How AI Will Shape Society Over The Next 20 Years

Summary

在这段视频脚本中，讨论了人工智能（AI）的未来及其对世界的影响。讨论涉及AI在医疗、教育、交通和资源管理等领域的潜力，以及AI如何帮助提高人类能力，实现个性化教育，并可能在20年内使道路上25%的车辆实现某种形式的自主性。同时，讨论了AI带来的挑战，包括就业变化、数据隐私和去中心化问题。专家们对AI的长期影响持乐观态度，认为尽管存在风险和误解，AI技术的发展将为社会带来积极的变化，并强调了在AI发展中考虑激励机制和经济学的重要性。

Takeaways

• 🤖 **AI发展速度**：人工智能的发展速度比以前的技术革命快得多，例如CRISPR技术经过多年才获得FDA批准，而AI技术的普及速度则更快。
• 🌐 **AI的普及**：预计在未来20年内，大约20-25%的车辆将具有某种形式的自主性，但不太可能超过这个比例。
• 🚀 **AI在教育中的应用**：AI将使教育更加个性化，允许人类教师快速了解每个学习者的状态，并且可以远程进行，提高教育的可及性。
• 🔍 **AI的风险与治理**：尽管AI带来了许多积极的可能性，但也存在风险，如因AI导致的死亡事件，社会需要找到方法来管理和适应这些风险。
• 🧠 **AI与人类智能**：AI将帮助我们更好地学习，提高我们的认知能力，使人类在某些领域如绘画或歌曲创作方面变得更好。
• 💼 **AI在资源管理中的应用**：AI在资源管理方面，如大数据或分布式资源分配，已被证明是有效的，并将看到更广泛的应用。
• 🌱 **AI在可持续发展目标中的作用**：AI的生产力提升可能会改变某些领域的经济效益，如水资源和农业，吸引更多的顶尖人才。
• 📈 **AI经济学**：AI将改变许多职业的经济效益，使得以前昂贵的服务如法律咨询变得更加便宜和可及。
• 🌐 **去中心化的AI**：AI的未来可能在于去中心化，包括数据、计算和治理的去中心化，以及通过联邦学习和专业化模型实现。
• 🚗 **自动驾驶车辆**：自动驾驶车辆的发展将是一个渐进的过程，预计不会完全自动化，而是会与人类驾驶员共存。
• 🧪 **AI在复杂系统中的应用**：AI在处理如微生物组这样复杂的系统时，可以提供帮助，通过分析大量样本来理解整体功能。

Q & A

Ramesh Raskar是如何看待AI在未来20年内对人类生活的影响的？

Ramesh Raskar是一个技术乐观主义者，他预计AI将在未来20年内改善我们的健康，特别是在通过植入物提高记忆力方面。他还预测，大约20%到25%的路面车辆将实现某种形式的自主性。他认为AI将帮助我们更好地学习，并改善我们的学习方式。

Hari Balakrishnan最近启动了什么项目，它在AI领域有什么作用？

Hari Balakrishnan最近启动了CMI，这是世界上最大的远程信息技术服务提供商，旨在帮助在道路上保持安全。

在AI领域，我们如何从以往的技术革命中学习并找到平衡点？

以往技术革命，如互联网、CRISPR基因编辑技术、核能等，都引发了大规模的变化讨论，包括风险和治理。我们从这些技术中学到的是，最终我们能够找到一种平衡，使得益处明显超过风险，并且风险可以得到管理。AI的发展速度比以往技术快，我们需要从这些历史中学习，以期达到类似的平衡。

AI在教育领域的潜力是什么，它将如何改变教育？

AI在教育领域的潜力在于提供个性化学习体验。它将允许人类教师快速了解特定学习者的状态，从而提供定制化的教学。此外，AI还可以使远程教育变得更加有效，通过提高教师的教学效率，让教育规模化的同时更加个性化。

AI在交通领域的应用将如何发展，特别是在车辆自主性方面？

在未来20年内，我们可以预见到20%到25%的路面车辆将实现某种形式的自主性。但这种自主性可能不会超过这个比例，因为实现完全的自动驾驶还面临着许多技术和伦理挑战。

