Speaker 1: Welcome to Tech Stuff, a production of I Heart Radios
Speaker 1: How Stuff Works. Hey there, and welcome to tech Stuff.
Speaker 1: I'm your host, Jonathan Strickling them an executive producer with
Speaker 1: How Stuff Works in I Heart Radio and I Love
Speaker 1: all Things Tech. And you know, guys, there's no shortage
Speaker 1: of scenarios in which AI proves to be our downfall.
Speaker 1: You've got popular films like The Terminator and Matrix series
Speaker 1: in which we have artificial intelligence literally revolting against us
Speaker 1: and then subjugating us. To the numerous predictions that automation
Speaker 1: is going to displace every job, and we run the
Speaker 1: gamut of all these different scenarios where AI is going
Speaker 1: to be our end And then we have various companies
Speaker 1: and organizations that are investing billions of dollars to develop
Speaker 1: an advance artificial intelligence who are saying, no, no, no,
Speaker 1: no, no no no, you don't need to worry about that.
Speaker 1: AI is not gonna totally story of the world. It's
Speaker 1: gonna make our world better. It's going to take over
Speaker 1: the more repetitive, dull, and dangerous parts of our jobs,
Speaker 1: and it's going to free us up to concentrate on
Speaker 1: more rewarding activities. So can we get to any truth
Speaker 1: in the matter? Is there some sort of truth we
Speaker 1: can suss out from these extremes? Well, today I'm joined
Speaker 1: by Oz and Kara, the hosts of the series Sleepwalkers,
Speaker 1: a show all about AI and if you haven't checked
Speaker 1: it out yet, I highly recommend you do because it
Speaker 1: is a phenomenal show. Guys, welcome to tech Stuff. Hi,
Speaker 1: thank you for having us. Yeah, thank you so much. Jonathan.
Speaker 1: We're huge fans of tech stuff and delights to be
Speaker 1: joining the house stuff works. My heart family in the
Speaker 1: and and made part of the tech Stuff networks. So
Speaker 1: thank you. Well, thank you because you know you've you
Speaker 1: have lifted up the boat of tech stuff, certainly because
Speaker 1: your work is really inspiring. Before we jump into this conversation,
Speaker 1: if you could just take a couple of moments and
Speaker 1: let my listeners know kind of you know what Sleepwalkers is,
Speaker 1: how you would describe that show to somebody. Let's say
Speaker 1: you're at a cocktail party and you are asked what
Speaker 1: do you do for a living? You say, well, I'm
Speaker 1: working on this show. How do you describe it? So
Speaker 1: they called I think they call it an elevator pitch.
Speaker 1: But this is a cocktail pitch. Yeah, and were based
Speaker 1: in New York, so we spend a whole lives of
Speaker 1: cocktail parties. I was born at a cocktail party and
Speaker 1: committee which happened in elevators in New York, as I
Speaker 1: understand exactly, or apartments, the size of elevators. Um. So,
Speaker 1: Sleepwalkers is a podcast that actually Oz came to me with.
Speaker 1: It was his idea, his brain child. But I will
Speaker 1: say first, you know, I've I used to report on
Speaker 1: tech and science at the Huffington Post, and I had
Speaker 1: a show called Talk Nerdy to me and when Oz
Speaker 1: came to me and said, you know, I want to
Speaker 1: I want to really make a show that deals with
Speaker 1: all of the human touch points that AI could possibly
Speaker 1: come in contact with, so healthcare, agriculture or uh, you know,
Speaker 1: science in general. Love you know, all of these places
Speaker 1: where people aren't necessarily thinking that a I will have
Speaker 1: an impact, but they already should be basically, And you know,
Speaker 1: I said yes very quickly because I'm very interested in
Speaker 1: all of those touch points. So each episode really is
Speaker 1: a deep dive into one of those areas, as I said,
Speaker 1: whether it be healthcare, transhumanism, agriculture, the military, for example, um,
Speaker 1: you know, these are these are places where we're going
Speaker 1: to see the presence of AI. Were already seeing the
Speaker 1: presence of AI, and and the show really tries to
Speaker 1: explore that. Yeah, I think it's uh, it's really pretty
Speaker 1: incredible when you sit down and look at where AI
Speaker 1: has already kind of crept into our day to day experience,
Speaker 1: sometimes in ways that we wouldn't necessarily associate with AI.
Speaker 1: Like one report I read said you could argue it's
Speaker 1: a very limited application of AI, but that things like
Speaker 1: spell check and grammar check, which are now standard in
Speaker 1: apps and clients and smartphones and browsers, that that's a
Speaker 1: type of artificial intelligence that if it's doing something besides
Speaker 1: just detecting No, this sequence of letters doesn't correspond with
Speaker 1: any words in the language you are writing in. It's
Speaker 1: also perhaps looking at context, like saying, well, you use
Speaker 1: the word weather, but you use the word weather as
Speaker 1: in the types of meteor logical activity that are outside
Speaker 1: the window, as opposed to whether or not you should
Speaker 1: do something. And so you think about that and you'll realize, oh, yeah,
Speaker 1: I guess, I guess there is a lot more to
Speaker 1: it than I thought, which kind of brings me to
Speaker 1: the first point I wanted to make before we dive
Speaker 1: into the various doom and gloom scenarios of AI, which is,
Speaker 1: how do you guys define artificial intelligence? Because I found
Speaker 1: that the this concept it's so broad that often you
Speaker 1: can have two people trying to have a meaningful conversation
Speaker 1: about AI and they're not able to meet in the middle.
Speaker 1: But it's not because they don't agree with each other.
Speaker 1: It's simply because they're working from vastly different definitions of
Speaker 1: what artificial intelligence actually is. I think that's a great point, Jonathan.
Speaker 1: Just to back up a little bit, UM, I want
Speaker 1: to tell you how I count with the name Sleepwalkers
Speaker 1: for the series UM, and then I'd love to dive
Speaker 1: into I think the excellent point you make, which is
Speaker 1: that effectively yesterday's AI is today is computing. UM. But
Speaker 1: so I was very struck about eighteen months ago when
Speaker 1: several of the senior and early employees of Facebook, people
Speaker 1: like Sean Parker, the first president of Facebook, who have
Speaker 1: now left the firm, obviously Chris Hughes more recently coming
Speaker 1: out and saying, you know, I wouldn't let my children
Speaker 1: use technology. Steve Jobs famously said, um, Steve Jobs, we
Speaker 1: had an audio shoot. I was repeat that. Steve Jobs
Speaker 1: famously gave an interview to Nick Bilton where he said that,
Speaker 1: you know, he wouldn't let his children use the iPad.
Speaker 1: But when the Facebook he's actually just came out and
Speaker 1: one after the other said that they didn't want their
Speaker 1: children using this technology kind of made me sit up
Speaker 1: and think, you know, if the people creating this technology
Speaker 1: don't want their kids to use it, what does that say.
Speaker 1: I mean, it's like, would you go to a restaurant
Speaker 1: where the owner didn't let their children eat? I certainly wouldn't.
