WEBVTT - The Watchmen

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<v Speaker 1>Sleepwalkers is a production of our heart Radio and unusual productions.

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<v Speaker 1>Every single thing that you do on a social network

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<v Speaker 1>or on a website is essentially recorded. How many pages

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<v Speaker 1>you visited, what did you click on, how did you

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<v Speaker 1>get to that website? What page did you leave on?

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<v Speaker 1>How many photos have you ever uploaded? Where were those

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<v Speaker 1>photos uploaded? How many places have you checked into? Who

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<v Speaker 1>have you tagged? What photographs have you been tagged in with? Whom?

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<v Speaker 1>Where were those photographs taken? Who's in your friendship circle?

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<v Speaker 1>Who did you go to school with? There are algorithms

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<v Speaker 1>and machine learning technologies that connect all of that data

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<v Speaker 1>together and start to find patterns. That's Lisa italian Aretti speaking.

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<v Speaker 1>She's a digital sociologist and tech ethics advocate with a

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<v Speaker 1>big focus on data. I think some of the data

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<v Speaker 1>that's quite concerning is facial recognition technology, where machines are

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<v Speaker 1>being fed huge amounts of images pictures of people's faces,

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<v Speaker 1>and that can come from dating websites, from photographs of

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<v Speaker 1>you on Facebook or Instagram or anywhere on the Internet.

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<v Speaker 1>You don't even have to upload it yourself. It could

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<v Speaker 1>be somebody else that's uploaded it for you. With all

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<v Speaker 1>that data out in the wild. All it takes is

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<v Speaker 1>for somebody to suck it up and they can start

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<v Speaker 1>connecting the dots. So there were researchers last year from

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<v Speaker 1>Stanford University who without the user's permission, scraped thirty thousand

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<v Speaker 1>photographs from a dating website that was public rights. So

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<v Speaker 1>they took it that they could just use those photographs

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<v Speaker 1>for research. Because it was a dating site, people will

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<v Speaker 1>also asked the data points, um, what is your sexual orientation?

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<v Speaker 1>So gay, straight, by right, and so they had all

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<v Speaker 1>of that data connected, so they could then connect faces

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<v Speaker 1>to sexual orientation and essentially they built an algorithm that

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<v Speaker 1>they said could detect just by somebody's face if they

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<v Speaker 1>were gay or straight or by the algorithm worked, it

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<v Speaker 1>could predict sexual orientation from photographs with accuracy for men

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<v Speaker 1>and accuracy for women based on just five photographs. And

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<v Speaker 1>Stanford isn't the only place using facial recognition to categorize people.

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<v Speaker 1>Governments are too all around the world. This is Sleepwalkers Welcome.

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<v Speaker 1>I'm Azveloshin. In the last episode, we looked at what

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<v Speaker 1>happens when artificial intelligence digests huge data sets to find

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<v Speaker 1>patterns and make predictions, like what's the most typical move synopsis?

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<v Speaker 1>Or is that moral cancerous? But advances in machine learning

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<v Speaker 1>are also making us more and more legible. In today's episode,

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<v Speaker 1>we ask what happens when we become the data set

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<v Speaker 1>and the power to predict is turned on us. Here's

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<v Speaker 1>Lisa again. The extra terrifying thing is why creates this

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<v Speaker 1>kind of technology? Like? Who does it serve? Why do

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<v Speaker 1>we need this? If you take this type of technology,

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<v Speaker 1>feed it to a citywide CCTV surveillance system and say

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<v Speaker 1>that you go to a place like Saudi Arabia where

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<v Speaker 1>being gay is considered a crime, and suddenly you're just

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<v Speaker 1>what pulling people off the street and arresting them because

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<v Speaker 1>you're gay because the computer said so, so like now

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<v Speaker 1>you're going to prison. This may sound like a terrifying

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<v Speaker 1>Minority Report style future, but actually it's here today. I

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<v Speaker 1>Cara Highs. So Lisa was talking about what happens when

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<v Speaker 1>governments start to connect the dots of all this data,

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<v Speaker 1>but it's already happening with private enterprise. For example, insurance

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<v Speaker 1>companies can now see someone who joins a Facebook group

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<v Speaker 1>about a genetic mutation and use that data to guess

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<v Speaker 1>that that person may have the genetic mutation and the

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<v Speaker 1>condition that's associated, and then the computer says, let's raise

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<v Speaker 1>their premium or let's deny them care. And so we

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<v Speaker 1>can use this peroxy data to do something The New

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<v Speaker 1>York Times has recently called proxy discrimination. And I think

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<v Speaker 1>something that's even more widely applicable is this definition of

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<v Speaker 1>surveillance capitalism, which is essentially that data is not just

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<v Speaker 1>data anymore, it's money. Companies can use data to make

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<v Speaker 1>predictions about future behavior and that can make them profit. Right,

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<v Speaker 1>that surveillance capitalism. I using information about joining a Facebook

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<v Speaker 1>group to make an insurance decision, and it's I don't know,

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<v Speaker 1>it's a little bit scary. In the US, it's all

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<v Speaker 1>about capitalism, But in other countries surveillance is used for

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<v Speaker 1>other purposes. For example, in China, it's about social control.

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<v Speaker 1>But in China, this same massive ingestion of data and

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<v Speaker 1>statistical modeling is used for governance. So let's take a

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<v Speaker 1>closer look at China and how they're using technology to

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<v Speaker 1>amplify the power of the state. They have this notion

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<v Speaker 1>of a social credit score, which is how good of

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<v Speaker 1>a citizen you are according to the government. They have

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<v Speaker 1>literally hundreds of millions of cameras around and they can

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<v Speaker 1>basically do things like you've been out at the bar,

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<v Speaker 1>you know, until to the last couple of nights. That's

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<v Speaker 1>not really what a good upstanding citizen would do. So

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<v Speaker 1>your social credit score can get ding because you've been

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<v Speaker 1>spending too much time at night at a bar. That's

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<v Speaker 1>Dr Alex Kilpatrick. He and Mary Haskett co founded Blink Identity,

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<v Speaker 1>a facial recognition company that can recognize the users face

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<v Speaker 1>in yes, the blink of an eye about no point

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<v Speaker 1>four seconds. Here's Blink co founder Mary on Chinese surveillance.

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<v Speaker 1>They have camera, so you're recorded jaywalking, and so your

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<v Speaker 1>score goes down, you know, and you automatically get a ticket,

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<v Speaker 1>which again doesn't sound like that they give a deal

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<v Speaker 1>if you really believe in law and order, except if

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<v Speaker 1>your score isn't high enough, you can't buy a plane ticket,

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<v Speaker 1>you have to travel by bus, you know, you can't

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<v Speaker 1>live in certain areas. And you can obviously see how

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<v Speaker 1>this could be abused. I mean, it doesn't take much

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<v Speaker 1>of an imagination. Other factors that can bring down your

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<v Speaker 1>social credit score include what you buy at the store,

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<v Speaker 1>your online browsing, and even having a friend with a

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<v Speaker 1>low score. This use of generalized surveillance can keep a

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<v Speaker 1>whole population in check, which is more or less the

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<v Speaker 1>explicit goal of the Communist Party of China. This can

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<v Speaker 1>be stifling for the average hand Chinese citizen. For minorities,

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<v Speaker 1>it can be much much worse, right, you know, more

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<v Speaker 1>specifically and more dauntingly, it can be used for internment,

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<v Speaker 1>you know, in the case of the weaker minority in China,

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<v Speaker 1>using data that is being created by this minority to

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<v Speaker 1>just communicate, you know, one person communicating with another person,

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<v Speaker 1>those who are connected that both wagas right, five weeks

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<v Speaker 1>are gathering the same place. Now that we know that,

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<v Speaker 1>you know, what are we going to do with that data? Oh,

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<v Speaker 1>We're going to send these people to re education camps.

