Speaker 1: Sleepwalkers is a production of our Heart Radio and Unusual Productions. Hi,
Speaker 1: I'm Aloan and I'm care Price. Welcome to a special
Speaker 1: bonus episode of Sleepwalkers. Well, Cara, it's very good to
Speaker 1: be back to the office. That you It is good
Speaker 1: to be back, as I just want to apologize to
Speaker 1: our listeners. We don't have an algorithm that's going to
Speaker 1: create season two, so things have taken a little longer. Yes, unfortunately,
Speaker 1: unfortunately a I can't do everything yet. But we are
Speaker 1: hard at work on season two of Sleepwalkers, and we're
Speaker 1: focusing on stories that really contextualize the implications of AI,
Speaker 1: what it's doing, what the future is, and how it's
Speaker 1: affecting us. You know, we had such a good time
Speaker 1: in season one of Sleepwalkers wrapping our heads around the
Speaker 1: meaning of artificial intelligence. It becomes basically a principle of
Speaker 1: statistics prediction, how we're using data to inform our decisions,
Speaker 1: and how that's becoming ingrained in products and services and
Speaker 1: everything we do. Really, it's true the Pandora's box of
Speaker 1: AI has been opened, but we still have the black
Speaker 1: box problem that is true explainable AI. We can't tell
Speaker 1: what neural networks are doing yet, but people are working
Speaker 1: on it, and that's a story we're going to be
Speaker 1: covering closely in season two. But in this bonus episode
Speaker 1: of Sleepwalkers, we're going to take a look back at
Speaker 1: some of the most poignant stories and interesting applications of
Speaker 1: AI that we talked about in the first season, and
Speaker 1: later in this episode, I'm going to give you a
Speaker 1: preview of a fascinating new company that's using AI to
Speaker 1: predict very specific consumer taste, as in preferences, not like
Speaker 1: tasting clothing. Before we get through though us, when you
Speaker 1: look back at season one, what stands out to you
Speaker 1: the most? One of the things that stands out to
Speaker 1: me most is the story you did about liar Bird.
Speaker 1: Thank you, but seriously, the way they use an algorithm
Speaker 1: to create a deep fake of your voice. But in
Speaker 1: that particular piece, the questions it raised about mortality and
Speaker 1: would you want to hear your father's voice beyond the
Speaker 1: grave has stuck with me and I was one of
Speaker 1: the most powerful moments in the whole podcast. Yeah so,
Speaker 1: Jose Satello, one of the founders of Liar Bird, had
Speaker 1: me sit down on a microphone for an hour and
Speaker 1: just speak which was a personal dream of mine, and
Speaker 1: then using that out me interrupting. Using that voice data,
Speaker 1: liar Bird's algorithms created a version of my voice that
Speaker 1: could turn any written text into something that sounded like me.
Speaker 1: But Jose actually explained it better. So can you hit
Speaker 1: the clip? Not an AI scientist, but we do have
Speaker 1: the sophistication to roll tape. I know it might sound
Speaker 1: a bit like magic, but in reality, the way that
Speaker 1: our algorithm's work is basically they are just a battern
Speaker 1: matching algorithms, and so it's trying to figure out how
Speaker 1: to identify the patterns in your voice by comparing it
Speaker 1: against thousands of other voices a shoually tens of thousands
Speaker 1: of other voices, and trying to figure out what is
Speaker 1: it that makes your voice unique. Once Jose's algorithms identified
Speaker 1: what was unique about my voice, obviously everything they had
Speaker 1: the building blocks they needed to make a fake. Then
Speaker 1: we sent Jose a set of sentences we wanted robot
Speaker 1: care to say, and he used another set of algorithms
Speaker 1: to turn the text into what we heard. The way
Speaker 1: they do this is they use it's called a generative
Speaker 1: adversarial network again, which is a system where one neural
Speaker 1: net tries to trick another one a thousand times per second,
Speaker 1: So each time the second network to texts of fake,
Speaker 1: the first one tries again. It basically learns from its mistakes,
Speaker 1: and once it tricks its adversary, it's ready to show
Speaker 1: its results. In our case, liar bird pits my fake
Speaker 1: voice against my real voice until it sounds like this
Speaker 1: sub dog Scara Karen. One of the reasons why this
Speaker 1: story has stuck with me is because it feels like
Speaker 1: we're just at the beginning of tapping the potential and
Speaker 1: the potential for harm of deep fakes. May be remembered
Speaker 1: as the year of the first significant deep fake crime.
Speaker 1: An employee at a UK based energy firm believed he
Speaker 1: was on the phone to his boss and followed instructions
Speaker 1: to transfer two hundred thousand pounds to a scammers bank account.
