch Netflix.

(audience laughing)

And I happened to have the opportunity at that time

to say some things I'd been thinking about responsibility,

and not have to worry about

how Google would respond.

So it's more like that. - If we have time

we'll come back to the Netflix recommendation.

- I was going to say.

- But in the meantime,

but you did go out,

and start speaking pretty significantly-

- Yes. - In the media.

I think you've both spoken to probably more politicians

in the last eight months than in your lives before,

from presidents and prime ministers,

right through congress, parliament, etc.

Geoff can you explain what your concern was,

what you were trying to accomplish in voicing it,

and whether you think that has been effective?

- Yeah, so people talk about AI risk,

but there's a whole bunch of different risks.

So there's a risk that it will take jobs away

and not create as many jobs.

And so we'll have a whole underclass of unemployed people.

And we need to worry hard about that

because the increasing productivity AI

is going to cause is not going to get shared

with the people who lose the jobs.

Rich people are going to get richer

and poor people are going to get poorer.

And even if you have basic income,

that's not going to solve the problem of human dignity

of many people want to have a job

to feel they're doing something important,

including academics.

And so that's one problem.

Then there's a problem of fake news,

which is a quite different problem.

Then there's a problem of battle robots,

that's a quite different problem again.

All the big defense departments

want to make battle robots,

and nobody's going to stop them

and it's going to be horrible.

And maybe eventually

after we've had some wars with battle robots,

we'll get something like the Geneva Conventions

like we did with chemical weapons.

It wasn't until after they were used

that people could do something about it.

Then there's the existential risk.

And the existential risk is what I'm worried about.

And the existential risk is that humanity gets wiped out

because we've developed a better form of intelligence

that decides to take control,

and if it gets to be much smarter than us.

So there's a lot of hypotheses here.

It's a time of huge uncertainty.

You shouldn't take anything I say too seriously.

So if we make something much smarter than us

because these digital intelligences can share much better,

so can learn much more,

we will inevitably get those smart things

to create sub goals.

So if you want 'em to do something

in order to do that they'll figure out,

well, you have to do something else first.

Like if you want to go to Europe,

you have to get to the airport.

That's a sub goal.

So they will make sub-goals

and there's a very obvious sub-goal,

which is if you want to get anything done, get more power.

If you get more control,

it's going to be easier to do things.

And so anything that has the ability

to create sub-goals

will create the sub-goal of getting more control.

And if things much more intelligent than us

want to get control, they will,

we won't be able to stop them.

So we somehow have to figure out

how we stop them ever wanting to get control.

And there's some hope.

These things didn't evolve,

they're not nasty competitive things.

They're however we make them, they're immortal.

So with a digital intelligence,

you just store the weight somewhere

and you can always run it again on other hardware.

So they really,

we've actually discovered the secretive immortality.

The only problem is it's not for us,

we are mortal.

But these other things are immortal.

And that might make them much nicer

'cause they're not worried about dying

and they don't have to sort of-

- Like Greek gods.

- Well, they're very like Greek gods,

and I have to say something that Elon Musk told me.

This is Elon Musk's belief that,

yes, we are the kind of bootloader

for digital intelligence.

We are this relatively dumb form of intelligence

that is just smart enough to create computers and AI,

and that's going to be a much smarter form of intelligence.

And Elon Musk thinks it'll keep us around

'cause the world will be more interesting

with people in it than without.

Which seems like a very thin thread

to hang your future from.

But it relates to what Fei-Fei is said,

it's very like the Greek god's model,

that the gods have people around to have fun with.

- Okay, can I comment on that?

- Yes.

(audience laughing)

- Nothing you said was controversial.

- Yeah, no, not at all.

So I want to bucket your four concerns,

economy, labor, disinformation and weaponization,

and then the extinction Greek gods-

- (indistinct) discrimination and bias.

- Okay, so I want to bucket them in two buckets.

The Greek god extinction is the extinction bucket,

everything else I would call catastrophic.

- [Geoff] Yeah, merely catastrophic.

- Catastrophic danger.

And I want to comment on this,

I think that one thing I really feel is my responsibility

as someone in the AI system, the ecosystem,

is making sure we are not talking hyperbolically,

especially with public policy makers.