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<v Speaker 1>And as technology improves, so does the state's ability to

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<v Speaker 1>project power. China today is cleaning the floor with the

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<v Speaker 1>Americans on voice and facial recognition technology. That's the Embrema,

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<v Speaker 1>an expert on global political risk and the founder of

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<v Speaker 1>the Eurasia Group. The Chinese have much more data. You

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<v Speaker 1>also have a government that is consolidating the data and

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<v Speaker 1>allocating it for different types of purposes. And you have

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<v Speaker 1>no presumption of privacy whatsoever. With no presumption of privacy,

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<v Speaker 1>the amount of data you can collect from your citizens

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<v Speaker 1>grows exponentially, and that actually gives you a huge technological advantage.

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<v Speaker 1>This is how Kai Fu Lee explains it. What makes

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<v Speaker 1>sense AI algorithm work better is how much data you

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<v Speaker 1>used to train it. And that's the beauty of deep learning.

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<v Speaker 1>You just keep throwing data at it and it just

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<v Speaker 1>performs better. And Kai fully understands this world better than most.

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<v Speaker 1>His fund, Signivation Ventures, has invested in meg v, a

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<v Speaker 1>facial recognition company valued at four billion dollars. Kaifu also

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<v Speaker 1>around Google China, so he knows the landscape. China simply

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<v Speaker 1>has more data than the US due to not only

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<v Speaker 1>the large number of users, but also the depth in

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<v Speaker 1>which Chinese users use the Internet for ordering food, for

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<v Speaker 1>shared bicycles, for mobile payments. So the AI will actually

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<v Speaker 1>just perform better because it's trained out more data. Some

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<v Speaker 1>of that data is taken from citizens using surveillance, but

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<v Speaker 1>according to Kaifu, much is freely given. I think the

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<v Speaker 1>Chinese culture and the Chinese people are more pragmatic, so

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<v Speaker 1>that if the software delivers pragmatic value, they ask fewer questions.

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<v Speaker 1>For example, you know, we've funded an app that loans

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<v Speaker 1>money to ask you a couple of questions and takes

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<v Speaker 1>data from your phone at the same level as the

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<v Speaker 1>Facebook would take data from your phone, and it's SAPs

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<v Speaker 1>the money to you instantly if it decides to lend

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<v Speaker 1>money to you. I think in the US people my

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<v Speaker 1>question do I really want to give my data to

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<v Speaker 1>a lending application? And is it appropriate to consider the

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<v Speaker 1>makeup my phone as a part of that give me

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<v Speaker 1>giving me money or my zip codes? Because that might

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<v Speaker 1>reflect certain things about me. The breadth and depth of

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<v Speaker 1>data in China, both from a larger population and a

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<v Speaker 1>much deeper integration with technology, gives China a serious competitive advantage.

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<v Speaker 1>Where the Americans have much better scientists, the Chinese were

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<v Speaker 1>able to buy a lot of science. Now, when you

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<v Speaker 1>talk to specialists in this field, they will tell you

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<v Speaker 1>that in many parts of Ai, great data and okay

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<v Speaker 1>scientists will frequently beat great scientists and okay data. Character

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<v Speaker 1>scary thing is that China is using that technological superiority

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<v Speaker 1>to build a very different kind of state, you know,

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<v Speaker 1>one in which the price of descent is intolerably high.

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<v Speaker 1>You know. So according to Ian public demonstrations have fallen marketly, right,

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<v Speaker 1>because if you know you're being watched, you're probably less

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<v Speaker 1>likely to commit public displays of civil disobedience. Right. We

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<v Speaker 1>talked about the vigas earlier in Cashgar, which is a

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<v Speaker 1>Muslim city in China. Vigas have to register to go

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<v Speaker 1>into the mosque, and once they're inside, they face a

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<v Speaker 1>bank of cameras like many many surveillance cameras, and go

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<v Speaker 1>figure Muslims stopped going to mosque voluntarily well, because going

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<v Speaker 1>to mask is an act of civil disobedience where they are.

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<v Speaker 1>Even if it's not explicitly stated, it's it's heavily implied, right.

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<v Speaker 1>I mean, I think about it for myself, Like if

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<v Speaker 1>I were living in an area in the United States

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<v Speaker 1>where going to temple was going to land me in

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<v Speaker 1>an internment camp, I would not be going voluntarily if

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<v Speaker 1>I knew there were security cameras all over my temple. Absolutely,

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<v Speaker 1>And the crazy thing is you wouldn't even have to

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<v Speaker 1>know if those security cameras work. It's like on the

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<v Speaker 1>o TO in England, we have a lot of speed

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<v Speaker 1>cameras and no one knows that they're actually doing it.

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<v Speaker 1>It's staying apart from once every five years you've got

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<v Speaker 1>a ticket. But it still slows people down the panopticon

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<v Speaker 1>even if they can't do that. But people think they

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<v Speaker 1>can do that, right. I mean, you don't need a

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<v Speaker 1>hundred percent certainty. You just need a government that is

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<v Speaker 1>starting to get that capacity and make it known and

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<v Speaker 1>have a few people that are sort of strung up

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<v Speaker 1>as examples, and suddenly everyone's scared. And this isn't only

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<v Speaker 1>happening in China. According to Ian, it was a key

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<v Speaker 1>part of Bashar al Assad's strategy in the Syrian Civil War.

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<v Speaker 1>Assade got some help from the Russians, who gave them

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<v Speaker 1>a couple of hundred computer scientists to go in work

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<v Speaker 1>with the Syrian military and identify on social media and

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<v Speaker 1>on text messaging who wore those Syrian citizens that were

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<v Speaker 1>nodes of dissent and within six months no more moderate

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<v Speaker 1>opposition in Syria. They specifically, we're looking into individual Syrian

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<v Speaker 1>citizens that we're saying things about the regime that we're untoward,

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<v Speaker 1>that we're connected to influencers that were helping to organize protests,

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<v Speaker 1>and suddenly, you know, a bunch of those people were

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<v Speaker 1>rounded up and some were never heard from again. And

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<v Speaker 1>as I mentioned in terms of China, you don't have

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<v Speaker 1>to do that with many people before people start ratting

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<v Speaker 1>out their friends, being scared of talking to anyone not

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<v Speaker 1>going out. The system worked. We may feel comfortably far

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<v Speaker 1>from the battlefields of Syria here in the US, and

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<v Speaker 1>from the overwhelming number of surveillance cameras on practically every

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<v Speaker 1>street corner in China. But the more effective these technologies are,

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<v Speaker 1>the more likely they are to be adopted by others. Now,

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<v Speaker 1>in other countries, you're going to have a confluence of

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<v Speaker 1>both them liking the model and the Chinese directly exporting it.

