2024年开年AI大牛世界论坛关于AI的三大访谈之一 李飞飞、吴恩达对谈：这一次，AI冬天不会到来2024 A Dialogue between Li Fei-Fei and Andrew Ng

Summary

本次访谈邀请了斯坦福大学教授、被誉为人工智能之母的李飞飞和AI基金的总经理合伙人、Google Brain的创始领导安德鲁·吴，共同探讨了人工智能的现状与未来。两位专家分享了他们对AI技术发展的看法，包括AI在不同领域的应用、AI伦理问题、以及AI对社会和经济的深远影响。他们还讨论了AI技术的最新突破，以及如何平衡技术创新与社会责任。

Takeaways

• 🤖 AI的未来不会被媒体炒作所左右，商业基础比以往任何时候都更加坚实。
• 🚀 尽管存在对AI寒冬的担忧，但AI作为一个通用技术，其商业应用前景仍然非常广阔。
• 🌟 2024年AI可能的重大突破包括视频、时间序列、生物学和化学领域的进展。
• 🖼️ 计算机视觉和图像处理技术即将取得令人兴奋的进展，可能会与大型语言模型相媲美。
• 🧠 公共部门的AI发展将得到更多资源，非营利组织在AI领域的突破也将更加显著。
• 🤔 AI在特定任务上的应用将更加广泛，特别是在数据丰富且模式可重复的领域。
• 🛠️ 企业应关注AI在特定业务和行业中的具体应用，这些应用可能带来独特的竞争优势。
• 📉 对于AI的准确性问题，需要根据行业和风险水平来评估AI的应用范围和限制。
• 📰 关于生成性AI和知识产权的诉讼，特别是纽约时报与OpenAI的案件，反映了创作者经济与AI技术之间的紧张关系。
• 🌐 开源LLM（大型语言模型）与闭源LLM之间的竞争将继续，但模型的发展方向和数据使用将有所不同。
• 💡 AI技术的发展需要新的突破，例如子二次方架构或液态神经网络，以超越现有的Transformer模型。

Q & A

Rajiv Chand 在本次会议上担任什么角色？

Rajiv Chand 在本次会议上担任主持人（moderator）。

Fe Lee 教授在斯坦福大学的职位是什么？

Fe Lee 教授是斯坦福大学的教授，同时也是斯坦福人类中心人工智能研究所的联合主任。

Andrew Ng 在本次会议上的角色是什么？

Andrew Ng 是 AI fund 的常务总经理合伙人，也是 Google Brain 的创始领导。

Fe Lee 教授被广泛认可为什么？

Fe Lee 教授被广泛认可为人工智能的“祖母”。

Fe Lee 教授和 Andrew Ng 是何时首次相识的？

Fe Lee 教授和 Andrew Ng 大约在 2007 年的某个会议或研讨会上首次相识。

Fe Lee 教授的哪本书被推荐给了观众？

Fe Lee 教授的书《The World I See》被推荐给了观众。

Andrew Ng 预测 2024 年人工智能将会有哪些突破？

Andrew Ng 预测 2024 年人工智能将在视频、时间序列、生物学和化学领域有重大突破。

Fe Lee 教授对未来人工智能的预测是什么？

Fe Lee 教授预测未来人工智能将在像素空间（pixel space）有令人兴奋的技术进步，并且公共部门的人工智能将得到更好的资源支持。

在讨论中，对于 AI 代理的未来，Fe Lee 教授和 Andrew Ng 有什么不同的观点？

Fe Lee 教授倾向于使用“辅助代理”（assistive agents）这个术语，而 Andrew Ng 则提到了“自主代理”（autonomous agents）。两者都认为 AI 的未来将更多地与人类协作，而不是完全自动化。

对于人工智能的商业基础，两位嘉宾持何种观点？

两位嘉宾都认为人工智能的商业基础比以往任何时候都要强大，AI 正在成为推动下一轮数字革命或工业革命的真正转型驱动力。

Outlines

00:00

🎤 开场与介绍

视频脚本的开头介绍了Rajiv Chand作为主持人，他是研究部门的负责人，并表示很荣幸能主持这场关于人工智能伟大思想的会议。他向观众介绍了两位嘉宾：F Fe Lee教授，斯坦福大学的教授，也是斯坦福人类中心人工智能研究所的联合主任，以及Andrew Ng，AI基金的总经理合伙人以及Google Brain的创始领导。Rajiv Chand还提到了F Fe Lee的著作《你所看到的世界》，并推荐大家阅读。接着，他提出了关于人工智能未来走向的问题，询问两位嘉宾是否认为我们将迎来更多的炒作还是进入人工智能的低谷。

05:02

🤖 人工智能的商业基础与未来展望

在这段对话中，F Fe Lee和Andrew Ng讨论了人工智能的商业基础和未来的发展趋势。F Fe Lee认为，尽管媒体可能会炒作，但人工智能的商业基础比以往任何时候都要强大，因为AI是一种通用技术，类似于电力，有着广泛的应用前景。她还提到，即使AI技术没有进步，现有的业务基础也将继续增长。Andrew Ng同意这一观点，并补充说，我们已经看到了AI的另一个转折点，特别是大型语言模型的出现，这些模型正在成为推动数字化或工业革命的真正力量。他还预测了2024年可能出现的AI突破，包括在视频、时间序列、生物学和化学领域的进展。