Speaker 1: So that was the first sort of point of inspiration
Speaker 1: for for sleepwalkers, in other words, not being aware of
Speaker 1: the future we may be going into. And then there
Speaker 1: was a Zuckerberg hearings in the Senate, and Mark Zuckerberg
Speaker 1: sat there looking increasingly from from slightly nervous two relieved
Speaker 1: and calm to actively smug as it became abundantly clear
Speaker 1: that the senators were not going to be able to
Speaker 1: hold into account. I think the idea of those hearings
Speaker 1: were Senator orn Hatch asking Mark Zuckerberg how the platform
Speaker 1: made my the if it was free and Zugerberg smirkingly replied, Senator,
Speaker 1: we run ads um. And so between those two things,
Speaker 1: between the Facebook is not wanting their own children on
Speaker 1: the platform and the grown ups are either senators not
Speaker 1: being able to hold Facebook to account. I thought, Okay,
Speaker 1: what's going on here? And how can we wake up
Speaker 1: and make sure that we don't sort of flush our
Speaker 1: democracy down the toilet and pollute our children's minds by
Speaker 1: not asking some fundamental questions about how technology is changing
Speaker 1: how we already live. And that brings me to your
Speaker 1: second question, Jonathan, what is AI? And it's a fantastic
Speaker 1: question because AI is everywhere, and it's not just the
Speaker 1: robot future that you see in sci fi films that
Speaker 1: you mentioned, and it's not the future facing products that
Speaker 1: you know many brands tell us they're developing. And it's
Speaker 1: basically just statistics and probability, which has got better and
Speaker 1: better and better over time. But one of the things
Speaker 1: that we make clear to our listeners in the first
Speaker 1: episode is that they've already encountered AI ten times or
Speaker 1: hundred times by the time they listened to this podcast
Speaker 1: in their day, because if they took an uber to
Speaker 1: work in the morning, likely the driver was matched to
Speaker 1: them and the route was chosen with AI. If they
Speaker 1: woke up next to somebody this morning who they met
Speaker 1: through a dating app, AI effectively intervened in their romantic
Speaker 1: life and connected them with somebody who they matched with.
Speaker 1: And even even if you're listening to this podcast right now,
Speaker 1: there are algorithms AI algorithms at work smoothing our voices,
Speaker 1: compressing the audio, helping with the editing techniques. So AI
Speaker 1: is everywhere, and it's already changing our perception of the
Speaker 1: world and how we relate to the world around us
Speaker 1: and each other. Yeah, you could even argue at this
Speaker 1: point that AI is really just a slightly more focused
Speaker 1: branch of computer science and that it's It's almost the
Speaker 1: same as saying will computer science save us or doom us?
Speaker 1: It is too big of a question. You have to
Speaker 1: start narrowing things down. I think the real issue is
Speaker 1: that for the longest time, we've associated artificial intelligence with
Speaker 1: the concept of strong AI, which is that idea that
Speaker 1: we would create a machine that was either capable of
Speaker 1: or so close to capable that we can't tell the
Speaker 1: difference of thinking like a human or processing information like
Speaker 1: a human and coming to decisions like a human would,
Speaker 1: possibly with the added elements of consciousness and self awareness. UM.
Speaker 1: And and you know, I talk about how many times
Speaker 1: in this show. How that's a very complicated thing even
Speaker 1: for us to talk about just as human beings without
Speaker 1: bringing machines into it. So I'm sorry, I can't do that, Jonathan, Yes, yes,
Speaker 1: how HOW or or IBM if you prefer. Uh, you know,
Speaker 1: they're just three letters off UM. But yeah, it's it's
Speaker 1: ah that wonderful, that wonderful feeling that that's the only
Speaker 1: thing that AI really is, right, It's it's the super
Speaker 1: intelligent deep thought or how computer that's capable of processing
Speaker 1: information typically in natural language. Uh, it's the Watson platform
Speaker 1: participating on Jeopardy. Like we've we've precipitated this, uh, this thought,
Speaker 1: this this concept of AI, and we've reinforced it with
Speaker 1: entertainment and with applications that try to emulate the stuff
Speaker 1: that we saw on entertainment. But as you point out,
Speaker 1: AI is is a much more broad concept than this
Speaker 1: super intelligent machine. It's a whole bunch of stuff that's
Speaker 1: all about processing information in a particular way, typically to
Speaker 1: come to some sort of uh, decision or action upon
Speaker 1: information that has been automated. So it might be something
Speaker 1: like Facebook's algorithm, which is all designed ultimately. What Facebook's
Speaker 1: algorithm is designed to do is to keep you on Facebook.
Speaker 1: It's it's ultimately ultimately designed so that you will see
Speaker 1: the next thing on Facebook. It's it's reinforcing that desire
Speaker 1: and uh. And so that's what once the algorithm quote
Speaker 1: unquote figures you out, that's why you're gonna start seeing
Speaker 1: a pretty uh, a pretty consistent presentation of what you
Speaker 1: would see on a day to day basis. UM. But
Speaker 1: that would be one example of that. So, as you
Speaker 1: point out, we do interact with AI all the time,
Speaker 1: whether it's on social media with those algorithms, UM, whether
Speaker 1: it's with an app. Maybe we have one of those
Speaker 1: personal assistants in our home that that uses AI to
Speaker 1: various extents. UM. I talked about just recently on the radio.
Speaker 1: I had a conversation about how Comcast is coming out
Speaker 1: with sensors that are meant to monitor the health of
Speaker 1: people living in homes that have been outfitted with these
Speaker 1: ambient sensors, and they monitor things like how often you
Speaker 1: get up to go to the bathroom or whether you
Speaker 1: stay in bed a longer time than normal. And to
Speaker 1: be perfectly fair to Comcast, they're they're pitching this as
Speaker 1: something to help the elderly or people who otherwise need
Speaker 1: caretakers to give them more independence in their own homes.
Speaker 1: But you could also very easily, without much imagination at all,
Speaker 1: start to come up with scenarios where that could become
Speaker 1: truly invasive. Oh yeah, So I was bringing up Second
Speaker 1: Chance AI, which was a project that came out of
Speaker 1: the University of Washington, which was a was designed to
Speaker 1: detect opioid overdoses early on, using UM an opioid users
Speaker 1: cell phone to detect changes in breath and really act
Speaker 1: as a monitor for people who were long time or
Speaker 1: short time heroin and opioid users. So that device would
Speaker 1: then be able to detect this overdose and allow family
Speaker 1: members to know or also alert the person who is
Speaker 1: overdosing that they're in a bad way. So in the
Speaker 1: case of opioid users, it's worth the trade off because
Speaker 1: um you know, it's very helpful and potentially life saving
Speaker 1: for them to know based on previous breathing patterns and
Speaker 1: previous movements, what's likely to happen next. In an overdose scenario.
Speaker 1: And for most Facebook users, they indeed get to see
Speaker 1: the ads which are relevant to them. But the problem
Speaker 1: with AI is that can't discriminate between individuals and general population.
Speaker 1: So although it's more probable that somebody who have a
Speaker 1: successful pregnancy than not, is very painful for the edge cases,
Speaker 1: and AI can't effectively discriminate for them. And I just
Speaker 1: want to say really quickly, and I think this is
Speaker 1: an important point to make, especially about second Chance. So
Speaker 1: second chance basically is harnessing the power of a cell
Speaker 1: phone's microphone, which is the same microphone that you can
Speaker 1: either choose to turn on or off when you're in Instagram,
Speaker 1: that can listen to what you're saying, and basically then
Speaker 1: use data that's collected to target you with products that
Speaker 1: you probably don't need, like another pair of shoes designed
Speaker 1: by a company that you've never heard of, but that
Speaker 1: you might like. So my point is is that this
Speaker 1: microphone that you know, as Oz was saying, could you
Speaker 1: know in a inappropriately spy on you essentially unless you
Speaker 1: are taking control of it, is also a microphone that
Speaker 1: could save a life of somebody who is in the
Speaker 1: early stages of an opioid overdose. So I think that
Speaker 1: kind of rocks my world when I think about the
Speaker 1: two existing on the same piece of technology. Um, again,
Speaker 1: it's that they're being used for different things, and two
Speaker 1: different things that are you know, have hugely different outcomes.
Speaker 1: But they're all about making guesses about what's going to
Speaker 1: happen in the future based on what's happened in the past,
Speaker 1: and that can be liberating or constraining, depending on the
Speaker 1: technology and the intention and your interaction with it. Yeah,
Speaker 1: I'm reminded of something similar that was it was an
Speaker 1: interesting use of AI that ended up being ah. Another
Speaker 1: embarrassing and emotionally traumatic story that broke a few years ago.
