Meet John. These days John spends an awful lot of time on phone calls and meetings.

It's pretty mind-numbing to say the least. Tell me about it.

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Good morning, Skeletally Pie Hotel Reservation. This is Marie Cruz speaking. How can I help you?

Hi, I would like to book a room for next month.

Before we proceed to the reservation process, may I know who is on the line?

This is Shania Marese again.

Could you spell your first name for me please?

S-H-Y-N-I-A-H

Thank you for that information, Ms. Marese again. May I know which date would you like to make your reservation?

Do you have any vacancies for December 25th to 27th?

Yes, ma'am. We have several rooms available for that particular date. May I know what type of room would you like to stay?

Do you have any premier rooms available?

Okay, ma'am. Just give me a minute. Let me check.

Okay.

Thank you for waiting, ma'am. Unfortunately, all of our premier rooms have been booked for the 25th of December. How about a deluxe room instead? We can transfer you once there is availability.

Okay, no problem.

Perhaps you didn't know that we have new room rates. Do you find it acceptable, ma'am?

Well, what's the rate for the room?

For a deluxe room, we have it at 6,405 pesos per room per night, including taxes with a complimentary breakfast.

Okay, that's a reasonable price.

Now, as for the room, ma'am, do you prefer smoking or non-smoking?

Definitely non-smoking.

Non-smoking. Now, is a queen-size bed okay, ma'am?

Well, a queen-size three.

Would you prefer a room with a view of the alley?

If that room is available, I would love to.

Great. Would you like to confirm your reservation, ma'am?

Yes, I would like to confirm my reservation.

Can we have the name of the person who will be staying, ma'am?

Mr. and Mrs. Villaluz.

Okay, Ms. Maricigan, your reservation is now verified, so all you need to know is your contact number.

It's 0929-876-5432.

Let me repeat that. 0929-876-5432. Am I right?

Absolutely.

Okay, ma'am, and how will the bill be settled?

Do you accept credit cards?

Yes, ma'am. Now, I will need to know your credit card information to reserve the room for you.

What type of card is this?

Visa. The number is 987-654-321.

And what is the name of the cardholder?

Shania H. Maricigan.

Shania H. Maricigan.

Alright, Ms. Maricigan, your reservation has been made for the 25th until 27th of December for a deluxe room with a queen-size bed and, of course, with a view of the Alley.

Your bills will be settled via Visa amounting to 12,810 pesos. Check-in is at 2 o'clock. Is that correct, ma'am?

Absolutely.

We wish to inform you about our 6 o'clock policy. Please be advised that you have to check-in not later than 6 p.m.

Otherwise, the hotel reserves the right to give the room to the waitlisted clients.

Okay, thank you for informing me.

My pleasure. Is there anything else I can do for you, ma'am?

No, that would be all. Thank you.

Thank you for calling, Ms. Maricigan. We'll see you in December. Have a nice day.

Okay, bye-bye.

Bye-bye.

It is my pleasure to welcome Dr. Andrew Ng tonight. Andrew is the Managing General Partner

of AI Fund, founder of Deep Learning AI and Landing AI, chairman and co-founder of Coursera,

and an adjunct professor of computer science here at Stanford. Previously, he had started

and led the Google Brain Team, which had helped Google adopt modern AI, and he was also director

of the Stanford AI Lab. About 8 million people, one in 1,000 persons on the planet, have taken

an AI class from him. And through both his education and his AI work, he has changed

numerous lives. Please welcome Dr. Andrew Ng.

Thank you, Lisa. It's good to see everyone. So what I want to do today is talk to you

about some opportunities in AI.

I've been saying AI is the new electricity. One of the difficult things to understand about AI

is that it is a general purpose technology, meaning that it's not useful only for one thing,

but it's useful for lots of different applications, kind of like electricity.

If I was to ask you, what is electricity good for? You know, it's not any one thing,

it's a lot of things. So what I'd like to do is start off sharing with you how I view the

technology landscape, and this will lead into the set of opportunities. So a lot of hype,

lots of excitement about AI, and I think a good way to think about AI is as a collection of tools.