AI如何帮助解决复杂的全球性问题，比如水资源短缺？

AI可以通过模拟和优化来帮助解决复杂的全球性问题。例如，AI可以模拟水流和分配，帮助我们更好地理解和管理水资源。此外，AI还可以帮助设计更高效的灌溉系统和水处理技术，从而提高水资源的利用效率。

在AI领域，为什么需要考虑去中心化的重要性？

去中心化在AI领域很重要，因为它可以防止数据和计算能力集中在少数大公司手中。去中心化可以促进创新，让更多的参与者进入市场，并且可以更好地保护个人隐私和数据安全。此外，去中心化还可以提高系统的鲁棒性和抗风险能力。

AI在模拟环境中的作用是什么，它如何帮助人类提高技能？

AI在模拟环境中可以进行大量的训练和学习，比如在棋类游戏中，AI可以快速学习并掌握游戏规则，达到超越人类专家的水平。这种能力可以帮助人类通过与AI对弈来提高自己的技能，同时也可以让AI帮助人类探索新的策略和创意。

AI在资源管理方面有哪些应用，它如何提高效率？

AI在资源管理方面的应用包括在大数据中心里分配资源、优化分布式计算资源的使用等。AI可以帮助预测资源需求，自动调整资源分配，从而提高资源利用效率，降低运营成本。

AI在现实世界中的应用面临的最大挑战是什么？

AI在现实世界中的应用面临的最大挑战是如何将AI技术与现实世界的复杂性结合起来。现实世界的不确定性和多变性要求AI系统不仅要有高度的智能，还要有良好的适应性和鲁棒性。此外，还需要解决伦理、法律和社会接受度等问题。

AI技术发展的速度是否超过了我们的治理能力？

是的，AI技术的发展速度非常快，这在一定程度上超过了我们的治理能力。这意味着我们需要加快制定相关的法律法规，建立伦理标准，并提高公众对AI技术的理解，以确保AI技术的健康发展。

AI技术在短期内是否会导致大规模的失业？

AI技术在短期内可能会导致某些工作岗位的消失，但历史上的技术变革也证明了，新技术的出现往往也会创造新的工作岗位和职业机会。关键在于社会如何适应这种变化，以及如何通过教育和培训帮助劳动力转型。

Outlines

00:00

🤖 AI的未来与社会适应

第一段主要讨论了AI的未来发展及其对社会的影响。提到了rames rcar和Hari bar Christen两位专家，分别在媒体实验室和计算机科学领域有所成就。讨论了AI可能带来的变革，包括对教育、工作和日常生活的影响。强调了AI发展速度之快，以及如何从以往的技术革命中学习，找到风险与收益之间的平衡。同时，表达了对AI在医疗、交通和教育领域应用的乐观态度，但也指出了AI在现实世界中应用的挑战。

05:02

🚀 AI的积极展望与潜在风险

第二段中，讨论者表达了对AI未来发展的乐观态度，认为AI不会带来人类的灭绝。他们预见了AI在医疗、交通和教育领域的积极作用，尤其是AI在个性化教育中的应用。同时，也提到了AI可能带来的风险，如因AI导致的死亡和对工作的影响。讨论了AI在资源管理中的应用，以及它如何可能改变人类的工作方式，包括创造新的工作机会和提高人类的创造力。

10:04

🌐 AI的去中心化趋势

第三段探讨了AI的去中心化趋势，以及这一趋势如何影响全球经济和各个行业。讨论了苏联模式的失败和AI在资源分配中的作用，以及AI如何在不吸引大投资的情况下帮助解决全球性问题。强调了AI在提高生产力和改变经济模式方面的潜力，以及它如何可能带来新的商业机会和挑战现有的经济结构。

15:05

🧠 AI与人类智能的结合

第四段讨论了AI与人类智能结合的可能性，以及这种结合如何帮助解决复杂的社会问题。提到了AI作为顾问在提升中低技能人群能力方面的潜力，以及AI在法律等专业领域的应用。讨论了AI在解决水危机等全球性问题中的潜在作用，以及AI如何帮助科学家和工程师在复杂系统中找到解决方案。

