How AI Could Empower Any Business | Andrew Ng | TED

When I think about the rise of AI,

I'm reminded by the rise of literacy.

A few hundred years ago,

many people in society thought

that maybe not everyone needed to be able to read and write.

Back then, many people were tending fields or herding sheep,

so maybe there was less need for written communication.

And all that was needed

was for the high priests and priestesses and monks

to be able to read the Holy Book,

and the rest of us could just go to the temple or church

or the holy building

and sit and listen to the high priest and priestesses read to us.

Fortunately, it was since figured out that we can build a much richer society

if lots of people can read and write.

Today, AI is in the hands of the high priests and priestesses.

These are the highly skilled AI engineers,

many of whom work in the big tech companies.

And most people have access only to the AI that they build for them.

I think that we can build a much richer society

if we can enable everyone to help to write the future.

But why is AI largely concentrated in the big tech companies?

Because many of these AI projects have been expensive to build.

They may require dozens of highly skilled engineers,

and they may cost millions or tens of millions of dollars

to build an AI system.

And the large tech companies,

particularly the ones with hundreds of millions

or even billions of users,

have been better than anyone else at making these investments pay off

because, for them, a one-size-fits-all AI system,

such as one that improves web search

or that recommends better products for online shopping,

can be applied to [these] very large numbers of users

to generate a massive amount of revenue.

But this recipe for AI does not work

once you go outside the tech and internet sectors to other places

where, for the most part,

there are hardly any projects that apply to 100 million people

or that generate comparable economics.

Let me illustrate an example.

Many weekends, I drive a few minutes from my house to a local pizza store

to buy a slice of Hawaiian pizza

from the gentleman that owns this pizza store.

And his pizza is great,

but he always has a lot of cold pizzas sitting around,

and every weekend some different flavor of pizza is out of stock.

But when I watch him operate his store,

I get excited,

because by selling pizza,

he is generating data.

And this is data that he can take advantage of

if he had access to AI.

AI systems are good at spotting patterns when given access to the right data,

and perhaps an AI system could spot if Mediterranean pizzas sell really well

on a Friday night,

maybe it could suggest to him to make more of it on a Friday afternoon.

Now you might say to me, "Hey, Andrew, this is a small pizza store.

What's the big deal?"

And I say, to the gentleman that owns this pizza store,

something that could help him improve his revenues

by a few thousand dollars a year, that will be a huge deal to him.

I know that there is a lot of hype about AI's need for massive data sets,

and having more data does help.

But contrary to the hype,

AI can often work just fine

even on modest amounts of data,

such as the data generated by a single pizza store.

So the real problem is not

that there isn’t enough data from the pizza store.

The real problem is that the small pizza store

could never serve enough customers

to justify the cost of hiring an AI team.

I know that in the United States

there are about half a million independent restaurants.

And collectively, these restaurants do serve tens of millions of customers.

But every restaurant is different with a different menu,

different customers, different ways of recording sales

that no one-size-fits-all AI would work for all of them.

What would it be like if we could enable small businesses

and especially local businesses to use AI?

Let's take a look at what it might look like

at a company that makes and sells T-shirts.

I would love if an accountant working for the T-shirt company

can use AI for demand forecasting.

Say, figure out what funny memes to prints on T-shirts

that would drive sales,

by looking at what's trending on social media.

Or for product placement,

why can’t a front-of-store manager take pictures of what the store looks like

and show it to an AI

and have an AI recommend where to place products to improve sales?

Supply chain.

Can an AI recommend to a buyer whether or not they should pay 20 dollars

per yard for a piece of fabric now,

or if they should keep looking

because they might be able to find it cheaper elsewhere?

Or quality control.

A quality inspector should be able to use AI

to automatically scan pictures of the fabric they use to make T-shirts

to check if there are any tears or discolorations in the cloth.

Today, large tech companies routinely use AI to solve problems like these

and to great effect.

But a typical T-shirt company or a typical auto mechanic

or retailer or school or local farm

will be using AI for exactly zero of these applications today.

Every T-shirt maker is sufficiently different from every other T-shirt maker

that there is no one-size-fits-all AI that will work for all of them.