So this includes a technique called supervised learning, which is very good at recognizing

things or labeling things, and generative AI, which is a relatively new, exciting development.

If you're familiar with AI, you may have heard of other tools, but I'm going to talk less about

these additional tools, and I'll focus today on what I think are currently the two most important

tools, which are supervised learning and generative AI. So supervised learning is very

good at labeling things, or very good at computing input-to-output, or A-to-B mappings. Give me an

input A, give me an output B. For example, given an email, we can use supervised learning to label

it as spam or not spam.

The most lucrative application of this that I've ever worked on is probably online advertising where given an ad, we can label if a user is likely to click on it and therefore show more relevant ads.

For self-driving cars, given the sense of readings of a car, we can label it with where are the other cars.

One project that my team at EFN worked on was ship route optimization where given a route that a ship is taking or considering taking, we can label that with how much fuel we think those will consume and use this to make ships more fuel efficient.

It does a lot of work in automated visual inspection in factories so you can take a picture of a smartphone that is just manufactured and label is there a scratch or any other defect in it.

Or if you want to build a restaurant review reputation monitoring system, you can have a little piece of software that looks at online restaurant reviews and labels that as positive or negative sentiment.

So one nice thing, one cool thing about supervised learning is that it's not useful for one thing. It's useful for all of these different applications and many more besides.

Let me just walk through concretely the workflow of one example of a supervised learning labeling things kind of project.

If you want to build a system to label restaurant reviews, you then collect a few data points or collect a data set where say the pastrami sandwich is great.

You say that is positive.

Sirloin is low. That's negative.

My favorite chicken curry is positive.

And here I've shown three data points,

where you're building this,

you may get thousands of data points like this,

or thousands of training examples, we call it.

And the workflow of a machine learning project,

like AI project, is you get labeled data,

maybe thousands of data points,

then you have an AI engineering team

train an AI model to learn from this data,

and then finally you would find maybe a cloud service

to run the trained AI model,

and then you can feed it, you know,

that's the most you've ever had,

and that's positive sentiment.

And so I think the last decade was maybe the decade

of large-scale supervised learning.

What we found starting about 10, 15 years ago,

was if you were to train a small AI model,

so train a small neural network,

a small deep learning algorithm,

basically a small AI model,

maybe not on a very powerful computer,

then as you fed it more data,

its performance would get better for a little bit,

but then it would flatten out, it would plateau,

and it would stop being able to use the data

to get better and better.

But if you were to train a very large AI model,

lots of compute on maybe powerful, you know, GPUs,

then as we scaled up the amount of data

we gave the machine learning model,

its performance would kind of keep on

getting better and better.

So this is why when I started and led the Google Brain team,

the primary mission that I directed the team to solve

at the time was let's just build

really, really large neural networks

that we then fed a lot of data to,

and that recipe fortunately worked,

and I think the idea of driving large compute

and large scale of data,

that recipes really helped us

driven a lot of AI progress over the last decade.

So if that was the last decade of AI,

I think this decade is turning out to be

also doing everything we had in SuperVisor,

I think by adding to it,

the exciting two of Genes of AI.

So many of you, maybe all of you,

who have played with Chai GPT and Bard and so on,

but just, you know, given a piece of text,

which you call a prompt, like I love eating,

if you run this multiple times,

maybe you get Vegas Cream Cheese

or My Mother's Meatloaf or Out to Friends,

and the AI system can generate output like that.

Given the amount of buzz and excitement

about Genes of AI,

I thought I'd take just half a slide

to say a little bit about how this works.

So it turns out that in terms of AI, at least the type of text generation, the core of it

is using supervised learning that inputs output mappings to repeatedly predict the next word.

And so if your system reads on the internet a sentence like, my favorite food is a bagel

with cream cheese and lox, then this is translated into a few data points where it sees my favorite

food is a, in this case, try to guess that the right next word was bagel, or my favorite

food is a bagel, try to guess the next word is with, and similarly if it sees that, in

this case, the right guess for the next word would have been cream.

So by taking text that you find on the internet or other sources, and by using this input

output, supervised learning, to try to repeatedly predict the next word, if you train a very

large AI system on hundreds of billions of words, or in the case of the largest model

is now more than a trillion words, then you get a large language model like ChaiGP.

