Name: We’re already using AI more than we realize（We’re already using AI more than we realize）
Uploaded: 2024-02-29T02:15:36.000Z
Duration: 6 min 32 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

Of all the interactions you have with technology in a day,

interacting with artificial intelligence —

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

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

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

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.

asked Americans to identify whether each of those used AI

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

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.

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.

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.

This episode is presented by Microsoft Copilot

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To learn more, you can go to Microsoft.com/copilotforwork.

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.

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

There has been this tendency over the last ten plus years

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We’re already using AI more than we realize（We’re already using AI more than we realize）

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