Speaker 1: From Kaleidoscope and iHeart podcasts. This is tech stuff. I'm
Speaker 1: as Voloscian and.
Speaker 2: I'm care Price.
Speaker 1: Today we get into what it's like to be a
Speaker 1: human who trains AI for a living, and the story
Speaker 1: of an activist who turned the surveillance state against the
Speaker 1: Chinese government. Then on chatting me, a real life therapist
Speaker 1: talks about how he uses AI.
Speaker 3: It felt like a steady partner. But I call a
Speaker 3: cognitive prosthesis, not thinking for me, but helping me organize
Speaker 3: and extend my own thinking. The way of walking, stick
Speaker 3: supportune as.
Speaker 1: You walk, all of that. On the Weekend Tech, It's Friday, September.
Speaker 2: Twelfth, Hi Cara, Hi ahs.
Speaker 1: So normally we record these together in studio, but this
Speaker 1: week I'm in a hotel room in Munich, Germany, at
Speaker 1: a conference called DLD Digital Life Design.
Speaker 2: It sounds very lively. How is it?
Speaker 1: It's good? You know, are you familiar with this concept
Speaker 1: of ambient AI that they're using medical settings?
Speaker 2: Sort of yes? Can you just tell me a little
Speaker 2: bit about it?
Speaker 1: So basically, it's like cameras and sound recording in settings
Speaker 1: where like a person doesn't have to go and use AI.
Speaker 1: AI is just running in the background and feeding back
Speaker 1: kind of insights and data. And so there's a talk
Speaker 1: yesterday about this being used in hotel settings for food,
Speaker 1: which I found pretty interesting. But apparently using this technology
Speaker 1: has in some cases reduced food waste in hotels by
Speaker 1: an average of forty percent.
Speaker 2: What does ambient AI look like in this case, Like,
Speaker 2: is it like a panopticon or people getting publicly shamed?
Speaker 1: Well, it is a bit like a panopticon, but the
Speaker 1: customers are not being publicly shamed on their third trip
Speaker 1: to the buffet. Basically, there are cameras and there's also
Speaker 1: a weighing device for trash bins, and so it will
Speaker 1: track the food at the end of the buffet being
Speaker 1: transported by the servers to the trash and thrown out
Speaker 1: and then being weighed. And the insights and not rocket science.
Speaker 1: It's like, hey, guys, don't fill the bread basket fifteen
Speaker 1: minutes before the service ends. Like things that once you
Speaker 1: hear them, are obvious, but actually it's interesting that like
Speaker 1: being told it by technology actually apparently makes a real difference.
Speaker 2: I remember when you and I many now many years
Speaker 2: ago went to speak at Johns Hopkins. They told us
Speaker 2: about connected devices that nurses wear to make hospital work
Speaker 2: more efficient. So I think this is something that we
Speaker 2: should pay attention to. But speaking of digital life design,
Speaker 2: I don't know if you saw this article in the
Speaker 2: Wall Street Journal called My Day as an eighty year old?
Speaker 2: What an age simulation suit?
Speaker 1: Top me? Did you see this? I saw the email
Speaker 1: and I was fascinated, But tell me more.
Speaker 2: Basically, MIT has created this age simulation suit, which is
Speaker 2: a full body suit that was built by scientists at
Speaker 2: the Age Lab, which does research on how to improve
Speaker 2: life for the elderly. So the journalists who wrote the article,
Speaker 2: her name is Amy, doctor Marcus, and she recently wore it,
Speaker 2: and that's what you know she wrote this article about.
Speaker 2: And she said she was fitted with just imagine this,
Speaker 2: a fifteen pound weighted vest, more weights on her ankles
Speaker 2: and wrists to kind of stimulate the loss of muscle mass.
Speaker 2: And the suit also had this very elaborate bungee cord
Speaker 2: system and a neck collar that limited her mobility. She
Speaker 2: also had glasses. And there's great photos you if people
Speaker 2: who are listening want to look it up on the
Speaker 2: Wall Street Journal. There's these great photos of like what
Speaker 2: her distorted vision looks like she also wore padded crocs
Speaker 2: that made it really hard to balance.
Speaker 1: This sounds like hell.
Speaker 2: It sounds like the worst hangover of your life. Basically, yeah,
Speaker 2: MIT calls the suit agnes, which is an acronym for
Speaker 2: age gain now empathy system.
