We’re already using AI more than we realize

Summary

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Outlines

00:00

🤖 人工智能在日常生活中的无处不在

这一段讨论了人工智能（AI）如何在不知不觉中成为我们日常生活的一部分。从智能手表的运动跟踪到推荐播放列表，从智能门铃到垃圾邮件过滤，再到客户支持聊天机器人，这些都是AI技术的应用实例。尽管这些AI应用已经变得普遍，但根据皮尤研究中心的调查，美国人只有大约60%的时间能正确识别出使用了AI的技术。调查还发现，尽管大多数美国成年人每天都会多次上网，但几乎一半的人认为他们并不经常与AI互动。这种认识上的差距部分原因是“人工智能”这一术语的定义十分宽泛，涵盖了多种技术和应用。

05:00

🌐 AI技术的深远影响

第二段深入探讨了AI技术如何影响我们生活的各个方面，从搜索结果的排名到面部识别解锁手机，再到语音助手和自动文本预测等。除了这些显而易见的应用外，AI也正逐渐渗透到更多领域，如政治广告的定向、职位申请的筛选、贷款资格的判断等，这些用途往往带有历史数据中的偏见。因此，提高公众对AI参与日常决策过程的认识变得尤为重要，以促进更广泛的讨论关于AI的道德和伦理界限。

10:45

🔄 人工智能的定义与演变

第三段重复了第一段的某些内容，强调了人们对AI应用广泛存在的认识差距，并提出了这个问题的根源：AI的定义非常宽泛，包含了从计算机视觉到自然语言处理等不同的子领域。随着时间的推移，机器学习和深度学习成为了AI领域的重点，使得计算机不再需要人类编写具体规则来识别模式，而是可以通过大量数据训练自行学习。这一段还提到了这个视频的赞助商Microsoft Copilot for Microsoft 365，强调了AI技术在工作中的应用，如会议总结、草稿编写等。

22:13

🔍 人工智能的起源和发展

这一段通过Karen Hao的视角，探讨了人工智能的起源、定义和技术发展。她强调了AI作为一个涵盖广泛技术和应用的术语是如何随时间发展的，从早期的尝试模拟人脑的各个方面，到现在主要聚焦于机器学习和深度学习。这段还提到了Hao即将出版的关于AI的书籍，她对AI商业化的批判，以及深度学习技术如何变得普遍。

27:27

🤔 公众对AI的认知与影响

第五段讨论了公众对AI技术认知的重要性和其对社会的潜在影响。一方面，虽然大多数美国人表示至少略知一二关于AI，但真正深入了解的比例并不高。提高公众对AI的认知被视为参与讨论AI道德和伦理界限的第一步。教育水平被认为是影响人们对AI认知程度的一个重要因素，高学历群体通常对AI有更深入的了解。这一段强调了加强AI知识普及的重要性，以促进更广泛的社会参与和对AI技术的理解。

32:31

🌟 AI技术的社会影响与公众讨论

最后一段深入探讨了AI技术对社会的影响，特别是在健康、医疗、就业和执法等领域的应用可能带来的问题。公众对AI技术的认知增加可能会引起对其应用的更多担忧。研究人员致力于开发问题和调查方法，旨在更好地理解公众对AI的态度和认知，以及他们对于AI在敏感领域应用的看法。这一部分强调了进行这些研究的重要性，以促进有关AI应用和影响的公共讨论，确保技术发展能够公平、有益地服务于全社会。

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Highlights

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Transcripts

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 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.

This episode is presented by Microsoft Copilot

for Microsoft 365, your AI assistant at work.

Copilot can help you solve your most complex problems at work,

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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

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Recommendations for shows.

Recommendations

We'll hear more about.

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

We'll hear more about that.

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

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

at least a little about it, but only a third say they've heard a lot about it.

Sort of a deep or rich knowledge in this matters, Right. Why?

Why do we care about awareness? Why do we study it?