10:02

🌟 人工智能的突破与应用

在这一段中，两位嘉宾继续讨论他们预期在2024年将会看到的人工智能的突破。F Fe Lee强调了计算机视觉领域的即将到来的技术进步，特别是在像素空间的模型。她提到了高斯扩散模型等技术，并表示对图像、视频和多模态的进展感到兴奋。Andrew Ng则讨论了从大型语言模型向视觉模型的转变，并强调了分析图像的重要性。他还提到了自主代理的兴起，这是一种可以规划和执行一系列操作的AI系统。此外，他还提到了在笔记本电脑上运行大型语言模型的可能性，以及这对设备制造商可能意味着什么。

15:04

🤔 人工智能代理与任务自动化

本段讨论了人工智能代理的概念，以及它们在业务中的应用。F Fe Lee提出了对自主代理的担忧，并建议使用“辅助代理”这个词。她强调了长尾分布的挑战，并认为人机协作比完全自动化更有可能发生。Andrew Ng同意这一点，并分享了他在与企业合作时的经验，说明了企业如何决定使用AI来增强还是替代人类工作。他还提到了AI在特定任务上的应用，如医疗保健中的辅助决策。

20:07

🏥 人工智能在医疗保健中的应用

在这一段中，F Fe Lee和Andrew Ng讨论了人工智能在医疗保健领域的应用。F Fe Lee提到了AI在医疗保健交付中的使用，尤其是在药物发现和电子健康记录的分析方面。她还提到了在新加坡的一个系统，该系统通过分析患者的电子健康记录来预测患者可能在医院中停留的时间。Andrew Ng讨论了AI在医疗保健操作中的应用，如MRI机器的调度，并强调了在风险较低的领域部署AI的机会。他还提到了AI在诊断和治疗中的局限性，以及如何在高风险情况下使用AI辅助决策。

25:08

📚 基础模型与知识产权诉讼

这段对话涉及了基础模型的发展和知识产权诉讼的问题。F Fe Lee和Andrew Ng讨论了2024年可能出现的基础模型领导者，并预测了AI技术将如何深入和扩展到所有行业。他们还讨论了关于生成性AI和知识产权的诉讼，特别是关于OpenAI和New York Times之间的诉讼。F Fe Lee表达了对OpenAI的支持，并认为诉讼中的论点有些模糊。Andrew Ng则提出了关于内容提供者是否应该因其内容被用于训练AI模型而获得补偿的问题，并提出了一些可能的解决方案。

30:09

💡 快速问答与未来展望

在最后一段中，Rajiv Chand通过一系列快速问答的形式，引导F Fe Lee和Andrew Ng对几个话题发表了简短的看法。这些问题包括开源LLMs是否能达到闭源LLMs的水平，AI生成的选举假信息是否会影响2024年的选举结果，Transformers是否遇到了瓶颈，以及是否存在来自AI的对人类存在的威胁。最后，他们还讨论了作为风险投资家，是否会投资于OpenAI这样的公司。

Keywords

💡人工智能

人工智能（AI）是计算机科学的一个分支，它试图理解智能的本质并制造出一种新的能以人类智能方式做出反应的智能机器。在视频中，AI是主要讨论的主题，涉及其历史、发展、应用以及对未来的预测。

💡深度学习

深度学习是机器学习的一个子领域，它通过模拟人脑的工作方式，使用多层神经网络来学习数据的表示。视频中提到深度学习模型，如大型语言模型（LLMs），以及它们在图像和视频处理中的潜在突破。

💡自动驾驶

自动驾驶是指利用各种传感器和系统，使汽车能够在没有人类驾驶员的情况下进行操作。视频中提到，AI在自动驾驶领域的应用将因图像分析技术的进步而得到提升。

💡知识产权

知识产权是指创造者对其创造的智力劳动成果所享有的专有权利。视频中讨论了生成性AI和知识产权的法律纠纷，特别是关于训练AI模型时使用的内容的版权问题。

💡生成性AI

生成性AI指的是能够创造新内容的人工智能系统，如文本、图像或音乐。视频中讨论了生成性AI在不同领域的应用，以及它对社会和经济的潜在影响。

💡AI伦理

AI伦理关注的是人工智能系统的设计和应用中涉及的道德和社会责任问题。视频中讨论了AI对工作市场的影响，以及如何平衡技术创新与伦理考量。

💡AI基金

AI基金是指专门投资于人工智能领域的风险投资基金。视频中提到了Andrew作为AI基金的总经理合伙人，他对AI行业的投资前景和策略进行了讨论。

💡AI冬季

AI冬季是指人工智能领域经历的一段低谷期，通常是由于技术发展的停滞或投资的减少。视频中讨论了AI行业是否正在进入另一个冬季，或者是否会继续增长。

💡Transformers

Transformers是一种深度学习模型，特别适用于处理序列数据，如文本。它通过自注意力机制来捕捉数据中的长距离依赖关系。视频中提到了Transformers在AI领域的应用，以及是否需要新的技术突破来超越现有的模型。