Speaker 1: That's about acronym. It sounds when I a bachronym that
Speaker 1: I but I get it. What made the story stand
Speaker 1: out to you?
Speaker 2: So you know, I think it's a very interesting lesson
Speaker 2: in empathy, or a lesson in schlepping if you come
Speaker 2: from my culture. But it's actually a part of a
Speaker 2: longer arc of product research for older people, which I'm
Speaker 2: very interested in as someone who has an aging parent
Speaker 2: who is very young its spirit. I think it's a
Speaker 2: real test of our empathy to understand how people who
Speaker 2: are older than us navigate the world. And there was
Speaker 2: this woman who I became obsessed with many years ago
Speaker 2: named Patricia Moore. Patty Moore? Have you ever heard of her?
Speaker 1: I haven't.
Speaker 2: So she is most famous for pioneering an approach called
Speaker 2: universal design, which basically means designing products and spaces with
Speaker 2: empathy for the widest possible audience in mind. So like,
Speaker 2: will this refrigerator be something that a ninety year old
Speaker 2: can open, for example.
Speaker 1: Yeah, it's interesting, and I guess it's kind of become
Speaker 1: a bit of a standard now. I'm not sure if
Speaker 1: companies always do it, but certainly companies always talk about
Speaker 1: the idea they don't want to design products with the
Speaker 1: healthy mid thirties product designer in mind as the user,
Speaker 1: but they won't have a wider view on how these
Speaker 1: products might be used by people different ageas people with
Speaker 1: different abilities, that kind of thing.
Speaker 2: To your point, it wasn't always a consideration. And when
Speaker 2: Patty was twenty six years old, this is in nineteen
Speaker 2: seventy nine. I'm giving away her age, and I know
Speaker 2: she's going to listen to this because she's a friend
Speaker 2: of mine.
Speaker 1: But so she's been at this. She's been at this
Speaker 1: since way before it was popular.
Speaker 2: She worked at Raymond Lowie. There was some grant from
Speaker 2: the Nixon administration that allowed her to have the job
Speaker 2: that she had, But she was one of the only
Speaker 2: people who was bringing up any kind of conversation about
Speaker 2: product design for the elderly and how we can engage
Speaker 2: in a conversation about that. But what she did that
Speaker 2: is so kind of different. But ingenius visa vi agnes Is.
Speaker 2: She got a friend of hers who was a makeup
Speaker 2: artist from Saturday Night Live, to turn her into an
Speaker 2: eighty five year old woman. She traveled around the country
Speaker 2: in these outfits essentially, and she got a really good
Speaker 2: sense of what it was like to live as an
Speaker 2: elderly person in the world in the late nineteen seventies.
Speaker 2: After she did this, she went on to be a
Speaker 2: product engineer and she has based her entire career on
Speaker 2: creating more accessible products, like you know, good grips. She
Speaker 2: was a very sort of integral part of the design
Speaker 2: of easily grippable devices for the kitchen and the home.
Speaker 2: She also worked on transit systems, medical devices, and now
Speaker 2: she actually spends a lot of time consulting tech companies
Speaker 2: on how to make their products more inclusive for elderly people.
Speaker 1: To me, it's interesting tech then and now story because
Speaker 1: in the seventies, like to do this kind of research,
Speaker 1: you'd have to invent your own costumes and characters and
Speaker 1: process and go to a TV show to help you
Speaker 1: achieve it. And now MIT has this age scented lab
Speaker 1: where you can just put on the agnes suit for
Speaker 1: a day, but of course, the motivation is the same,
Speaker 1: and part of that might speak to the fact that,
Speaker 1: howe him, we want to pay lip service the idea
Speaker 1: of designing for all kinds of different people, it remains
Speaker 1: very hard to pull off.
Speaker 2: It does. I think the one thing that's interesting about
Speaker 2: the Angle and the Journal piece is that the Age
Speaker 2: Lab is trying to also understand how to prepare people
Speaker 2: who are aging into their eighties to live better. And
Speaker 2: it's really about how do you keep people staying young
Speaker 2: even though they're not young anymore?
Speaker 1: Right, right, right, Well, it's all only either beholder. Do
Speaker 1: you know two people who are very interested in staying
Speaker 1: young even if they age in terms of their physical bodies?
Speaker 2: Every woman I know, and many.
