Luyện nói tiếng Anh bằng Shadowing qua video: We’re already using AI more than we realize

Of all the interactions you have with technology in a day, 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 worlds, 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 Most 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 U .S 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 AI, but it's not very 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 Howe.

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

Giving a 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 programmed.

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, the 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 that 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 assistance, and voice -enabled TV remotes.

And they predict text in auto -complete and auto -correct.

But AI is seeping into more than that.

There has been this tendency over the last 10 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 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 AI should be used for and what it shouldn't be used for.

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