20:06

🌿 AI的民主化与资源限制

第五段强调了AI民主化的重要性，以及如何通过民主化来缩小技术拥有者和非拥有者之间的差距。讨论了AI硬件的发展，特别是为特定任务设计的高效硬件，以及AI在解决实际问题中的应用。同时，也提到了数据和资源限制对AI发展的影响，以及如何通过联邦学习和分布式系统来克服这些限制。

25:07

📈 AI的投资周期与市场潜力

第六段讨论了AI技术的投资周期，以及当前对某些AI领域的过度投资和对其他领域的投资不足。提到了互联网时代投资周期的类比，以及在基础设施建设之后，基于这些技术的应用层公司如何取得更大的成功。强调了中国和印度等国家在AI领域的快速发展，以及这些地区可能成为未来AI技术和应用的重要来源。

Keywords

The video is abnormal, and we are working hard to fix it.
Please replace the link and try again.

Highlights

Ramesh Raskar 是媒体实验室的同事，也是 SIGGRAPH 奖的获得者，拥有约 100 项专利，最近创立了 C10 AI Ventures，这是一个风险工作室，他担任首席科学家。

Hari Balakrishnan 是计算机科学和 AI 领域的知名人物，最近创立了 CMI，这是世界上最大的远程信息服务平台，致力于在道路上保障安全。

讨论了 AI 的未来方向，以及世界如何适应 AI，特别是从 MIT 的专家角度来看。

提到了关于机器人接管世界的普遍担忧，以及这些担忧如何与 AI 发展相关联。

讨论了 AI 在未来 20 年可能对世界产生的影响，包括教育、工作角色和社会角色的变化。

强调了以往技术革命（如互联网、基因编辑、核能）带来的变革，以及我们如何找到合理的平衡点。

Ramesh Raskar 表达了对 AI 未来 20 年的乐观态度，认为 AI 将帮助改善健康和提高生活质量。

预计在未来 20 年内，大约 20-25% 的车辆将实现某种形式的自主性。

讨论了 AI 在教育领域的潜力，以及它如何使大规模教育变得更加个性化。

提到了 AI 在未来可能导致人类死亡的风险，以及社会将如何应对这些风险。

讨论了 AI 对就业的影响，以及人们如何适应技术变革带来的职业变化。

提出了 AI 将如何帮助人类学习新事物，并提高我们的智能。

讨论了 AI 在模拟环境中的表现，以及它如何使人类在某些领域（如国际象棋）中变得更好。

强调了 AI 在资源管理方面的潜力，以及它在大数据和分布式资源分配中的应用。

讨论了 AI 在现实世界中的挑战，包括机器人技术、医学和工厂自动化等领域。

预测了 AI 将如何通过提高生产力来改变联合国可持续发展目标相关领域的经济模式。

讨论了 AI 在法律等专业领域的潜力，以及它如何使法律服务变得更加经济实惠和易于获取。

强调了 AI 在解决复杂问题（如微生物组研究）方面的潜力，以及它如何帮助我们理解整体功能的复杂性。

讨论了 AI 在大型系统中的应用，以及它如何帮助我们处理复杂的交互和多维数据。

Ramesh Raskar 表达了对 AI 民主化和分散化的看法，以及它如何影响硬件发展和专用芯片的设计。

讨论了 AI 生态系统中的经济力量和激励机制，以及它们如何影响 AI 的发展和应用。

预测了 AI 技术在接下来的 3 到 5 年内将在目前投资不足的领域取得显著进展。

提醒听众，西方公司并不是地球上唯一的参与者，中国和印度等国家的 AI 投资周期和数据系统增长非常迅速。

Transcripts

you know so we're going to talk a little

bit about that so we have rames rcar who

is a colleague of mine here in the media

lab uh he's the winner of the llon prize

sigraph prize you have what a 100

patents now something like that oh crazy

uh and uh he just started C10 uh AI

Ventures which is a venture studio uh

where you're the chief scientist so we

hope to hear less about that and Hari

bar Christen who's over in computer

science and AI uh and is very famous but

more recently he started something

called CMI which is the world's largest

telematic service provider helps keep

you safe on the roads Fair okay good so

what I thought we would do here is have

a conversation about where is AI going

how is the world going to adapt to it uh

at from the perspective of the people

that you see here right so this is like

the

MIT these people are are the best that

we have here right so good okay so I

thought I'd start with uh something that

was the motivation for this panel to

begin with back at the beginning which

is so the robot overlords are coming

right the robot overlords are going to

kill us all or take over we're going to

work for them and then they're going to

decide that we aren't any good and

they're going to get rid of us that's

the sort of thing you hear a lot lot of

people say uh and all of this is due to

AI somehow and I wonder if you guys have

sort of an idea what the world is going

to look like in say 20 years because of

all this AI stuff I mean I don't think

either of you are in the extinction Camp

I don't think so I don't think so two of

my both daughters are here sitting in

the audience you know they're under 15

both of them and when I think about

first of all what should they learn in

school uh to all the way to what will

our role be all three of us are

professors but also entrepreneurs um

what it would be and also what's the

role for society I think it's a very

interesting time uh to think about that

I think without nobody knows uh what's

going to happen but if you think about

the previous revolutions that have

projected massive changes whether it's

internet whether it's crisper uh whether

it's you know nuclear energy uh nuclear

power

uh there have been a lot of discussions

uh about this risks uh and their

governance and eventually we have

figured out a reasonable equilibrium uh

where the

benefits uh will definitely outweigh the

risks and the risks can be managed I

think what we can learn in AI is is is

going to be very interesting

unfortunately it's moving much faster

than the previous two technologies I

mean crisper only now we have an FDA

approval approved approved uh treatment

so it took years after you know

chrisopher came on the scene uh with AI

That's not the case so can we learn from

previous risky but exciting Innovations

or not is is an open question oh har

yeah um so I'm I'm a techno optimist

about about these things and I think

that there's a few things that will work

in the next 20 years and a few things

that I think we are crazy to imagine

that it'll work so I think on the crazy

side I don't think this is going to lead

to Extinction or any of those um types

of uh possibilities um I think that on