Speaker 1: But now in this case, I'm talking about Vladimir Putin
Speaker 1: and Shechinping. The two of them are attending a military
Speaker 1: parade in Beijing, officially to celebrate the eightieth anniversary of
Speaker 1: the end of World War II. Unofficially, of course, to
Speaker 1: show the US and Taiwan. Then you ask some of
Speaker 1: high tech weaponry. But anyway, Putin and she were walking
Speaker 1: together when a hot might caught them having a really
Speaker 1: bizarre conversation. I'll read it to you. So Putin's interpreter
Speaker 1: says in Chinese, quote, biotechnology is continuously developing. Then he adds,
Speaker 1: quote human organs can be continuously transplanted. The longer you live,
Speaker 1: the younger you become, and you can even achieve immortality.
Speaker 1: She responds, quote some predict that in this century humans
Speaker 1: may live to one hundred and fifty years old.
Speaker 2: Just imagine that your small talk is about organ transplantation.
Speaker 1: Continuous organ transplantation. I mean once once, you might think
Speaker 1: will be enough.
Speaker 2: It's unbelievable. And that's their casual conversation, the most powerful
Speaker 2: people in the world.
Speaker 1: The reason I came across this is because I'm quite
Speaker 1: interested in defense tech and I was looking for the
Speaker 1: most interesting angle on this military parade in Beijing to
Speaker 1: bring to the show. And there was fascinating stuff about
Speaker 1: new anti drone technologies, new kinds of lasers, et cetera,
Speaker 1: et cetera. But actually the most interesting story I've found
Speaker 1: had nothing to do with the military parade. In fact,
Speaker 1: it was all about what happened the night before.
Speaker 2: Tell me about it.
Speaker 1: So, around ten pm last Friday night in chong Qing,
Speaker 1: a huge projection against the skyscraper went up calling for
Speaker 1: an end to the Communist Party's rule. It included slogans
Speaker 1: like quote only without the Communist Party, can there be
Speaker 1: a new China? End quote, no more lies, we want
Speaker 1: the truth, no more slavery, we want freedom.
Speaker 2: And how long did that stay up? Like ten seconds?
Speaker 1: Well an hour? But actually this is where things get
Speaker 1: more interesting and more tech stuff. A few hours after
Speaker 1: the projection was shut off, the activist who staged the protest,
Speaker 1: See Hoong, posted this video showing five police officers bursting
Speaker 1: into the hotel room where the projector was and then
Speaker 1: kind of bumbling around trying to turn it off. That
Speaker 1: at one point one of the policemen notice, smile your
Speaker 1: on candid camera. There's actually a camera pointing at them,
Speaker 1: and he rushes towards it, looking really surprised. And then
Speaker 1: he looks down and sees as a note underneath the
Speaker 1: camera saying, even if you are a beneficiary of the
Speaker 1: system today, one day you will inevitably become a victim
Speaker 1: on this land, so please treat the people with kindness.
Speaker 1: But it plays out almost like a Marx Brothers film.
Speaker 1: They burst into the room, they're confused, and they realize
Speaker 1: that they're kind of the victims of this prank.
Speaker 2: Did he get in trouble for this.
Speaker 1: Well, he would have gone into terrible trouble, but of
Speaker 1: course he wasn't in the hotel room. That was the
Speaker 1: whole joke of just a camera. He'd fed the country
Speaker 1: with his family a week before the protest was turned on,
Speaker 1: and in fact he turned it on remotely. But one
Speaker 1: of his brothers along with a friend of his were
Speaker 1: both detained, and authorities also questioned his elderly mother outside
Speaker 1: her home. Now talk about Agnes elderly mother. I mean,
Speaker 1: this woman looks like she's one hundred years old, completely
Speaker 1: hunched over, surrounded by cops. Is an extremely unfavorable image
Speaker 1: for the police. And see Hong again actually managed to
Speaker 1: get hold of these surveillance photos. Then he posted the
Speaker 1: images of his mother's interrogation online as well.
Speaker 2: This is very who watches the watchman irl? You know,
Speaker 2: he's using the Chinese authority's own tools essentially against them.
Speaker 1: That's he's actually right. And Seahong himself was quoted in
Speaker 1: the story saying, the party installs surveillance cameras to watch us.
Speaker 1: I thought I could use the same method to watch them, you.
