interacting with artificial intelligence —

or not — feels like a choice.

But in some ways it isn't.

Over the past decade, we've become surrounded by AI systems

that perceive our world,

that support our decisions and that mimic our ability to create.

Whether we're aware of it or not is another story.

Imagine a day like this.

You do some exercise with a smartwatch, put on a suggested playlist,

go to a friend's house and ring their camera

doorbell, browse recommended shows on Netflix.

Check your spam folder for an email you've been waiting for.

And when you can't find it, talk to a customer support chatbot.

Each of those things are made possible by technologies

that fall under the umbrella of artificial intelligence.

But when a Pew survey

asked Americans to identify whether each of those used AI

or not, they only got it right

about 60% of the time.

Some of these applications of AI have become fairly ubiquitous.

They almost exist in the background, and it's not terribly apparent to folks

that the tools or services they're using are being powered

by this technology.

That's Alec Tyson, one of the researchers behind that Pew study.

When Tyson and his team asked respondents how often they think they use AI,

almost half didn't think they regularly interact with it at all.

Some of them might be right, but most probably just don't know it.

We know about 85% of US adults are online every day, multiple times a day.

Some folks are online almost all the time.

This suggests a bit of a gap where there seem to be some folks

who really must be interacting with a AI, but it's never salient to them.

They don't perceive it.

So why does that gap exist?

Part of the problem is that the term artificial

intelligence has been used to refer to a lot of different things.

Artificial intelligence is totally this giant umbrella tent term

that now has become a kitchen sink of everything.

That's Karen Hao. She's a reporter who covers artificial intelligence and society.

In the past, there were distinct disciplines

about which aspect of the human brain do we want to recreate?

Like do we want to recreate the vision part?

Do we want to recreate our ability to hear? Our ability to write and speak?

Giving the machine the ability to see became the field of computer vision.

Giving the machine the ability to write and speak

became the field of natural language processing.

But on their own, these tasks still required a machine to be programed.

If we wanted machines to recognize spam emails, we had to explicitly program them

to look out for specific things, like poor spelling and urgent phrasing.

That meant the tools weren't very adaptable to complex situations.

But that all changed when we

started recreating the brain's ability to learn.

This became the subfield of machine learning,

where computers are trained on massive amounts of data so that instead of

needing to hand-code rules about what to see or speak or write,

those computers can develop rules on their own.

With machine learning, a computer could learn to recognize new spam emails

by reviewing thousands of existing emails that humans have labeled as spam.

The machine recognizes patterns in this structured

data and creates its own rules to help identify those patterns.

When the training data hasn't been structured and labeled by humans,

that method is called “deep learning.”

Most of the time people talk about AI now,

they're not talking about the whole field, but specifically these two methods.

We'll hear more about that

after a word from this video's sponsor.

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Now back to our video.

Improvements in computing power, together with the massive amounts of data

generated on the Internet, made possible a whole new generation of technologies

that leveraged machine learning. And existing ones swapped out

their algorithms for machine learning too.

A lot of the “how” in the back has been swapped into AI over time

because people have realized, “oh wait, we can actually get an even better

performance of this product if we just swap our original algorithm,

our original code out for a deep learning model.”

Now, machine learning and deep learning models power recommendation

for shows, music, videos, products and advertisements.

They determine the ranking of items every time we browse search results

or social media feeds. They recognize images like faces to unlock phones

or use filters, and the handwriting on remote deposit checks.

They recognize speech in transcription,

voice assistants, and voice-enabled

TV remotes. And they predict text in autocomplete and autocorrect.

But AI is seeping into more than that.

There has been this tendency over the last ten plus years

where people have started

putting AI into absolutely everything.

Machine learning algorithms are already

being used to decide which political ads we see, which jobs we qualify for,

and whether we qualify for loans or government benefits,

and often carry the same biases as the human decisions that preceded them.

Are you actually automating the poor decision

making that happened in the past and just bringing it into the future?

If you're going to use historical data to predict

what's going to happen in the future,

you're just going to end up with a future that looks like the past.

And that's part of the reason why it matters to close that gap

between those who knowingly interact with the AI every day

and those who don't quite know it yet.