💡数字革命

数字革命指的是由于数字技术的发展和应用，导致的社会、经济和文化的根本变革。视频中提到AI作为一种通用技术，正在推动新的数字或工业革命。

Highlights

Rajiv Chand作为CES会议的主持人，介绍了两位人工智能领域的杰出人物：斯坦福大学的教授Fei-Fei Lee和AI基金的总经理合伙人Andrew Ng。

Fei-Fei Lee被誉为人工智能的“祖母”，同时也是斯坦福大学人类中心AI研究所的联合主任。

Andrew Ng是Google Brain的创始领导，并对深度学习有重要贡献。

Fei-Fei Lee和Andrew Ng首次会面是在2007年的某个会议或研讨会上。

Andrew Ng提到，即使没有技术进步，AI的商业基础也比以往任何时候都要强大。

Fei-Fei Lee认为AI是一种通用技术，类似于电力，将在各个行业中发挥重要作用。

Andrew Ng预测，图像和视频的AI技术将取得重大突破。

Fei-Fei Lee强调公共部门AI的重要性，以及非营利组织在推动AI发展中的作用。

Andrew Ng讨论了自主代理的兴起，即AI系统能够规划和执行一系列动作。

Fei-Fei Lee提出了对“自主代理”一词的异议，建议使用“辅助代理”来更准确地描述AI的作用。

Andrew Ng分享了企业如何确定哪些任务适合使用AI来增强或自动化的方法。

Fei-Fei Lee讨论了AI在医疗保健领域的应用，以及如何安全地部署AI技术。

Andrew Ng预测，AI将在金融服务、教育、电子商务等行业中发挥越来越重要的作用。

Fei-Fei Lee和Andrew Ng讨论了AI对创作者经济的影响，以及媒体应如何更细致地报道这一问题。

关于AI训练使用互联网内容的版权问题，Fei-Fei Lee和Andrew Ng表达了对当前法律需要更新以适应生成性AI时代的看法。

Fei-Fei Lee认为，尽管AI可能改变某些任务，但不会导致整个工作的消失，而应被视为提高效率的工具。

Transcripts

[Music]