the positive side I I'm looking forward

to a time when AI can help improve our

health um you know with implants with

not only for physical stuff but also for

uh you know to improve our memory and to

make that uh so that it's almost like

you have that as an assistant um I think

those things are going to become real um

I suspect that in 20 years we will

probably see 20 to 25% % of the vehicles

on the road worldwide have a significant

form of autonomy um it's probably not

going to be more than that worldwide um

and I suspect that the real challenge uh

that we will continue to tackle is

operationalizing AI um you know I think

there are going to be some use cases

where it completely replaces humans and

it's completely automated and you know

everything is actuated and done with AI

but I suspect that in a lot of other

cases figuring out how to operationalize

that in the rest of the business work

flow um would be a significant Challenge

and I think just specifically with

education I think you know it we've

always wanted technology to improve

education and largely speaking very few

technologies have I suspect AI will

actually make a huge difference um but

it's an example where um it's not going

to replace uh human teachers I think

that it actually will allow uh education

at scale to become far more personalized

by allowing the human teacher to very

quickly get a summary of where that

particular learner stands and you could

make this completely remote and have

great telepresence so you know they

could be in a different you completely

different part of the world so I think

there's a lot of that that will happen

and I think 20 years is a good time

frame for that I think on the negative

side we will see people die due to Ai

and I think there will be a reaction to

that but we'll eventually cope with it

because I think it's a technology that

can't be stopped I think one of the

things that um I was had my attention

drawn to recently is the first wave of

AI which was in the 50s one part of it

was about logic but the other thing was

optimal resource uh allocation which is

essentially linear solving under

constraints and this was the thing that

was going to save the Soviet economy it

was the planned economy and it didn't

work out real well but on the other hand

uh every spreadsheet on the planet has

this sort of thing built into it it's

the most common con reputation out there

so it may be that all the things about

you know robot overlords or infinite uh

uh you know advanced intelligence and

stuff isn't going to happen any better

than the Soviet Union planned economy

but we're going to see it

everywhere and and I think s pick up on

the thing that you just said which is

that so what happens to the people so

one of the sets of Visions people have

about this is that all the people will

be out of work what are we going to do

we got to have Universal basic income

things like that the other thing that is

the more uh historical thing is is we

find new things to do and that new sort

of productivity helps what what what

I'll start go the other direction since

you brought it up so I mean the short

answer is we are all guessing as to

what's going to happen to the uh to the

employment or to to the occupation or or

Hobbies of of people I mean it's clear

that some jobs will vanish but that's

always been true with any technological

change that's happened things things

just kind of vanish I think that what um

if we're sort of addressing people who

are probably younger than 25 or 30 um I

think that no longer already it's not

the case that what you learn before the

age of 20 or 25 gives you the ability to

have a 40 to 45 year career that's just

gone actually it's tough for us we're

learning new things from our students

every day more so than they learning

from us and it's it's it's a you're

supposed to tell people that yeah I know

but but I think AI will help us learn

better it'll help us learn how to learn

new things it'll help us improve it and

I do believe this idea of us becoming a

lot smarter um you know if we choose to

become smarter uh would be a positive as

for what people will do you know the

story was you know the AI will do all of

the hard stuff The Drudge work and you

know we can write poetry it looks like

the opposite is happening so um I feel

that at the end of the day there are

three classes of AI very very broad

generalization I'm going to a lot of

stuff there's AI that works really well

in simulation you know this is it'll

beat everybody playing any game like

chess or go or whatever but actually the

Advent of AI and algorithms in chess has

made people much more excited about

