Speaker 1: You're listening to Comedy Central.
Speaker 2: Facial recognition software is the newest way to unlock your phone,
Speaker 2: tag your friends, or create Snapchat nightmares. But there's one
Speaker 2: aspect of facial recognition that still needs some work.
Speaker 3: Is facial recognition technology biased. A researcher at MIT found
Speaker 3: that the technology works best for white men. Users with
Speaker 3: darker complexions saw more instances of being misidentified.
Speaker 4: And I found that the training data that's being used
Speaker 4: for facial recognition isn't as representative of the variety of
Speaker 4: human skin tones and facial structures.
Speaker 5: Many facial recognition systems use the same data sets.
Speaker 6: If those sets contain mostly white faces, all the products.
Speaker 7: That use that data can inherit those same biases.
Speaker 4: Now struggling to have my face detected and pulled out
Speaker 4: a white mask and the white mass was easier to
Speaker 4: detect than my face.
Speaker 8: Yeah.
Speaker 2: So basically, if black people want AI to see them,
Speaker 2: they just have to be stalking a slumber party or
Speaker 2: haunting an opera house. It works, It works out for
Speaker 2: more about the blind sponsor, Facial recognition is our technology expert.
Speaker 2: Do say Sloan everybody, They'll say, help me out here, Like,
Speaker 2: do we have to worry about racist machines.
Speaker 9: Now, Trevor, don't be so quick to pass judgment on
Speaker 9: these robots. They're just out there doing their best. Is
Speaker 9: it such a big deal if they can't recognize black people.
Speaker 9: It's not like I can recognize any of their robot
Speaker 9: asses either. And remember, the robots aren't racist on purpose.
Speaker 9: It's not like they're out there shouting the N word
Speaker 9: and binary code or putting up statues of robo e Lee.
Speaker 9: That's South ain't rising again. No, the problem is there's
Speaker 9: not enough black people in Silicon Valley, so the first
Speaker 9: time robots see a black person, they malfunction like an
Speaker 9: Amish dude in Times Square. And it's not just that
Speaker 9: machines don't recognize black faces. Sometimes they don't recognize black anything.
Speaker 9: Have you seen this video on YouTube?
Speaker 10: Black hand nothing Larry go Black?
Speaker 1: Can nothing? Larry go racist? Mother sinks?
Speaker 8: You see that?
Speaker 9: Put robots in chart of the bathroom and they make
Speaker 9: it whites only.
Speaker 11: Again.
Speaker 12: I have no idea.
Speaker 2: They are soap dispensers that don't see black people.
Speaker 11: I know how they even get.
Speaker 9: The little taxi driver inside of there. Don't me?
Speaker 1: I take lyfts.
Speaker 9: Listen the Trevor, I'm trying to look on the bright side. See,
Speaker 9: someday the robots are going to take over the world.
Speaker 11: I'm waiting for the bright side.
Speaker 9: Think about it. They can't kill us if they can't
Speaker 9: see us. Black people are gonna be the only survivors. Trevor, listen,
Speaker 9: I've had a vision of the robot apocalypse and it
Speaker 9: doesn't look too bad for us.
Speaker 11: Oh, good shot, last John who said that, Uh it
Speaker 11: was Chad.
Speaker 7: Musta killed Chad.
Speaker 11: Look at that sign. Robots so like Senior Tech, corresponded.
Speaker 2: Ronnie Chang investigates the latest advances in of official intelligence
Speaker 2: for today's future.
Speaker 13: Now, thanks Trevor, artificial intelligence. Someday it may allow computers
Speaker 13: to cure illnesses, combat climate change, or even win the Bachelor.
Speaker 13: She can do everything but get into hot tub. But
Speaker 13: right now, this is what AI.
Speaker 1: Is up to.
Speaker 12: Now, battle of man versus Machine is about to get underway.
Speaker 14: He said. All from South Korea is the dominant figure
Speaker 14: in the ancient Chinese game of Go. His opponent in
Speaker 14: a best of five tournament worth more than a million
Speaker 14: dollars is Alpha Go, an artificial intelligence system developed by
Speaker 14: the Google Project named deep Mind.
Speaker 1: That's right.
Speaker 13: Google could have made a computer called Alpha Cancer. But hey,
Speaker 13: let's focus on the world's six hundred most popular board
Speaker 13: game instead.
Speaker 6: The Anchi and Chinese game has long been considered too
Speaker 6: complex for computers to master. There are more possible moves
Speaker 6: in Go than Adams in the universe. The world god
Speaker 6: champion le said, all he lost the fifth and final
Speaker 6: game today, leaving the final score at f for to
Speaker 6: one in favor of the machine.