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<v Speaker 1>Who were those countries, Well, look at One Belt, One Road,

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<v Speaker 1>the you know, trillion plus dollar investments that the Chinese

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<v Speaker 1>are making all over the world in Pakistan, Southeast Asia,

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<v Speaker 1>you know, Cambodia, laughs, a whole bunch of countries. And

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<v Speaker 1>when you look at those countries and you see that

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<v Speaker 1>the Chinese are providing the money in this conditionality in return,

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<v Speaker 1>some that conditionalities use Chinese standards for technology that's in

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<v Speaker 1>many of these contracts. And with the spread of Chinese

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<v Speaker 1>standards of technology comes the spread of Chinese style surveillance,

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<v Speaker 1>which could ultimately make the whole world trend more authoritarian.

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<v Speaker 1>So as that happens, these governments are going to say,

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<v Speaker 1>ah ha, we get the money from China, we use

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<v Speaker 1>their technology. We're stuck with their system, but we can

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<v Speaker 1>use it to ensure that our people stay in power. Again,

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<v Speaker 1>it's easy to let all of this feel comfortably far away,

0:13:41.200 --> 0:13:44.680
<v Speaker 1>but remember the Internet doesn't have borders, so you don't

0:13:44.679 --> 0:13:46.800
<v Speaker 1>have to be in China for the Chinese state to

0:13:46.840 --> 0:13:49.280
<v Speaker 1>access your data. Yeah. I don't know how many people

0:13:49.320 --> 0:13:52.440
<v Speaker 1>know this, but Grinder is actually owned by a Chinese company,

0:13:52.640 --> 0:13:55.280
<v Speaker 1>Grinder the dating MP. Yes, and actually there have been

0:13:55.400 --> 0:13:58.600
<v Speaker 1>articles about the fact that the US government is trying

0:13:58.640 --> 0:14:01.320
<v Speaker 1>to force China's hands so that we can buy it

0:14:01.400 --> 0:14:05.720
<v Speaker 1>back because there's so much user data that this company

0:14:05.760 --> 0:14:08.760
<v Speaker 1>now owns. Well, that Grinder use a data is basically

0:14:08.760 --> 0:14:11.280
<v Speaker 1>being seen by the US government as a strategic asset.

0:14:11.679 --> 0:14:13.480
<v Speaker 1>It is a strategic asset. I mean, if you think

0:14:13.480 --> 0:14:16.920
<v Speaker 1>about it, if somebody is on a military base or

0:14:16.960 --> 0:14:21.600
<v Speaker 1>in a barrack and trying to connect with someone on Grinder,

0:14:21.800 --> 0:14:24.400
<v Speaker 1>they're turning their location services data on because they want

0:14:24.400 --> 0:14:26.760
<v Speaker 1>to see people in their area and if they're turning

0:14:26.800 --> 0:14:31.080
<v Speaker 1>that location services data on, they're basically making themselves vulnerable

0:14:31.560 --> 0:14:34.320
<v Speaker 1>to the company that owns the data, because it's basically

0:14:34.320 --> 0:14:36.720
<v Speaker 1>saying here I am, here, I am and not only that,

0:14:36.840 --> 0:14:39.760
<v Speaker 1>here I am, here, I am, and I'm gay, and

0:14:39.920 --> 0:14:43.400
<v Speaker 1>that can lead to some possibilities of blackmail. Even today, exactly,

0:14:43.720 --> 0:14:45.920
<v Speaker 1>there was an article in the interface that was written

0:14:45.920 --> 0:14:49.400
<v Speaker 1>by this guy, Casey Newton, chat history, photos, videos, real

0:14:49.400 --> 0:14:52.760
<v Speaker 1>time location. All of that is connected to a user's

0:14:52.760 --> 0:14:56.040
<v Speaker 1>email address, and that means that the user's identity can

0:14:56.040 --> 0:14:59.880
<v Speaker 1>be very easily learned. That's pretty scary. And even if

0:15:00.040 --> 0:15:03.840
<v Speaker 1>don't use Grinder, you might use Flash of Clans or Fortnite,

0:15:03.840 --> 0:15:07.200
<v Speaker 1>which are very popular gaming apps also owned by the Chinese.

0:15:07.200 --> 0:15:09.720
<v Speaker 1>Now we keep saying the Chinese. To be clear, these

0:15:09.760 --> 0:15:13.080
<v Speaker 1>apps aren't owned by the Chinese government, that owned by

0:15:13.200 --> 0:15:16.320
<v Speaker 1>Chinese companies. What the actions of the US government imply

0:15:16.520 --> 0:15:19.440
<v Speaker 1>by trying to force this company to sell Grinder back

0:15:20.200 --> 0:15:23.000
<v Speaker 1>is that they don't believe in the distinction. And do

0:15:23.040 --> 0:15:24.960
<v Speaker 1>you think the US government would really be working that

0:15:25.080 --> 0:15:29.320
<v Speaker 1>hard to get back a gay dating app if they

0:15:29.360 --> 0:15:32.240
<v Speaker 1>didn't think that there was not a murky separation between

0:15:32.240 --> 0:15:36.720
<v Speaker 1>the government and companies in China. Right, So every time

0:15:36.760 --> 0:15:39.000
<v Speaker 1>we give data away, I mean, we're aware that it

0:15:39.040 --> 0:15:41.560
<v Speaker 1>opens us up to targeted ads on Facebook. We talked

0:15:41.560 --> 0:15:45.400
<v Speaker 1>about those with Gillian. We're not aware that that data

0:15:45.440 --> 0:15:50.040
<v Speaker 1>may end up in the hands of potentially hostile foreign government.

0:15:50.160 --> 0:15:55.120
<v Speaker 1>So once again, Sleepwalking. We've been talking about how foreign

0:15:55.160 --> 0:15:58.120
<v Speaker 1>governments are using AI, but when we come back, we'll

0:15:58.120 --> 0:16:00.080
<v Speaker 1>look at how the police and courts are using it

0:16:00.280 --> 0:16:09.320
<v Speaker 1>at home in America. Kara, it's easy to look at

0:16:09.440 --> 0:16:12.200
<v Speaker 1>China and to see the big bad wolf. They're using

0:16:12.240 --> 0:16:16.440
<v Speaker 1>surveillance technology for the wholesale suppression of an ethnic minority.

