How AI Will Shape Society Over The Next 20 Years

Summary

The transcript features a discussion on the future of AI with a focus on its integration into society and the potential impact on various sectors. The conversation involves Rames RCar, a media lab colleague and winner of the SIGGRAPH prize, and Hari Balachandran, a computer science and AI expert who has started CMI, a large telematic service provider. They explore the idea that AI will not lead to human extinction but will instead improve health through technology, increase vehicle autonomy, and revolutionize education by personalizing learning experiences. The speakers also address the challenges of operationalizing AI, the potential for AI to create new job opportunities, and the importance of learning from previous technological revolutions to manage the risks and benefits of AI. They emphasize the need for a balance between centralization and decentralization in AI development and the role of AI in solving complex global issues like the water crisis. The discussion concludes with thoughts on the current hype cycle of AI and the importance of investing in underexplored areas of AI technology.

Takeaways

• 🤖 **AI in Real World Applications**: AI is expected to make significant strides in practical applications such as health improvements through implants and autonomous vehicles, though the extent of vehicle autonomy may be limited to 20-25% worldwide.
• 🚀 **Technological Optimism**: The speakers are optimistic about AI's potential to improve health and safety without leading to human extinction or mass unemployment.
• 🧠 **AI and Human Symbiosis**: There is a belief that AI will not replace human roles but will instead enhance human capabilities, leading to a symbiotic relationship where AI assists in areas like personalized education.
• 🚨 **Risks and Governance**: Concerns are raised about AI-related accidents and the need for proper governance and risk management, much like previous technological revolutions.
• 📈 **Economic Shifts**: AI is anticipated to change economic landscapes, potentially increasing productivity and margins in sectors previously not lucrative enough to attract significant investment.
• 🌐 **Decentralization of AI**: There's a push towards decentralizing AI, which could democratize the technology and prevent a monopoly by a single entity, promoting a more equitable distribution of advancements.
• 📚 **Changing Education**: AI is expected to revolutionize education by personalizing learning experiences and enabling teachers to better understand and cater to individual student needs.
• 🛠️ **AI in Resource Management**: AI's capability in resource allocation and management, such as in large data centers, is seen as a strong suit with potential for further expansion.
• 🧮 **AI and Complex Systems**: AI's potential to model and understand complex systems, like the microbiome, is highlighted as a promising area where traditional reductionist approaches fall short.
• 💹 **Investment Trends**: There is a current overinvestment in some AI sectors, while others, particularly real-world applications, are underinvested. The focus is expected to shift towards areas that yield tangible benefits.
• ⏳ **Hype Cycle**: The AI field may be nearing a peak in the hype cycle, which could lead to a downturn in investment and interest, but the long-term outlook for AI remains positive with growth expected in underinvested areas.

Q & A

What is Rames RCar's background and his current role at C10 AI Ventures?

Rames RCar is a colleague at the Media Lab and has won the SIGGRAPH prize. He holds around 100 patents and has recently started C10 AI Ventures, where he serves as the Chief Scientist.

What is Hari Balachandran's contribution to the field of computer science and AI?

Hari Balachandran is a renowned figure in computer science and AI. He has recently started CMI, which is the world's largest telematic service provider aimed at enhancing road safety.

How does the panelist view the future of AI in terms of societal adaptation?

The panelist believes that AI will bring significant changes, but it is not likely to lead to human extinction. They foresee AI improving health, with advancements in implants and personalized education, but also acknowledge potential negative consequences such as AI-related fatalities.

What is the panelist's perspective on the future of autonomous vehicles?

The panelist predicts that in 20 years, 20 to 25% of vehicles worldwide will have a significant form of autonomy, but it is unlikely to be more than that on a global scale.

How does the panelist envision the role of AI in education?

AI is expected to make a huge difference in education by personalizing learning at scale. It will not replace human teachers but will act as an assistant, allowing teachers to quickly understand the needs of individual learners.

What are the panelist's thoughts on the impact of AI on employment?

While some jobs will vanish due to AI, new opportunities will arise. The panelist suggests that lifelong learning will become more important, and AI will help people learn new skills and improve their ability to learn.

What are the three classes of AI that the panelist mentioned?