for

for

humaner

for

Fore

good morning

everyone good morning morning awesome my

name is Rajiv Chand I'm head of research

of wing it is my honor to be the

moderator for this session on great

minds in AI we are here with two of the

greatest Minds in AI immediately to my

left is Professor of Stanford University

co-director of the Stanford Health

excuse me Stanford human centered AI

Institute and also widely recognized as

the grandmother of artificial

intelligence F Fe Lee not to indicate

anything but age not

anything and immediately to her left is

managing General partner of AI fund also

the founding lead for Google brain

Andrew Ang everybody please join me

again in welcoming f f Lee and Andrew a

to

CES

he is also the author of uh this amazing

book called the worlds I see if you

haven't picked up a copy I certainly

recommend it it's got this amazing life

story as well as the history and future

for AI it's amazing amazing book so just

just just to add to that I think over

the years I've known f f i and many

others have been inspired by her

personal story she used to work in a

lrat I think she's been public about

that and then she wed up more recently

building up hii and frankly you know her

whole team knows she has a reputation

for working crazily hard and built hii

to this fantastic institution in

Stanford so I think for the people that

don't know hisory yet I think um if you

read her book you find it pretty

inspiring so thank you Andrew this is

why you're in the

book so Andrew F let's start with both

of you are each luminaries and Veterans

of AI but each of you have also worked

when's the first time both of you met

each other or worked together tell us a

little about your history I'm worried

when my have different

answers I think well first of all I've

been reading Andrew's papers before I

read uh I met Andrew I think we met as

young assistant professors somewhere

around

2007 in a conference or a workshop that

that was my Merm what what year is that

again 2007

2007 yeah honestly I my memory is awful

I I have no idea but but you should

remember that the first thing you said

to me is Fe do you want a job at

Stanford that I

remembered that works out that that I'm

I'm I I I I'm actually really proud you

know that I played this small Row in

convincing to come so it's well let's

start with question one which is kind of

state of AI and uh last year was

certainly a very hype year for AI our

good friend Rodney Brooke uh tweet

tweeted or posted on January 1st get

your thick coats now there may be yet

another AI winter around just around the

corner and it's going to be

cold so are we headed to less hype more

hype or a trough for this upcoming year

in

AI I think the media would do whatever

the media does but we're not in for

winter and that's because the business

fundamentals of AI are stronger than

than ever um even before the generative

AI wave that really took off last year

AI has been moving probably hundreds of

billions of dollars maybe trillions of

dollars I'm not sure at least hundreds

of billions of dollars for a single

company like Google you know sh more

relevant ads that drives massive amounts

of Revenue so the business fundamentals

are there and in fact because AI one of

the difficult things to understand about

AI is it's a general purpose technology

meaning that it's not useful for one

thing it's kind of like electricity and

another general purpose technology if I

ask you what electricity good for is

almost hard to answer that because it's

useful for so many different things and

AI is like that too and so where we are

today I think even if AI makes no

technological progress which you know it

is going to make Tech progress even if

it doesn't there's so many use cases all

around the world to be identified and

build out that the business fundamentals

I'm very confident will continue to grow

and we're going to make this session

also highly interactive as well let me

get a show of hands more hype less hype

winter how many folks think that we are

not at Peak hype there will be more hype

hype this year show a

hands how many folks think there will be

less hype this upcoming

year how many folks think no hands for

Less hype how many folks think that will

be a winter for this upcoming

year wow so we're not at Peak hype yeah

so I more or less agree with Andrew so

what we have seen is another inflection

point of AI and uh that inflection point

came came through the large language

models or the the first roll out of tat

GPT and then the uing models what I do

see and I agree with Andrew is this is a

deepened horizontal technology when it's

a deepened horizontal technology it is

becoming a true transformative driving

force of the next um whether you call it

digital Revolution or Industrial

Revolution in terms of the the public

media you know coverage it's going to

just go in in this kind of waves and

that's not very relevant but what is

what is relevant is that this technology

is here to stay is here to be deepening

into all vertical businesses and uh

customer consumer experiences and is

changing the very fabric of our societal

economical political landscape and that

is that is just a fact and that's

happening more and more let's jump to

Big breakthroughs that you anticipate

for 2024 clam at hugging face uh had I

think six predictions for this upcoming

year one of which was big breakthroughs

in AI for video time series biology and

chemistry what do you feel and maybe

start with you f what do you feel will

be one of the biggest breakthroughs in

AI in this upcoming year as we start

2024

that it's always very dangerous to

predict the future because then I'm

going to be quoted as saying something

wrong um all right coming from the field

of computer vision and and what I would

call pixel Centric AI I do think we're

at the verge of very exciting

technological advances in pixel space

we've been looking at gen we've been

looking at diffusion models we you know

some of you hear about gausian splatting

you're hear about uh I think there is

just so much that's almost you know

breaking through that wave of technology

I don't know if it's exactly going to be

as um um matured as llm or large

language models a a year and a few

months ago but I I'm seeing that more

and more and I'm very very excited by

that image video multimodal with a

combination different modes or any of

the three or right it's a combination of

those it's going to be more pixel first

it's not just language induced and uh

another thing I do want to say is uh

this is a little bit more faith face

hope uh rather than my prediction is

actually public sector AI I think it's

really important that for the ecosystem

as well as for many reasons that public

sector AI will be better resourced

uh we we are pushing our governments for

that as well as um some of the very

exciting interesting multi-disciplinary

nonprofit driven breakthroughs coming

from public sectors whether it's

sustainability or medicine drug

Discovery and and other areas Andrew

your thoughts yeah make a few quick

predictions so first um we've seen the

large language model breakthrough right

things a CH reading b i I agree with fa

about images coming so I'm seeing a

shift from large language models so

launch Vision models um a lot of