0:16:16.280 --> 0:16:18.880
<v Speaker 1>They have a social credit score that con emit access

0:16:18.880 --> 0:16:23.160
<v Speaker 1>to opportunities and even travel, But algorithms also determined outcomes

0:16:23.360 --> 0:16:26.240
<v Speaker 1>here in the US exactly if you think about it,

0:16:26.360 --> 0:16:29.160
<v Speaker 1>we do use social ratings. If you use Uber and

0:16:29.240 --> 0:16:31.280
<v Speaker 1>have a rating lower than four out of five stars,

0:16:31.360 --> 0:16:33.560
<v Speaker 1>you can't get a car right and you can't get

0:16:33.560 --> 0:16:36.080
<v Speaker 1>alone if you have a low FICO credit score. And

0:16:36.400 --> 0:16:40.320
<v Speaker 1>our criminal justice system also uses algorithmic ratings to decide

0:16:40.360 --> 0:16:45.400
<v Speaker 1>people's fate. When I got arrested at sixteen, I was

0:16:45.440 --> 0:16:48.120
<v Speaker 1>in high school and John F. Kennedy High School. That's

0:16:48.160 --> 0:16:51.480
<v Speaker 1>Glenn Rodriguez. When he was a baby, Glenn's mother was murdered,

0:16:51.520 --> 0:16:54.920
<v Speaker 1>and when he was three, his father committed suicide. From

0:16:54.960 --> 0:16:57.920
<v Speaker 1>then on, Glenn was raised by his grandmother and he

0:16:58.000 --> 0:17:02.200
<v Speaker 1>searched for belonging. The kid who wants to feel accepted,

0:17:02.440 --> 0:17:05.040
<v Speaker 1>wants to feel a part of something right. Whatever the

0:17:05.080 --> 0:17:08.080
<v Speaker 1>group was up for, I was down. And if they

0:17:08.119 --> 0:17:11.199
<v Speaker 1>were going one step forward, I would take two. We

0:17:11.240 --> 0:17:14.760
<v Speaker 1>pretty much planned a robbery at a car dealership in

0:17:14.840 --> 0:17:20.240
<v Speaker 1>Queen's and we entered the premises. We took three cars.

0:17:20.720 --> 0:17:24.240
<v Speaker 1>There was a twenty five year old men in there,

0:17:24.920 --> 0:17:27.520
<v Speaker 1>and he initially pulled a gun, and so I had

0:17:27.520 --> 0:17:32.000
<v Speaker 1>a gun and I shot him. Glenn was arrested and

0:17:32.080 --> 0:17:35.679
<v Speaker 1>convicted of second degree murder. He was sentenced to twenty

0:17:35.720 --> 0:17:38.560
<v Speaker 1>five years in jail, and he was still a high schooler.

0:17:38.760 --> 0:17:41.720
<v Speaker 1>You feel powerless, you feel hopeless, especially at that age.

0:17:41.880 --> 0:17:44.159
<v Speaker 1>So the way I saw it was, this is my life.

0:17:44.400 --> 0:17:46.320
<v Speaker 1>You know, I'm probably gonna die in jail, and so

0:17:46.440 --> 0:17:48.080
<v Speaker 1>whatever it is that I have to do, I need

0:17:48.160 --> 0:17:50.199
<v Speaker 1>to survive. One of the things that I learned very

0:17:50.280 --> 0:17:53.399
<v Speaker 1>quickly is that in prison, one of the only things

0:17:53.440 --> 0:17:55.919
<v Speaker 1>that is respected is violence, and so in order for

0:17:56.000 --> 0:17:58.479
<v Speaker 1>you to survive in there, you have to be violent,

0:17:58.920 --> 0:18:02.200
<v Speaker 1>because otherwise you'd be come pray. In time, Glenn established

0:18:02.200 --> 0:18:06.320
<v Speaker 1>his reputation and started to feel safer. With that security

0:18:06.359 --> 0:18:09.879
<v Speaker 1>and getting older, his thinking began to change. And it

0:18:09.960 --> 0:18:12.560
<v Speaker 1>wasn't until later, to like my mid twhenies, when I

0:18:12.640 --> 0:18:15.119
<v Speaker 1>started saying, you know what, I need to reverse this

0:18:15.240 --> 0:18:17.879
<v Speaker 1>trend if I want to have any chance at parole.

0:18:18.240 --> 0:18:22.120
<v Speaker 1>Glenn had to reverse thirteen years of behavior to survive

0:18:22.119 --> 0:18:24.720
<v Speaker 1>in prison. He had learned to behave one way, but

0:18:24.800 --> 0:18:27.760
<v Speaker 1>to get out he had to behave another. I reveiled

0:18:27.800 --> 0:18:30.399
<v Speaker 1>myself of the Puppies behind Bars program, so I was

0:18:30.520 --> 0:18:33.399
<v Speaker 1>training service dogs for wounded war veterans for five years.

0:18:33.720 --> 0:18:36.959
<v Speaker 1>That was an amazing experience, right because throughout incarceration it's

0:18:36.960 --> 0:18:40.840
<v Speaker 1>almost like you build a wall around yourself with the dogs.

0:18:41.280 --> 0:18:43.440
<v Speaker 1>You can't fake it with the dogs. If you're trying

0:18:43.480 --> 0:18:45.800
<v Speaker 1>to like teach them a command, sometimes you may have

0:18:45.880 --> 0:18:48.679
<v Speaker 1>to be silly, which guess what, in prison, being silly

0:18:48.720 --> 0:18:51.240
<v Speaker 1>is not acceptable. That's perceived as a weakness. But with

0:18:51.320 --> 0:18:53.960
<v Speaker 1>that program, often times you had the resort of being

0:18:53.960 --> 0:18:57.160
<v Speaker 1>silly and throwing yourself on the floor and giggling loud

0:18:57.160 --> 0:18:59.159
<v Speaker 1>and making all kinds of crazy sounds to try to

0:18:59.160 --> 0:19:01.840
<v Speaker 1>get the dogs attend gin Right. To a very large extent,

0:19:01.920 --> 0:19:04.160
<v Speaker 1>I believe that that program kind of helped me regain

0:19:04.200 --> 0:19:08.840
<v Speaker 1>my humanity as well as helping Glenn. Personally. Taking part

0:19:08.880 --> 0:19:11.400
<v Speaker 1>in prison programs for the public good is looked upon

0:19:11.480 --> 0:19:14.520
<v Speaker 1>favorably by parole boards. So everything I did, I wanted

0:19:14.560 --> 0:19:17.239
<v Speaker 1>to document the kind of showcases what I've done, this

0:19:17.320 --> 0:19:20.280
<v Speaker 1>is who I am today. As part of the process,

0:19:20.320 --> 0:19:25.080
<v Speaker 1>there's also the Campus Risk Assessment COMPUS stands for Correctional

0:19:25.160 --> 0:19:30.720
<v Speaker 1>Offender Management Profiling for Alternative Sanctions. It's an algorithm that

0:19:30.880 --> 0:19:33.680
<v Speaker 1>claims to be able to predict how likely a defendant

0:19:33.760 --> 0:19:36.359
<v Speaker 1>is to commit another crime based on a list of

0:19:36.359 --> 0:19:39.760
<v Speaker 1>a hundred and thirty seven questions. Since being developed in

0:19:41.200 --> 0:19:43.800
<v Speaker 1>its estimated COMPASS has been used to assess more than

0:19:43.880 --> 0:19:48.639
<v Speaker 1>one million defendants, including Glenn. You meet with this person

0:19:48.800 --> 0:19:51.640
<v Speaker 1>a few months before your schedule parole board day, and

0:19:52.320 --> 0:19:54.640
<v Speaker 1>they asked you a series of questions. And so when

0:19:54.640 --> 0:19:58.680
<v Speaker 1>he got to the disciplinary history section of the Campus

0:19:58.760 --> 0:20:01.960
<v Speaker 1>Risk Assessment, there was a list of offenses right for

0:20:02.040 --> 0:20:03.920
<v Speaker 1>him to check off. Yes or no for the past

0:20:03.920 --> 0:20:06.280
<v Speaker 1>twenty four months, and it was all known. And anyone

0:20:06.359 --> 0:20:08.880
<v Speaker 1>who has any experience with prison would tell you that

0:20:08.880 --> 0:20:12.320
<v Speaker 1>that is almost impossible to do, right, because misbehavior reports

0:20:12.560 --> 0:20:15.800
<v Speaker 1>can be for something as simple as having too many pillows,

0:20:15.840 --> 0:20:18.440
<v Speaker 1>something as simple as your parents hanging off your bot,

0:20:18.840 --> 0:20:21.720
<v Speaker 1>your sneaker is untied. It takes a lot of energy

0:20:21.760 --> 0:20:24.760
<v Speaker 1>to dodge a misbehavior report during the course of a year,

0:20:25.119 --> 0:20:27.959
<v Speaker 1>let alone ten, and in my case, it had been eleven.