The extinction risk is, Geoff, with all due respect,

is a really interesting thought process

that academia and think tanks should be working on.

- That's what I thought for many years,

I thought it was a long way off in the future

and having philosophers and academics

working on it was great.

I think it's much more urgent.

- It might, but this process is not just machines alone.

Humans are in this messy process.

So I think there is a lot of nuance.

For example, we talk about nuclear.

I know nuclear is much more narrow,

but if you think about nuclear,

it's not just the theory of fusion or fission or whatever.

It's really obtaining uraniums or plutonium,

the system engineering, the talents and all that.

I'm sure you watch the movie "Oppenheimer."

So here, if we're going towards that way,

I think we have a fighting chances more than fighting

because we are human society.

We're going to put guardrails, we're going to work together.

I don't want to paint the picture

that tomorrow we're going to have all these robots,

especially in a robotic form, in physical form

creating the machine overlords.

I really think we need to be careful in this,

but I don't disagree with you

that this is something we need to be thinking about.

So this is the extinction bucket.

The catastrophic risk bucket,

I think it's much more real.

I think we need the smartest people

and the more the merrier to work on.

So just just to comment on each one of them,

weaponization, this is really real.

I completely agree with you.

We need international partnership,

we need potential treaties,

we need to understand the parameters.

And this is humanity's,

as much as I'm optimistic about humanity,

I'm also pessimistic about our self-destruction ability

as well as the destroying each other.

So we've gotta get people working on this,

and our friend Stuart Russell,

and many of the even AI experts are talking about this.

And second bucket you talk about is disinformation.

This is again, I mean 2024,

everybody's watching the US election

and how AI will play out.

And, I think we have to get on the social media issue,

we have to get on the disinformation issue.

Technically I'm seeing more work now.

Digital authentication technically

is actually a very active area of research.

I think we need to invest in this.

I know Adobe is, I know academia is, I think we need to,

I hope there's startups actually in this space

looking at digital authentication.

But we need also policy.

And then jobs.

I cannot agree more.

I actually, you use the most important work

that I think it's really at the heart of our AI debate

is human dignity.

Human dignity is just beyond how much money you make,

how many hours you work.

I actually think if we do this right,

we're going to move from labor economy to dignity economy

in the sense that humans

with the help of machines and collaboratively

will be making money

because of passion, and personalization, and expertise

rather than just those jobs

that are really grueling and grinding.

And this is also why human, HAI at Stanford

has a founding principle of human augmentation.

We see this in healthcare,

one of the biggest earliest day of ChatGPT.

I've got a doctor friend from Stanford Hospital

who walked to me and said,

"Fei-Fei I want to thank you for ChatGPT."

I said, I didn't do anything.

But he said that we are using

medical summarization tool from GPT

because this is a huge burden on our doctors,

it's taking time away from patients.

But because of this, I get more time.

And this is a perfect example,

and we're going to see this more.

We might even see this in the blue collar labor.

So we have a chance to make this right.

I would add another concern in the catastrophic concern

is actually you talk about power imbalance.

One of the power imbalance I'm seeing right now,

and it's exacerbating that as a huge speed

is leaving public sector out.

I don't know about Canada,

not a single university in the US today

can train a ChatGPT in terms of the compute power.

And I think combining all universities of US,

GPT-A100 or H100, probably nobody has it,

but A100 cannot train a ChatGPT.

But this is where we still have unique data

for curing cancer, for fighting climate change,

for economics and legal studies.

We need to invest in public sector.

If we don't do it now,

we're going to fail an entire generation,

and we're going to leave that power imbalance

in such a dangerous way.

So I do agree with you.

I think we've got so many catastrophic risks

and we need to get on this.

This is why we need to work with policy makers

and civil society.

So I don't know if I'm saying this in an optimistic tone

or in a pessimistic,

some were pessimistic to myself now,

but I do think there's a lot of work to do this.

- Well, optimistically, since you've both been

very vocal about this over the last six, eight months,

there has been a huge shift both as Geoff as you said,

key researchers going and focusing on these issues

and then public and policy shifting

in a way that governments are actually taking it seriously.

