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  • 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 calleddeep 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,

  • going far beyond simple questions and answers. From getting up to speed

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  • And it's all built on Microsoft's comprehensive approach to security,

  • privacy, compliance and responsible AI.

  • Microsoft does not influence the editorial process of our videos,

  • but they do help make videos like this possible.

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

  • 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 thehowin 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

  • 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.

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

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We’re already using AI more than we realize

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