And there are additional other important technical details, I talked about predicting the next

word, technically these systems predict the next sub-word or part of word called a token,

and then there are other techniques like RHF for further tuning the AI output to be more

helpful, honest, and harmless. But at the heart of it is this using supervised learning

to repeatedly predict the next word, that's really what's enabling the exciting, really

fantastic progress on large languages.

So, while many people have seen large-language models as a fantastic consumer tool, you can

go to a website like TrackGV's website or BOTS or other large-language models and use

it.

I think it's fantastic, too.

There's one other trend that I think is still underappreciated, which is the power of large-language

models, not just as a consumer tool, but as a developer tool.

So it turns out that there are applications that used to take me months to build that

a lot of people can now build much faster by using a large-language model.

So specifically, the workflow for supervised learning, building the restaurant review system,

say, would be that you need to get a bunch of label data, and maybe that takes a month

to get a few thousand data points.

And then have an AI team train and tune and really get, you know, optimized performance

on your AI model.

Maybe that'll take three months.

Then find a cloud service to run it, make sure it's running robustly, make sure it's

regularized.

Maybe that'll take another three months.

So a pretty realistic timeline for building a commercial-grade machine learning system

is like 6 to 12 months.

So teams I've led will often take roughly 6 to 12 months to build and deploy these systems,

and some of them turn out to be really valuable,

but this is a realistic timeline for building and deploying a commercial-grade AI system.

In contrast, with prompt-based AI, where you write a prompt,

this is what the workflow looks like.

You can specify a prompt that takes maybe minutes or hours,

and then you can deploy it to the cloud, and that takes maybe hours or days.

So there are now certain AI applications that used to take me, you know,

literally 6 months, maybe a year to build,

that many teams around the world can now build in maybe a week.

And I think this is already starting, but the best is still yet to come.

This is starting to open up a flood of a lot more AI applications that can be built by a lot of people.

So I think many people still underestimate the magnitude of the flood of custom AI applications

that I think is going to come down the pipe.

I know you probably were not expecting me to write code in this presentation,

but that's what I'm going to do.

So it turns out this is all the code I need in order to write a sentiment classifier.

So I'm going to, you know, some of you will know Python, I guess.

Import some tools from OpenAI.

And then I have this prompt that says,

Classify the text below, delimited by three dashes, as having either a positive or negative sentiment.

I don't know.

I had a fantastic time at Stanford GSB.

Learned a lot and also made great new friends.

Alright, so that's my prompt.

And now I'm just going to run it.

And I've never run it before, so I really hope, thank goodness, you got the right answer.

And this is literally all the code that it takes to build a sentiment classifier.

And so today, you know, developers around the world can take literally maybe like ten minutes to build a system like this.

And that's a very exciting.

So, one of the things I've been working on was trying to teach online classes about how

to use prompting, not just as a consumer tool, but as a developer tool.

So, to start off the technology landscape, let me now share my thoughts on what are some

of the AI opportunities I see.

This shows what I think is the value of different AI technologies today, and I'll talk about

it a few years from now.

But the vast majority of financial value from AI today is, I think, supervised learning,

where for a single company like Google, it can be worth more than $100 billion US a year.

And also, there are millions of developers building supervised learning applications,

so it's already massively valuable, and also with tremendous momentum behind it, just because

of the sheer effort in finding applications and building applications.

And in terms of AI, it's the really exciting new entrant, which is much smaller right now.

And then there are the other two that I'm including for completeness.

The size of these circles represent the value today.

This is what I think it might grow to in three years.

So supervised learning, already really massive, may double, say, in the next three years from

truly massive to even more massive.

And generative AI, which is much smaller today, I think will much more than double in the

next three years because of the number of amounts of developer interest, the amount

of venture capital investments, the number of large corporates exploring applications.

And I also just want to point out, three years is a very short time horizon.

If it continues to compound at anything near this rate, then in six years, you know, it

will be even vastly larger.

But this light shaded region in green or orange, that light shaded region is where the opportunities

for either new startups or for large companies, incumbents, to create and to enjoy value capture.