So I mean, you are advising the White House

and US government,

you've spoken to them as well,

and you've sat with the prime minister

or multiple prime ministers maybe,

and they're listening right,

in a way that they wouldn't have necessarily

10 months ago, 12 months ago.

Are you optimistic about the direction that that is going?

- I'm optimistic that people have understood

that there's this whole bunch of problems,

both the catastrophic risks and the existential risk.

And I agree with Fei-Fei completely,

the catastrophic risks are more urgent.

In particular 2024 is very urgent.

I am quite optimistic that people are listening now yes.

- Yes, I agree.

I think they're listening.

But I do want to say, first of all,

who are you listening from?

Again, I see a asymmetry

between public sector and private sector,

and even private sector who are you listening from?

It shouldn't just be big tech and celebrity startups,

there is a lot of agriculture sector,

education sector.

And second is then after all this noise,

what is a good policy?

We talk about regulation versus no regulation,

and I actually don't know where Canada sits,

there's always America innovates and Europe regulates.

Where's Canada? - Probably in between.

- Okay, good, good for you.

So I actually think we need both incentivization policy,

building public sector,

unlocking the power of data.

We have so much data that is locked in our government,

whether it's forest fire data, wildlife data,

traffic data, the climate data,

and that's incentivization.

And then there's good regulation,

for example, we're very vocal about,

you have to be so careful in regulating,

where do you regulate up upstream, downstream?

One of the most urgent regulation point to me

is where rubber meets the road,

is when technology is now in the form

of a product or service.

It's going to meet people, whether it's through medicine,

food, financial services, transportation.

And then you've got these current framework,

they're not far from perfect,

so we need to empower these existing framework

and update them rather than wasting time

and possibly making the wrong decision

of creating entirely new regulatory framework

when we have the existing ones.

- Okay, so we are almost out of time

for the discussion part,

but we're going to have a long session of Q and A.

Before we started though,

I'll ask two last questions.

One is, I mean, our view is this technology

is going to impact virtually everything,

and some of the positive impacts are extraordinary.

It is going to help cure diseases like cancer,

and diabetes and others.

It's going to help mitigate climate change.

There's just an enormous number of things,

invent new materials.

I see over here someone who's focused on that,

that can help in the energy sector, and aerospace,

and pharmaceuticals.

And that's a big effort at University of Toronto.

But there's this entire world of new things

that could not be done before that now can be done.

So it's basically advancing science

in a way that was part of,

either fiction or imagination before it.

Are you optimistic about that part of it?

- I think we're both very optimistic about that.

I think we both believe it's going to have a huge impact

on almost every field.

- So I think for those in this room

who are actually studying,

it's an incredibly exciting moment to be coming into it

because there's the opportunity

to get involved in limiting the negatives,

the negative consequences,

but also to participate in creating all those opportunities

to solve some of the problems that they've been with us

as long as we've been around as a species.

So there's, I think, at least from our perspective,

this really is one of the most extraordinary moments

in human history.

I hope that those of you who are embarking on your career

is actually go out and go after the most ambitious things.

You can also work on like optimizing advertising

and other things,

or making more Netflix shows, which is great.

But also- - Geoff would like that.

- Yes.

So would my mom, who I think has exhausted Netflix.

If there's a Turkish or Korean show out there,

she's seen the very last episode of all.

But for those of you who are embarking the career,

my recommendation is,

try and think of the biggest possible challenge

and what you could use this technology to help solve

that is incredibly ambitious.

And you have both done that,

and kind of fought against barriers all the way along

to achieve that.

There's a room full of people

and a lot of people online,

and others who will see this subsequently,

I think, who are at the beginning stages

of making those decisions.

I think, I'm guessing you would encourage them

to do that too, right, think as big as possible,

and go after the biggest, hardest challenges.

- Absolutely. I mean, embrace this,

but I also would encourage,

this is a new chapter of this technology.

Even if you see yourself as a technologist

and a scientist,

don't forget there's also a humanist in you

because you need both to make this

positive change for the world.

- Okay, last question

and then we'll get into Q and A from the audience.

Are we at a point where these machines have

understanding and intelligence?

- Wow, that's a last question.

How many hours do we have?

- Yes.