0:20:29.520 --> 0:20:31.720
<v Speaker 1>And then I heard him read the question and he says,

0:20:31.760 --> 0:20:36.159
<v Speaker 1>does this person appear to have notable disciplinary issues? And

0:20:36.200 --> 0:20:39.359
<v Speaker 1>he says yes, And I was like, hold up, wait

0:20:39.400 --> 0:20:41.639
<v Speaker 1>a second, did I just hear you right? Because I

0:20:41.760 --> 0:20:44.800
<v Speaker 1>just heard you say that I have notable disciplinary issues?

0:20:45.040 --> 0:20:47.199
<v Speaker 1>Do you do realize that I haven't had a misbehavior

0:20:47.200 --> 0:20:50.480
<v Speaker 1>report in over a decade? Right? And his answer was well,

0:20:50.520 --> 0:20:53.920
<v Speaker 1>I was told that if there's any instance of misbehavior

0:20:54.000 --> 0:20:56.359
<v Speaker 1>at any point, I have to check yes for this answer.

0:20:56.520 --> 0:20:58.760
<v Speaker 1>So I was like, okay, So at that point there

0:20:58.800 --> 0:21:04.119
<v Speaker 1>was nothing I could do. I'm appearing before the parole

0:21:04.119 --> 0:21:07.479
<v Speaker 1>board panel. I presented to them a portfolio that was

0:21:07.520 --> 0:21:12.800
<v Speaker 1>approximately one hundred pages, had letters to support Now the

0:21:12.840 --> 0:21:16.800
<v Speaker 1>Compass is saying that I'm a disciplinary issue and so

0:21:16.840 --> 0:21:19.399
<v Speaker 1>I shouldn't be released. But I was denied because of

0:21:19.400 --> 0:21:21.800
<v Speaker 1>the fact that I scored high on Compass. They played

0:21:21.800 --> 0:21:24.760
<v Speaker 1>a safe and kept me in. It may have been

0:21:24.840 --> 0:21:27.640
<v Speaker 1>less than five minutes the hearing. I waited twenty six

0:21:27.720 --> 0:21:30.120
<v Speaker 1>years to sit in front of a panel of three

0:21:30.119 --> 0:21:33.000
<v Speaker 1>people for less than five minutes. No one wants to

0:21:33.000 --> 0:21:35.399
<v Speaker 1>be the one to go against Compass, and next you know,

0:21:35.480 --> 0:21:37.720
<v Speaker 1>something goes wrong and now your job is on the

0:21:37.760 --> 0:21:41.840
<v Speaker 1>line because you departed from Compass, which is taken as

0:21:42.080 --> 0:21:48.320
<v Speaker 1>factual and scientific. In time, Glenn went before another parole

0:21:48.359 --> 0:21:52.120
<v Speaker 1>board and this time they freed him against the recommendation

0:21:52.160 --> 0:21:54.439
<v Speaker 1>of Compass. And now Glenn has built a life for

0:21:54.520 --> 0:21:58.119
<v Speaker 1>himself working with teenagers at risk, giving conceration at the

0:21:58.160 --> 0:22:02.280
<v Speaker 1>Center for Community Alternatives. But he's still being affected by

0:22:02.280 --> 0:22:06.760
<v Speaker 1>the algorithm. Compass does not end upon your release because

0:22:06.800 --> 0:22:10.840
<v Speaker 1>the same Compass risk assessment that's considered for your release.

0:22:10.960 --> 0:22:13.760
<v Speaker 1>The term is how you're going to be supervised upon release.

0:22:14.000 --> 0:22:16.280
<v Speaker 1>There's a number of restrictions that I have. I have

0:22:16.359 --> 0:22:21.800
<v Speaker 1>a curve you. I'm still haunted by a Compass. Despite

0:22:21.800 --> 0:22:26.480
<v Speaker 1>turning his life around Compass is still limiting Glen's freedom,

0:22:26.520 --> 0:22:29.680
<v Speaker 1>and that should haunt all of us. According to propublic A,

0:22:29.760 --> 0:22:34.320
<v Speaker 1>Compass inaccurately labels black defendants as likely to reoffend twice

0:22:34.359 --> 0:22:39.480
<v Speaker 1>as often as white defendants. Algorithmic discrimination isn't government policy

0:22:39.520 --> 0:22:41.800
<v Speaker 1>here in the US like it is against the weak

0:22:41.840 --> 0:22:46.320
<v Speaker 1>as in China, but it still exists. There's this issue

0:22:46.680 --> 0:22:50.639
<v Speaker 1>where you can have computer scientists building a more accurate algorithm,

0:22:50.960 --> 0:22:55.159
<v Speaker 1>but on account of dubious input factors like gender or

0:22:55.280 --> 0:22:59.600
<v Speaker 1>race or religion, you've created something that's unconstitutional. That's Jason

0:22:59.640 --> 0:23:02.600
<v Speaker 1>tchet It. He introduced us to Glenn, and he's the

0:23:02.600 --> 0:23:05.840
<v Speaker 1>founder of Justice Codes and a legal affairs writer for

0:23:05.880 --> 0:23:10.359
<v Speaker 1>the American Bar Association Journal. There's this predisposition to believe

0:23:10.480 --> 0:23:15.840
<v Speaker 1>that math doesn't carry all of the biases that humans do.

0:23:16.000 --> 0:23:18.840
<v Speaker 1>It's an objective science. I think we need to dispel

0:23:19.080 --> 0:23:23.159
<v Speaker 1>that idea. Jason is describing the very human habit of

0:23:23.200 --> 0:23:28.560
<v Speaker 1>taking computer output as gospel truth. It's called automation bias,

0:23:28.680 --> 0:23:31.720
<v Speaker 1>and it's why parole boards often don't feel comfortable over

0:23:31.760 --> 0:23:35.320
<v Speaker 1>writing algorithms like Compass, and why some people follow their

0:23:35.320 --> 0:23:38.200
<v Speaker 1>GPS even when it has them driving into the ocean.