The panelist categorizes AI into three classes: AI that works well in simulations (e.g., games like chess or go), AI in resource management (e.g., big data centers), and AI in the real world (e.g., robotics, medicine, factory floors) which will require a new form of machine-human symbiosis.

How does the panelist view the potential of AI in optimizing resource allocation?

AI has the potential to significantly improve resource allocation, which could lead to higher productivity and better margins in sectors like water and agriculture, making them more attractive for investment and talent.

What are the panelist's thoughts on the decentralization of AI?

The panelist supports the idea of decentralization in AI, suggesting that it will lead to democratization of the technology, more innovation, and a more equitable distribution of benefits.

What are the potential challenges and risks associated with AI that the panelist discussed?

The panelist discussed the risks of AI-related fatalities and the potential negative societal reaction to these events. They also mentioned the importance of proper incentivization mechanisms and the need for AI to be developed with decentralized and real-world applications in mind.

How does the panelist perceive the current hype cycle of AI and its future trajectory?

The panelist believes there is a lot of overinvestment in certain AI areas and underinvestment in others. They predict that within 18 months to 2 years, there might be a correction in the market, but the areas that are underinvested will eventually boom, leading to significant advancements in real-world applications of AI.

Outlines

00:00

🤖 The Future of AI and Society

The paragraph introduces a discussion on the future of AI, led by Rames RCar, a colleague at the media lab and winner of the Sigraph prize, and Hari Balachandran, a computer science and AI expert who started CMI, a large telematic service provider. The conversation aims to explore the direction of AI and how the world will adapt to it, considering the perspectives of leading figures at MIT. The speaker expresses optimism about AI's potential to improve health, enhance learning, and increase vehicle autonomy, while acknowledging potential challenges in operationalizing AI and the impact on employment.

05:02

🚀 AI's Impact on Economy and Employment

This paragraph delves into the historical context of AI and its potential effects on the economy and employment. The speaker discusses the initial wave of AI in the 1950s and its evolution, emphasizing the importance of AI in resource allocation and its prevalence in modern tools like spreadsheets. There's an acknowledgment that while AI will cause job displacement, it will also create new opportunities. The speaker suggests that AI will help improve learning and enable smarter human capabilities, leading to a more productive society where humans and AI collaborate.

10:04

🌐 Centralization vs. Decentralization of AI

The paragraph discusses the potential shift from a centralized model of AI to a decentralized one. The speaker predicts a future where AI is more distributed, similar to the transition from mainframes to personal computers and mobile devices. They also touch on the Soviet model of centralized governance and its failure, suggesting that a decentralized approach to AI could prevent similar outcomes. The paragraph highlights the importance of productivity gains in real-world sectors and the potential for AI to make these sectors more lucrative and attractive to top talent.

15:05

🧠 AI as a Tool for Complex Problem Solving

This paragraph explores AI's role in addressing complex systems and phenomena, such as the microbiome. The speaker suggests that AI can help understand and model the interactions within these systems, which are too complicated for reductionist approaches. They also discuss the potential for AI to democratize access to expertise, making legal services more affordable and accessible, and to solve problems in areas like water management and agriculture by behaving like a scientist.

20:06

🌿 Democratization and Decentralization of AI

The paragraph focuses on the decentralization of AI and its potential to democratize access to technology. The speaker advocates for a future where AI is not dominated by a single company or entity, but rather shared among many players. They discuss the need for specialized AI models, federated learning, and the importance of creating incentive mechanisms for participation in the AI ecosystem. The speaker also emphasizes the importance of considering economic forces and the role of anthropologists in governing AI.

25:07

📈 Investment Trends and the Hype Cycle in AI

The final paragraph addresses the current state of investment in AI and where it stands in the hype cycle. The speaker warns of potential overinvestment in some areas while noting underinvestment in others, particularly in real-world sectors that are ripe for AI innovation. They predict that these sectors will experience significant growth in the next 3 to 5 years. The speaker also points out the different investment cycles in various parts of the world, highlighting the importance of considering global perspectives in AI development.

Keywords

💡AI

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the video, AI is central to discussions about future technological advancements, its impact on society, and how it will shape various sectors like healthcare, education, and transportation.