human chess because humans are becoming

better at chess due to those machines

and I think that we will see a lot of

that happen in other walks of life we'll

start to see how humans can become

better painters or better songwriters of

what have you using those tools so I

feel like that will happen and AI will

absolutely uh change those fields the

second is in things like Resource

Management like in Big Data Centers or

allocating large scale distributed

decentralized resources and I think

there AI research has already shown that

it's going to work I think we'll just

see more and more of that happening the

biggest challenge for AI and I think

where humans will continue to be in the

loop is AI in the real world whether it

be robotics whether it be medicine

whether it be in Factory floors and so

on and I think that it is going to be a

new form of machine human symbiosis um

where

um one possibility is that humans act in

a more supervisory role the other

possibility is the AI acts in a more

supervisory role and be interesting to

see how it how it turns out what yeah

I'm really glad you brought this point

of um does optimization take us into new

regimes because if you think about you

know un's sustainable development goals

which are about water and health and

poverty and Agriculture and so on um the

the challenge has has been that many of

those fields just don't have the unit

economics to become you know multi-

trillion dollar businesses so there's no

trillion dollar company in the sectors I

just mentioned and and the reason for

that is the margins are so low that

doesn't attract the top talent um but

what could happen over time just the way

it took a long time for solar to become

comparable to fossil fuels and only now

the unit economics works I think the

productivity gains because of AI will

similarly bring unit economics so it'll

be exciting to work in water and

agriculture because the productivity is

so high that right now you your margins

may be 10% or minus 10% but very soon

the margins might be 100% to 200% so I

think that's an exciting time that as as

starts coming into the real world uh

you'll start seeing this exciting

opportunity and even for C10 lab which

is a venture Studio we launched here

that's the single hypothesis which is

productivity gains in are areas that are

unexplored will give you unreal you know

you know tremendous gains they'll become

very very lucrative the question is the

world GDP is1 trillion how can we figure

out what are the sectors that'll impact

in the next two years versus be impacted

in the next 10 years and if you can do

the right matchmaking between

opportunist in Ai and opport in the real

world sectors I think we can go very far

and then one quick point to add to your

question about the Soviet model is

unfortunately that's how it's working

right now actually shantu bachara who's

a scientist in our group gave me the

same analogy for Soviet he said you know

when we're thinking about C

supercomputers or IBM mainframes in the

80s we thought that's the way computer

is going to play out you know right here

on one Route 128 but very quickly we had

the PC and the mobile and the iot and

things became highly decentralized so

very kind of a Soviet mindset of

centralize everything the way we are

doing it with big companies right now

centralized data centralized compute

centralized

governance and we just use their apis

you know gp4 and um cloud and so on um I

think that model is going to shatter

very soon uh and we'll see this

decentralization of a come in so I think

the intersection of productivity gains

in the real world and ability to do this

in a very decentralized way are two

major trends that are going to intersect

very soon can I push back on something

yes please okay so I sort of agree with

the decentralization but I think that's

because of the highly data Centric

nature of it and maybe we'll cover that

later with the fact that more and more

organizations and entities don't want

all of the data to be shipped to some

centralized place I totally agree with

you but I think with AI it's not clear

to me that this long tale of um

businesses or u a lot opportunities for

society where there's not enough of a

market to justify big investment uh AI

is really well suited because in the

short term I actually feel from some

experience we've had at my company and

other startups I talk to that the

short-term costs of everything from

large scale data acquisition to training

to everything else actually you're far

better off hiring a bunch of very smart

human beings more economical to get

going like that so I wonder about

whether this hypothesis that AI is going

to help us scale the heavy tail which