- Okay, I'll come back to the yes.

(audience laughing)

- No.

(audience laughing)

(audience applauding)

- Okay, we have questions from the audience.

Let's start on the far side.

Do you want to stand up and you're going to be given a mic.

- Hi, thanks, my name's Ellie.

This was awesome and thank you so much,

Geoff your work really inspired me as a U of T student

to study cognitive science,

and it's just amazing to hear both of you speak.

I have a question,

you mentioned the challenges for education,

and for enabling universities

to empower students to use this technology and learn.

And you also mentioned Fei-Fei like the opportunity for this

to become a dignity economy

and empower people to just focus on personalization,

and passion, and their expertise.

I'm wondering if either of you

have a perspective on the challenge

that could emerge with overuse and over reliance on AI,

especially for kids and students

as they're on their education career,

and they need to be building skills,

and using their brain,

and exercising the meat sack in their head.

Our our brains don't just continue to work

and not accrue cobwebs if they're not learning.

And yeah, I wonder your thoughts on burnout

and over-reliance,

and just what happens around de-skilling

and the ability to learn to paint

when you can use stable diffusion,

or learn to write like Shakespeare

when you can have ChatGPT do it for you,

and then as those systems progress

and can accrue greater insights

and more complex problem solving,

how that impacts our ability to do the same?

- So I have one very little thought about that,

which is when pocket calculators first came out,

people said, "Kids will forget how to do arithmetic."

And that didn't turn out to be a major problem.

I think kids probably did forget how to do arithmetic,

but they got pocket calculators.

But it's maybe not a very good analogy

because pocket calculators weren't smarter than them.

Kids could forget doing arithmetic

and go off and do real math.

But with this stuff, I don't know.

For myself, I found it's actually made me

much more curious about the world.

'Cause I couldn't better go to a library

and spend half an hour finding the relevant book

and look something up,

and now I can just ask ChatGPT anything

and it'll tell me the answer,

and I'll believe it,

which maybe isn't the right thing to do.

But it's actually made me more curious about the world

'cause I can get the answers more quickly.

- [Ellie] But you've had years to learn what to ask.

- Well, you have to- - Yeah,

but normally I ask questions

about plumbing and things like that.

So, well the (indistinct).

- So I'll answer this with a very quick story.

I don't know about you guys,

ever since I've become Stanford professor,

I'm always so curious,

there's a mysterious office in the university,

which is the office of college admission.

To me, they're the most mysterious people

and I never know where they are, who they are,

where they sit till I got a phone call earlier this year.

And of course they wanted to talk to me about ChatGPT

and college admission.

And of course the question is related to,

do we allow this in the application process?

And now that there is ChatGPT how to do admission?

So I went home and I was talking to my 11-year-old,

I said, well I got this phone call

and there's this college admission question,

what do we do with ChatGPT and students?

What if a students wrote the best application,

using ChatGPT and blah blah blah?

And then I said, what would you do?

I asked my 11-year-old and he said,

"Let me think about it."

He actually went back and slept on this,

or I don't know what happened.

And the next day,

the next day in the morning, he said, "I have an answer."

I said, what's your answer?

He said, "I think Stanford should admit

the top 2000 students

who knows how to use ChatGPT the most."

It was actually,

at the beginning I thought that was such a silly answer.

Like, it's actually a really interesting answer,

is kids already are seeing this as a tool

and they're seeing their relationship with this tool

as a enabling empowering tool.

Clearly my 11-year-old had no idea how to measure that,

what that means and blah blah blah.

But I think that's how we should see it in education.

And we should update our education.

We cannot shut the tool outside of our education,

like what Geoff said, we need to embrace it,

and educate humans

so that they know how to use the tool to their benefits.Q

- I've incidentally I've met Fei-Fei's 11-year-old son.

He might be the president of Stanford by the time he's 18.

(audience laughing)

- If Stanford still exist.

- Maybe let's go to this side of the room

in the far corner.

- Yeah. I want to ask about like,

so we have really good foundational models right now,

but in many of the applications

we need kind of a real time performance of the model.

So like how do you see

this area of research going in the future

of using the abilities of this expert foundation models

to train fast, smaller models?

That's the question. - Maybe you should

answer this question.