Speaker 1: I want to say it was Target that sent coupons
Speaker 1: like maternity coupons to a young woman who her father
Speaker 1: had intercepted the thing and was incensed that Target would
Speaker 1: send these to his his daughter. Uh. And then because
Speaker 1: the father, the father of the young woman, did not
Speaker 1: realize that she was actually pregnant, she had not told him,
Speaker 1: and so he was upset and he was very angry
Speaker 1: at Target, you know, saying how dare you suggest this?
Speaker 1: Then discovered that she was pregnant after all, and it
Speaker 1: shows again that it was the intent was trying to
Speaker 1: be helpful. You could see that at least from you know,
Speaker 1: from a thousand yards away, you could see that where
Speaker 1: it say a company that says, you know you're going
Speaker 1: to have need of these things, here are some coupons
Speaker 1: for those things. If you shop with us, we can
Speaker 1: get you some deals. So you know it's gonna be
Speaker 1: a mutually beneficial kind of arrangement. But then you realize, oh,
Speaker 1: but this is on a subject that is extremely personal
Speaker 1: and in this case had this unintended consequence. It was
Speaker 1: the same sort of predictive approach, and they were able
Speaker 1: to predict the fact that she was pregnant based upon
Speaker 1: her browsing history. So they were proactively acting on this
Speaker 1: data that had been kind of gathered through her browsing activity.
Speaker 1: And then uh, that's what ended up causing this sort
Speaker 1: of a uh scandal is probably too strong of a
Speaker 1: word for it, but certainly a bruhaha. I think if
Speaker 1: we're looking at the grand scheme of of of how
Speaker 1: do we determine the level of of awkwardness, embarrassment, and
Speaker 1: potential emotional trauma? Um? So yes, please, one of the
Speaker 1: things that can me think about? Jonathan is a study
Speaker 1: at Stanford which basically turned AI onto identifying sexual orientation
Speaker 1: from photographs. So they took a data set publicly available
Speaker 1: data set of images of people's faces from dating websites
Speaker 1: which have been tagged bisexual preference I, straight, gay or bisexual.
Speaker 1: Then they train the algorithm on which faces corresponded to
Speaker 1: which express sexual preferences, and the algorithm, after this training
Speaker 1: was able to identify with accuracy for men and accuracy
Speaker 1: for women sexual orientation just from seeing five photographs of them.
Speaker 1: So again that technology by itself is more or less neutral.
Speaker 1: But you think about it being overlaid onto a citywide
Speaker 1: surveillance system in a country like Brunei or Saudi Arabia
Speaker 1: where homosexuality is punishable up to the death penalty, and
Speaker 1: it starts to become very very scared. Ry. Um. So
Speaker 1: we are in this world now where where technology is
Speaker 1: advancing and the ability to make these predictions based on
Speaker 1: past data is so advanced. It doesn't need to have
Speaker 1: consciousness to be killer, right right, Yeah, The fear of
Speaker 1: the matrix or terminator future, while compelling, turns out to
Speaker 1: not be necessary at all. Like that doesn't need to
Speaker 1: be a component for this to already be dangerous. Yeah,
Speaker 1: we'll we'll go into that in greater detail in just
Speaker 1: a moment, but first let's take a quick break. As
Speaker 1: you were saying just before the break, I mean, you
Speaker 1: made that great point about how AI has this potential
Speaker 1: to do potentially, you know, great harm as a as
Speaker 1: a possibility without the need for any sort of intelligence
Speaker 1: or malevolence on the part of the machine. In fact,
Speaker 1: it can just unthinkingly in human terms, cause some some
Speaker 1: pretty terrible consequences, unintended certainly, or at least we hope so,
Speaker 1: on the on the part of those who designed the systems.
Speaker 1: And I wanted to kind of talk a little bit
Speaker 1: more about that about how sometimes that can happen. And one,
Speaker 1: and I'm sure you've come across this in your reporting
Speaker 1: and in your podcasting. One problem that's not only confined
Speaker 1: to AI, but too and not just a tech but
Speaker 1: across the board is bias. Right, This idea that when
Speaker 1: you're designing a system, you're doing so from a particular
Speaker 1: point of view, and because of that, uh, you are
Speaker 1: likely excluding other points of view, maybe not consciously, but
Speaker 1: you are. And that ends up meaning that if it's
Speaker 1: a system that's supposed to apply to everyone, but it
Speaker 1: particularly applies well to people who are similar to the
Speaker 1: people who designed the system, and not so well to
Speaker 1: everybody else, that becomes a problem. And and we've certainly
Speaker 1: seen this in systems like UM Microsoft Connect. When Microsoft
Speaker 1: was pushing the Connect peripheral, which is the gesture recognition peripheral,
Speaker 1: where there's a camera had an infrared camera and a
Speaker 1: regular optical camera that could detect motions so that it
Speaker 1: could be translated into commands for the system. UM it
Speaker 1: was discovered pretty quickly that it worked great for white
Speaker 1: people but not so great for people of color. It
Speaker 1: had been designed by people who had not really worked
Speaker 1: with it in that regard, and so we see there.
Speaker 1: You could argue a fairly um harmless in the grand
Speaker 1: scheme of things failure of a system, but you look
Speaker 1: at something like computer vision for maybe an autonomous car,
Speaker 1: and you could argue, well, now you're talking about life
Speaker 1: or death situations. So to me, one of the big
Speaker 1: challenges in AI is making sure that you that the
Speaker 1: people designing the systems are doing their best to eliminate
Speaker 1: bias as best they can. And part of that I
Speaker 1: think falls to a real concentrated effort to increase diversity
Speaker 1: in the organization's companies that are actually designing these systems
Speaker 1: in the first place. Yeah. No, absolutely. I mean I
Speaker 1: think that the conversation about AI and bias has sort
Speaker 1: of reached critical critical mass. I guess, you know, I
Speaker 1: think it was yesterday or the day before, you know, UM,
Speaker 1: Alexander Rocascio Cortez was speaking out specifically about this problem
Speaker 1: as it pertains to facial recognition technology. UM, there was
Speaker 1: a very good M I. T study that recently came
Speaker 1: out that you know, a lot of these programs are
Speaker 1: developed by white men and therefore are extremely bias and
Speaker 1: and and I think politicians now are really trying to
Speaker 1: sound the alarm because I think it's, um, it's not
Speaker 1: something people think about in their everyday lives. You know.
Speaker 1: I don't think people are you know, walking around getting
Speaker 1: to their job that maybe they don't want to be at,
Speaker 1: you know, driving to work, driving their kids to school,
Speaker 1: you know, thinking about the implications of bias and facial
Speaker 1: recognition technology. I think people have other things to think about,
Speaker 1: but I think it's very important, UM, especially when you know,
Speaker 1: politicians start bringing up these problems, uh for sort of
Speaker 1: ordinary people to start to think, well, actually, wait a minute,
Speaker 1: I might encounter this technology UM at at border patrol,
Speaker 1: you know, when I'm flying out of the country, or
Speaker 1: you know, I might encounter this technology as I walk
Speaker 1: into a stadium that's now using you know, a quick lane.
Speaker 1: And I think when people start to listen to politicians
Speaker 1: who care about these issues, UM, they realize again that
Speaker 1: there are much more human touch points than we think.
Speaker 1: And then so issues of like bias and gender discrimine nation.
Speaker 1: Whereas before people weren't thinking about them as much in
Speaker 1: terms of technology and artificial intelligence, you know, now people
Speaker 1: are realizing that there's real I don't know, there's there's
Speaker 1: real issues in terms of who is developing these technologies
Speaker 1: and who is harmed by the inherent bias within these technologies.