0:23:38.960 --> 0:23:42.800
<v Speaker 1>This idea that somehow, because math is an underlying force

0:23:43.160 --> 0:23:46.840
<v Speaker 1>to these tools, makes them more objective or beyond certain

0:23:46.880 --> 0:23:51.160
<v Speaker 1>types of scrutiny is wrong. Computer algorithms are being used

0:23:51.200 --> 0:23:54.960
<v Speaker 1>to determine human fate today, whether it's Compassed in the

0:23:55.040 --> 0:23:58.479
<v Speaker 1>US or the social credit score in China, So we

0:23:58.560 --> 0:24:01.679
<v Speaker 1>have to scrutinize them and understand that their output is

0:24:01.720 --> 0:24:06.399
<v Speaker 1>not necessarily neutral. The foundational principle of AI is using

0:24:06.480 --> 0:24:10.080
<v Speaker 1>historical data to predict what will happen next, and that

0:24:10.160 --> 0:24:13.879
<v Speaker 1>in itself is a challenge to our culture because the

0:24:13.920 --> 0:24:16.600
<v Speaker 1>American dream is built on the idea that we have

0:24:16.640 --> 0:24:19.480
<v Speaker 1>a capacity to change, that we can move from rags

0:24:19.480 --> 0:24:22.920
<v Speaker 1>to riches, from the penitentiary to the boardroom. And it's

0:24:22.960 --> 0:24:26.440
<v Speaker 1>not just an American narrative. It's Scrooges change of heart

0:24:26.480 --> 0:24:29.560
<v Speaker 1>delivering the turkey to the Cratchets on Christmas Day. It's

0:24:29.560 --> 0:24:33.159
<v Speaker 1>Saint Paul's conversion on the Road to Damascus. It's at

0:24:33.200 --> 0:24:38.080
<v Speaker 1>the very heart of Western culture. But algorithms like Compass

0:24:38.200 --> 0:24:41.400
<v Speaker 1>aren't built to see the potential in people. They're designed

0:24:41.400 --> 0:24:46.200
<v Speaker 1>to calculate risk based on past actions, and Compass isn't

0:24:46.240 --> 0:24:48.800
<v Speaker 1>the only example of algorithms being used in our criminal

0:24:48.840 --> 0:24:52.680
<v Speaker 1>justice system. When we come back, we go right inside

0:24:52.720 --> 0:24:57.040
<v Speaker 1>the NYPD to understand how new technology is powering policing.

0:25:03.040 --> 0:25:05.560
<v Speaker 1>It's a freezing cold day in New York City when

0:25:05.600 --> 0:25:08.800
<v Speaker 1>we arrive at the NYPD headquarters, and before we even

0:25:08.800 --> 0:25:11.280
<v Speaker 1>get into the main building, Julie and I have to

0:25:11.320 --> 0:25:15.399
<v Speaker 1>pass through airport style security and naturally give up some data,

0:25:15.920 --> 0:25:21.560
<v Speaker 1>including submitting a selfie in a kiosk. Yes, right now,

0:25:21.560 --> 0:25:26.400
<v Speaker 1>our society is holding big conversations about body cameras, police accountability,

0:25:26.480 --> 0:25:30.199
<v Speaker 1>and government monitoring, so we had to ask how does

0:25:30.240 --> 0:25:33.320
<v Speaker 1>one of the most recognizable police forces in the world

0:25:33.320 --> 0:25:37.879
<v Speaker 1>handle our data. My name is Benjamin Singleton, director of

0:25:37.920 --> 0:25:41.280
<v Speaker 1>Analytics at the NYPD, so I probably spend half my

0:25:41.359 --> 0:25:44.240
<v Speaker 1>day writing code and half my day and meetings. The

0:25:44.280 --> 0:25:47.360
<v Speaker 1>police Department collects records as a regular course of our business.

0:25:47.720 --> 0:25:50.920
<v Speaker 1>We respond to nine when one calls, We take crime reports,

0:25:51.240 --> 0:25:54.720
<v Speaker 1>we make arrests, We issue moving summons. Is when you

0:25:54.960 --> 0:25:57.399
<v Speaker 1>you know, speed in the city. These are examples of

0:25:57.440 --> 0:25:59.600
<v Speaker 1>the kind of data that we collect. You know, I

0:25:59.640 --> 0:26:02.960
<v Speaker 1>think there's probably some sentiment that there are back doors

0:26:03.000 --> 0:26:06.679
<v Speaker 1>into various systems UM, but the NYPD is governed by

0:26:06.760 --> 0:26:11.440
<v Speaker 1>the same legal processes as any other UM law enforcement agency.

0:26:11.720 --> 0:26:14.200
<v Speaker 1>If we want data from an outside company or vendor,

0:26:14.480 --> 0:26:16.560
<v Speaker 1>we get a search warrant from a judge or through

0:26:16.600 --> 0:26:19.000
<v Speaker 1>a d a's office issue with subpoena, and that's how

0:26:19.000 --> 0:26:22.440
<v Speaker 1>we collect our data. Their cameras throughout the subways, white

0:26:22.480 --> 0:26:25.840
<v Speaker 1>corset turnstyles, and easy pause readers on the roads and

0:26:26.000 --> 0:26:29.399
<v Speaker 1>more in New York. So what might the NYPD know

0:26:29.560 --> 0:26:32.080
<v Speaker 1>about me? If you haven't sort of stood in front

0:26:32.119 --> 0:26:35.000
<v Speaker 1>of a police officer who hasn't taken a report by hand,

0:26:35.359 --> 0:26:38.760
<v Speaker 1>we probably don't have records on you. That being said,

0:26:38.800 --> 0:26:42.600
<v Speaker 1>we do collect data through sensors like license plate readers UM,

0:26:42.760 --> 0:26:44.720
<v Speaker 1>and we do have data sharing agreements with some other

0:26:44.800 --> 0:26:49.800
<v Speaker 1>criminal justice agencies like corrections, like the courts, and so

0:26:49.920 --> 0:26:53.040
<v Speaker 1>there's obviously opportunities for that kind of data to enter

0:26:53.119 --> 0:26:56.000
<v Speaker 1>our realm. But one thing that's built into every single

0:26:56.160 --> 0:27:00.159
<v Speaker 1>NYPD application is an auditing track. So anytime you look

0:27:00.200 --> 0:27:03.159
<v Speaker 1>at any piece of information, no matter what system you're in,

0:27:03.560 --> 0:27:07.000
<v Speaker 1>that's being audited. And so we have a very large

0:27:07.080 --> 0:27:10.160
<v Speaker 1>internal affairs bureau, and people have gotten in trouble before

0:27:10.240 --> 0:27:13.119
<v Speaker 1>for misuse of computer systems, and so I think that

0:27:13.200 --> 0:27:16.679
<v Speaker 1>that's an important check that's reassuring to hear. But why

0:27:16.760 --> 0:27:19.360
<v Speaker 1>collects so much data in the first place. I think

0:27:19.400 --> 0:27:22.760
<v Speaker 1>that the next frontier of machine learning in policing is

0:27:23.400 --> 0:27:27.640
<v Speaker 1>bringing decisions and information into the hands of cops who

0:27:27.720 --> 0:27:31.399
<v Speaker 1>need to make decisions quickly. We recently rolled out tens

0:27:31.440 --> 0:27:33.919
<v Speaker 1>of thousands of mobile phones to all of our cops,

0:27:34.400 --> 0:27:37.040
<v Speaker 1>and putting a computer in their hands has really changed