But one thing I hope you take away from this slide is that all of these technologies are

general purpose technologies.

So in the case of supervised learning, a lot of the work that had to be done over the last

decade but is continuing for the next decade is to identify and to execute on the concrete

use cases.

And that process is also kicking off for generative AI.

So for this part of the presentation, I hope you take away from it that general purpose

technologies are useful for many different tasks.

A lot of value remains to be created using supervised learning.

And even though we're nowhere near finishing figuring out the exciting use cases of supervised

learning, we have this other fantastic tool of generative AI, which further expands the

set of things we can now do using AI.

But one caveat, which is that there will be short term fads along the way.

So I don't know if some of you might remember the app called Lenza.

This is the app that will let you upload pictures of yourself and they'll render a cool picture

of you as an astronaut or a scientist or something.

And it was a good idea and people liked it.

And it took off like crazy like that through last December.

And then it did that.

And that's because Lenzer, it was a good idea.

People liked it.

But it was a relatively thin software layer on top of

someone else's really powerful APIs.

And so even though it was a useful product, it was in a

defensible business.

And when I think about apps like Lenzer, I'm actually

reminded of when Steve Jobs gave us the iPhone.

Shortly after, someone wrote an app that I paid $1.99 for

to do this, to turn on the LED, to turn the

phone into a flashlight.

And that was also a good idea, to write an app to turn on the

LED light, but it also didn't create very long-term value,

because it was easily replicated and underpriced,

and then eventually incorporated into iOS.

But with the rise of iOS, with the rise of iPhone, someone

also figured out how to build things like Uber and Airbnb

and Tinder, the very long-term, very defensible

businesses that created sustaining value.

And I think with the rise of gen-dev AI or the rise of new AI tools, I think really what

excites me is the opportunity to create those really deep, really hard applications that

hopefully can create very long-term value.

So the first trend I want to share is AI as a general purpose technology, and a lot of

work that lies ahead of us is to find the very diverse use cases and to build them.

There's a second trend I want to share with you, which relates to why AI isn't more widely

adopted yet.

It feels like a bunch of us have been talking about AI for like 15 years or something, but

if you look at where the value of AI is today, a lot of it is still very concentrated in

consumer software internet.

Once you go outside, you know, tech or consumer software internet, there's some AI adoption,

but the law feels very early.

So why is that?

It turns out if you were to take all current and potential AI projects and sort them in

decreasing order of value...

Then to the left of this curve, the head of this curve, are the multi-billion dollar projects

like advertising or web search or e-commerce, product recommendations, a company like Amazon.

And it turns out that about 10, 15 years ago, various of my friends and I, we figured out

a recipe for how to hire, say, 100 engineers to write one piece of software to serve more

relevant ads and apply that one piece of software to billion users and generate massive financial

value.

So that works.

But once you go outside consumer software internet, hardly anyone has 100 million or

a billion users that you can write and apply one piece of software to.

So once you go to other industries, as you go from the head of this curve on the left

over to the long tail, these are some of the projects I see and I'm excited about.

I was working with a pizza maker that was taking pictures of the pizza they were making

because they needed to do things like make sure that the cheese is spread evenly.

So this is about a $5 million project, but that recipe of hiring 100 engineers or dozens

of engineers to work on a $5 million project, that doesn't make sense.

Or another great example, working with an agriculture company that, with them, we figured

out that if we use cameras to find out how tall is the wheat, and wheat is often denser

because of wind or rain or something, and we can chop off the wheat at the right height,

then that results in more food for the farmer to sell and is also better for the environment.

But this is another $5 million project that that old recipe of hiring a large group of

highly skilled engineers to work on this one project, that doesn't make sense.

And similarly, materials grading, cloth grading, sheet metal grading, many projects like this.

So where it's to the left, in the head of this curve, there's a small number of, let's

say, multi-billion dollar projects, and we know how to execute those delivering value.

In other industries, I'm seeing a very long tail of tens of thousands of, let's call them,

$5 million projects that, until now, have been very difficult to execute on because

of the high cost of customization.

The trend that I think is exciting is that the AI community has been building better

tools that let us aggregate these use cases and make it easy for the end user to do the

customization.