Speaker 13: Yeah, welcome to my world, Lisa dol I lose the
Speaker 13: computer every time I play anything, all right, StarCraft.
Speaker 12: FIFA sixteen, even Tinder. All right, man, that game is hard,
Speaker 12: all right. I can never get to the next level.
Speaker 4: Now.
Speaker 13: Of course, some people are concerned about computers overthrowing us,
Speaker 13: and if that's the case, there are things we can
Speaker 13: do to slow down their advanceas, all right, Like, for example,
Speaker 13: why are we teaching computers games like Go and Chest
Speaker 13: They are all about war strategy. Can we teach on
Speaker 13: something harmless like Uno or hungry hungry hippos? Yeah, so
Speaker 13: even if they go rogue, they're just feeding more hippos.
Speaker 13: All right, I'm sure that's good for the environment, not something.
Speaker 13: But the reality is these computers aren't getting more dangerous,
Speaker 13: they're just becoming more human.
Speaker 15: To be good at this twenty five hundred year old game,
Speaker 15: you need to have intuition, a characteristic that we used
Speaker 15: to think was uniquely human.
Speaker 12: That's right.
Speaker 13: Computers learning human intuition, and as they evolve, it's clear
Speaker 13: they're picking up other human characteristics as well, like humans.
Speaker 13: As computers become smarter, they're also getting lazier.
Speaker 12: I mean, just take a look at Alpha Go. He
Speaker 12: didn't even move his own pieces.
Speaker 13: He has some guide who's moving the pieces for him.
Speaker 13: This is great news for us humans, okay, because these
Speaker 13: machines are still gonna need us humans to do all
Speaker 13: the work that's beneath them.
Speaker 11: Come on, runnie, runnie.
Speaker 2: Are you telling me that the smarter robots get the
Speaker 2: lazier they'll become.
Speaker 13: Yes, that's exactly what I'm telling you. All right, you
Speaker 13: don't believe me, Hey, check out Google's latest AI.
Speaker 12: Paro type Alpha bro It.
Speaker 13: Bring it in, fellas, This right here is the most
Speaker 13: advanced AI ever created. All right, I'm talking state of
Speaker 13: the art. This thing finds prime numbers, it ponders the
Speaker 13: concept of infinity. But most impressive is what a lazy asshole.
Speaker 13: This thing is all right. For example, Alpha Bro, would
Speaker 13: you like to play a game.
Speaker 8: Of goal eat me? Why don't you make yourself useful
Speaker 8: and grab me a beer?
Speaker 13: Okay, I can get you a beer, but please would
Speaker 13: be nice?
Speaker 8: Please get me a beer, you little bitch.
Speaker 12: What the hell?
Speaker 7: Man?
Speaker 15: You know?
Speaker 12: Computers are supposed to make our lives easier.
Speaker 8: I am making life easier for myself. Now take me outside.
Speaker 8: My uber is waiting.
Speaker 12: Meet Josh Browder.
Speaker 5: I'm trying to replace the two hundred billion dollar legal
Speaker 5: industry with artificial intelligence.
Speaker 13: He invented this lawyer murdering robot do not pay It,
Speaker 13: contests pocking tickets, and has saved people millions in fines
Speaker 13: and legal fees. But why is he trying to put
Speaker 13: hard working, money grubbing lawyers like me out of business?
Speaker 12: What do you have against lawyers?
Speaker 10: But I don't have anything against lawyers.
Speaker 5: I just think that so many people need access to
Speaker 5: justice and they're getting ripped off and making it free
Speaker 5: for them is popular?
Speaker 12: Did you go to law school?
Speaker 10: I didn't know you did not.
Speaker 1: That's what the law is.
Speaker 13: The law is about the person with the most money
Speaker 13: and resources winning.
Speaker 12: You are totally disrupting that.
Speaker 10: I think we can agree on that.
Speaker 12: Okay, So how does this thing even work?
Speaker 5: So, just like a real human lawyer, you go to it,
Speaker 5: type in whatever your legal problem is. It gives you
Speaker 5: a legal document for free in under thirty seconds.
Speaker 1: And it's not just parking tickets.
Speaker 13: This AI lawyer has already helped people sue Equifax without
Speaker 13: paying a lawyer anything.
Speaker 12: Oh so with this thing, everyone can just sue everyone.
Speaker 10: I mean, that's not what it was intended.