It's really the first step towards a broader public engagement

with the host of moral and ethical questions that A.I.

raises for society.

So we're at a really interesting moment here

where most Americans are generally aware of artificial intelligence,

But deep knowledge, intimate knowledge is still fairly modest.

And it is growing. Absolutely, it's growing.

But it needs to grow for folks to be able to participate

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

What I should be used for and what it shouldn't be used for.

So that's part of why we study awareness at the center

and why we feel it's important.

Know Yeah,

well, one thing that we do know is that not everyone brings

the same level of awareness to understanding

something like artificial intelligence and a really big factor.

Probably the biggest factor is level of formal education, right?

Where college graduates and those with post-graduate degrees,

they express higher self-reported awareness

and they score better on our awareness sort of scale that we've developed.

And that's important.

It just sort of underscores that

these conversations are a bit different depending on what circles you're in,

whether it's you're in a formal education

setting or a job that requires this technology

or maybe you're not in those settings.

So absolutely, there are differences across the public that reflect things

like formal education, job type and even types of conversation

or social circles that really matter when it comes to where folks are in terms

of understanding, interpreting and how they feel about artificial intelligence.

So, well, look, there's so many big questions

with artificial intelligence, but access and equity are certainly among them.

We'll folks who do understand this technology, who can use it well

and leverage it to their advantage, these prime equity and access questions.

And there's the conversation around that or the solutions are very complex.

But that's certainly one thing at play here

is there's immense power with some of these technologies.

They may not be equally available or understood by all

spheres of the public in Is that okay?

That's a public conversation to have.

That's part of what our research can do

is provide a foundation for having that conversation right.

So, well, I'm in trouble now.

I've gone too far with Shad to find a way to take back at all.

I hear the heavy hand of lies and deceit.

Absolutely.

I mean, part of what we do

at the center, an enormous amount of time goes into question or development.

The ultimate question we end up on really represents a fraction

of all the versions and items and ideas being discussed.

And we had to balance considerations here.

One is we have to make this accessible to folks

we can't use necessarily highly technical or almost esoteric examples

which don't resonate well with folks who are giving their time to take a survey.

So there's many other ways of imagining an awareness index

and there's more to do on this front, both by us and others.

But we thought a good first step would be to try and identify

some applications that are fairly well known in everyday life,

or at least these of the tools they're powering are fairly widely used.

We thought that was a good first place to start.

There may be other ways to go deeper.

Some of the more complex or technical or industry specific uses,

that's a great opportunity for future research.

But we wanted to start with ten folks even recognize where this technology

is, that play and even some of the most common.

We use tools online today, like online shopping, like email, like fitness

trackers.

Let's start there and see what we can learn.

Well, that's

that is the question is as folks become more aware, as expressions of

I increasingly shape the way we work live, how are attitudes going to change?

What do we need to understand going forward?

Right now, there's a fair amount of caution

among the public about A.I., right?

Far more say they're concerned and excited about its growing role in life.

How will that change going forward?

A little more awareness only reinforce or grow concern or a critic go in the other

direction, whereas the more aware folks get, the more positive they get.

We've seen examples of both in our past research there.

There is it's a story that's yet to be written, right?

But it's one that's important to follow.

Early indications are even in the past year or two,

that the public is actually more concerned, not less, as they become

a bit more aware of the ways in which air is involved in their own life.

Spaces like health and medicine, spaces like jobs and hiring,

perhaps some potential uses in law enforcement right there.

There's some concern out there among Americans

about how this is all going to play out

in the.

Yeah, absolutely.

And they're really sort of multiple prongs to our research plan here at the center,

which is for some of the most controversial uses of I like A.I.

and Health and medicine,

we focus a little bit less on awareness and more about attitudes.

How would you feel?

Would you feel comfortable with air and your own primary care?

Would you be open to using AI and something like an image review

and skin cancer detection?

So in some of the most you could call the most

perhaps potentially impactful on people's own lives, we go directly to attitudes.