So specifically, I'm seeing a lot of exciting low-code and no-code tools that enable the

user to customize the AI system.

What this means is instead of me needing to worry that much about pictures of Pisa, we

have tools.

We're starting to see tools that can enable the IT departments of the Pisa-making factory

to train an AI system on their own pictures of Pisa to realize this $5 million worth of

value.

And by the way, the pictures of Pisa, they don't exist on the Internet, so Google and

Bing don't have access to these pictures.

We need tools that can be used by really the Pisa factory themselves to build and deploy

and maintain their own custom AI system that works on their own pictures of Pisa.

And broadly, the technology for enabling this, some of it is prompting, text prompting,

visual prompting, but really large language models and similar tools like that, or a technology

called data-centric AI, whereby instead of asking the Pisa factory to write a lot of

code, which is challenging, we can ask them to provide data, which turns out to be more

feasible.

And I think the second trend is important

because I think this is a key part of the recipe

for taking the value of AI,

which so far still feels very concentrated

in the tech world and consumer software internet world,

and pushing this out to all industries,

really to the rest of the economy,

which sometimes is easy to forget.

The rest of the economy is much bigger than the tech world.

So, the two trends are shared.

AI as a general purpose technology,

lots of concrete use cases to be realized,

as well as low-code, no-code, easy-to-use tools,

enabling AI to be deployed in more industries.

How do we go after these opportunities?

So, about five years ago,

there was a puzzle I wanted to solve,

which is I felt that many valuable AI projects

are now possible.

I was thinking, how do we get them done?

And having led AI teams in Google and Baidu

in big tech companies,

I had a hard time figuring out

how I could operate a team in a big tech company

to go after a very diverse set of opportunities,

and everything from maritime shipping,

to education, to financial services,

to healthcare, and on and on.

And so, it's very diverse use cases,

it's very diverse go-to-markets,

and very diverse customer bases and applications.

And I felt that the most efficient way to do this

would be if we can start a lot of different companies

to pursue these very diverse opportunities.

So, that's why I ended up starting AI Fund,

which is a venture studio that builds startups

to pursue a diverse set of AI opportunities.

And of course, in addition to lots of startups, incumbent companies also have a lot of opportunities

to integrate AI into existing businesses.

In fact, one pattern I'm seeing for incumbent businesses is distribution is often one of

the cyclical advantages of incumbent companies, that they play the cards right, can allow

them to integrate AI into their products quite efficiently.

But just to be concrete, where are the opportunities?

So I think of this as a, this is what I think of as the AI stack.

At the bottom level is the hardware semiconductor layer.

Fantastic opportunities there, but very capital intensive, very concentrated, so it means

a lot of resources, relatively few winners.

So some people can and should play there, I personally don't like to play there myself.

There's also the infrastructure layer.

Also fantastic opportunities, but very capital intensive, very concentrated, so I tend not

to play there myself either.

And then there's the developer tool layer.

What I showed you just now was, I was actually using OpenAI's API as a developer tool.

And then I think the developer tool sector is hyper competitive, look at all the startups

chasing OpenAI right now, but there will be some mega winners.

And so I sometimes play here, but primarily when I think of a meaningful technology advantage

because I think that earns you the right or earns you a better shot at being one of the

mega winners.

And then lastly, even though a lot of the media attention and the buzz is in the infrastructure

and developer tooling layer, it turns out that that layer can be successful only if

the application layer is even more successful.

And we saw this with the rise of SaaS as well.

A lot of the buzz, the excitement is on the technology, the tooling layer, which is fine,

nothing wrong with that.

But the only way for that to be successful is if the application layer is even more successful

so that frankly, they can generate enough revenue to pay the infrastructure and the

tooling layer.

So actually, let me mention one example, ArmourEye, I was actually just texting the CEO yesterday,

but ArmourEye is a company that we built that uses AI for romantic relationship coaching,

right?

And he just points out, I'm an AI guy, and I feel like I know...

nothing really about romance.

And if you don't believe me, you can ask my wife.

She will confirm that I know nothing about romance.

But we want to build this, we wanted to get it together

with the former CEO of Tinder, Renata Naibog,

and with my team's expertise in AI

and her expertise in relationships.