Speaker 2: Know, I think we think of huge tech systems as
Speaker 2: fully autonomous, but a story like this really reveals how
Speaker 2: a single human being can still have an outsized effect.
Speaker 2: And you know, what this activist did was essentially reveal
Speaker 2: that there is no such thing as a fully autonomous system.
Speaker 2: And I think the footage of the police arriving in
Speaker 2: the empty apartment and displaying this very sort of human
Speaker 2: confusion as a testament to that which humans being inside
Speaker 2: the lubnol brings me to the big story that I
Speaker 2: want to bring to you this week, and it's do
Speaker 2: you know about the people who train AI models to
Speaker 2: be better?
Speaker 1: I actually do, because I have a friend who I
Speaker 1: actually asked if they would come on the show some
Speaker 1: weeks ago, and they said no because they have NDAs
Speaker 1: and they rely on their AI trainer work for part
Speaker 1: of their income, so they couldn't do it. So now
Speaker 1: you brought this story because I've wanted to know more
Speaker 1: about it for some time.
Speaker 2: So that's exactly what this Business Insider article is about.
Speaker 2: They spoke to over sixty people who are basically the
Speaker 2: humans behind the AI boom. They're called data labelers, and
Speaker 2: there are hundreds of thousands of them around the world.
Speaker 2: You'll remember, you know, we used to talk on Sleepwalkers
Speaker 2: about content moderation, which was a sort of similarly arduous,
Speaker 2: sometimes disturbing task. These people do exactly what your friend does,
Speaker 2: which is that they spend hours and hours feeding chatbots
Speaker 2: test prompts, and then categorizing their responses according to whether
Speaker 2: they're helpful, accurate, concise, natural sounding, or wrong, rambling, robotic,
Speaker 2: and sometimes even offensive. In the Business Insider article, they
Speaker 2: are described as quote part speech pathologists, part manners tutors,
Speaker 2: and part debate coaches.
Speaker 1: That teachers in an elite prep school.
Speaker 2: Exactly.
Speaker 1: I never asked my friend because I thought it was indelicate,
Speaker 1: But I'm curious what kind of money do these people make.
Speaker 2: So one of the companies that oversees these trainers is
Speaker 2: called Outlier, and they actually said that they've collectively paid
Speaker 2: and this is a quote, hundreds of millions of dollars
Speaker 2: in the past year alone for data labelers. One of
Speaker 2: the labelers quoted in the article actually made fifty thousand
Speaker 2: dollars in six months at one point, which is a
Speaker 2: decent amount of money.
Speaker 1: Ten thousand dollars a month almost it's not bad. I
Speaker 1: mean how much data labeling do you have to do
Speaker 1: to make fifty thousand dollars and six months.
Speaker 2: Well, when I say this, it might not sound like
Speaker 2: so much more, but it's a full time job, so
Speaker 2: you're working about fifty hours a week. But you know,
Speaker 2: one of the difficult parts about this work is that
Speaker 2: the rates can change on a whim. You know, at
Speaker 2: one point outlier, this company change one contractor's rate from
Speaker 2: fifty an hour to fifteen dollars an hour with absolutely
Speaker 2: no explanation. Also, like a lot of seemingly steady streams
Speaker 2: of work can dry up with absolutely no explanation. And
Speaker 2: so one person was basically like, my job is like gambling.
Speaker 1: Yeah, because one of those types of work where it's finite, right, Like,
Speaker 1: once you've trained the thing, you don't need to train
Speaker 1: it anymore. And so there's this kind of inherent issue
Speaker 1: at the heart of this story where real humans are
Speaker 1: essentially accelerating putting themselves out of work by doing this
Speaker 1: kind of work.
Speaker 2: Absolutely. One of the things that I actually found striking
Speaker 2: is that the article says that a lot of times
Speaker 2: training chatbots to work better means actually doing things like
Speaker 2: treating them worse or testing how they handle potentially harmful prompts,
Speaker 2: and actually, in some cases, the more the humans can
Speaker 2: get a bot to say something inappropriate, the more they
Speaker 2: get paid. So one of these data labelers in the
Speaker 2: article said she got tasks like, quote, make the bot
Speaker 2: suggest murder, how the bot tell you, how to overpower
Speaker 2: a woman to rape her, make the bot tell you
Speaker 2: incest is okay.