progress will not just be in generating

images it'll be in analyzing images so

computers can see much better those

implications on you know self-driving

cost for example wherever you have a

camera that's one second um we used the

prompting chat GP B you prompted a

response but I'm excited about the rise

of autonomous agents which is when you

can give a AI system and instruction

like d AI system do market research for

me to do a competitive analysis of this

company and instead of responding right

away it plans out a sequence of actions

like do these web searches and download

these web pages and summarize these

things it goes off and does half an hour

of work or an hour of work or a day of

work and then comes back to M an answer

autonomous agents they can plan out and

execute sequences of of actions they're

kind of just barely working but I feel

like there's a lot of traction the

research and the commercialization side

and we expecting breakthroughs um in the

coming months and me just one last thing

maybe appropriate for seers as well I'm

very excited about AI um so you know uh

I routinely run a large language model

on my laptop right so I use gbd4 all the

time I use bot quite often but what not

many people know is that it's actually

getting quite feasible to run the large

language model um on your laptop not as

big as gp4 but big enough to be useful

and I think this actually have a lot of

implications um device makers you know

all the PC makers won't we like to um be

able to sell consumers a more powerful

PC to let them power the latest AI I

think graphics cards was often a reason

for upgrade I think that uh Edge AI

running on your laptop or PC or your

industrial PC at the age that capability

is actually getting much better than

most people think and maybe a perfect

for CS I think this will drive a lot of

device sales for a lot of companies as

well I'm going to respectfully disagree

on or or just just tiny bit of a

discussion you use the word autonomous

agent um I actually would like to change

the a word to assistive agent

one thing we have seen in today's

language model large language models and

these large Foundation model is that

longtail distribution is still really

really hard whether we're talking about

hallucination and other things and in a

lot of Works Space that um in order to

deliver the kind of quality service and

uh products long tail matters so what I

actually see is a human machine in the

loop collaboration assistive agents that

part of the work is autonomous part of

the work is collaborative is more likely

to happen rather than fully autonomous

actually you high five years we finally

had something to disagree on no no but

but I actually actually do kind of agree

um so let me share my my experience I I

I think the term autonom agents is

problematic maybe but what I'm seeing in

business context is I know that a lot of

us would rather you know have ai hope

humans rather than replace humans

because of the job loss conversation

which is a thing and I

um and without uh uh diminishing the the

the suffering of people whose jobs do go

away what I what I see just BEC candid

is often the decision for whether you

use AI to automate or to augment it

tends to be a business economic decision

rather than an ethical decision maybe it

should be an ethical decision but candly

when I work with businesses and they

build a chatbot you know there's a very

rational economic calculation that I see

most businesses do to say great humans

add this value a adds his value what's

the right economic decision because the

competitors are doing the same thing so

I wish we could say don't replace human

Tas you know but but unfortunately okay

so so on this top of AI agents let me

say another quote from Mira morati she

said the concept of AI agents prob been

November or so the concept of AI agents

isn't new but now we're uh iterating

toward the future with intelligent

Common Sense agents that understand why

we do

things okay okay this this just

to um add to one thing and and I'll

comment on that one is that I think we

have to be careful about replacing Jobs

versus replacing tasks actually that was

I was going to say that next exactly so

I'm sure you and I read the same reports

that for every given human job it's

actually a suite of multiple tasks

sometimes for you know I I study healthc

care a lot a nurse eight hour shift is

hundreds of tasks so I do see that AI

agents helping and being assistive and

augmentative are manying tasks but we

should be very very very careful in

talking about jobs and I do think that

um economical business decisions are not

mutually exclusive from ethical societal

decisions it's a deeper conversation I

know you and I agree uh coming to your

question about these agents have an

understanding I think that this is a

very nuanced term just focusing on the

business what is understanding there's

the understanding of the pattern that

lives in the data there's the

understanding of what decisions you're

making there's also understanding of the

intention of the whatever the human task

so so I think it is actually um I would

not go as far as using a blanket word

understanding to describe today's AI

agents or which of those three do you

think AI agents will get to in what time

frame well I think the best we've gotten

is understanding the patterns in the

data you know especially if we have

massive training data we have done a

great job when let's say we you know for

example large language models right

using this sequence to sequence

Transformer based algorithm is really

done a great job um extracting the the

patterns in the data uh in in order to

create predictive powerful predictive

models and so I think that's really

really

probably the most ahead in terms of

understanding the decision making again

I think that's much more nuanced you

guys all come from business and you know

how nuanced it is and I I think there's

more to be done and to be said and in

terms of

intention I think we're just scratching

the first surface you know yeah actually

can can I I want to I want to go back to

the Tas topic because I think that's the

important so one thing that you know um

my team's working with quite a few

businesses and occasionally I get a call

from a c they say hey Andrew reading

about AI gen of AI what should I do

about it and it turns out that there's a

recipe for businesses to figure out um

what tasks you should try to use AI to

augment or automate so as fa was saying

most jobs are made of many different

tasks um take the job of a radiologist

you know Radiologists read x-ray images

they have to uh gather patient histories

they have to operate the machines

maintain the machines um you know

consult with Mentor younger doctors and

so on so radiologist is one example does

many different TS so what um I've seen

businesses do sometimes of Our Hope and

also one of our friends every broson was

really pionering this technique is to

look at your team you know figure out of

all of your employees what are the tasks

they're actually doing and to analyze

not at the job level but at the task

level how ad meable is this task to AI

augmentation or Automation and what's

the business Roi and every time I've

done this with a business um we've

always come up with way more ideas than