We actually ran Tinder.

She knows more about relationships

than anyone I know.

We were able to build something pretty unique

using AI for kind of romantic relationship mentoring.

And the interesting thing about applications like these

is when we look around,

how many teams in the world are simultaneously

expert in AI and in relationships?

And so at the application layer,

I'm seeing a lot of exciting opportunities

that seem to have a very large market,

but where the competition set is very light

relative to the magnitude of the opportunity.

It's not that there are no competitors,

it's just much less intense

compared to the developer two

or the infrastructure layer, I'd say.

And so...

Because I've spent a lot of time iterating on a process of building startups,

what I'm going to do is just, you know, very transparently tell you

the recipe we've developed for building startups.

And so after many years of iteration and improvement,

this is how we now build startups.

My team has always had access to a lot of different ideas,

internally generated ideas from partners,

and I want to walk through this with one example of something we did,

which is a company, Bering AI, which uses AI to make ships more fuel-efficient.

So this idea came to me when, a few years ago,

a large Japanese conglomerate called Mitsui,

that is a major shareholder and sort of operates major shipping lines,

they came to me and they said,

hey, Andrew, you should build a business to use AI to make ships more fuel-efficient.

And the specific idea was, you know, think of it as Google Maps for ships.

We can suggest a ship or tell a ship how to steer

so that you still get to your destination on time,

but using, it turns out, about 10% less fuel.

And so what we now do is we spend about a month validating the idea.

So double check, is this idea even technically feasible?

And then talk to prospective customers to make sure there's a market need.

So we spend up to about a month doing that.

And if it passes this stage, then we will go and recruit a CEO to work with us on the project.

When I was starting out, I used to spend a long time working on a project myself before bringing on a CEO.

But after iterating, we realized that bringing on a leader at the very beginning to work with us,

it reduces a lot of the burden of having to transfer knowledge or having a CEO come in and having to revalidate what we discovered.

So the process, we've learned, is much more efficient, which is bringing the leader at the very start.

And so in the case of Bering AI, we found a fantastic CEO, Dylan Kyle, who was a repeat entrepreneur, one successful exhibit before.

And then we spent three months, six two-week sprints to work with them to build a prototype as well as do deep customer validation.

If it survives this stage,

and we have about a two-thirds,

66 percent survival rate,

we then write the first check-in,

which then gives the company resources to hire an executive team,

build a key team, get the MVP working,

minimum viable product working,

and get some real customers.

Then after that, hopefully,

then successfully raises additional external rounds of funding.

You can keep on growing and scaling.

So I'm really proud of the work that my team was able to do to

support Mitsui's idea and Dylan Cao as CEO.

Today, there are hundreds of ships on the high seas right

now that are steering themselves differently because of Bering AI.

Ten percent fuel savings translates the rough order amount to

maybe $450,000 in savings in fuel per ship per year.

Of course, it's also, frankly,

quite a bit better for the environment.

I think this startup would not have existed if not for Dylan's fantastic work,

and then also Mitsui brings this idea to me.

I like this example because this is another one.

It's like, this is a startup idea that just to point out,

I would never have come up with myself.

Because I've been on a boat,

but what do I know about maritime shipping?

But it's the deep subject matter expertise of Mitsui that had

this insight together with Dylan and then my team's expertise in AI.

that made this possible.

And so, as I operate in AI, one thing I've learned

is my swim lane is AI, and that's it.

Because I don't have time, it's very difficult

for me to be expert in maritime shipping,

and romantic relationships, and healthcare,

and financial services, and on and on and on.

And so I've learned that if I can just

help get the accurate technical validation,

and then use AI resources to make sure

the AI tech is built quickly and well,

and I think we've always managed to help

the companies build a strong technical team,

then partnering with such experts

often results in exciting opportunities.

And I want to share with you one other weird aspect,

one other weird lesson I've learned

about building startups, which is,

I like to engage only when there's a concrete idea.

And this runs counter to a lot of the advice

you hear from the design thinking methodology,

which often says, don't rush the solutioning.

Explore a lot of alternatives before you get a solution.