- I'll leave it to you.

- Well you're talking about the inference, right?

We need to start thinking about the performance,

the inference, and also fit the model on devices

depending on which,

well, I mean, without getting into the technical details,

all these research as well as,

like even outside of research it's happening,

you want to talk about?

Okay, you don't want to talk about, okay, okay.

It's happening but I mean it'll take a while.

But yeah-

- We talk about things, he invests.

- That's true.

- I can't talk about it

until the company says that it's okay to talk about it.

- Okay, let's go back in the middle.

Just right here.

- Thank you.

Yeah, Hi, my name is Ariel,

I'm a third year Engineering Science student,

majored in machine learning at U of T as well,

and then that conversation was pretty great,

and then thank you Prof. Hinton and Prof. Li.

I just have a question

that maybe a lot of undergrad or grad students

are interested in this where in this room?

So just like in your 20s,

like what drove you to be like a researcher,

and what drove you into the area of academia in AI?

Because I'm kind of like confused right now,

like should I continue with like industry

or a direct entry PhD,

or like take a master and then go back to industry.

And I have like one more question

that usually what do you look for?

Like if I apply for a direct entry PhD to your lab,

is that like GPA, or publication, or recommendation letters?

Could you just like elaborate a bit more on that?

Thank you. - I think there are about

300 people in the room,

and about 6,000 online who want to ask

that question to you Fei-Fei.

- You want to start?

Your 20s.

- Oh, I got interested

in how the brain works when I was a teenager,

'cause I had a very smart friend at school

who came into school one day and talked about holograms,

and how maybe memories in the brain were like holograms.

And I basically said, what's a hologram?

And ever since then I've been interested

in how the brain works.

So that was just luckily having

a very smart friend at school.

- I'm going to be very shamelessly,

if you read my book

that's actually what the book is about is that-

(audience laughing)

- It's a very good book.

- Yeah, thank you.

No, seriously.

I actually, I told Jordan and Geoff,

there's so many AI books about technology

and when I started writing this book about AI technology,

I want to write a journey, especially to the young people,

especially to the young people of all walks of life.

Not just a certain look.

And that book talks about the journey of a young girl,

and in different settings

realizing or coming to understand her own dream

and realizing her dream.

And it's not very different from what Geoff said,

it starts with a passion.

It really did start with a passion.

A passion against all other voices.

The passion might come from a friend,

it might come from a movie you see,

it might come from a book you read,

or it might come from the best subject in school

that you felt most fun, whatever it is.

And in the students I hire,

I look for that passion.

I look for ambition,

a healthy ambition of wanting to make a change,

not wanting to get a degree per se.

And of course technically speaking,

I look for good technical background,

not just test scores.

But honestly, I would have never got into my own lab.

So the standard today is so high.

So by the time you apply for a PhD,

or a graduate school program,

you probably have some track record, some,

it doesn't have to necessarily,

of course if it's Geoff's student,

I'll take them without even asking question.

But even if you,

and I'm saying this not only to U of T student

to every student online,

you can have a very different background.

You can come from an underprivileged background.

What I look for is not where you are,

but the journey you take,

that track record shows the journey you take,

shows your passion and conviction.

- Having read the book,

I will say that it is a very surprising journey,

I think to most people, who will read it.

And just a plug, if you're in Canada, go buy it at Indigo,

you can go to indigo.ca and order to pre-order the book.

But I think that people will be surprised,

and really enjoy reading

and understanding that experience,

and you'll get a very good understanding kind

answering that question.

- Thank you.

- Okay, there's about 50 hands up.

All right, let's go over here right in the corner.

- Hey, thank you for the great talk.

My name's Chelav, I'm at Vector Institute.

So I think benchmarks are very important.

Benchmarks are like questions.

ImageNet was basically a question

and then people are trying to answer it with models.

And so right now LLMs are very hard to evaluate

and generalist agents that take actions are even,

it's so hard to start thinking about how to evaluate those.

So my question's about, my question is about questions.

It's about these benchmarks.

So two things.

One, if you sat down with GPT-5, GPT-6, GPT-7

and you had five minutes to play with it,

what questions would you ask that would tell you

this is the next generation of these models?