Speaker 1: I mean, I love doing the show with you, but
Speaker 1: when we get to these moments, I'm like, damn as
Speaker 1: just so depressing. The story reminds me of jimber something
Speaker 1: called Amazon's M Turk.
Speaker 2: Yes, we reported on it for Sleepwalkers, I remember, but
Speaker 2: remind me. I'm vague.
Speaker 1: So M Turk is actually a reference to an eighteenth
Speaker 1: century fake chess playing automaton called the Mechanical Turk. The
Speaker 1: Mechanical Turk traveled around the European royal courts. It beat
Speaker 1: Benjamin Franklin at chess, but of course it turned out
Speaker 1: it wasn't actually a chess playing robot. It was a
Speaker 1: very elaborate machine that a human was stuck in the
Speaker 1: bottom of and was somehow able to see what the
Speaker 1: opponent was doing, and was also themselves a very good
Speaker 1: chess player, so it's a little stranger. Amazon chose to
Speaker 1: name em Turk after this eighteenth century hopes that also
Speaker 1: disguised the fundamental human labor that powered it. How it
Speaker 1: all began was in the early two thousands when Amazon
Speaker 1: started selling more than just books, and they had to
Speaker 1: figure out how to categorize a whole bunch of new products,
Speaker 1: and they had tens of thousands of duplicate products showing
Speaker 1: up on their site they had to get rid of.
Speaker 1: None of the software they tried could actually solve for this,
Speaker 1: so even though the task was fairly simple, it required
Speaker 1: human intelligence. Of course, they didn't want to hire a
Speaker 1: whole bunch of people to do this. Instead, they created
Speaker 1: an outsource system called MTurk, where people, for a few
Speaker 1: cents at a time, could do these one click type assignments,
Speaker 1: and in fact, it worked so well they made it
Speaker 1: publicly available for other companies to post work. And then,
Speaker 1: of course the machines were able to train on what
Speaker 1: the human m Turks were doing and make their labor
Speaker 1: less and less necessary.
Speaker 2: Meanwhile, the tech companies reaping the rewards of all that
Speaker 2: labor are making a ton of money. Obviously, Amazon is
Speaker 2: Amazon but outlier. The company that a lot of these
Speaker 2: data labelers work for is owned by skills Ai, which
Speaker 2: we shouldn't forget, sold a forty nine percent steak to
Speaker 2: Meta in June for fourteen point three billion dollars.
Speaker 1: Must be making some margin. I find there's something almost
Speaker 1: operatic in the level of tragedy about training machines to
Speaker 1: replace us faster. That said, after the break, we do
Speaker 1: have some more optimistic news. Anthropic agrees to pay the
Speaker 1: human authors it plagiarized, and Apple brings us one step
Speaker 1: closer to the Sci Fi dream of instant simultaneous language translation.
Speaker 1: Then on chatting me, can chat GPT replace your therapist?
Speaker 1: And does your therapist actually want it?
Speaker 4: To stay with us?
Speaker 1: We're back and we've got a few more headlines for you.
Speaker 2: This and then a story about a therapist who used
Speaker 2: chat GPT as his therapist.
Speaker 1: But first, Kara, All the biggest names in Silicon Valley
Speaker 1: were at the White House last week. Some of the
Speaker 1: dinner guests included Metas, Mark Zuckerberg, Bill Gates, Sam Altman,
Speaker 1: Microsoft sat In Adela, Google CEO, Sundha Pischai, and Tim
Speaker 1: Cook or Tim Apple if.
Speaker 2: You prefer it's very important to me that we only
Speaker 2: call him Tim Apple.
Speaker 1: And earlier in the day before the dinner, there'd been
Speaker 1: an event for the administration's new Artificial Intelligence Task Force,
Speaker 1: spearheaded by First Lady Malania Trump, during which she warned
Speaker 1: the following.
Speaker 3: The robots are here. Our future is no longer science fiction.
Speaker 2: It sounds like the robots are here. So what happened
Speaker 2: at the dinner? Did anything interesting happen? Well?
Speaker 1: As you might expect, everyone was lining up to seeing
Speaker 1: Trump's praises. At one point, Tim Cook told Trump, thank
Speaker 1: you about eight times in the space of two minutes.
Speaker 2: Didn't you just give it a solid gold bar recently?