And in fact, once you go outside the internet and tech sectors

in other industries, even large companies

such as the pharmaceutical companies,

the car makers, the hospitals,

also struggle with this.

This is the long-tail problem of AI.

If you were to take all current and potential AI projects

and sort them in decreasing order of value and plot them,

you get a graph that looks like this.

Maybe the single most valuable AI system

is something that decides what ads to show people on the internet.

Maybe the second most valuable is a web search engine,

maybe the third most valuable is an online shopping product recommendation system.

But when you go to the right of this curve,

you then get projects like T-shirt product placement

or T-shirt demand forecasting or pizzeria demand forecasting.

And each of these is a unique project that needs to be custom-built.

Even T-shirt demand forecasting,

if it depends on trending memes on social media,

is a very different project than pizzeria demand forecasting,

if that depends on the pizzeria sales data.

So today there are millions of projects

sitting on the tail of this distribution that no one is working on,

but whose aggregate value is massive.

So how can we enable small businesses and individuals

to build AI systems that matter to them?

For most of the last few decades,

if you wanted to build an AI system, this is what you have to do.

You have to write pages and pages of code.

And while I would love for everyone to learn to code,

and in fact, online education and also offline education

are helping more people than ever learn to code,

unfortunately, not everyone has the time to do this.

But there is an emerging new way

to build AI systems that will let more people participate.

Just as pen and paper,

which are a vastly superior technology to stone tablet and chisel,

were instrumental to widespread literacy,

there are emerging new AI development platforms

that shift the focus from asking you to write lots of code

to asking you to focus on providing data.

And this turns out to be much easier for a lot of people to do.

Today, there are multiple companies working on platforms like these.

Let me illustrate a few of the concepts using one that my team has been building.

Take the example of an inspector

wanting AI to help detect defects in fabric.

An inspector can take pictures of the fabric

and upload it to a platform like this,

and they can go in to show the AI what tears in the fabric look like

by drawing rectangles.

And they can also go in to show the AI

what discoloration on the fabric looks like

by drawing rectangles.

So these pictures,

together with the green and pink rectangles

that the inspector's drawn,

are data created by the inspector

to explain to AI how to find tears and discoloration.

After the AI examines this data,

we may find that it has seen enough pictures of tears,

but not yet enough pictures of discolorations.

This is akin to if a junior inspector had learned to reliably spot tears,

but still needs to further hone their judgment about discolorations.

So the inspector can go back and take more pictures of discolorations

to show to the AI,

to help it deepen this understanding.

By adjusting the data you give to the AI,

you can help the AI get smarter.

So an inspector using an accessible platform like this

can, in a few hours to a few days,

and with purchasing a suitable camera set up,

be able to build a custom AI system to detect defects,

tears and discolorations in all the fabric

being used to make T-shirts throughout the factory.

And once again, you may say,

"Hey, Andrew, this is one factory.

Why is this a big deal?"

And I say to you,

this is a big deal to that inspector whose life this makes easier

and equally, this type of technology can empower a baker to use AI

to check for the quality of the cakes they're making,

or an organic farmer to check the quality of the vegetables,

or a furniture maker to check the quality of the wood they're using.

Platforms like these will probably still need a few more years

before they're easy enough to use for every pizzeria owner.

But many of these platforms are coming along,

and some of them are getting to be quite useful

to someone that is tech savvy today,

with just a bit of training.

But what this means is that,

rather than relying on the high priests and priestesses

to write AI systems for everyone else,

we can start to empower every accountant,

every store manager,

every buyer and every quality inspector to build their own AI systems.

I hope that the pizzeria owner

and many other small business owners like him

will also take advantage of this technology

because AI is creating tremendous wealth

and will continue to create tremendous wealth.

And it's only by democratizing access to AI

that we can ensure that this wealth is spread far and wide across society.

Hundreds of years ago.

I think hardly anyone understood the impact

that widespread literacy will have.

Today, I think hardly anyone understands

the impact that democratizing access to AI will have.

Building AI systems has been out of reach for most people,

but that does not have to be the case.

In the coming era for AI,

we’ll empower everyone to build AI systems for themselves,

and I think that will be incredibly exciting future.

Thank you very much.

(Applause)