Speaker 1: And I just want to say something really quickly. One
Speaker 1: hypocrisy that I think is is really wild and worth
Speaker 1: noting is, you know, the European Union has recently released
Speaker 1: basically a list of seven I don't know, I don't
Speaker 1: even know what you call them, but bullet points about
Speaker 1: you know, the way in which we should be talking
Speaker 1: about and regulating artificial intelligence, and you know, one of them,
Speaker 1: one of like the main bullet points is to say,
Speaker 1: you know, we really have to focus on uh by
Speaker 1: the inherent bias UM within these you know, both algorithms
Speaker 1: and the way this technology is built. UM, we don't
Speaker 1: we want to make sure that it doesn't get ahead
Speaker 1: of us essentially, right. And at the same time, the
Speaker 1: European Union in Latvia and Hungary and Greece is using,
Speaker 1: is piloting a program called Eyeborder Control UM, which is
Speaker 1: basically being tested and run by border patrol agents. UM
Speaker 1: two match people's faces on a very very large amount
Speaker 1: of data and then decide if a person should be
Speaker 1: detained for further questioning. Right. So, I think right now,
Speaker 1: both politically and socially, there is a reckoning that's going
Speaker 1: on which was like, Okay, we want to use algorithms
Speaker 1: to quote unquote make our borders safer, but we also
Speaker 1: don't want to allow these same things to get ahead
Speaker 1: of us so far that you know, we no longer
Speaker 1: have control over them. And I think that human beings
Speaker 1: in general and specifically politicians are having a really difficult
Speaker 1: time reckoning with the sort of inherent hypocrisy of wanting
Speaker 1: to harness the power of AI to you know, make
Speaker 1: smarter predictions, uh, make policing easier, but also regulating these things. Yeah,
Speaker 1: we're seeing it in in business too, right, Like we're
Speaker 1: seeing businesses that rely heavily upon algorithms. They're not necessarily
Speaker 1: nearly as as critical as the sort of decisions that
Speaker 1: would take place at a border where you could potentially
Speaker 1: really disrupt a person's life unfairly, and that would that's terrible.
Speaker 1: But like I just did an episode recently about the
Speaker 1: YouTube ad apocalypse. You know, this idea of advertisers pulling
Speaker 1: their money and they're they're advertising out of YouTube and
Speaker 1: how that hurt a lot of content creators and sort
Speaker 1: of the problems that YouTube faces. One of the big
Speaker 1: ones being that, you know, they have a pretty aggressive
Speaker 1: algorithm that goes again, goes in and tags videos and
Speaker 1: has them as being potentially uh not family friendly and
Speaker 1: therefore they cannot be monetized. Uh. And the reason why
Speaker 1: YouTube has to depend upon that is because you have
Speaker 1: more than four fifty hours of content being uploaded every
Speaker 1: single minute, So there's no way you could actually have
Speaker 1: human gate keepers who could review all the video footage
Speaker 1: that's being uploaded to YouTube every day and determine whether
Speaker 1: or not this actually merits being allowed into the monetization
Speaker 1: camp versus being demonetized. So you see from the scale
Speaker 1: that they have to rely on it, but you also
Speaker 1: see from the limitation of the algorithms themselves, how all
Speaker 1: these different cases that if a human were to review
Speaker 1: would probably be considered perfectly fine for monetization get you know, excluded.
Speaker 1: So we're seeing that as well. This idea that we're
Speaker 1: seeing the limitations of artificial intelligence where they're working off
Speaker 1: a certain set of criteria, but they aren't always able
Speaker 1: to apply them in the same way that a human would, right,
Speaker 1: they don't they don't take in all the context. So
Speaker 1: we see a lot of videos that are covering sensitive
Speaker 1: subjects like news about the l g B t Q communities, uh,
Speaker 1: news about places that are full of conflict, and these
Speaker 1: are meaningful and useful and educational videos. They're not sensationalized,
Speaker 1: they're not you know, trying to to exploit anyone, and
Speaker 1: the creators are trying to monetize the videos in order
Speaker 1: to be able to fund their efforts, but then they
Speaker 1: get demonetized. So again we're seeing where artificial intelligence can
Speaker 1: cause harm um in ways that we wouldn't have necessarily
Speaker 1: anticipated back when you know, folks like Arthur C. Clarke,
Speaker 1: we're writing about artificial and aligence. One of the things
Speaker 1: that we've found very exciting about Sleepwalkers is that we've
Speaker 1: been able to get access to a lot of kind
Speaker 1: of hard to get into places. So we went to
Speaker 1: the Facebook headquarters in Palo Alto to meet Nathaniel Glika,
Speaker 1: who runs cybersecurity policy for Facebook. And we went to
Speaker 1: the NYPD headquarters to meet the director of Analytics, the
Speaker 1: guy who makes the calls and helps develop the software,
Speaker 1: on what kind of predictive policing is acceptable, what kind
Speaker 1: of policing predicative policing is not acceptable, And we went
Speaker 1: to Google. We went to Google twice. We went to
Speaker 1: Google x, which is the kind of secret lab which
Speaker 1: invents the future, like the self driving cars, the balloons
Speaker 1: which sail in the stratosphere to deliver Internet too hard
Speaker 1: to reach places. But we also went to a very
Speaker 1: interesting program at Google called Jigsaw, and Jigsaw's mission is
Speaker 1: to right some of the wrongs of the Internet, and
Speaker 1: one of the big projects they're working on is sentiment analysis,
Speaker 1: because you know the early promise of the internet, which
Speaker 1: Jonathan you may remember better than me and karaoke. No,
Speaker 1: that was not. He meant more than your podcasts podcast.
Speaker 1: That's fair. That's fair that the podcast. I don't say.
Speaker 1: I put up with that with Tori, but I don't
Speaker 1: need that go ahead. Was comments, right, The Internet was comments,
Speaker 1: It was comment boards, and it was MSN messenger with
Speaker 1: random people you've never met before. And then all of
Speaker 1: a sudden, comments became this morass of utter hatred, and
Speaker 1: most websites stopped accepting comments because it was just too
Speaker 1: horrific and they couldn't afford to have moderators to to
Speaker 1: make it a safe space. So this program at Google Jigsaw,
Speaker 1: one of the things they're working on is sentiment analysis,
Speaker 1: so putting a bunch of comments through an algorithm to
Speaker 1: detect whether or not the comments are hateful. And the
Speaker 1: technology is now being used by the New York Times,
Speaker 1: who are trying to reintroduce a comments section on their
Speaker 1: web site. The problem is these um algorithms learn from
Speaker 1: how humans have historically perceived the negativity or positivity of language,
Speaker 1: and so guess what gay black female was originally considered
Speaker 1: by the algorithm to be hate speech, and white Man
Speaker 1: was considered positive. So you know, there's a lot of
Speaker 1: work to be done to make sure these algorithms don't
Speaker 1: reproduce are very painful history and in trench it right. Yeah,
Speaker 1: that's an excellent point, and it also kind of reminds me.
Speaker 1: I created an outline for this episode, and I'm sort
Speaker 1: of generally making my way through it. Uh, this is
Speaker 1: sort of my milieu, but I was I was thinking
Speaker 1: also that this plays into another component of AI that
Speaker 1: doesn't have anything to do with the AI natively, but
Speaker 1: rather our interactions with AI, and this comes was something
Speaker 1: that humans are particularly good at that AI isn't good at,
Speaker 1: and humans are really good at sussing out what the
Speaker 1: high level operations are for a system and then figuring
Speaker 1: out how to game that system. So we also see
Speaker 1: a lot of examples of people who have recognized how
Speaker 1: the AI is going about detecting something and then they
Speaker 1: end up using that to their own advantage. And in fact,
Speaker 1: I listened to one of your recent episodes of Sleepwalkers,
Speaker 1: the poker Face episode. First of all, Kara, amazing, Lady Gaga.