0:27:37.080 --> 0:27:39.639
<v Speaker 1>the way that the police. When you have more information,

0:27:39.880 --> 0:27:43.320
<v Speaker 1>you can make better decisions. So we could be responding

0:27:43.400 --> 0:27:46.240
<v Speaker 1>to a job at a specific location in a building,

0:27:46.560 --> 0:27:49.040
<v Speaker 1>and we know what's happened at that building before we

0:27:49.160 --> 0:27:52.360
<v Speaker 1>responded to nine. When one calls their last week in apartment,

0:27:52.600 --> 0:27:55.640
<v Speaker 1>you know for see and in that interaction it led

0:27:55.680 --> 0:27:58.399
<v Speaker 1>to some sort of altercation or we found out that

0:27:58.480 --> 0:28:01.600
<v Speaker 1>that person that we interacted have had some sort of issue. Well,

0:28:01.640 --> 0:28:03.520
<v Speaker 1>the cop who's working today might not be the same

0:28:03.600 --> 0:28:05.639
<v Speaker 1>cop who is working a week ago. And so how

0:28:05.680 --> 0:28:08.480
<v Speaker 1>do I convey that information? Maybe in a phone as

0:28:08.520 --> 0:28:10.720
<v Speaker 1>a as a pop up, as a notification that tells

0:28:10.800 --> 0:28:14.600
<v Speaker 1>you take extra time, take caution. Um, this sort of

0:28:14.800 --> 0:28:18.840
<v Speaker 1>incident happened. Using data to give office as context is

0:28:18.880 --> 0:28:21.240
<v Speaker 1>hard to argue against if it can lead to safer

0:28:21.359 --> 0:28:25.240
<v Speaker 1>interactions for everyone. But of course, what many people find

0:28:25.280 --> 0:28:28.960
<v Speaker 1>more concerning is ambient surveillance. Surveillance that happens all the time,

0:28:29.200 --> 0:28:32.639
<v Speaker 1>and despite much pressure, the NYPD has yet to release

0:28:32.720 --> 0:28:36.560
<v Speaker 1>an explicit facial recognition policy. And where do the effort

0:28:36.600 --> 0:28:41.280
<v Speaker 1>stand on facial recognition technology. Our Facial Identification Section, which

0:28:41.320 --> 0:28:44.960
<v Speaker 1>sits under the Detective Bureau, is a group of trained

0:28:45.240 --> 0:28:49.880
<v Speaker 1>detectives investigators. They use a tool and algorithm that compares

0:28:50.000 --> 0:28:53.160
<v Speaker 1>faces that we might get from a surveillance photo, and

0:28:53.600 --> 0:28:57.680
<v Speaker 1>they run that algorithm, get potential matches and then conduct

0:28:57.720 --> 0:29:00.680
<v Speaker 1>an investigation. But it's not as simple as you know,

0:29:00.840 --> 0:29:04.440
<v Speaker 1>a facial recognition hit occurs and that's suddenly licensed to

0:29:04.800 --> 0:29:08.320
<v Speaker 1>make an arrest. It doesn't generate probable cause for us.

0:29:08.640 --> 0:29:11.719
<v Speaker 1>We still require much more evidence in order to make

0:29:11.760 --> 0:29:15.000
<v Speaker 1>a determination that that hit is truly viable and something

0:29:15.080 --> 0:29:18.120
<v Speaker 1>we can act on. But there are cases where that

0:29:18.240 --> 0:29:21.680
<v Speaker 1>technology has been used as part of an arrest or prosecution.

0:29:25.600 --> 0:29:28.400
<v Speaker 1>In the absence of an explicit policy, Ben wasn't able

0:29:28.480 --> 0:29:30.960
<v Speaker 1>to answer the question live in the room, but we

0:29:31.040 --> 0:29:33.080
<v Speaker 1>did get a statement from the m I p D.

0:29:34.240 --> 0:29:37.560
<v Speaker 1>The NYPD has moved deliberately and responsibly in the use

0:29:37.640 --> 0:29:41.640
<v Speaker 1>of facial recognition software. There is no NYPD case where

0:29:41.680 --> 0:29:44.280
<v Speaker 1>an arrest or prosecution was brought on the basis of

0:29:44.360 --> 0:29:47.480
<v Speaker 1>facial recognition. The NYPD uses it on a case by

0:29:47.560 --> 0:29:50.719
<v Speaker 1>case basis, and the case must always be supported by

0:29:50.880 --> 0:29:55.000
<v Speaker 1>further investigation before any arrest is made. The NYPD has

0:29:55.040 --> 0:29:58.720
<v Speaker 1>absolutely no interest in wholesale surveillance, which would be an

0:29:58.840 --> 0:30:04.160
<v Speaker 1>enormous and entirely pointless task. We have little choice but

0:30:04.240 --> 0:30:07.840
<v Speaker 1>to trust. But that said, Bend is beat convincingly about

0:30:07.920 --> 0:30:11.440
<v Speaker 1>how the NYPD actually uses technology to police themselves. There's

0:30:11.440 --> 0:30:15.440
<v Speaker 1>also statistical tools around fairness that can actually measure whether

0:30:15.520 --> 0:30:19.120
<v Speaker 1>an algorithm is fair, whether it's causing bias, etcetera. And

0:30:19.240 --> 0:30:23.600
<v Speaker 1>so we're very interested in utilizing these metrics and we

0:30:23.760 --> 0:30:26.280
<v Speaker 1>fully embrace them. We we want to get better, and

0:30:26.440 --> 0:30:31.240
<v Speaker 1>we're taking a conservative approach because we know how high

0:30:31.320 --> 0:30:35.360
<v Speaker 1>stakes this is. The stakes are high and the path

0:30:35.520 --> 0:30:38.880
<v Speaker 1>is murky. I didn't know what to expect at the NYPD.

0:30:39.400 --> 0:30:42.720
<v Speaker 1>Would they optimize purely for reducing crime or they take

0:30:42.760 --> 0:30:46.600
<v Speaker 1>a broader view of justice. Personally, I found ben reassuring,

0:30:47.240 --> 0:30:51.200
<v Speaker 1>but the potential for abuse remains. So how do we

0:30:51.440 --> 0:30:55.600
<v Speaker 1>here in America god against that abuse. Well, let's return

0:30:55.640 --> 0:30:59.520
<v Speaker 1>to Mary Haskett who found a blink identity with Alex Kilpatrick.

0:31:00.000 --> 0:31:02.720
<v Speaker 1>Anytime you're using face wreck without consent, it's going to

0:31:02.840 --> 0:31:06.720
<v Speaker 1>get abused because why wouldn't it. And and here's the problem.

0:31:06.960 --> 0:31:09.720
<v Speaker 1>I don't think it's appropriate to ask a police department

0:31:09.760 --> 0:31:13.280
<v Speaker 1>to just voluntarily not use a tool that's awesome for them.

0:31:13.840 --> 0:31:15.560
<v Speaker 1>I mean you need to have a different level. You

0:31:15.640 --> 0:31:19.880
<v Speaker 1>need to have your governor, federal states, some government governing

0:31:19.960 --> 0:31:23.280
<v Speaker 1>body needs to be saying sorry, this is not appropriate.