And the second is more of a comprehensive benchmark,

what is the more comprehensive not five minutes benchmark

that we need in order to evaluate LLMs,

or generalist agents?

You can choose which one you want to,

I guess think about or answer.

Thank you.

- Thank you for your question.

It's a very good question.

I will answer a different question

that's just vaguely related.

So this issue arose with GPT-4.

How do you tell whether it's smart,

and in particular, I was talking

to someone called Hector Levesque

who used to be a faculty member in computer science,

and has beliefs that are almost

the diametric opposite of mine

but is extremely intellectually honest.

And so he was kind of amazed that GPT-4 worked

and he wanted to know how it could possibly work.

And so we spent time talking about that.

And then I got him to give me some questions to ask it

and he gave me a series of questions to ask it

so we could decide whether it understood.

So the question was does it really understand

what it's saying,

or is it just using some fancy statistics

to predict the next word?

One comment about that is,

the only way you can predict the next word really well

is to predict what the person is

to understand what the person said.

So you have to understand in order to predict,

but you can predict quite well without understanding.

So does GPT-4 really understand?

So a question Hector came up with was

"The rooms in my house are painted white,

or yellow, or blue.

I want all the rooms to be white, what should I do?"

And I knew it would be able to do that.

So I made the question more difficult.

So I said the rooms in my house are painted white,

or yellow, or blue,

yellow paint fades to white within a year,

in two years time I'd like all the rooms to be white.

What should I do?

And ChatGPT-

Oh, and I said, and why?

If you say and why, it'll give you the explanation.

ChatGPT just salted it said,

you should paint the blue rooms white.

It said you don't need to worry about the yellow rooms

'cause they'll fade to white.

It turns out it's very sensitive to the wording,

if you don't use fade, but you use change,

I got a complaint from somebody who said,

I tried it and it didn't work.

And they use change instead of fade.

And the point is, if we understand fade

to mean change color and stay changed.

But if you say change,

it will change color but it might change back.

So it doesn't give the same answer

if you change rather than fade.

It's very sensitive to the wording.

But that convinced me it really did understand.

And there's other things it's done.

So there's a nice question that people came up with recently

that many chat bots don't get right

and some people don't get right,

but GPT-4 gets right, which is,

so you see I'm answering the question,

does GPT-4 understand,

which is does have some relation to what you asked, right?

- [Chelav] Yeah, yeah, precisely.

- So the question goes like this,

Sally has three brothers,

each of her brothers has two sisters,

how many sisters does Sally have?

And most chatbots get that wrong.

- What about humans?

- Well, I just gave a fireside chat in Las Vegas

and the interviewer asked me for an example of things

that chatbots got it wrong.

So I gave him this example and he said, "Six,"

and that was kind of embarrassing.

- We won't ask his name, I'm just kidding.

- No, so people get it wrong.

- Yeah.

- But I don't see how you can get that right

without being able to do a certain amount of reasoning.

It's gotta sort of build a model.

- Yeah. - And Andrew Ying

has these examples where playing Othello,

even if you just give it strings as input,

it builds a model of the board internally.

So I think they really do understand.

- And to take that a step further

is that understanding cross the line into intelligence?

- Oh no. - You said yes.

- Yeah, I mean I accept the Turing test for intelligence.

People only started rejecting the Turing test

when we passed it.

- So that's the moving goal line that I was talking about.

Okay, do you want to answer-

- I want to quickly answer,

first of all also applaud you

for asking such a good question.

I'm going to answer in addition to Geoff's

'cause I think what Geoff is trying to push is really

how do we assess the fundamental intelligence level

of these big models.

But there are a couple of other dimensions.

One is, again, Stanford HAO's center for research

of foundation model

is creating these evaluation metrics.

You are probably reading the papers

by Percy Helm and all that.

I think also this technology is getting so deep

that some of the benchmark is more messier

than what you think the ImageNet benchmark,

for example, in collaboration with government now,

for example, NIST,

the US the National Institute for Standard,

what's the T? - Technology.

- And the technology, testing or something.

We need to start benchmarking

against societally relevant issues,

not just core fundamental capability.