Speaker 1: Second of all, you're welcome as like Karaoke King that
Speaker 1: was actually a robot version of me doing that was
Speaker 1: my head is off to the to robo then but
Speaker 1: the but yeah, the the there was the discussion, There
Speaker 1: was the the the professor who was talking about how
Speaker 1: students had figured out how to uh to insert keywords
Speaker 1: in their cvs, but they used white text on white
Speaker 1: background so it wouldn't show up to a human reviewer.
Speaker 1: But it was the sort of stuff that machines could read.
Speaker 1: So machines were picking up on the cvs that had
Speaker 1: these words that typically we're going to very prestigious schools.
Speaker 1: It was. It was linking things back to things like
Speaker 1: Harvard or Cambridge, and so their cvs were popping up
Speaker 1: at the top of the pile for consideration, because the
Speaker 1: machines were the ones in charge of going through the
Speaker 1: first pass of these cvs, and then humans would look
Speaker 1: at the next pass, and so it increased your your
Speaker 1: chances getting called in for an interview. And meanwhile, the
Speaker 1: humans are none the wiser because they don't they don't
Speaker 1: see this hidden text, which I thought was a fascinating point.
Speaker 1: It reminded me actually of the early days of S E.
Speaker 1: O and web search where people would just flood a
Speaker 1: web page with all the top searched topics at the
Speaker 1: bottom of the page, even even though they had nothing
Speaker 1: to do with whatever the intent of the page was.
Speaker 1: It was the same sort of thing. They were gaming
Speaker 1: the system. And that's another way that AI could potentially
Speaker 1: become harmful. You know, in this case, I don't think
Speaker 1: it's harmful. I think it's brilliant. The kids are doing
Speaker 1: this because, you know, any way to get your foot
Speaker 1: in the door. If you're the best candidate for the role,
Speaker 1: you should definitely give that interview. But well, especially if
Speaker 1: the game is rigged exactly. Yes, that's another great point.
Speaker 1: Julian and I have Julian's our producer, and uh, Julian
Speaker 1: and I have talked about how we hope to see
Speaker 1: much more cyber I don't know cyberpunk rock in the future,
Speaker 1: whereas you know, I think, yes, cyberpunk is not cyberpunk
Speaker 1: future cyberpunk rock. We don't want cyberpunk rock because that
Speaker 1: would be bad music created by an algorithm. But you know,
Speaker 1: there are it's fun, it's I mean, it's kind of fun.
Speaker 1: I think when deep fakes can get tricky, but it's
Speaker 1: sometimes fun to see how people are gaming computers. You know,
Speaker 1: I was talking about this thing, uh, the reflectacles, which
Speaker 1: were actually designed We're part of a Kickstarter campaign actually
Speaker 1: to UM raise money to design these glasses that would
Speaker 1: basically direct natural light right back into a camera that
Speaker 1: was equipped with facial recognition technology. So it was sort
Speaker 1: of a way for kids to dodge cameras that we're
Speaker 1: trying to recognize them. And I, you know, I just
Speaker 1: I don't know. I guess that my rebellious side really
Speaker 1: really UM is warmed by by things like that. It's
Speaker 1: nice that we can still resist. I mean, you know,
Speaker 1: if she feels so overwhelming technology. And we may talk
Speaker 1: about China later on. You know, part of the problem
Speaker 1: of this kind of surveillance architecture we have is that
Speaker 1: it kind of demotivates you to even try and resist.
Speaker 1: But the issue of these students and peppering the applications
Speaker 1: with with with keywords like Harvard and Stanford on their
Speaker 1: applications in white X versus white background does bring up
Speaker 1: another concern or issue, which is what we call data poisoning. UH.
Speaker 1: And data poisoning is is a military term that we
Speaker 1: heard from the former Secretary of State. Sorry is a
Speaker 1: military term that we heard from the former Navy secretary
Speaker 1: under President Clinton, Richard Danzig, who's a guest on our
Speaker 1: podcast Sleepwalkers. He said that, you know, as we're relying
Speaker 1: on algorithms more and more to make decisions in the battlefield,
Speaker 1: decisions about which targets are threatening, which targets to civilian,
Speaker 1: whether an adversary is preparing for an attack or not.
Speaker 1: And we're relying on algorithms to make these calls for us,
Speaker 1: or at least to inform our decisions. You know, smart
Speaker 1: enemies can start to feed the algorithms they know exist
Speaker 1: poison data. In other words, you know, they can put
Speaker 1: on their own reflecticles and use our technological infrastructure against
Speaker 1: us by tricking our algorithms into thinking things are happening
Speaker 1: that aren't happening. Yeah, that's a another scary concept. It
Speaker 1: reminds me the last little point I have on my
Speaker 1: on my outline will will loop back in a second.
Speaker 1: But this uh the the various cases of false alarms
Speaker 1: that have happened since the nineteen fifties in the early
Speaker 1: warning systems for various nuclear programs. This has happened both
Speaker 1: in the United States and the former Soviet Union. Uh,
Speaker 1: we have seen cases where there were systems that detected
Speaker 1: a nuclear strike when in fact that it never happened.
Speaker 1: But but these were, you know, again, automated systems designed
Speaker 1: to detect patterns, something that AI is really that's one
Speaker 1: of the main things that AI does is look for
Speaker 1: patterns and then uh, start to predict things based upon
Speaker 1: the patterns that have been observed. And it was a
Speaker 1: couple of different cases of mistaken things that were not
Speaker 1: actually patterns but were interpreted as patterns, and that we
Speaker 1: thus saw very near miss into going into full nuclear war.
Speaker 1: And the only reason we did it is because there
Speaker 1: were actually human beings who said, hang on, let me,
Speaker 1: let me triple check this before we commit to mutually
Speaker 1: assured destruction. And uh, you know, we were very fortunate
Speaker 1: in that case that we had clear thinking individuals who
Speaker 1: were second guessing the systems. The danger I see is
Speaker 1: that we start to depend more and more heavily upon
Speaker 1: the systems, where we are less likely to resist the
Speaker 1: decisions coming out. And um, we'll talk a little bit
Speaker 1: more about that again in just a moment, but first
Speaker 1: let's take another quick break. So I was talking about
Speaker 1: the early warning systems. That kind of relates to another
Speaker 1: problem that we hear in AI. This one's uh one
Speaker 1: I hear side by side with bias as being one
Speaker 1: of the big concerns about AI, and that's what is
Speaker 1: commonly referred to as the black box problem, which is
Speaker 1: where you've designed a system that is so uh complicated
Speaker 1: or perhaps purposefully Obvius skated, that you cannot see how
Speaker 1: the system actually operates, and so you're getting output from
Speaker 1: this system, and the output appears to be good, but
Speaker 1: you don't necessarily understand all the steps that went through
Speaker 1: the system to come to that. And we see this
Speaker 1: in machine learning in particular, where you've got, you know,
Speaker 1: these artificial neural networks that have different weights on different decisions,
Speaker 1: and then they give you what is, at least statistically speaking,
Speaker 1: the most correct answer for whatever it is you're looking for.
Speaker 1: If we don't know how the machine is coming into
Speaker 1: that decision, then we can't be fully sure that it
Speaker 1: is the best one. And so there have been a
Speaker 1: lot of people that I've seen arguing for more transparent
Speaker 1: approaches to AI to make sure that it's sort of
Speaker 1: the system that we can audit so that we do
Speaker 1: feel reasonably certain it's working as intended and not producing
Speaker 1: results that could be less than ideal or even harmful. Um,
Speaker 1: it's one of the big concerns I've seen over the
Speaker 1: recent years that you know, the bias one being on
Speaker 1: one side and the black box problem being on the other.
Speaker 1: Have you guys encountered any of that in your work
Speaker 1: so far? Yeah, we have actually and and and just
Speaker 1: in a lot of recent news. Um, the black box
Speaker 1: AI problem it kind of feels like a Ponzi scheme
Speaker 1: where it's like, Okay, we have these returns that we
Speaker 1: know are good, and someone selling you these returns. They're
Speaker 1: not telling you how these returns are happening, but you
Speaker 1: trust that because you want to see your money grow exponentially,
Speaker 1: You're going to give them the money that you have
Speaker 1: now and expect to see those returns. And that's how
Speaker 1: people get tainted. I mean, that's how people It's not funny,
Speaker 1: but it's sort of you know, how Ponzi schemes work. Um.