will never get that big is really true I

mean if you look at all the big

companies right now forget the oil era

of Exxon and Exon Mobile and so on but

nearly all top 10 fortune f companies

have all made their their their revenue

on the long tail you know Facebook um uh

meta sorry Facebook that's not long T

that's 3 billion four billion people

using I mean all of us are buying

$500,000 mobile phones as opposed to

companies that are selling large servers

uh same thing if you look at the catalog

of of Amazon there's a huge number of

long the long is where the money treat

those uniformly is what makes it

profitable yeah yeah I think your

question is also about centralization of

talent because I was talking about

centralization of you know compute data

and and and governance centralization of

talent is a very important thing but

like you said AI is going to change

education so the smart kids in Tanzania

will be 80 90% of the talent level as

the best people out there so I think

we're going to see a lot of

decentralization of those opportunities

as well um and it remains to be seen

whether you know there's always going to

be a Delta between you know a talent

available in Tanzania versus Talent

available somewhere else so and

decentralization on the other hand

because of Regulation because of Trade

Secrets you know if I think about the

health system in Tanzania just to pick

an example you know the insurance

players there the hospitals there the

government systems are not going to wake

up one day say like let me send all my

data to Sam and Sam is going to build

this really nice model for us and we'll

just use Sam's apis and and you know run

the healthare system what I see in all

the companies that I work with is

they're all building little AI models on

their data they use some like something

like llama to begin with but then they

specialize it with their process data

right and and that what they hope to do

is get something that's better than just

a linear constraint solver or an expert

system and they do it's not that hard

and because a graph is going to be much

a graph network is going to be much more

stable we're talking about risks and

opportunities in this panel you know a

graph is much more stable than you know

a a a hierarchical tree um so I mean you

you mentioned the the different ways of

AI you know Marvin miny called it the

Society of the mind he didn't call it

the mind like one Mega AI he said a

society of multiple small AIS and we are

all by the way are multiple small AIS

like even a large organization has an HR

department and a sales department and

marketing department and tech department

are all small AIS human AIS uh a CEO

doesn't run all those departments and uh

I think the same thing is true for you

know future so one of the the most

hopeful things is that most of the

experiments people have done on making

AI as an advisor helps middle skill

people or lower skill people more than

High skll people and and that's actually

really interesting because that allows

you to onboard people it allows you to

to make up some of the skills Gap there

was an interesting illustration I ran

into yesterday which is someone was

talking about AI for law so AI is very

effective in law but currently law is so

expensive that you can't afford to hire

a lawyer you can't get representative if

you could get something that was pretty

good yeah cheaper that would help lots

of people again economics it's changing

the yeah it's changing the economics of

the whole thing yeah I think it's true

in many professions I just don't know

about some of these like solving the

Water Crisis for example whether AI

magically tackles those types of the

problems yeah I mean if you have I think

that's a good question so like you know

no like it's not considered a glamorous

job to go figure out water but if a

starts behaving like a scientist and you

know you have this you know amazing

tools not AI as a as a assistant a as an

engineer but a as a scientist uh then

they can create all the solutions on the

Fly you know the geometric design the

mechanical design you know the cfds and

so on and a lot of these issues can be

solved same thing with on the

incentivization of how do you not just

invent but how do you have a diffusion

of those inventions in the society and

again you know unfortunately the whole

wave of decentralized Finance didn't

work out as we wanted but the

incentivization mechanisms if they if

they are done right and S is going to do

it for us um you know both the invention

as well as dissemination of those

invention I think one of the worst

things that happened is all of the

crypto stuff where you don't have