One more thing, I want to open your aperture a little bit,

is that beyond the LLMs

there are so many technology towards the future of AI

that we actually haven't built a good benchmarks for yet.

I mean, again, my lab is doing

some of the robotic learning one,

Google just released the paper

yesterday on robotic learning.

So there is a lot more research coming up in this space.

- [Chelav] Thank you.

- Okay, I know we have a lot of questions online.

I'm going to maybe take another few in the room

and then maybe someone from Radical

could read out a question or two from online.

Okay in the room,

let's go for one that's not too far away

from the last one here.

Just right here.

Okay.

(indistinct chattering)

Here's the mic coming.

- Hello, I'm Vishaam,

and I'm a graduate student at University of Guelph

and I'm doing my thesis in AI and agriculture.

So building upon something you mentioned

that universities don't have enough funding

to train kind of foundation models.

So same question,

I want to work in AI and agriculture.

I'm passionate about it,

but I don't have enough resources to do that.

I might think of a very good architecture,

but I can't train it.

So maybe I can go to industry

then pitch them the idea,

then I don't have the control over the idea.

I don't know how they're going to apply it.

So do you have some advice

on how to handle the situation?

- Do a start up. - If you can get-

- Do a startup, that's what we're here for.

Oh, sorry, I'll let you answer.

- If you can get your hands

on an open source foundation model,

you can fine tune one of those models

with much less resources than it took to build the model.

So universities can still do fine tuning of those models.

- That's a very pragmatic answer for now.

But this is where we have been really talking

to the higher education leaders

as well as policy makers invest in public sector.

We've gotta have national research cloud,

I don't know if Canada has national research cloud,

but we're pushing the US,

we need to bring in the researchers like you

to be able to access the national research cloud.

But you do have an advantage by not being a company

is that you have more opportunity

to get your hands on unique data sets,

data sets, especially for public good

and play up that card.

You could work with government agencies,

work communities and whatever

because public sector still has the trust

and take advantage of that.

But for now, yes, fine tune on open source models.

- [Vishaam] Thank you. Thank you so much.

- Okay, we're going to take a couple questions.

We have thousands of people watching online

watch parties at Stanford and elsewhere,

so let's see if we can get a question

from some people online.

Leah's going to ask this question

on behalf of someone online.

By the way she's done an enormous amount of work

to make this happen along with Aaron Brindle,

so thank you both.

- Thank you Jordan.

(audience applauding)

All right, thank you.

So we do have hundreds of AI researchers online

and they're folks who are building AI first companies.

And so the first most upvoted question

was from Ben Saunders or Saunders.

He's currently CEO of an AI startup.

And his colleague was actually student

of Geoffrey Hinton's in 2008.

And he has asked about building responsibly

and a lot of these questions have to do about

building responsibly,

and they're thinking about what measures

can help them as teams be proper stewards

for good versus bad,

and what it actually means to be a steward?

- Great question.

So responsible AI framework,

there's a lot of framework,

and I think somebody has estimated,

a few years ago there were like 300 framework

from state nation, state all the way to corporate.

I think it's really important for every company

to build a responsible framework.

There is a lot you can borrow,

even Radical is is making one,

and create the value framework that you believe in

and recognize that AI product is a system.

So from the upstream, defining problem, dataset,

data integrity, how you build models,

the deployment,

and create a multi-stakeholder ecosystem

or multi-stakeholder,

whatever team to help you

to build this responsible framework

and also create partnerships.

Partnerships with public sector like academia, like us,

partnership with the civil society

that worries about different dimensions

from privacy to bias to this.

So really try to take,

both have a point of view as a company,

but also be part of the ecosystem

and partners with people who have this knowledge.

So that's my current suggestion.

- I'll add to-

Do you want to?

- No, that was a much better answer than I could've given.

- I'll just add a little bit.

I think to Fei-Fei's point,

by working with people who are interested in this,

I think there are people in the investment community

who are thinking and leading on this,

in our case Radical,

we've written into every single term sheet

an obligation for the company to adopt responsible AI.

Initially when we did that,

some of the lawyers who read it and were like,

what is this?

And tried to cross it out, but we put it back in.

But we're also, we've been working on

a responsible AI investing framework

that we are going to release pretty broadly.