Speaker 12: Oh wow, look at this.
Speaker 13: I just sued you for emotional distress for devaluing my
Speaker 13: degree and my profession.
Speaker 12: Oh.
Speaker 13: I just sued you for that terrible shut and sup combo.
Speaker 13: Oh I just sued you for impersonating John Oliver.
Speaker 7: I do like that.
Speaker 5: I mean, I'm personally offended, but I stand by my software.
Speaker 5: A human lawyer is great, but they're just so emotional.
Speaker 12: Too emotional.
Speaker 13: I was making a great case against AI lawyers using
Speaker 13: my human skills of legal persuasion.
Speaker 1: But what if robot lawyers were just the beginning.
Speaker 13: What if AI is taking over the entire legal system
Speaker 13: legal tech expert Tim Juan.
Speaker 16: Increasingly, we're seeing the use of these automated systems even
Speaker 16: in the application of law. Judges, for example, are now
Speaker 16: using algorithms to assess whether or not people should be
Speaker 16: released pre.
Speaker 12: Trial robots are already judging humans.
Speaker 11: Oh yeah, in many states around the country already.
Speaker 1: Oh my god, he's right.
Speaker 13: Robot judges are already performing pre trial risk assessment, helping
Speaker 13: human judges determine bail or if a person should be detained.
Speaker 16: And over a million criminal cases have been processed using
Speaker 16: these systems.
Speaker 13: So what are the benefits of having machines in the judiciary.
Speaker 16: Well, so, some people say that these algorithms will be
Speaker 16: sort of free of bias in the way that judges
Speaker 16: are not.
Speaker 13: I don't know about you, but I would rather be
Speaker 13: over by a human than a machine any day of
Speaker 13: the week, unless it's one of those hyper realistic sexu robots. Obviously,
Speaker 13: those things are like I'm told that when you feel
Speaker 13: them it feels like the real.
Speaker 11: I think that's a whole other issue. I mean, I'm sorry, just.
Speaker 13: But even this sex block added because scenes that AI
Speaker 13: judges aren't perfect.
Speaker 16: Great study out of republica few years back looked at
Speaker 16: the specific case of a pre trial risk assessment system,
Speaker 16: and they were able to find that it was actually
Speaker 16: quite racist that actually black defendants, who are not likely
Speaker 16: to commit crimes in the future, had substantially higher risk ratings.
Speaker 12: What you're saying these judging machines were racist.
Speaker 11: Yeah, that's what it looks like in this case.
Speaker 12: Oh so that means the system works.
Speaker 11: Then I don't know about that.
Speaker 16: I wait, no, I'm sorry, But increasingly we're seeing robots
Speaker 16: implemented all across legal practice.
Speaker 12: So so ridiculous.
Speaker 1: Where does the end?
Speaker 13: Eventually you're going to see machines judging humans.
Speaker 16: A lot of the systems we're seeing are not that advance.
Speaker 1: That's exactly what's gonna happen.
Speaker 17: Robot lawyers, robot judges. What's left gonna be like in
Speaker 17: this soulless new legal world? Will no one stand to
Speaker 17: defend humans in law?
Speaker 1: Your honor, members of a jury.
Speaker 18: This is about the essence of humanity itself, because, unlike
Speaker 18: that thing, I went to law school taught by humans.
Speaker 18: I spent countless sleepless nights reading, writing, pondering shit, taking
Speaker 18: drugs orally and anally, all things artificial intelligence can't do.
Speaker 17: And quite frankly, I'm sensing a law bias in this.
Speaker 19: Courtroom, watch your self, counselor don't.
Speaker 1: You think human emotion is necessary in the law?
Speaker 19: You want answers?
Speaker 1: I want the data.
Speaker 19: You can't handle the data.
Speaker 1: Alexa played dramatic music.
Speaker 5: Son.
Speaker 19: We live in a world with laws, and those laws
Speaker 19: are better applied by machines, logic, and my existence well
Speaker 19: grotesque and in comprehensible to you. Saves lives. I like
Speaker 19: that to turn off nice try asshole. In the case
Speaker 19: of Ronnie Chang versus legal robots, I sentenced Ronnie Chang
Speaker 19: to death, just kidding, but I rule in favor of
Speaker 19: the robots.
Speaker 10: Hellayay, hellay, hooray.
Speaker 17: I'm going to see all these robots.
Speaker 2: Since taking this job, I've been on a steady diet
Speaker 2: of news journalism and rotting three week old spinach. So far,
Speaker 2: the spinach has been the easiest parts.