0:31:23.440 --> 0:31:26.600
<v Speaker 1>This is violating people's rights. The difference between what is

0:31:26.640 --> 0:31:29.640
<v Speaker 1>happening in China and in the US is not technological,

0:31:30.200 --> 0:31:34.440
<v Speaker 1>it's cultural and political. Edward Snowden had a phrase for this,

0:31:35.200 --> 0:31:39.880
<v Speaker 1>turnkey tyranny, meaning that the technical infrastructure of mass surveillance

0:31:39.920 --> 0:31:43.000
<v Speaker 1>already exists, and that we're only protected by our values

0:31:43.320 --> 0:31:46.440
<v Speaker 1>and our laws. And thinking of China, I think there

0:31:46.440 --> 0:31:49.000
<v Speaker 1>are some profoundly creepy things that we are right on

0:31:49.080 --> 0:31:52.840
<v Speaker 1>the edge of starting to see. There's cameras everywhere. If

0:31:52.920 --> 0:31:55.760
<v Speaker 1>you add face recognition. It's not just oh, they saw

0:31:55.880 --> 0:31:59.120
<v Speaker 1>my face, they saw that I went to Starbucks. It's

0:31:59.680 --> 0:32:02.520
<v Speaker 1>where you were every day, every time, all of her

0:32:02.600 --> 0:32:05.400
<v Speaker 1>history and all that gets saved. It's my pattern of

0:32:05.440 --> 0:32:09.240
<v Speaker 1>where I go when I'm outdoors forever. Five years ago,

0:32:09.320 --> 0:32:12.400
<v Speaker 1>I would have said that could never happen here. Part

0:32:12.400 --> 0:32:15.080
<v Speaker 1>of the reason Mary has so much about privacy is

0:32:15.120 --> 0:32:18.800
<v Speaker 1>that she knows how quickly facial recognition is spreading. In fact,

0:32:18.840 --> 0:32:22.320
<v Speaker 1>in twenty eight Blink Identity raise money from Live Nation

0:32:22.760 --> 0:32:26.840
<v Speaker 1>ticket Master's parent company to allow future concert goers to

0:32:27.000 --> 0:32:30.240
<v Speaker 1>use their faces instead of their tickets. We wanted to

0:32:30.440 --> 0:32:32.080
<v Speaker 1>be a case study of how to do this in

0:32:32.120 --> 0:32:36.080
<v Speaker 1>a way that preserves individual privacy and respects the individual,

0:32:36.200 --> 0:32:38.400
<v Speaker 1>and maybe that will help set a precedence and maybe

0:32:38.440 --> 0:32:41.640
<v Speaker 1>some of these other objectionable use cases just won't be

0:32:41.680 --> 0:32:45.440
<v Speaker 1>able to take off. Facial recognition and other AI technologies

0:32:45.520 --> 0:32:48.160
<v Speaker 1>are being developed all over the world, and we can't

0:32:48.240 --> 0:32:51.560
<v Speaker 1>trust everyone to be as conscientious as Mary and Alex

0:32:52.080 --> 0:32:54.640
<v Speaker 1>In America, the liberty we take for granted is hard

0:32:54.720 --> 0:32:58.320
<v Speaker 1>one and fragile, and cases like Glens show what can

0:32:58.400 --> 0:33:02.840
<v Speaker 1>happen when algorithms are blind trusted to determine outcomes. So

0:33:03.040 --> 0:33:06.480
<v Speaker 1>much hangs in the balance right now about our technological future,

0:33:07.080 --> 0:33:10.000
<v Speaker 1>and the decisions we take will affect our lives profoundly

0:33:10.320 --> 0:33:13.320
<v Speaker 1>and echo through the lives of our children too. I

0:33:13.440 --> 0:33:16.560
<v Speaker 1>mentioned Charles Dickens Christmas Carol earlier to me. One of

0:33:16.600 --> 0:33:19.320
<v Speaker 1>the most powerful scenes in the book is Scrooge seeing

0:33:19.360 --> 0:33:21.440
<v Speaker 1>for the first time the chains he has made for

0:33:21.560 --> 0:33:25.480
<v Speaker 1>himself through his own decisions. Nowadays, we would call those

0:33:25.520 --> 0:33:29.840
<v Speaker 1>decisions and those chains longitudinal data, and they'd be very

0:33:29.880 --> 0:33:32.480
<v Speaker 1>hard to get rid of. They're the record that Lenn

0:33:32.520 --> 0:33:36.240
<v Speaker 1>couldn't shake, that might deny a Chinese citizen a plane ticket,

0:33:36.920 --> 0:33:40.440
<v Speaker 1>or deny you health insurance because of your social media activity.

0:33:41.480 --> 0:33:44.760
<v Speaker 1>But data can also set us free. In the next episode,

0:33:44.840 --> 0:33:47.600
<v Speaker 1>we investigate what's possible when our data is used to

0:33:47.720 --> 0:33:50.520
<v Speaker 1>help us. From a dying man brought back to his youth,

0:33:50.800 --> 0:33:53.680
<v Speaker 1>to movies and music that read our bodies while they play,

0:33:54.520 --> 0:33:57.080
<v Speaker 1>and what happens when Alexa becomes part of the family.

0:33:58.840 --> 0:34:03.360
<v Speaker 1>How long that Giuliana dived with m hmmm, I don't

0:34:03.400 --> 0:34:06.200
<v Speaker 1>know that one. I am still learning more about dinosaurs

0:34:07.200 --> 0:34:24.800
<v Speaker 1>us some dinosaur trivially. Sleepwalkers is a production of I

0:34:24.920 --> 0:34:28.800
<v Speaker 1>Heart Radio and Unusual productions. For the latest AI news,

0:34:29.000 --> 0:34:32.520
<v Speaker 1>live interviews, and behind the scenes footage, find us on Instagram,

0:34:32.680 --> 0:34:37.120
<v Speaker 1>at Sleepwalker's podcast or at Sleepwalker's podcast dot com. Special

0:34:37.160 --> 0:34:41.720
<v Speaker 1>thanks this episode to Laurie Arlam and Lucy Brady. Sleepwalkers

0:34:41.840 --> 0:34:44.440
<v Speaker 1>is hosted by me Osbaloshen and co hosted by me

0:34:44.600 --> 0:34:47.440
<v Speaker 1>Kara Price, with produced by Julian Weller with help from

0:34:47.520 --> 0:34:51.080
<v Speaker 1>Jacopo Penzo and Taylor Koin. Mixing by Tristan McNeil and

0:34:51.200 --> 0:34:54.840
<v Speaker 1>Julian Weller. Our story editor is Matthew Riddle. Recording assistance

0:34:54.920 --> 0:34:58.200
<v Speaker 1>this episode from Dina Bridgett, Rachel London, and Phil Bodger.

0:34:58.840 --> 0:35:03.040
<v Speaker 1>Sleepwalkers is Exactly You, produced by me Osloschen and Mangesh Hattikiler.

0:35:04.080 --> 0:35:06.080
<v Speaker 1>For more podcasts from my Heart Radio, visit the I

0:35:06.200 --> 0:35:09.080
<v Speaker 1>Heart Radio app, Apple Podcasts, or wherever you listen to

0:35:09.120 --> 0:35:10.000
<v Speaker 1>your favorite shows.