identity you don't have all sort of

normal sort of guard rails on it and as

a consequence theft and fraud in in

crypto has ended up uh just destroying a

lot of things bad reputation for a lot

of this stuff come back funded 50% of

the North Korean uh nuclear missile

program I mean come on this is this is

pretty bad so there are some real

downsides to this stuff right um one of

the more interesting things that we've

run into is uh looking at really

complicated things like the microbiome

and it turns out that you know we don't

it's millions of organisms and and

hundreds of thousands of of little RNA

fragments in there but it turns out that

if you actually get enough samples of

that you can begin understanding how the

thing as a whole functions and we've

been able to do it for you know uh CO2

reduction and and are looking at it for

human health so some there's a lot of

things where the reductionist approach

just sort of doesn't work right because

it's all connected and and maybe this is

some way where we can begin to get a

handle on really complex phenomena

you're not in your head so I'm going to

yeah I agree yeah I mean we see this in

uh large scale systems right of any kind

where uh you really have to you know

it's really they're all decomposed into

individual components but the

interactions are so complicated that AI

as a tool uh has been tremendously

useful and I'm not even talking about

generative AI just things like

enforcement learning work really

well yeah I mean I think this is

something that people don't appreciate

is that a lot of the reason the tools we

have today don't work is because they're

sort of abstractions of the situation

and the real situation is a lot more

complicated and one of the strengths of

this sort of thing is is it has lots of

Dimensions so you can begin to actually

model some of that complexity raes what

what have you seen in this sort of space

um I think I think the the

I mean I'm a I'm a kind of a

decentralization maximalist here and I

think the the equilibrium is going to

come when we have democratization of AI

so that's enough players and you don't

have this big gap between the halves and

Have Nots because the moment that

happens when you only have one big chip

company like it's happening with Nvidia

if you have only one big co-pilot

company like Microsoft versus when there

are a lot of players in this space uh

and they're distributed geographically

as well I think we're going to see this

you know interesting back and fourth you

do need the leaders to bring the

Innovation first you know Nvidia has to

spend billions of dollars to bring us

you know highly compact chips that have

a very small energy footprint and so on

so I just worry about how long does it

take to go from the SC super computers

to iot uh and so on so that's one of the

biggest things that that bothers me

about how long is it going to take us to

go this is one of the things that

everybody talks about is it's going to

take a trillion dollars to scale this

stuff and you know but wait I don't

really want an AI that speaks Romanian

it doesn't do me any good special I want

narrow things that solve real problems

and those are lots easier and that's

sort of this beginning path to like AI

everywhere as opposed to the over Uber

things and then I see a lot of things in

Hardware too uh as6 being built special

for this sort of stuff that are much

more efficient than the current gpus

what what do you think is going to

happen in this ecology how how are we

going to get to sort of AI everywhere

where AI is maybe with small letters

rather than big letters yeah I think

it's inevitable I I don't know if it's

going to be completely decentralized the

way you're talking but I think there are

two big trends people don't in many

cases don't want all of the data shipped

to some other entity there are

regulations against it uh people feel

uncomfortable about sharing their own

like you know you take your home audio

Alexa type system um but I think that

the way this will come about is

um specialized models and highly

Federated learning and uh the way I like

to think about it is there's sort of

this big model but then it's a big

distributed system and you end up being

able to partition that amongst different

pieces and different entities which each

has some well- defined set of things

that it provides into the higher layers

so in some cases you have completely

independent autonomous decision making

in some other cases they do need to

collaborate and you don't have to do

this with uh just you know some

generative AI you could use graphical

neural networks there are many many

different techniques that we've