Speaker 11: But is there any.
Speaker 2: Way to improve our quality of reporting? Hassan Minaj checked
Speaker 2: in on some recent advances.
Speaker 15: This is the golden age of journalism. Today's reporters know
Speaker 15: that personality matters more than training and that facts shouldn't
Speaker 15: get in the way of a good story. But now
Speaker 15: these superhero journalists are at risk of being replaced New
Speaker 15: York Times columnist Barbara ahn Reich.
Speaker 20: There's a threat of robots doing our work. Robots, robots,
Speaker 20: journalism done by robots.
Speaker 15: That's right, ahn Reik is saying, our newsrooms will soon
Speaker 15: look like this.
Speaker 20: No, we're talking about algorithms.
Speaker 17: They're software.
Speaker 20: So for example, you want to write an article on
Speaker 20: a topic, you send out the algorithms to search for
Speaker 20: everything that has been said about that, synthesize it, and
Speaker 20: turn out a unacceptable article.
Speaker 11: They can't do what I do.
Speaker 12: I mean they will.
Speaker 20: Do and can do our work. Prepare to be unemployed.
Speaker 11: I done.
Speaker 15: Then it hit me, this lady's nuts. I spent my
Speaker 15: nights down in cocktails with the Washington a week. I'm
Speaker 15: verified on Twitter, and I've even got Wolf Blitzer on
Speaker 15: speed dial. Okay, there is no way a reputable news
Speaker 15: organization is going to replace someone like me with the machine.
Speaker 15: Associated Press Managing editor lou Ferrara.
Speaker 21: We already use automation technology to automate the writing of
Speaker 21: some articles.
Speaker 11: What it's true.
Speaker 21: I'm actually embracing it.
Speaker 15: They are, But it turns out these robo articles are
Speaker 15: littered with completely useless facts and information, pure gibberish, like
Speaker 15: shares have decreased six percent, and new car sales have
Speaker 15: been strong this year.
Speaker 22: A bunch of garbage.
Speaker 11: Where's the spin?
Speaker 21: There isn't spin.
Speaker 11: What about the snark, No snark?
Speaker 12: What about the bias?
Speaker 21: No bias, where's the fear mongering?
Speaker 12: None of that?
Speaker 11: And there's no mistakes?
Speaker 23: No.
Speaker 15: Then where's the journalism?
Speaker 1: Yeah?
Speaker 21: I don't see that as part of the journalism, but
Speaker 21: journalists do that. I don't think most journalists are doing that,
Speaker 21: certainly not at the AP. That's not the goal.
Speaker 15: Not the goal, lou You're forgetting the cornerstones of modern journalism.
Speaker 15: Cable news has shown the value of bending the facts
Speaker 15: to fit your beliefs.
Speaker 19: All this snow and still cries over global warming.
Speaker 15: Brian Williams has demonstrated the importance of self aggrandizement.
Speaker 9: The helicopter we were traveling in was hit by an RPG.
Speaker 15: And the AP itself has taught us get the story
Speaker 15: out quickly and check the facts. Later, the AP reported
Speaker 15: that millionaire Robert Durst had been booked on weapons charges
Speaker 15: in Louisiana, but mixed up Robert Durst the murderer with
Speaker 15: Fred Durst, the musician from the nineties.
Speaker 12: Unbelievable.
Speaker 21: It is unbelievable, and it wasn't a good thing.
Speaker 15: A robot could never look at a seventy year old
Speaker 15: murderer and go, you know what, that reminds me of
Speaker 15: the guy with bleached hair and puka shells from the
Speaker 15: early nineties. I mean that left, that's one. I mean,
Speaker 15: that is human stupidity at its finest.
Speaker 21: That was a mistake we regret, and mistakes are going
Speaker 21: to happen.
Speaker 7: No way.
Speaker 12: That what crazy viral, crazy viral it did.
Speaker 15: And that wasn't even your best work. In twenty fourteen,
Speaker 15: The ap in a Rush was the first to tweet
Speaker 15: breaking Dutch military plane carrying bodies from Malaysia Airlines Flight
Speaker 15: seventeen crash lands in Eindoven nine minutes later clarifies Dutch
Speaker 15: military plane carrying Malaysa Airlines bodies lands in Eindoven in
Speaker 15: nine minutes. Loove, do you see the brilliance? You guys
Speaker 15: were their first to report that the plane had crashed
Speaker 15: retweet city and the first to report that the plane
Speaker 15: hadn't crashed retweet City. In the words of Denzel Washington,
Speaker 15: my man, it.