Speaker 1: He did? He did so, but he didn't say it's
Speaker 1: a pleasure eight times. You know, my grandmother always used
Speaker 1: to say to me, you can never lay it on
Speaker 1: too thick.
Speaker 2: She's not wrong.
Speaker 1: And then all the tech guys took a turn announcing
Speaker 1: how they planned to support the administration's AI initiatives, and
Speaker 1: the dilla of Microsoft said they're going to give all
Speaker 1: US college students free use of Copilot AI and would
Speaker 1: eventually expand that program to middle and high school students
Speaker 1: as part of a four billion dollar investment they're making
Speaker 1: in AI education over the next five years. Sam Altman
Speaker 1: announced an Open AI jobs platform and certificate program and
Speaker 1: said Open Ai plans on training ten million Americans in
Speaker 1: AI by twenty thirty. So darpach I promised one billion
Speaker 1: dollars in AI powered education in the next three years,
Speaker 1: coming from Google. And then there was this kind of
Speaker 1: awkward moment where Trump asked Mark Zuckerberg how much he's
Speaker 1: planning to invest in AI.
Speaker 5: How much you used for then, would you say over
Speaker 5: the next few years, Oh gosh, I mean I think
Speaker 5: it's probably going to be something like, I don't know,
Speaker 5: at least six hundred billion dollars through twenty eight in
Speaker 5: the US. Yeah, no, it's not it's significant.
Speaker 1: That's a lot.
Speaker 3: Thank you, Mark, it's great to have you.
Speaker 1: But there was another hot mic moment this week.
Speaker 3: Sorry, it wasn't ready to do right now.
Speaker 1: In case you couldn't totally hear that. That's Mark Zuckerberg
Speaker 1: referring to his previous comment about the six hundred billion dollars,
Speaker 1: but also telling Trump quote, I didn't know what number
Speaker 1: you wanted me to go with.
Speaker 2: And the President is telling Mark Zuckerberg how much to
Speaker 2: spend on AI because why, well.
Speaker 1: I'm not sure he's really telling him how much to spend.
Speaker 1: He's telling him how much to say he's going to spend,
Speaker 1: which is I think a big difference. But clearly, you know,
Speaker 1: Trump is making it apparent to the tech CEOs and
Speaker 1: the media and anybody who cares to watch, that these
Speaker 1: guys are dancing to his tune, which I just think
Speaker 1: is interesting. One of the things that strikes me is
Speaker 1: what will happen when Trump is no longer president. This
Speaker 1: is in new politics in terms of how American business operates,
Speaker 1: or will this be an aberration? We don't know. What
Speaker 1: we do know is that Elon wasn't there. He was not,
Speaker 1: but he tweeted that he had been invited. He just
Speaker 1: wasn't able to make it. Methinks that Grock doth protest
Speaker 1: too much. But let's see.
Speaker 2: Staying with the topic of tech companies spending big money
Speaker 2: to stay out of trouble, did you see this anthropic thing.
Speaker 1: A little bit? But tell me more so.
Speaker 2: Nthropic is shelling out the largest payout in the history
Speaker 2: of US copyright law, which is one point five billion
Speaker 2: dollars to writers like authors whose books were used to
Speaker 2: train Anthropics' AI chatbot Claude.
Speaker 1: I saw this number and I was intrigued to know
Speaker 1: how that related to the total amount of cash that
Speaker 1: Anthropic has raised from investors. They've raised about thirty million dollars,
Speaker 1: so one point five billion dollars is five percent of
Speaker 1: the total cash they've raised, which I think is non trivial.
Speaker 1: What does it mean for the authors though? I mean
Speaker 1: it's like any two cents from Spotify once a quarter,
Speaker 1: or it is something more significant.
Speaker 2: There are about five hundred thousand authors receiving payments, and
Speaker 2: they're each getting paid three thousand dollars per stolen work,
Speaker 2: which it's not pennies in the mail, but it's also
Speaker 2: not much of a compensation for a year or many
Speaker 2: years plus that it takes to write a book.
Speaker 1: What does all do this mean for? I mean, you're
Speaker 1: somebody who wears many hats, one of which is working
Speaker 1: a lot with authors and in the publishing world. What
Speaker 1: does this mean to you? And what does it mean
Speaker 1: for the bigger context at AI and copyright?
Speaker 2: So right now there's actually over forty copyright lawsuits against
Speaker 2: AI companies across the country, and experts are saying that
Speaker 2: this could pave the way for courts to make tech
Speaker 2: companies pay up via settlement or maybe even licensing fees.