Awareness needs to grow for folks to be able to participate

in some of these conversations about the moral and ethical boundaries,

what air should be used for, and what it shouldn't be used for.

Over the past decade,

we've become surrounded by AI systems that perceive our worlds

that support our decisions, and that mimic our ability

to create.

Over the past

decade, we've become surrounded by AI systems that perceive our worlds,

that support our decisions, and that mimic our ability to create.

But over the past decade, we've become surrounded by AI systems

that perceive our worlds, that support our decisions

and that mimic our ability to create.

Over the past decade, we've become surrounded by AI systems

that perceive our worlds,

that support our decisions, and that mimic our ability to create.

Whether we're aware of it or not is another story.

But when a Pew survey asked Americans to identify whether these technologies use

AI, they only got it right about 60% of the time.

But when a Pew survey asked

Americans to identify whether these technologies use AI,

they only got it right about 60% of the time.

But when but when a.

But when a Pew survey

but when a Pew survey asked.

But when a pew.

But when a Pew survey asked.

But when a Pew survey asked Americans

to identify whether these technologies use AI,

they only got it right about 60% of the time.

But when a Pew survey asked Americans

but when a Pew survey asked

Americans to identify whether these technologies use AI,

they only got it right about 60% of the time.

We'll hear more about that

after a word from this video sponsor.

We'll hear

we're going to hear more about that after.

We'll hear more about that after a word from this video's sponsor.

We'll hear more about that.

We'll hear more about that after a word from this video sponsor

now, machine learning.

Now machine learning and deep learning.

Now, machine learning and deep learning models Power, recommendations

for shows, recommendations.

Recommendations for shows.

Recommendations

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We'll hear more about that after a word from this video sponsor.

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sponsor.

Okay,

great.

Hi, I'm

Karen Howe and I am a reporter that's been covering

artificial intelligence for over five years.

And I am currently also working on a book about opening

AI for Penguin Press

and totally,

yeah,

yeah,

yeah.

Artificial Intelligence is totally this giant umbrella tent term that now

it's become a kitchen sink of everything but the origins of the term.

And this sort of helps to understand why it's so broad.

The origins of the term are from an academic field, the founding

of an academic field called A.I., and that happened in the 1950s,

and it was a group of academics in the US that actually had a meeting

at Dartmouth University to decide that they wanted to create a brand new field.

They wanted to be the founding fathers of this field, and specifically

they wanted to try and attain human level intelligence in computers.

And there were there have been over the decades, many different hypotheses

from like a scientific perspective about how to do this.

From what?

Like one of those hypotheses

is that we are intelligent because we know things,

and so we should build intelligent computers by encoding

all of the rules that we know about the universe into a computer.

Another theory has been we are intelligent because we can learn very quickly,

so we should build intelligent computers by building learning machines.

And so that theory that second one has become

the dominant paradigm of everything.

Basically everything that we see today

and those learning machines is now called machine learning

and machine learning has continued to advance

in the last decade and a half or so from just simple machine

learning like statistical calculations to deep learning, which means

fancier statistical calculations.

And there's a whole range of commercial products that have spun out of this

particular technology

that have really nothing to do with trying to recreate the human brain.

But more just companies can make money off of it.

And so they're going to keep doing that.

So deep learning technologies include things like voice assistants,

self-driving cars, facial recognition

and all the way up today to gravity and stable diffusion.

All of these count as deep learning.

And what deep learning ultimately is doing

is it's these techniques that allow

to become fairly ubiquitous.

They almost exist in the background, and it's not terribly apparent to folks

that what they're the tools or services they're using are being

powered by this technology.

And I return to the point we talked about earlier that there are others

where it's more apparent

chatbots, for example, we didn't get it into generative AI

in this example,

but that's one where it's more front and center

that that a computer is doing some thinking

or at least mimicking some thinking in a way that's very directly

more associated with artificial intelligence, where

some of the consumer technology has become a little bit more in the background.

And it's harder for folks to perceive that AI is influencing or helping them

go about their lives

and it

well, one thing that we know

is a question we ask very simple question how much have you heard or read about?

A You know, on the one hand, 90% of Americans say, well, I've heard