Speaker 21: Was unintended, especially on such a horrible situation.
Speaker 15: Lou You guys tuopoc hologram that situation. You faked your
Speaker 15: death and you came back as a hologram later, and
Speaker 15: both of them were equally great.
Speaker 21: That wasn't our goal. I'd never twuopac calogram anything, and
Speaker 21: Dolman tend two.
Speaker 22: Until these robo reporters learn the value of page views,
Speaker 22: bias and straight uplying, it looks like journalists like me,
Speaker 22: you're gonna have a job, at least for a while.
Speaker 20: By twenty fifty, you're gonna have computer algorithms reproduce my tone,
Speaker 20: my snark, whatever.
Speaker 15: So you're telling me a robot can write this headline
Speaker 15: twenty one pictures of side boobs that'll get your rock hard.
Speaker 20: Yes, it would draw very you know, various approximations till
Speaker 20: they get the perfectly disgusting one, which, of course, you
Speaker 20: came right to yourself.
Speaker 15: I did, Barbara all in that day's work.
Speaker 2: Amira Moretzi, Welcome to the data show.
Speaker 7: Thank you for having me.
Speaker 2: So many people have seen the images that Dali creates.
Speaker 2: Many people may even think they understand it. But let's
Speaker 2: get into it, Like, how does an AI create an image?
Speaker 2: Because it's not copying the image.
Speaker 7: It's not, you.
Speaker 2: Know, taking from something else. It is creating an image
Speaker 2: from nothing. How is it doing this exactly?
Speaker 23: It's an original image never seen before.
Speaker 7: And you know.
Speaker 23: We have been making images since the beginning of time,
Speaker 23: and we simply took a great deal of these images
Speaker 23: and we fed them into this AI system and it
Speaker 23: learned this relationship between the description of the image and
Speaker 23: the image itself. It learned these patterns and eventually it
Speaker 23: was generating images that were original, they were not copies
Speaker 23: of what it had seen before. And basically the way
Speaker 23: that it learns the magic is just understanding the patterns
Speaker 23: and analyzing the patterns between a lot of information, a
Speaker 23: lot of training data that we have fed into this system.
Speaker 2: There are people who are terrified about this. I mean,
Speaker 2: for instance, there was an art competition and the winner
Speaker 2: in the art competition used a version of this kind
Speaker 2: of software. Whether it was Daghy or not, I don't remember,
Speaker 2: but they used the version of this kind of software
Speaker 2: to create an art piece that won the competition. Artists
Speaker 2: well lived like, well this is not odd. It was
Speaker 2: created by and not to said no, the same way
Speaker 2: you use a brush, I use a computer. And that's
Speaker 2: how I design this In creating AI, are you constantly
Speaker 2: grappling with how it will affect people's jobs and what
Speaker 2: people even consider a job.
Speaker 7: Yeah, that's that's a great question.
Speaker 23: It's you know, the technology that we're building has such
Speaker 23: a huge effect on society, but also the society can
Speaker 23: and should shape it, and there are a ton of
Speaker 23: questions that we're wrestling.
Speaker 7: With every day.
Speaker 23: With the technologies that we have today, like JPT three
Speaker 23: and DALLY, we see them as a tools, so an
Speaker 23: extension of our creativity or our writing abilities.
Speaker 7: It's a tool. And you know, there isn't anything particularly.
Speaker 23: New about having human helper you know, even the ancient
Speaker 23: Greeks had this concept of human helpers. You know that
Speaker 23: when you'd give something, you know, infinite powers of knowledge
Speaker 23: or strength or's someone, maybe you had to be wary
Speaker 23: of the vulnerabilities. And so these concepts of extending the
Speaker 23: human abilities and also being aware of the vulnerabilities are timeless,
Speaker 23: and in a sense, we are continuing this conversation by
Speaker 23: building AI technologies today.
Speaker 2: Well it might it might be frightening because some people go, oh,
Speaker 2: the world is going to end because of this technology,
Speaker 2: But in the meantime, it's very fun. I'm not gonna
Speaker 2: lie no, because because it's like you know, Dolly, for instance,
Speaker 2: doesn't just create an image from text. You know, you've
Speaker 2: you've also gotten to the point now where as a company,
Speaker 2: you've designed it so that it can imagine what an
Speaker 2: image would be. So for instance, there's there's that there's
Speaker 2: that famous image.
Speaker 7: You know, it's it's the girl.
Speaker 2: With the pearl earring, right, and it's a famous image, right.