developed I mean distributed

reinforcement learning and so I think we

should be thinking about it very broadly

but it's inevitable it's going to happen

and there are many cases where there are

fundamental resource constraints that

prevent you from delivering all of the

data all the time to a central entity I

mean if you imagine the 1.4 billion

vehicles in the world all delivering 60

frames a second video all the time from

you know continuously there's not really

it's going to kill the networks and I

don't think anyone actually even wants

to do that and you would end up with a

high degree of uh dis I mean if if you

think about human you know how the human

society behaves uh and I'm sure

anthropologists are going to play a big

role in how kind of governance of you

know AI are going to work and and so you

know as a human society we have figured

out how each of us have expertise and we

help each other out and you know we

can't imagine you know a super brilliant

person governing all of us at least for

most of the time most of the time uh

most of the time and and so I think this

this uh this the challenge of

decentralization is key but that alone

is not going to solve the problem

because you also want the economic

forces so you need the incentivization

of why should somebody should some why

should somebody participate in the AI

ecosystem are they going to get paid in

dollars or this fuzzy tokens from

cryptocurrencies or they're going to get

paid in compute or they get ahead of the

queue because so I think there's going

to be a lot of incentive mechanisms that

you have to design as well so some of

the research we do in our group here uh

in decentralized AI is actually thinking

about data markets model markets and

incentive mechanisms um and it's one of

those areas that seems adjacent to uh

progress in AI but I think they'll

converge very soon instead of behavior

economics we'll call it AI economics

okay so we're almost out of time here

one last question where are we in the

hype

cycle well with generative we're near

the peak or maybe we're about so we're

going to crash next year well I think

that what people will find is a

tremendous amount of misinformation and

I don't know if it'll crash next year

but uh 18 months 18 months okay there

you are get your investments in get out

in 18 months well then go back up well

no it's always goes back up but that

could be 10 years right yeah um I I

would say there's a absolutely a lot of

overinvestment in certain areas but

surprisingly underinvestment in many

other areas and uh if you can I would

say there's underinvestment in most of

the areas so as we said earlier many

real world sectors are just not getting

enough attention that's right and

they're going to they're going to just

Bloom they're just going to Boom uh in

the next 3 to five years not right away

because it takes a long time for new

technology to be absorbed you know to

see the productivity gains for rest of

the ecosystem to have right protocols to

work with each other and so that'll take

some time but over the next 3 to 5 years

you'll see these highly underinvested

areas really take off and you can use

analogy from the internet era you know

in the beginning everybody bought stock

in you know chip companies and Os

companies right and Cisco and IBM and so

on but very soon what was built on top

of that which is the applied internet

you know eBay Yahoo Google Facebook and

so on they're so much bigger than

anybody who's selling I mean not right

now with Nvidia but but in the internet

era anybody who was selling chips or you

know internet infrastructure okay

infrastructure play which is highly over

invested right now is uh I don't know 18

months two years okay and let me just

remind people that Western companies are

not the only ones on the planet and that

the investment cycle in China is very

different and they have a lot of

Engineers and they're are 100% on it and

they don't worry too much about who owns

the data uh and the fastest growing area

in the world in terms of data systems is

India and the surrounding countries

there they've gone from essentially zero

to a couple billion on some of their

systems in the last two three

years guess what that's where a lot of

it's going to come from not from the

people in this room so keep your radar

out I just kind I just mention um so the

decentralized AI team working on that is

downstairs and they're showing demos on

how you can use decentral AI for the

Indian stack so please go check them out

I think they're right on the bottom

floor sounds good that's the backend

people yeah backend people yeah okay

good okay thank you know who's supposed

to be up next