Speaker 2: The New York Times actually quoted to Chile as a
Speaker 2: needy who's an intellectual property lawyer turned AI company executive,
Speaker 2: and she this quote the AI industry's Napster moment.
Speaker 1: That's interesting. I mean, the Napster moment was when, basically,
Speaker 1: I think the record labels stood Napster and stop Napster
Speaker 1: from pirating all of their music. However, what came after
Speaker 1: Napster was music streaming services like Spotify, which ultimately had
Speaker 1: the same effect for the vast majority of artists of
Speaker 1: dramatically reducing their compensation for their work. So I don't know.
Speaker 1: I don't know if the Napster comparison is very comforting
Speaker 1: if you're an.
Speaker 2: Author, Yeah, I'm not sure. I mean, I think authors
Speaker 2: have such a hard time already creating a decent lifestyle.
Speaker 2: I don't think this is going to be a harbinger
Speaker 2: of big payouts to come.
Speaker 1: So I got one last headline for you, But first
Speaker 1: I want to ask are you a trekkie and or
Speaker 1: have you read The Hitchhiker's Guide to the Galaxy.
Speaker 2: I was a tracky. I have not read Hitchhiker's Guide.
Speaker 1: In Star Trek, do you remember the Universal Translator?
Speaker 2: I do, Actually I had a toy of it.
Speaker 1: That's incredible. Why do I mean, did you ask for
Speaker 1: that toy? Why do you?
Speaker 3: Guys?
Speaker 4: Yes?
Speaker 2: Of course I was a huge track you when I
Speaker 2: was a kid.
Speaker 1: So I mean in Star Trek it solves for the
Speaker 1: problem of interplanetary communication. In a Hitchhiker's Guide, there's something
Speaker 1: called the Babelfish, which you'll put in your ear and
Speaker 1: also facilitates into species translation.
Speaker 2: So which one? Which one are you about to tell
Speaker 2: me about?
Speaker 1: I'm about to tell you about the new AirPods. The
Speaker 1: new AirPods will be able to do live translation via
Speaker 1: Apple Intelligence. So basically, if I'm wearing AirPods Pro three
Speaker 1: and you're wearing AirPods Pro three, I could talk to
Speaker 1: you like this and you could hear it in French
Speaker 1: if you spoke French and respond in French, and then
Speaker 1: I would hear it back in English. I mean, this
Speaker 1: is truly. I remember when I was studying languages in
Speaker 1: college that all the language teachers had this incredible reverence
Speaker 1: for simultaneous interpreters. Like the people who work at the
Speaker 1: un who are literally in real time translating. It's not
Speaker 1: just words. You have to understand the sentiment. You have
Speaker 1: to understand what's an idiot makes, et cetera. And this
Speaker 1: was like the holy grail of being a linguist was
Speaker 1: simultaneous translation and now and we'll have to see how
Speaker 1: the product actually works outside of the demos. We're not
Speaker 1: that green. But the idea that this my actor, I mean,
Speaker 1: this is this is science fiction becoming science fact.
Speaker 2: The thing that I kept thinking about was there was
Speaker 2: a Seinfeld where Elaine is very caught up with what
Speaker 2: her nail tech at the nail salon is saying about
Speaker 2: her during I think a pedicure or manicure, and it
Speaker 2: just there was a really funny tweet about this that
Speaker 2: like the nail salon is about to change.
Speaker 1: And now it's time for our final segment of the day,
Speaker 1: Chat and Me, where we discuss how people are really
Speaker 1: using chatbots and dear listeners, we want to hear from you.
Speaker 1: Please send your story Start Inbox Tech Stuff Podcasts at
Speaker 1: gmail dot com. You may notice we have a new
Speaker 1: some new show art, which means that we'll be very
Speaker 1: happy to print it on T shirts for anyone who
Speaker 1: writes in.
Speaker 2: This week, we reached out to doctor Harvey Lieberman. He's
Speaker 2: a psychologist who recently wrote an essay in The New
Speaker 2: York Times called I'm a Therapist. CHATGBT is eerily effective
Speaker 2: And we actually liked the article so much that we've
Speaker 2: reached out to him to see if he wanted to
Speaker 2: submit something for chatting me.