💡Autonomous Vehicles

Autonomous vehicles are self-driving cars that use sensors, cameras, and AI to navigate and operate without human input. The script mentions the expectation that in 20 years, 20-25% of vehicles worldwide will have a significant form of autonomy, indicating a major shift in transportation.

💡Robot Overlords

The term 'robot overlords' is a colloquial and often humorous reference to a hypothetical future where robots or AI have control over humans. In the script, it is used to contrast the more optimistic view that AI will augment human capabilities rather than replace them.

💡AI in Education

AI in education refers to the use of AI technologies to enhance teaching and learning processes. The video discusses how AI could personalize education at scale, allowing teachers to quickly assess individual learner's progress and adapt their teaching methods accordingly.

💡Decentralization

Decentralization in the context of AI refers to the distribution of AI capabilities across various systems and platforms rather than concentrating it in a single, central entity. The script suggests that decentralization could lead to more equitable access to AI technologies and prevent the formation of AI monopolies.

💡Federated Learning

Federated learning is a machine learning approach where an AI model is trained across multiple devices or servers holding local data samples without exchanging them. It's mentioned in the script as a method to achieve decentralized AI, allowing for privacy-preserving data use.

💡AI Ethics and Governance

AI ethics and governance involve establishing principles and rules to guide the development and use of AI technologies responsibly. The video touches on the importance of managing risks and ensuring that AI benefits outweigh the risks, drawing parallels with historical technological revolutions.

💡Techno-Optimism

Techno-optimism is the belief that technology will lead to a better future. One of the speakers identifies as a techno-optimist, anticipating positive developments such as AI improving health through implants and enhancing human capabilities in various fields.

💡AI and Employment

The impact of AI on employment is a significant topic in the video, with discussions on how AI might change job roles, create new opportunities, and potentially displace certain types of work. It also addresses the need for lifelong learning and adaptability in the face of AI advancements.

💡Universal Basic Income (UBI)

UBI is a concept where every citizen receives a set amount of money from the government, regardless of their income or employment status. The script briefly mentions UBI as a potential societal response to job displacement due to AI and automation.

💡AI in Real World Applications

The script discusses the challenges and opportunities of applying AI in real-world scenarios such as robotics, medicine, and factory floors. It highlights the need for a new form of 'machine-human symbiosis' where humans and AI systems collaborate effectively.

Highlights

Rames RCar, a colleague at the Media Lab and winner of the SIGGRAPH prize, has started C10 AI Ventures, where he serves as the chief scientist.

Hari Balachandran, from computer science and AI, has initiated CMI, the world's largest telematic service provider for road safety.

The discussion focuses on the future of AI and how society will adapt to its rapid advancements.

Contrary to fears of AI leading to human extinction, the panelists are optimistic about AI's potential for improving health and safety.

AI is anticipated to enhance vehicles' autonomy, with 20-25% of vehicles predicted to have significant autonomy in 20 years.

Operationalizing AI in business workflows is identified as a significant challenge for the future.

AI is expected to revolutionize education by personalizing learning at scale and allowing teachers to understand individual learners' needs.

There will likely be instances where AI contributes to human fatalities, but society is expected to adapt and manage these risks.

The first wave of AI in the 1950s focused on logic and optimal resource allocation, which has influenced modern technology like spreadsheets.

The concern of widespread job loss due to AI is acknowledged, but historical context suggests new roles and opportunities will emerge.

AI is predicted to help humans learn continuously and improve their skills throughout their careers.

Three classes of AI are identified: simulation, resource management, and real-world application, each with its own challenges and opportunities.

Optimization through AI could lead to new economic opportunities in sectors like water and agriculture, which traditionally have not attracted large investments.

Decentralization of AI is seen as a significant trend, with the potential to democratize AI and prevent a monopoly by a few large companies.

The current centralized model of large companies controlling data and compute is compared to the Soviet model and is expected to change.

The importance of incentivization mechanisms in AI is discussed, as they will influence participation and contribution to the AI ecosystem.

The hype around generative AI may lead to overinvestment in some areas, but many real-world sectors are underinvested and have significant potential.

The panelists predict that within 3 to 5 years, the currently underinvested areas of AI will experience significant growth and impact.

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