Speaker 1: The black box A is similar to me, at least
Speaker 1: in my understanding, in that we don't really understand what
Speaker 1: linguistic patterns the networks are actually analyzing. We just know
Speaker 1: that they're analyzing them. And that to me, as someone
Speaker 1: who is, um not a computer scientist, I'm like, what, like,
Speaker 1: that's how is that possible? UM? And it's I mean,
Speaker 1: I think I think it's a bit alarming. And I
Speaker 1: know there are people there's a team at Google right
Speaker 1: now that's sort of working on this, working to fix it,
Speaker 1: and they sort of call it, you know, going I'm
Speaker 1: not a driver, so I don't know, popping the hood,
Speaker 1: going under the hood of of of AI to to
Speaker 1: you know, better understand what exactly is going on, UM,
Speaker 1: because I think, you know, again going back to what
Speaker 1: I was saying about the EU UM releasing these sort
Speaker 1: of seven guidelines. You know, one of them is transparency, right,
Speaker 1: and that's not only transparency and sort of how we're
Speaker 1: using AI and you know, various touch points in human life,
Speaker 1: but also how AI or how algorithms actually work. And
Speaker 1: I think, you know, not only do people not understand
Speaker 1: how many human touch points daily you know, consist of
Speaker 1: some form of artificial intelligence, they don't understand exactly how
Speaker 1: the AI is working. I mean that's an even that's
Speaker 1: more difficult. And so I think this idea that even
Speaker 1: the people who are feeding data into these algorithms, don't
Speaker 1: know exactly how the algorithms are treating the data. Is
Speaker 1: really a cause for alarm, and not not to not
Speaker 1: to not to be alarmists, but but I do think
Speaker 1: it's a cause for alarm, and and I do know
Speaker 1: there's a there's actually a lot of research going out
Speaker 1: in my t about it as well, because I think
Speaker 1: even for people who are in the field, it's something
Speaker 1: that worries them. I think it's worth mentioning that Henry Kissinger,
Speaker 1: who is obviously a controversial figure, wrote a piece about
Speaker 1: this last year for The Atlantic under the headline how
Speaker 1: the Enlightenment Ends. And you know, Kissinger is somebody who
Speaker 1: into his nineties, you know, likes being in the game
Speaker 1: and being hot. So and something he invested in their
Speaker 1: enough somebody he didn't he was involved, but so he
Speaker 1: you know, so he has an appetite for for these
Speaker 1: for these topics. On the other hand, you know, here's
Speaker 1: somebody in their nineties. And the piece was basically he
Speaker 1: convened as many of the leading minds in the world
Speaker 1: on AI that he could and wrote this piece, the
Speaker 1: State of the Nation piece on AI called how the
Speaker 1: enlightenment ends, And the main topic of the of this
Speaker 1: essay was about the black box problem. So Kissinger's point was,
Speaker 1: throughout human history we have been able to state why
Speaker 1: we did stuff, look at the outcome, argue about whether
Speaker 1: our reasoning that got us to that outcome was correct
Speaker 1: or faulty, and then improve our ability to reason. And
Speaker 1: when you have these black box AIA systems which make
Speaker 1: decisions but we're as as yet unable to understand why
Speaker 1: they made the decisions, it takes away the ability to
Speaker 1: have a debate. And that is such a fundamental part
Speaker 1: of what it means to be the human being in
Speaker 1: twenty one century liberal society, um that it's frightening to
Speaker 1: think about losing that ability. On the other hand, and
Speaker 1: the classic, you know, the classic illustration of this problem
Speaker 1: is called the trolley car problem. An autonomous car is
Speaker 1: driving along, it has to choose one person to kill.
Speaker 1: Does it choose as a swerve right and kill the
Speaker 1: child or swerve left and kill the old person, um,
Speaker 1: And it will never be able to explain why it
Speaker 1: made the decision it made. You know, that's probably true
Speaker 1: for most drivers as well, because they'll either have been
Speaker 1: killed themselves they'll have had so much trauma in the
Speaker 1: crash that they can't remember or they simply won't know.
Speaker 1: And as humans, we like to post rationalize things and
Speaker 1: then believe there are rationalizations are why we did what
Speaker 1: we did, But that also may not be true. So
Speaker 1: I don't know bash Ai too hard for being black box,
Speaker 1: because I think that humans, despite our best interests and
Speaker 1: thousands of years of our statilion onwards syllogisms and culture,
Speaker 1: you know, our logic and rationality, and is overlaid on
Speaker 1: some very hard to explain animal instincts. Yeah, and and
Speaker 1: when I think about this problem, so this isn't this
Speaker 1: isn't strictly a I but I have a very strong
Speaker 1: emotional response to the black box problem. But that's because
Speaker 1: I live in the state of Georgia. And in Georgia
Speaker 1: you may or may not know this, we rely heavily
Speaker 1: upon technologically ancient electronic voting machines that have no paper trail,
Speaker 1: so there's no way to audit them. They also have
Speaker 1: been proven to be vulnerable to to um attack, you know,
Speaker 1: to outside attack. And in fact, there's an enormous controversy
Speaker 1: in the State of Georgia that some servers may have
Speaker 1: been tampered with, and then the servers that may or
Speaker 1: may not have been tampered with were mysteriously wiped clear
Speaker 1: a couple of days before anyone could do an investigation
Speaker 1: of it. And so when you see something like that
Speaker 1: where that lack of transparency can have not just a
Speaker 1: direct impact on lives, I mean we're talking about actually
Speaker 1: threatening the very concept of the democratic process. Right. If
Speaker 1: you cannot trust the results of your election, you have
Speaker 1: undermined democracy. And so when I see that, that's why
Speaker 1: I end up having a very kind of heightened emotional
Speaker 1: response to the thought of these opaque systems. But Odds
Speaker 1: to your point, that is absolutely correct that people like
Speaker 1: we we don't necessarily hold people to that same standard.
Speaker 1: We will take them at their word if they tell us, oh, well,
Speaker 1: what what I was thinking when it happened was X, Y,
Speaker 1: and Z, When in reality, maybe they weren't thinking anything
Speaker 1: at all, Maybe they were reacting, but in in the
Speaker 1: post event, they have come up with a rationalization for
Speaker 1: that action that works within the narrative that they've constructed
Speaker 1: for their own lives. So maybe maybe that's because maybe
Speaker 1: that means I just need to give machines a little
Speaker 1: bit of the same slack I would give people. We
Speaker 1: do hold machines to a to an unreasonable expectation. I mean,
Speaker 1: you know how many people are killed every year on
Speaker 1: the roads by drunk driving, by unqualified driving, by poor driving,
Speaker 1: you know, and when that happens, we kind of take
Speaker 1: it as a you know, a necessary evil so that
Speaker 1: people can get around in cars. And yet if anyone
Speaker 1: is killed in a you know, an accident involving driver
Speaker 1: as car like that which has happened with Tesla, you know,
Speaker 1: it's news for runs for months and months and months.