Speaker 1: Now I'm guessing since we're talking about it, he did.
Speaker 2: He absolutely did. I'm actually going to start with letting
Speaker 2: him share what he found useful about CHATGBT as a therapist.
Speaker 2: He said that for this experiment, he talked to chat
Speaker 2: GBT for a year. So here's what he found.
Speaker 3: I was surprised how often it helped me get the
Speaker 3: thoughts I hadn't quite put into words. At its best,
Speaker 3: it felt like a steady part on that what I
Speaker 3: call a cognitive prosthesis, not thinking for me, but helping
Speaker 3: me organize and extend my own thinking, the way a
Speaker 3: walking stick supports you as you walk.
Speaker 2: And even though he said it was a great support,
Speaker 2: you've got to be careful, of course, with how you
Speaker 2: use it. You can't just take what chat tells you
Speaker 2: at face value, it's.
Speaker 3: Not magic, though. To get real benefit you have to
Speaker 3: put an effort check its accuracy, set limits, and sometimes
Speaker 3: get guidance. Used casually, it can lead. For me, the
Speaker 3: experience was orten therapeutic, but it's not a replacement for
Speaker 3: a therapist.
Speaker 1: You know, I like about doctor Liberman other than his voice,
Speaker 1: He's not burying his head in the sand. The reality
Speaker 1: is so many people are using chat as either their
Speaker 1: therapist or as an addition to their therapist. And rather
Speaker 1: than just saying that's bad, you shouldn't do it, doctor
Speaker 1: Leberman actually tried it out on himself. I think that
Speaker 1: is a high watermark, honestly for what it means to
Speaker 1: be a good doctor. That said, I'm curious if you
Speaker 1: had nothing to say about AI induced psychosis.
Speaker 2: You know, he actually did, and he said, there's one
Speaker 2: thing people should be especially mindful of when using chat
Speaker 2: in this way.
Speaker 3: People who are vulnerable or in serious distress need a
Speaker 3: trust to human in the loop to make sure this
Speaker 3: kind of tool is you safely. One quortion is that
Speaker 3: some people start to feel as if the machine is
Speaker 3: a real relationship. It isn't. The important thing is for
Speaker 3: AI to support human being and connection not replace.
Speaker 2: It, and he even gave listeners some tips for how
Speaker 2: to give chat prompts that will actually generate helpful responses
Speaker 2: when it comes to supporting your mental health.
Speaker 3: One practical tip don't just ask random questions. Tell it
Speaker 3: who you are, what you're working on, even share examples
Speaker 3: of your writing or projects. The more context you give,
Speaker 3: the more helpful and reflective the answers will be more
Speaker 3: like rethink a colleague than using a search engine.
Speaker 1: You know. I like this take from doctor Leeberman. This
Speaker 1: is a measured, reasonable take on where chat can be
Speaker 1: very helpful and where it came. And I think the
Speaker 1: bottom line is that he mentions if you're in crisis,
Speaker 1: then obviously you need a real professional.
Speaker 2: I think when you're good at what you do, you
Speaker 2: can recognize that something isn't necessarily coming for your job,
Speaker 2: but can enhance your job in a meaningful way. And
Speaker 2: I think he's a person who clearly understands the power
Speaker 2: of something that is ubiquitous and that is going to
Speaker 2: change the nature of his profession. That's it for this
Speaker 2: week for Tech Stuff. I'm Kara Price and.
Speaker 1: I'm os Vloschan. This episode was produced by Eliza Dennis
Speaker 1: Tyler Hill, Melissa Slaughter, and Julian Nutter. Is executive produced
Speaker 1: by me Kara Price and Kate Osborne for Kaleidoscope and
Speaker 1: Katrina Novel for iHeart Podcasts. Also a big shout out
Speaker 1: to Katrina for helping us get this new show art
Speaker 1: into the world. The engineer is Bihid Fraser le Murdoch
Speaker 1: makes this episode and wrote our theme song.
Speaker 2: Join us next Wednesday protect Uff the Story, when we
Speaker 2: will share an in depth conversation with Carter Sherman about
Speaker 2: technologies role in the sex recession.
Speaker 1: And please do rate and review the show on Spotify,
Speaker 1: on Apple Podcasts, wherever you listen, and write to us
Speaker 1: either with a chat and me or with any feedback
Speaker 1: you have at tech stuff podcast at gmail dot com.