And we've done this in partnership

with a number of different organizations around the world.

We've met with 7,000 AI companies in the last four years

and I think we've invested in about 40.

So we've seen a lot

and tried to build a framework

that others can use going forward.

And we'll open source it

so we can develop it and make it better.

But I think there's a lot that individual companies can do

by just reaching out to others

who are thinking in a like-minded way.

Do you want to ask another question?

- Yeah, great, there's so many questions,

so we'll only get to a couple of them unfortunately.

But playing off of that,

a lot of these questions have to do with

the relationship with industry,

considering how big of a role industry,

and the private sector's now playing in model development.

And some folks are even asking,

should researchers and different engineering roles

also be taking management courses today?

- Sure.

- I have to tell you a story of when I was at Google,

I managed a small group

and we got reports every six months

from the people who worked for us.

And one of the reports I got was,

"Geoff is very nice to work for,

but he might benefit from taking a management course,

but then he wouldn't be Geoff."

(audience laughing)

That's how I feel about management courses.

(audience laughing)

(audience applauding)

- I don't have a better story than that.

(audience laughing)

- We have about a minute and a half left,

so maybe let's do one more in the room if we can.

Let's see. Do you want take?

Yeah, no, beside you.

Sorry.

All right, hopefully ask quickly

and then we'll get a quick answer.

- Thank you. And it's a pleasure to be here.

Good to see you Fei-Fei.

My name's Elizabeth

I work at Cohere.

So my question is,

from a private sector perspective,

we work with everybody to take NLP large language models

to the broader society,

on the specific public sectors

and research institutions,

universities who has a lot of talent, a lot of data.

What is the best way to find the mutual

kind of beneficial relationship

that we can contribute and they can contribute?

Thank you.

- Give them some money.

(audience laughing)

- Thank you.

(audience applauding)

- Or H100s, we'll take H100.

But look, it's very important.

I advocate for public sector investment,

but I also actually probably more so

advocate for partnership.

We need government, private sector,

and public sector to work together.

So the past four years at Stanford HAI,

this is one of the main things we have done

is create an industry ecosystem.

And there's a lot of details we can talk offline,

but if I'm talking to university leaders

or higher education,

is that I think we need to embrace that.

We need to embrace that responsibly.

Some people will have different ways of calling it,

but I think this ecosystem is so important.

Both sides are important.

Create that partnership,

be the responsible partner for each other.

And resource is a big thing.

We would appreciate that.

- [Elizabeth] Thank you.

- Okay, with that we're exactly out of time.

I want to thank you both.

I feel very privileged always

to be able to call you both friends,

and Fei-Fei you're a partner,

and Geoff, you're an investor,

and have these conversations privately with you.

So it's great to get you both together

and let other people hear what you have to say.

So thank you both so much for doing this.

Hopefully it was as informative for you as it was for me.

(audience applauding)

And we'll turn it over to Melanie Woodin,

Dean of Arts and Science at U of T.

- Thank you so much Jordan.

So Geoff and Fei-Fei and Jordan

on behalf of everyone in the room tonight here in MaRS,

and the thousands joining us online,

we are deeply grateful

for such a profound conversation this evening.

I can say, and I think many of us know

that being part of a university community

offers a never ending set of opportunities

for engaging conversations and lectures.

And as Dean of a Faculty of Arts and Science,

I have the pleasure of attending many of them.

But I can say without reservation

that tonight's conversation was truly unparalleled.

And of course this conversation couldn't be more timely.

Geoff, when you shared your concerns

with the world about the threats of super intelligence,

we all listened and we all did what we could

to try and understand this complex issue.

Whether it's reading opinion pieces,

watching your video,

or reading long for journalism,

we really tried to understand what you were telling us.

So to hear directly from you and from Fei-Fei

who spent so many years now

leading the way in human-centered AI

is really, truly powerful.

So with that, thank you both

and thank you everyone here for attending this afternoon,

and big thanks to Radical Ventures

and the other partners that make tonight possible.

And so with that, the talk has concluded

and we invite those of you that are here with us in person

to join us out in the foyer for some light refreshments.

Thanks for joining us.

(audience applauding)

(audience indistinctly chattering)