Speaker 1: I'm not saying it shouldn't be news. I'm not saying
Speaker 1: it's not saying we should scrutinize. But we also know
Speaker 1: in order to enjoy the benefits of AI and technology,
Speaker 1: we have to accept that it comes with risks, just
Speaker 1: like the automobile itself comes with risks. Well, I'm sorry,
Speaker 1: go ahead, Kara, No I was gonna say. I was
Speaker 1: speaking at ODDS earlier today about this case of um,
Speaker 1: a man who basically was pitching around a AI powered
Speaker 1: hedge fund and is now in a lot of trouble
Speaker 1: because he lost a lot of money for people and
Speaker 1: you know, I think there's a I think it's an
Speaker 1: interesting story because you know, it's a legal battle that
Speaker 1: has emerged that it's sort of going to set up
Speaker 1: precedent for how you know, AI is incorporated into at
Speaker 1: least this facet of life, right in terms of making
Speaker 1: financial decisions for real human beings with real money, right,
Speaker 1: And if we're allowing computer programs to make decisions based
Speaker 1: on data and then those decisions lead to a significant
Speaker 1: loss of finding a significant loss of money. You know,
Speaker 1: who are we holding accountable? Are we holding the money
Speaker 1: manager accountable or reholding the program or you know, or
Speaker 1: holding the algorithm accountable, algorithm the person who wrote the
Speaker 1: algorithm accountable? You know, I think, and I actually don't
Speaker 1: think the American legal system. I don't think any legal
Speaker 1: system really knows how to handle this problem. And how
Speaker 1: would you How would you if you don't even know
Speaker 1: how the algorithm is working, and that you have no
Speaker 1: language for like human language for it. So I think,
Speaker 1: and we're going to see more and more cases of
Speaker 1: this because I think at the same time and Oz
Speaker 1: talks about this a lot with me, is you know,
Speaker 1: AI is used as such a strong marketing tool right
Speaker 1: now in all facets of life, and again in healthcare
Speaker 1: and agriculture, you know, in computing, in in in the
Speaker 1: automobile industry. And so I think people are very susceptible
Speaker 1: to being marketed with a I it's it's has a
Speaker 1: serious factor right now. But at the same time, are
Speaker 1: we willing to accept AI shortcomings? I mean, I think
Speaker 1: we have to be um But I think, you know,
Speaker 1: as Oz just said, like people are setting their expectations
Speaker 1: a bit high. I mean, they are computers, after all. Yeah,
Speaker 1: and well we've also we've lived in an era where
Speaker 1: we've seen such incredible advancement in computers that it starts
Speaker 1: to reinforce this idea that technology can accomplish just about anything.
Speaker 1: I mean, if you had told ten year old Jonathan
Speaker 1: that one day he would have a computer that would
Speaker 1: fit in his pocket and would allow him to communicate
Speaker 1: with everyone he knows, and whether it's through voice or
Speaker 1: video or text, that I would be able to tap
Speaker 1: into the world's you know, database of all human knowledge
Speaker 1: at a touch of a button, I would have thought
Speaker 1: you were crazy. That that would have seemed completely patently
Speaker 1: impossible to me. I mean, let's talking about an era
Speaker 1: where at that point the most sophisticated machine out there
Speaker 1: was a ma Cantosh computer or the IBM PC, and
Speaker 1: you look at that and you think, well, these are
Speaker 1: great machines, but no, there's no way I'm going to
Speaker 1: have one of these in my pocket, let alone be
Speaker 1: able to do all these other things you're talking about.
Speaker 1: So once you look into that, you start to realize, oh,
Speaker 1: we have now built up this expectation that because we
Speaker 1: have this amazing, uh incredibly rapid evolution of technology in
Speaker 1: our recent past, we start projecting that and thinking the
Speaker 1: same sort of progress is going to continue unabated. It's
Speaker 1: actually just going to pick up speed. And then we
Speaker 1: start thinking, oh, well, that means that before long we're
Speaker 1: gonna have the sort of uh, incredibly sophisticated, artificially intelligent
Speaker 1: constructs as part of our day to day lives. Uh.
Speaker 1: And that's not necessarily the case because of what it does.
Speaker 1: It assumes that all technological advancement proceeds at the same speed,
Speaker 1: which isn't That's not the case. What do you mean
Speaker 1: the chat bots, the chatbots that I was going to
Speaker 1: get fun? What are you talking? I'm sorry, I'm sorry.
Speaker 1: The chat bought past your two ring test. Well, one
Speaker 1: thing I did want to kind of end on because
Speaker 1: I think this is sort of the the the capper
Speaker 1: of discussions about how AI is potentially hazardous is this
Speaker 1: is a discussion that's come up many times of the past,
Speaker 1: i'd say three or four years, about how AI and
Speaker 1: automation are going to end up displacing people. It's going
Speaker 1: to end up eliminating jobs. And there are lots of
Speaker 1: different points of view on the subject. You've got people
Speaker 1: who say, yes, some jobs are going to go away.
Speaker 1: They are the very repetitive jobs, the ones the things
Speaker 1: that AI are good at, like being able to do
Speaker 1: the same thing over and over and over again with
Speaker 1: very little variation. You know, the more you vary from
Speaker 1: the norm, the more difficult it is for a machine
Speaker 1: to do. But those jobs will probably go away, but
Speaker 1: as a result, more jobs will be created. And other
Speaker 1: people are saying maybe in the short term, but in
Speaker 1: the long term, we're going to see automation take over
Speaker 1: everything and no one's gonna have a job, and we've
Speaker 1: got to figure this out, and something's gonna you know,
Speaker 1: the entire world economy is gonna collapse, or we're gonna
Speaker 1: have to go to some form of guaranteed basic income
Speaker 1: for the entire world, or we're gonna have to do
Speaker 1: away with the concept of money altogether. Um, now that
Speaker 1: we've divorced money from labor what we do, and so
Speaker 1: we're seeing like all these kind of conversations going around,
Speaker 1: and I thought I would tell you guys a bit
Speaker 1: because just for the heck of it, I found an M. I. T. H.
Speaker 1: Technology Review article from two thousand eighteen that gathered together
Speaker 1: all of the major predictions for what automation was going
Speaker 1: to do, um, like how many jobs it was going
Speaker 1: to destroy versus create. And Uh, I think it's pretty telling.
Speaker 1: I'm just gonna cite one year. They have years from
Speaker 1: two thousand sixteen up to let's see, but I'm just
Speaker 1: gonna do twenty twenty five two different predictions you had
Speaker 1: Forrester predicting that in the US, automation would destroy the
Speaker 1: words of the view not me, uh, twenty four million
Speaker 1: on six thousand, two hundred forty jobs and only create million,
Speaker 1: six hundred four thousand, seven hundred sixty jobs. So you're
Speaker 1: looking at a deficit of more than ten million jobs. Meanwhile,
Speaker 1: Science Alert said jobs destroyed three million, four hundred thousand,
Speaker 1: so twenty one million jobs fewer predicted than Forrester. So
Speaker 1: if you're looking at the twenty one million disparity between predictions,
Speaker 1: do you think it's safe to say we don't know yet.
Speaker 1: I don't think we know yet. I don't think you
Speaker 1: know yet. I think it's a very I think it.