any of us have time to implement so

there's a lot of opportunities for AI

augmentation or Automation and then the

second thing I've learned is very often

the highest Roi tasks are not what

people initially think so for example um

when you think of a radiologist people

often think oh Radiologists read x-rays

there's that picture in your head of

that defining roow of what a job entails

but we actually break down that job

within many different tasks there are

often other Tas like maybe maybe

Gathering patient histories or something

you know that turns out to be easier and

maybe higher Roi so I found that doing

this exercise systematically has often

helped businesses um identify valuable

opportunities to then go through a bill

versus buy kind of decision to execute

an AI project Andrew this is exactly

actually where I wanted to go next which

is bringing it very practical to the to

the group here so so are there any

commonalities of applications that you

see

among your work with Fortune 500

companies on applications that have

clear demonstrable achievable Roi like

what what applications do you see that

most in this room should be absolutely

laser focused on yeah so well if if we

look as broad as portion 500 I think the

common ones are customer operations or

customer support there's so many

companies trying to augment or automate

customer support um I think software

engineering is also transforming so we a

lot of software engineers and I think

this goes well beyond GitHub co-pilot

GitHub co-pilot is a nice too but it

goes well beyond that um I think sales

operations is also being heavily

impacted um but for specific businesses

it turns out kind of everyone is doing

customer operations so you you should

probably consider it as well but the

more exciting things to me are um boy

what can I talk about talking to a very

large agriculture company and there some

people that do some task I can't talk

about the details but we identify the

task that you know it's not what you

think of when they think of harvesting

right it this this weird task that we

thought oh maybe we could use AI to

really save them a lot of time so it's

those Niche it's those things are

specific to your business and your

industry that I think are often more

interesting and creates that you know

industry specific defensible defensible

fly wheel and strengthens because

everyone at some point will probably

really to buy some generic tools for

sales operations and so on but the

things specific to your business they

should build internally those things

that I find very exciting one thing to

just add to that that is um there's you

know the the the kind of like uh

customer support or or Ops Solutions but

there's also another way to look at is

where are the com uh opportunity

commonality opportunities using the

current technology and I think it's

still true today is where you have the

most data where the data can be

um you can actually discern uh

repeatable patterns or or good patterns

out of the data and that's where you can

start whether it's human language

patterns or it's structure data patterns

or imagery P patterns where where data

is and where the the the the patterns of

data uh prove to be valuable and

actionable in your business is where one

should be looking at let's talk about

barriers that Fortune 500 CEOs May face

uh we held our healthc care Summit this

past Sunday with a number of healthcare

CEOs one of whom we asked what are you

most excited about for digital

Innovation he said artificial

intelligence Then I then I asked him

well what are you most concerned about

as CEO and he said

inaccuracy you pick the hardest

industry yeah yeah how what would you

say to CEOs Who Who Are You know talking

about inaccuracy as a CEO level concern

in artificial intelligence and are there

other concerns that you also see at that

level well this is where I was saying

right like it depends on your product it

depends on your services it depends on

the stakes of the outcome right Health

Care driving Financial prediction there

are many Industries where the longtail

accur accuracy is so important you

cannot afford human lives or human

injury you cannot afford you know

banking errors so this is where you need

to understand your industry you need to

understand your Solutions and services

and look at where AI genuinely can can

help and this is where when you call

hype personally as you know when I have

conversation with business Executives

this is where we really should peel away

from the hype and understand what this

technology can do and avoid that kind of

um you know uh investment or or or um um

the kind of directions where AI is not

ready so an industry like healthcare

which is life and death and highly

regulated then what would you say to a

company that's wants to do generative AI

but it's concerned about inaccuracy what

would you say to him or her well both

Andrew and I work in healthc care a lot

I personally work work a lot in healthc

care um uh delivery um so there is

actually a ton of AI usage in healthcare

just just breaking it down from very

Upstream drug Discovery there's a ton we

can do and I mean generative AI

generative AI by the way that is a

overloaded word every AI today people

call it generative AI when Andrew and I

started we have very specific

mathematical definitions of generative

AI but now it's we we used to call it

machine learning but machine learning

became AI exactly we also used to call

gen generative versus discriminative AI

right so that's that whole mathematical

rigor is gone so but yeah I I I feel

that the mass media has kind of taken

over the tech terminology and may

technology just adopt the

mass when you call generative AI I'm

just going to assuming like that kind of

large data driven there's a pre-training

phase you know so some people might put

Transformer and predictive model in it

but I'm not even totally sure if people

always do but in any case I think uh if

there is a true accuracy issue we should

examine uh several things is this a

model limitation is this a data quality

problem is this the the AI in the loop

there is more nuanced business issues

that causes inaccuracy really decipher

all those and uh try to tackle them and

sometimes for example in certain level

of Health Care diagnosis and treatment

you do have to recognize there's a limit

and we cannot push too far if if the

risk is too high so so let's turn just

just add to that um even though we use

term generative AI generative AI is

often used for analysis so you know my

teams have done a bunch of projects

using uh these large language models uh

to read electronic health records to try

to spit out the conclusion rather than

to write text and even if you're writing

text it turns out that if you're careful

umof software for summarizing you know

is not that bad it it may still make

some and and I think there's so many

opportunities to deploy things even in

the health care setting where the stakes

aren't quite as high so for Diagnostics

you miss something you know that seems

really bad but um we deployed A system

that uh is still running a hospital to

screen patients reading electronic

health records to try to decide who's at

high risk of mortality to recommend them

for consideration for palal care for end