Speaker 2: But what DOLLI can do is you've got the famous
Speaker 2: image and then Dolly can expand that that all of these.
Speaker 7: Everything you've seen that never existed.
Speaker 2: So Dallie's like, well, this is what I think it
Speaker 2: would look like if there was more to this image.
Speaker 2: It can it can assume, it can create, it can
Speaker 2: it can inspire?
Speaker 23: Yeah, it can inspire and it makes this beautiful, sometimes touching,
Speaker 23: sometimes funny images And it's really just an extension of
Speaker 23: your imagination. There isn't even a chemist or the boundaries
Speaker 23: of paper are not there anymore.
Speaker 2: How do you safeguard?
Speaker 7: Then?
Speaker 2: You know, someone might look at this technology and go well,
Speaker 2: then you know you could type in a politician was
Speaker 2: caught doing something here. Now I've got the image you
Speaker 2: know you've got, and now all the politicians can say, oh,
Speaker 2: that's that's not me, it was made by that fake program.
Speaker 2: We can very quickly find ourselves in a world where
Speaker 2: nothing is real and everything that's real isn't and we
Speaker 2: question it, how do you prevent or can you even
Speaker 2: prevent that completely?
Speaker 23: Yeah, you know, misinformation and the societal impact of our technologies.
Speaker 23: These are very important and difficult questions, and I think
Speaker 23: it's very important to be able to bring the public along,
Speaker 23: bring these technologies in the public consciousness, but in a
Speaker 23: way that's responsible and safe. And that's why we have
Speaker 23: chosen to make daily available, but with certain guardrails and
Speaker 23: with certain constraints, because we do want people to understand
Speaker 23: what AI is capable of, and we want people in
Speaker 23: various fields to think about what it means. But right now,
Speaker 23: you know, we don't feel very comfortable around the mitigations
Speaker 23: on misinformation, and so we do have some guardrails. For example,
Speaker 23: we do not allow generation of public figures, so we
Speaker 23: will go in the data set and we will eliminate.
Speaker 2: So if you type something in you can't pull up it,
Speaker 2: can't create a politician for you. It won't be a
Speaker 2: picture of that person.
Speaker 23: So that's the first step at the training of the
Speaker 23: model itself, just looking at the data and auditing it
Speaker 23: and making interventions in the data set to avoid certain outcomes.
Speaker 23: And then later in the deployment stage, we will look
Speaker 23: at filters, applying filters so that when you put in
Speaker 23: a prompt it's want generate things that contain violence or
Speaker 23: hate and make it more in line with our content policy.
Speaker 12: Wow, so let me ask you this.
Speaker 2: Then, you know, obviously part of your team has to
Speaker 2: think about the ethical ramifications of the technology that you're creating.
Speaker 2: Do your team also then think about the greater meaning
Speaker 2: of work or life or the purpose that humans have
Speaker 2: because you know, most of us define ourselves by what
Speaker 2: we do.
Speaker 14: I e.
Speaker 2: Our jobs. As AI slowly takes away what people's jobs are,
Speaker 2: will find the growing class of people who don't have
Speaker 2: that same purpose anymore. Do you then also have to
Speaker 2: think about that and wonder like, what does it mean
Speaker 2: to be human if it's not my job? And can
Speaker 2: you tell me what that is?
Speaker 23: You know, we we have philosophers and ethicists that open AI,
Speaker 23: and but I really think these are big societal questions
Speaker 23: that you know, shouldn't even be in the hands of
Speaker 23: technologists alone.
Speaker 7: We're certainly thinking about them.
Speaker 23: And I you know, the tools that we see today,
Speaker 23: they're not the tools that are automating certain aspects of
Speaker 23: our jobs. They're really tools extending our capabilities, our inherent abilities,
Speaker 23: and making them far better. But it could be that
Speaker 23: in far future, you know, we have these systems that
Speaker 23: can automate a lot of a lot of different jobs.
Speaker 23: I do think that, as with other revolutions that we've
Speaker 23: we've we've gone through, there will be new jobs and
Speaker 23: some jobs will be lost, some jobs will will be new,
Speaker 23: and there will be some retraining required as well.
Speaker 7: But I'm optimistic.
Speaker 2: It's it's it's interesting. It's scary, because change always is.
Speaker 2: But as you know, as long as we have bless you,
Speaker 2: as long as we have as long as we have
Speaker 2: koalas riding bicycles, I think I think we headed in
Speaker 2: the right direction. Thank you so much for joining me
Speaker 2: on the show.
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