Speaker 1: I think the idea of unfortunately the line of jobs
Speaker 1: being lost is UH is part of the if it
Speaker 1: bleeds it leads, you know, method of journalism. I do
Speaker 1: think it's absolutely true that automation is not only on
Speaker 1: the horizon, it's here, you know. I mean, if we
Speaker 1: just talk about agriculture, for example, you know there is
Speaker 1: UM right now in Washington Can Washington State. UM is
Speaker 1: you know, piloting a harvesting robot that they are going
Speaker 1: to be using for the first time in this next
Speaker 1: harvest apple harvest where they're using this sort of huge
Speaker 1: hoover like vacuum to pick apples. Right. You know, Amazon
Speaker 1: is introducing uh some new automation technology that's going to
Speaker 1: UH cut the box building jobs that you see in
Speaker 1: some of their warehouses, so they're not they're displacing roles
Speaker 1: they're changing roles, right, so uh, instead of actually creating
Speaker 1: actually making boxes, they're still human beings putting boxes on
Speaker 1: conveyor belts, but they're not making the boxes because that
Speaker 1: leads to a lot of waste, right, because there's a
Speaker 1: lot of human error involved. Um. And also, these machines
Speaker 1: can crank out six hundred to seven hundred boxes, you know,
Speaker 1: per hour, which a human being cannot do. Um. So
Speaker 1: there are certain uh, there's there's there's no denying that
Speaker 1: machines are replacing human beings, and in that way, I
Speaker 1: don't I don't think it's like literal robots. I think
Speaker 1: that there are machines that are doing jobs that are
Speaker 1: very difficult and taxing on human beings. They're doing those
Speaker 1: jobs better and therefore, yes, displacing people. Um. You know
Speaker 1: what Amazon will say is that it's not so much
Speaker 1: about replacing people, it's about repurposing people and um giving
Speaker 1: people jobs that are more meaningful. I think that is
Speaker 1: a public relations line, um. But I also think there's
Speaker 1: a there's a certain element of truth to it, which is,
Speaker 1: you know, can we use machines to take people out
Speaker 1: of jobs that are both physically and emotionally taxing for them,
Speaker 1: I think certainly, and that could you know, it could
Speaker 1: be one of the upsides, but I think that, yeah,
Speaker 1: of course, there are jobs that are going to be
Speaker 1: replaced by machines that are, you know, not only faster,
Speaker 1: but have a much lower margin of error. And then
Speaker 1: maybe some you know, read distributive universal basic income solution
Speaker 1: to solve the practical problem of how will people eat,
Speaker 1: but it won't solve the bigger culture and a psychological problem,
Speaker 1: which is at the American dream and everything we're encouraged
Speaker 1: to think in this country is that through work you
Speaker 1: can better yourself and that this one major source of
Speaker 1: your identity and value in the world is your success
Speaker 1: in your career and how much you achieve and how
Speaker 1: many promotions you get. I mean, look at the famous
Speaker 1: Christmas movie, Um, what's it called? Sorry Christmas Carol? No, no no, no, nos,
Speaker 1: Oh It's a wonderful life. No, not even that one.
Speaker 1: It's along with the guy chevy Chase, Oh Christmas Vacation.
Speaker 1: I mean, look at and look at National Lampoon's Christmas Vacation.
Speaker 1: Chevy Chase's whole identity and worldview is predicated on that
Speaker 1: Christmas bonus and you know, we've been encouraged by a
Speaker 1: hundred years if not more, of this post industrial revolution
Speaker 1: world to equate our value in life with a financial
Speaker 1: value that we create. And we may be technically economically
Speaker 1: able to move away from that, but psychologically it's going
Speaker 1: to be intensely traumatic and we have not even begun
Speaker 1: to deal with the consequences of that or even think
Speaker 1: about them. Yeah, that's a good point, I think. Uh,
Speaker 1: you know, do you have the technologists who argue, and
Speaker 1: I think rightly so that they're going to be a
Speaker 1: lot of aspects that AI simply will not be ready
Speaker 1: to just take over. Again, the further out from the
Speaker 1: repetitive norm you get, the more challenging it is for
Speaker 1: a machine to do, whereas a human can pick up
Speaker 1: on it pretty quickly. We're really good at doing that.
Speaker 1: But um so there's gonna be certain things that, at
Speaker 1: least for the foreseeable future, are going to be really
Speaker 1: firmly in the realm of human beings. Uh. But you also,
Speaker 1: you know, end up having to think about who's messaging
Speaker 1: this out right, because that always creates that little question
Speaker 1: you have too. If it's IBM saying the technology where
Speaker 1: creating is going to augment people in the future, then
Speaker 1: you remember, oh, well, IBM is also designing those systems. UM.
Speaker 1: But I still think that there is truth to it.
Speaker 1: I mean, I think that there is truth that AI
Speaker 1: can augment people, and as you were saying, Kara can
Speaker 1: help take over parts of jobs that really humans are
Speaker 1: not very well suited for in the first place, and
Speaker 1: certainly wouldn't be considered the type of jobs that most
Speaker 1: people would find meaning from right that they wouldn't find
Speaker 1: value in that opportunity. They would be doing it because
Speaker 1: they would need to make ends meet, But it's not
Speaker 1: necessarily I don't think there's a lot of people who
Speaker 1: dream of making boxes UM. So I think it's it's
Speaker 1: one of those things where I think it always benefits
Speaker 1: you to kind of take a step back, think about
Speaker 1: who's messaging this um and and really take a look
Speaker 1: at what's actually going on. Because, as it turns out,
Speaker 1: when you look at a prediction and one since predicting
Speaker 1: that twenty four million jobs are going to be destroyed
Speaker 1: in and someone else is saying it's more like three
Speaker 1: million jobs, what it ultimately what it ultimately tells us
Speaker 1: is that nobody really knows and that that in itself
Speaker 1: is scary. It's not. It's not making us feel better
Speaker 1: about the future necessarily, But I think what it really
Speaker 1: tells us is the future is not set in stone
Speaker 1: at all, and that if we are going forward knowing
Speaker 1: the capabilities of AI, how it can work with us.
Speaker 1: If we hold companies and individuals accountable for designing AI
Speaker 1: systems that can uh be used in an ethical way
Speaker 1: and UH and then hold the people who are implementing
Speaker 1: those systems to make sure it's done in that ethical way,
Speaker 1: then we can see the benefits of AI. I think
Speaker 1: AI ultimately is a very complicated tool, but it's like
Speaker 1: other tools, which means you can use it for good
Speaker 1: or you can use it for evil, And ultimately comes
Speaker 1: down to the implementation and and vigilance. Right, we have
Speaker 1: to just make sure that we're paying attention to what's
Speaker 1: going on and not just trusting that the machines are
Speaker 1: doing everything correctly, because as far as the machines are concerned,
Speaker 1: they're doing everything correctly. It's just that the outcome is
Speaker 1: not so great for us. Um, a hammer is always
Speaker 1: doing its job. Yeah, it's just a matter of who's
Speaker 1: using Yeah, exactly. Yeah, It depends on whoever's holding the hammer,
Speaker 1: what he or she thinks of as a nail. That's
Speaker 1: what it really comes down to. Um, well, guys, thank
Speaker 1: you so much. We're going to have another episode coming
Speaker 1: up in next week guys, so so stay tuned because
Speaker 1: Os and carrere gonna be back. We're gonna talk about
Speaker 1: how different parts of the world are viewing a I
Speaker 1: from sort of a policy and regulations kind of perspective,
Speaker 1: as well as just like what are just the different
Speaker 1: approaches to artificial intelligence around the world, because, as it
Speaker 1: turns out, you know, Kara, you've already mentioned a couple
Speaker 1: of times how the EU has been taking steps to
Speaker 1: try and and think about this ahead of everybody else.
Speaker 1: But what's going on around the world. And I think
Speaker 1: you guys are going to be surprised. I know I
Speaker 1: was because I am so US centric in my show
Speaker 1: that I often have blinders on. So we'll have to
Speaker 1: join us for that episode that's coming out next week.
Speaker 1: And if you haven't already gone out and subscribe to Sleepwalkers,
Speaker 1: this is your reminder to go out and do that
Speaker 1: because the show is fantastic. You've got some great interviews,
Speaker 1: you have fantastic conversations between the two of you about
Speaker 1: these these subjects, and it's really educational and entertaining and
Speaker 1: thought provoking, and congratulations on creating such a really compelling show. Well,
Speaker 1: thank you, Jonathan. We're we're already enjoying working on Sleepwalkers,
Speaker 1: and you know, this conversation is has been fantastic for
Speaker 1: us to have a chance to step out of our
Speaker 1: own show and think about some of these ideas in
Speaker 1: conversation with you, so we already enjoyed it. Thank you, Jonathan.
Speaker 1: You're very welcome, and so guys, if you want to
Speaker 1: get in touch with me, send me an email the
Speaker 1: addresses tech stuff at how stuff works dot com. Pop
Speaker 1: on over to the website that's tech stuff podcast dot com.
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