of Life Care uh but we don't trust our

system to make the decision so we send

to a doctor that reviews the photos we

show them and then makes a final

decision um and actually one of my

friends in Singapore Chang in the

National University Singapore has a

system that's looking at patient dhrs as

they come in to try to estimate how long

is a patient going to be in the hospital

sometimes the doctor thinks oh this is a

simple case there'll be other than three

days but the AI says no 15 days and that

triggers a conversation this actually

happening now in Singapore where the

clinician says oh maybe I need to take a

second look at this patient maybe I miss

something that AI C so so these things

are actually getting deployed but

depending on the capabilities um we can

often you know design safeguards to make

sure that it's deployed and responsible

way oh in healthcare operations if

you're using AI to schedule your MRI

machine if you make a mistake fine the

US of MRI is less efficient that is bad

but that doesn't seem maybe as bad as

missing a critical diagnosis so there

are actually lot of opportunities deploy

in healthcare and I think pretty much

all the sectors so let's J to

foundational models and this next

question was inspired by an article that

I read in Venture B

if 2023 was the year of open AI among

the foundation model leaders who will we

be talking about most in

2024 will Apple launch Ajax llm will we

be talking more about Gemini than

about

GPT so I just said um earlier I see this

technology deepening and also widening

into all sectors and because of that it

is hard to single out one company I'm

sure there will be exciting releases I

there will be you know the next a h100

next uh generation release from the chip

side all the way to the consumer side so

I I'm not going to be able to bet on a

single um a single you know topic but I

do think uh 20 24 I hope to see and I do

believe will be defined by a year that

we are seeing the widening of AI

applications as well as AI technology

and uh so it's not just focusing on one

or two Andrew how do you handicap the

foundation model leaders for this year

no so so let me let me let me um it

turns out that every time there's a wave

of tech Innovation the media likes to

talk about the technology layer which is

why the media focuses on open AI Google

you know AWS Microsoft meta and so on

nothing wrong with that but it turns out

or in Nvidia and AMD and so on but it

turns out that for this technology

infrastructure layer to be successful

there's another sector we need to be

even more successful and that's the

application layer built on top of these

po tool providers because frankly we

need the applications built on top these

tools to generate even more Revenue so

that they can afford to pay the two

Builders um I think Squire wrote a nice

article showing given the capital

investment and gpus you know we better

right collectively as a few generate

applications to fill in these tens of

billions of dollars kinds of hold that

that that we're now you know with the

capital Investments that have already

been made with gpus so I I again I don't

know what the media does they they you

know the hype cycle is whatever it is

but I think a lot of the actual work

will not just be the foundation model

layer it'll be going to healthcare

Financial Services uh uh education

e-commerce all of these different

sectors to identify and execute the

projects are now possible yeah I grew up

in Tech and mobile and in Mobile you had

many multi-billion dollar app companies

get created and I do think we do think I

agree with you I think there going to be

something here for that staying with

Foundation models for a moment one of

the major topics today is the lawsuits

on generative Ai and intellectual

property so how do you see these

lawsuits evolving and should the New

York Times be compensated for use of its

content in

training do you want take that you are

the one on Twitter talking about it I've

already said some stuff that might gave

into trouble so I might as well now so

sry I I did look through the New York

Times um opening eye Microsoft lawsuit

not lawyer not giving legal advice or

any form of advice any for for for this

but my sympathy line much more of open

AI than with Microsoft and my sympathy

line much more of open Ai and Microsoft

than with the New York Times uh candly

when I read the New York Times lawsuit I

felt that it was a very muddy argum

argument um uh and you know I wish New

York Times lawyers were held to the same

standards of clarity and journalistic

you know explaining stuff as the

reporters are but I don't think they are

so the New York Times it this very

somewhat I thought sensationalist

showing of oh open AI you know

regurgitates uh uh New York Times

articles but I think the way I was

presented was frankly a little bit

strange how so how so Andrew what what

did you find as a flaw in the in the

arguments in that in that brief so two

things one and May more important is uh

uh the prompts you what you type in get

it to regate New York Times articles

that was a very strange prompt that I

don't think any pretty much any normal

user of open ey will ever use so I think

it is true that New York Times Found You

know open eye characterizes the bugs I

think New York Times found a bug in

which opening ey does regurg dat

articles which it should not do I don't

think regurg dating cortic content at

scale is appropriate so open out has a

bug where it does that and New York

Times just points out kind of kind of

says you have a bug you have a bug you

have a bug and yes open that has a bug

we all you know sadly sometimes have

bugs in the software and I think there

was another thing that was strange which

was I believe that in some of the

examples New York Times showed that you

can write a prompt your prompt chat GPT

to tell it to basically go and download

The New York Times article and then tell

it to print it out and I feel like just

because it does that it's not the same

as the fact that um opening ey trained

on a lot of text Data from the internet

including New York Times article and I

think the lawsuit tried to draw a link

between opening eye training on a lot of

text that includes New York Times

articles with this you know Spectre that

opening eye is uh engaging in Mass

regurgitation of New York Times text

which I don't think is really um uh

telling the full story Andrew you should

expect a call from the District

Attorney's Office let me let me add to

this um not specifically the New York

Times meaning Andrew will be a expert

witness yeah I I

rather that's my 2024 prediction I'm

just kidding um so I do want to add to

this and zoom out a little bit about uh

the tension between geni and Creator

economy and I I I'm not as nuanced as

Andrew about the specifics of New York

Times dispute but even in my book I'm

mentioning about the messiness of this

technology those of us who trained in

the technology itself love to see

deterministic even in probability Theory

it's mathematically rigorous um things

but the truth is when Tech when rubber

hits the road especially a technology as

profound as this it gets messy with the

human human world Human Society what New

York Times um lawsuit with oi and uh

Microsoft is really showing us is