Honestly, we tried that, it was very slow.

But what we've learned is that, at the ideation stage,

if someone comes to me and says,

hey Andrew, you should apply AI to financial services.

Because I'm not a subject matter expert

in financial services, it's very slow

for me to go and learn enough about financial services

so you can figure out what to do.

I mean, eventually you could get a good outcome,

but it's a very labor intensive,

very slow, very expensive process

for me to try to learn industry after industry.

In contrast, one of my partners wrote this idea

as a tongue-in-cheek, not really seriously.

But, you know, let's say a concrete idea is,

buy GPT, let's eliminate commercials

by automatically buying every product advertised

in exchange for not having seen the ads.

It's not a good idea, but it is a concrete idea.

And it turns out, concrete ideas can be validated or falsified efficiently.

They also give the team a clear direction to execute.

And I've learned that in today's world, especially with, you know, the excitement, the buzz,

the exposure to AI of a lot of people, it turns out that there are a lot of subject

matter experts in today's world that have deeply thought about a problem for months,

sometimes even, you know, one or two years, but they've not yet had a built partner.

And when we get together with them and they share the idea with us, it allows us to work

with them to very quickly go into validation and building.

And I find that this works because there are a lot of people that have already done the,

you know, design thinking thing of exploring a lot of ideas and wounding down to really

good ideas.

And I find that there are so many good ideas sitting out there that no one is working on,

that finding those good ideas that someone has already had and wants to share with us

and wants to build a partner for, that turns out to be a much more efficient engine.

So before I wrap up, we'll go to the question in a second.

Just a few slides to talk about risk and social impact.

So AI is a very powerful technology.

To state something you probably guessed, my teams and I, we only work on projects that

move humanity forward.

And, you know, we have multiple times killed projects that we assess to be financially

sound based on ethical grounds.

It turns out I've been surprised and sometimes dismayed at the creativity of people to come

up with good ideas, sorry, to come up with really bad ideas that seem profitable but

really should not be built.

We've killed a few projects on those grounds.

And then I think I have to acknowledge that AI today does have problems with bias, fairness,

accuracy, but also the technology is improving quickly.

So I see that AI systems today are less biased than six months ago and more fair than six

months ago, which is not to dismiss the importance of these problems.

They are problems and we should continue to work on them, but I'm also gratified at the

number of AI teams working hard on these issues to make them much better.

When I think of the biggest risk of AI, I think that the biggest risk, one of the biggest

risks is the disruption to jobs.

This is a diagram from a paper by our friends at the University of Pennsylvania and some

folks at OpenAI analyzing the exposure of different jobs to AI automation.

And it turns out that whereas the previous wave of automation, mainly the most exposed

jobs were often the lower-wage jobs, such as when we put robots into factories, with

this current wave of automation, it's actually the higher-wage jobs further to the right

of this axis that seems to have more of their tasks exposed to AI automation.

So, even as we create tremendous value using AI, I feel like as citizens and corporations

and governments and society, I feel a strong obligation to make sure that people, especially

people whose livelihoods are disrupted, are still well taken care of, are still treated

well.

And then lastly, there's also been, it feels like every time there's a big wave of progress

in AI, you know, there's a big wave of hype about artificial general intelligence as well.

When deep learning started to work really well 10 years ago, there was a lot of hype

about AGI, and now that general AI is working really well, there's another wave of hype

about AGI.

But I think that artificial and general intelligence, AI didn't do anything a human could do, is

still decades away.

You know, maybe 30 to 50 years, maybe even longer.

I hope we'll see it in our lifetimes, but I don't think there's any time soon.

One of the challenges is that the biological path to intelligence, like humans, and the

digital path to intelligence, you know, AI, they've taken very different paths.

And the funny thing about the definition of AGI is you're benchmarking this very different

digital path to intelligence with really the biological path to intelligence.

So I think, you know, large language models are smarter than any of us in certain key

dimensions, but much dumber than any of us in other dimensions.

And so forcing it to do everything a human can do is like a funny comparison, but I hope

we'll get there.

Maybe, hopefully, within our lifetimes.

And then...

There's also a lot of, I think, overblown hype about AI creating extinction risks for

humanity.