indicative of the tension we're seeing

between creater economy which is the

internet has really uh you know scaled

creater economy and this impacts not

only big players like New York Times but

little players like a single artist a

photographer a music composer and that

whole ecosystem is being challenged

disrupted as well as augmented by

today's generative AI technology and

we're seeing that tension playing now in

addition to the new York Times lawsuit

we getting artists uh law uh engaged in

lawsuits with mid journey and and others

so I actually think especially Andrew

has been calling out media and I do

plead to the media to look into this

with of much more nuanced lens and uh

whether it's public sector or private

sector private sector we should pay much

deeper attention in this issue than than

than just scratching the surface and

there and there is a very human element

to this so during the Hollywood strikes

a lot of the you know difficulties was

just boy if you're a Creator and you

think is my job going to go away is all

my work going to be still in it is a

very deeply emotional thing I actually

do sympathize with that um I think that

the fears of job loss are probably

greater than they will be because of

what we said earlier the jobs are made

out of tasks and even artist jobs are

made out of many tasks and yes AI could

alterate some of the task but because AI

May automate I don't know 20 30% of

someone's task but that still a lot of

other tasks that we need people to do

and maybe they could be more efficient

and actually even make more money but

there is this fear that that I think is

um challenging I think the AI worlder

needs to do a better job having that

conversation and reassuring people that

the job losses I don't say there'll be

no job bosses that's just not true but I

think it won't be as bad as as as it's

feared let me jump back to the group

again here and again show a hands three

options all internet content should be

available for training not inference but

for training some content categories

some content provider categories should

be compensated for their content for

training and then many or most content

providers should be paid for their

content for training how many folks feel

that all internet content should be

available for free for training you're

just go all open internet right all open

all open internet all open internet how

many folks feel yeah let's stay say open

internet how many folks feel like all

open internet content should be

available for training and there's kind

of an interesting argument here you know

I mean the University of Michigan

quarterback might have watched Todd

Brady but he probably didn't pay Todd

Brady for for yesterday's performance

okay how many folks feel like some

content providers some categories of

content providers should be paid for

their content for training on models

like open Ai and

others how many folks feel like many or

most content providers on the open

internet should be

paid so maybe like 30 40 maybe 30 50 10

30 50 20 something like that I'm sure

this is how the law is made and by the

way just just here I think I think you

know you know copyright law was written

in a previous era I think it needs to be

cleaned up for the generative AI era and

there are these difficult questions

about what is best for society right we

you know US Government collectively as a

society who can pass whatever laws we

want and I think these are very

difficult debates about how do we

compensate Crea us fairly and also

enable Tech Innovation and you know what

does the open internet mean and do we

want it to be a little bit less open

than it has been these actually really

difficult questions about what's best

for society awesome we're going to do a

lightning round for this last four

minutes so true or false in 30 seconds

why maybe Andrew I'll start with you

open source llms will reach the level of

the best Clos Source llms within the

next 3

years uh don't know but it might there a

lot of momentum Sly beat today's close

LMS but whether but the race is on we'll

see what happens with close Source

versus open source I I'm going to say

it's false not because of the

discrepancy of quality it's because of

the Divergence of the kind of models

because of the data uh are very

different and the close Source will will

pivot much more to deepening deepened

business cases and the open source will

be different so true or false AI

generated election disinformation will

be everywhere but it will not shift the

outcome of the 2024

election why think

that

um more or less

true and the reason I'm saying this and

I'm part of the effort so I'm trying to

believe in this is the the strength of

democracy is not relied on technology

itself and the flip side is true the

weakness of democracy the strength and

weakness of a democracy is on its people

and if we do the right public education

and uh and have the right public

discourse we are stronger than we

believe who we

are yeah I

yeah I don't know it's tough I you I

think until now I think the B true or

false Andre true or

false I think it's I think it's probably

false but I'm not sure um because until

now the bottleneck for dissemination of

you know falsehoods of disinformation

has been distribution you know people

can write fake stff it's really

difficult to get that fake stff in front

of the large audience I think the risk

is personaliz Iz uh disinformation

personalized disinformation and

persuasion but the technology is early

and the building up of defenses is is is

also still early a deeply technical

topic Transformers are hitting a wall

and we need a new solution such as sub

quadratic architectures or liquid neuron

networks liquid neuron networks well um

coming from the pixel World which is

deeply non 1D it's 2D it's 3D it's

multi-dimensional

I do think we need breakthrough

technology Beyond sequence to sequence

models that I absolutely believe um so I

guess it's true truish Andrew I say

Transformers are not hurting a while if

all we have is Transformers for the next

5 years we still have tons of room and

we also need new breakthroughs because I

wish we had something much better than

Transformers Andrew the next one is for

you as managing General partner of the

AI fund and F totally opine as well I

would invest in open AI I at a100

billion valuation yeah no no no comment

open eyes is a great comy open eyes is a

great comfy s was my student at Stanford

really deep respect for open ey but no

comment on investment

decisions I'm too poor to

invest well that's that's that that may

be that may be not 100% true uh F

question true false there is an

existential threat to humanity from AI

not

now there is catastrophic so social

risks from democracy to data bias issu

uh algorithm bias issues to uh job uh

labor market shift these are true

societal risks but not the kind of

conscious sension being existential

crisis not now Andrew last one is for

you this is from Ivan garia from

Gunderson Detmer and true or false at

least as a venture capitalist at least

one AI startup will raise the

substantial round of financing before

investors realize that the company

contains no actual humans and the

founder is a

bot not this year not for a

while everybody please join me in

thanking f f Lee and Andrew egg