Candidly, I don't see it.

I just don't see how AI creates any meaningful extinction risk for humanity.

I think that people worry we can't control AI, that we have lots of—AI will be more

powerful than any person, but with lots of experience steering very powerful entities,

such as corporations or nation states, that are far more powerful than any single person,

and making sure they, for the most part, benefit humanity.

And also, technology develops gradually.

The so-called hard takeoff scenario, where it's not really working today, and then suddenly,

one day overnight, it works brilliantly, and we achieve super intelligence, takes over

the world.

That's just not realistic.

And I think AI technology will develop slowly, and then it gives us plenty of time to make

sure that we provide oversight and can manage it to be saved.

And lastly, if you look at the real extinction risk to humanity, such as, finger crossed,

the next pandemic, or climate change, leading to a massive depopulation of some parts of

the planet, or much lower odds that maybe someday, an asteroid doing to us what it had

done to the dinosaurs.

I think if you look at the actual real extinction risk to humanity, AI having more intelligence,

even artificial intelligence in the world, would be a key part of the solution.

So I feel like if you want humanity to survive and thrive for the next thousand years, rather

than slowing AI down, which some people propose, I would rather make AI go as fast as possible.

So with that, just to summarize, I think that AI, as a general purpose technology, creates

a lot of new opportunities for everyone, and a lot of the exciting and important work that

lies ahead of us all is to go and build those concrete use cases, and hopefully in the future,

hopefully I have opportunities to maybe engage with more of you on those opportunities as well.

So with that, let me just say thank you all very much.

Thank you.

With the exciting new wireless features coming to iPhone 15, here's Dennis.

iPhone uses wireless technologies to make everyday activities easier, like streaming

videos with AirPlay or easily sharing contact information with NameDrop using near-field

communication.

iPhone was the first smartphone with ultra-wideband, which is great for identifying where your

keys are hiding in the couch.

Ultra-wideband is getting even better this year, because just like Apple Watch, iPhone

15 has our cutting-edge second-generation ultra-wideband chip, so iPhone 15 can connect

to other devices with this chip from up to three times farther away.

This chip also opens up a completely new way to use precision finding on iPhone 15 to find

your friends.

When you're in a crowded place, like a train station or a farmer's market, and your friends

share their location, you'll be guided right to them with clear directions and distance.

It's built into FindMy, so it has all the same privacy protections that users have come

to trust.

Of course, sometimes a phone call is the best way to reach someone.

iPhone already has fantastic 5G performance, and now audio quality on phone calls is getting better too.

Because iPhone 15 uses a more advanced machine learning model that automatically prioritizes your voice.

If you prefer to filter out even more distracting background noise, just select voice isolation and you'll come through loud and clear.

I'm at the farmer's market. Do you want... Hang on. It's noisy here.

Better? Great. Of course they have apples here.

It's a feature you can use every day.

Sometimes a call can't go through because you're off the grid.

When you need help in an emergency, iPhone introduced a breakthrough new service, Emergency SOS via Satellite.

It's having an extraordinary impact, including helping first responders rescue people after accidents and natural disasters.

And users have let their loved ones know that they're safe by updating their location with Find My via Satellite,

in places from the Shetland Islands in Scotland to Mount Wellington in Tasmania.

Emergency SOS and Find My via Satellite have expanded to 14 countries on three continents.

and they're coming to Spain and Switzerland this month.

Emergency SOS was built for serious emergencies that require a first responder.

But say you have car trouble and no cellular or Wi-Fi service.

For those times, we're introducing Roadside Assistance via satellite.

It's built on the same advanced technology as our existing satellite service,

and it's so easy to use.

Just text ROADSIDE ASSISTANCE and select what kind of help you need.

Then, the intuitive interface will guide you to connect to a satellite

and share that information with a Roadside Assistance provider.

They'll message you directly and dispatch help to your exact location

with the right equipment to get you moving.

It's launching in the U.S. with AAA, the country's largest Roadside Assistance provider.

Roadside service is covered by AAA membership

and is also available separately for non-members.

This is yet another way that iPhone is essential in our lives.

Now, back to Kaian.