Speaker 1: That certainly won't be the last deep fatefore we hear of,
Speaker 1: and it raises questions about responsibility and accountability. Who's liable
Speaker 1: in a case like this. Facebook has actually gone so
Speaker 1: far as to create a deep fake detection challenge to
Speaker 1: get the best minds thinking about deep fakes and how
Speaker 1: we might solve the problem and offering like a million
Speaker 1: dollar prize reach prize, which is like a dollar but
Speaker 1: it also shows how important the issue is, especially when
Speaker 1: a company like Facebook gets behind it. Um. There's another
Speaker 1: side to deep fake technology that actually highlights this dichotomy
Speaker 1: and tech anology right now, which is that it can
Speaker 1: be used for menacing purposes but also really powerful and
Speaker 1: beautiful applications. Jose goes on to talk about how liar
Speaker 1: Bird can be used with als patients and give them
Speaker 1: the ability to speak when they've lost all ability to speak,
Speaker 1: when it could give them the opportunity to speak in
Speaker 1: a version of their voice to their children again, which
Speaker 1: is quite profound. One area where I think technology is
Speaker 1: a powerhouse for change is in medicine. Technologists and doctors
Speaker 1: alike are looking at AI to predict, treat, and diagnose,
Speaker 1: you know, everything from depression to cancer, and that's a
Speaker 1: very wide spectrum. And it reminds me of one of
Speaker 1: my favorite interviews that you did, which was your interview
Speaker 1: with Saddartha. Muker g just so fascinated by this article
Speaker 1: he'd written for The New Yoker called AI versus m
Speaker 1: D which laid out all of the kind of benefits
Speaker 1: and potential applications of using AI in medicine, including some
Speaker 1: of the downside such as the black box problem of
Speaker 1: AI that you mentioned, not knowing why an algorithm has
Speaker 1: made a recommendation, and also another problem, which is that
Speaker 1: if we really too much on technology, it can erode
Speaker 1: human skills. There is a fear that AI could move
Speaker 1: us into a very black and white way of thinking.
Speaker 1: The computer says you have cancer, or the computer says
Speaker 1: you to have your liver removed, said Arthur, who is
Speaker 1: one of the world's foremost oncologists and a Pullet Surprise
Speaker 1: winning author. Provided a different perspective. There is something very
Speaker 1: fundamental about the human brain, a scientist's brain, a doctor's brain,
Speaker 1: and artist's brain that asks questions in a fundamentally different manner,
Speaker 1: the why question. Why did this happen in this person
Speaker 1: in this time? Why does the melanoma appear in the
Speaker 1: first calase? What is the molecular basis of that appearance.
Speaker 1: The most interesting mysteries of medicine remain mysteries that have
Speaker 1: to do with the why. Once we give up some
Speaker 1: of the diagnostic pattern recognition material to machines, it will
Speaker 1: be time to play. It will be the time to
Speaker 1: play in the arena of human therapeutics, human biology, the
Speaker 1: complexity of the human interaction, the art of medicine. My
Speaker 1: hope is that medicine, in being more playful, will become
Speaker 1: more compassionate, more able to take into account individuals and
Speaker 1: their individual destinies rather than bucketing people in big categories.
Speaker 1: It means having more time to spend with humans. You know,
Speaker 1: we are so constrained by time that even compassion gets
Speaker 1: three minutes, We won't become more robotic, will become less
Speaker 1: robotic as the robots enter our own So Dartha's point
Speaker 1: is that these tools could make doctors more efficient so
Speaker 1: that they can provide better care. It sort of takes
Speaker 1: the grunt work out of medicine and puts the patient
Speaker 1: care work back in the doctor's hands. This idea that
Speaker 1: technology can actually allow us to be more human, make
Speaker 1: us more empathetic, is fascinating, and it also raises the
Speaker 1: questions about new types of skills that may need to
Speaker 1: be developed in an age of AI. Yeah, and Regina
Speaker 1: Barslay from m I T spoke a lot about this.
Speaker 1: How doctors have to now equip themselves with new ways
Speaker 1: of translating data to patients, we still do not communicate
Speaker 1: it to the patient because I think now there is
Speaker 1: a walk to be done, not on computer science or AIPAD,
Speaker 1: but really on the clinical side. What is the best
Speaker 1: way to communicate it to the patient and what is
Speaker 1: um you know, the past that you're going to give them.
Speaker 1: It is not just enough to say you know you
Speaker 1: are high risk. You need to propose some suggestion and solution.
Speaker 1: So currently the clinical stuff is thinking and looking at
Speaker 1: the ways of effective you know, clinical engagement with a patient.
Speaker 1: You know, I speaking of data. You all know Harri
Speaker 1: was another person who made you and me think about
Speaker 1: humans as reducible to data. I think he's mostly known
Speaker 1: as a historian and for his book Sapiens, but you
Speaker 1: spoke with him about the data we produce as humans
Speaker 1: and how that influences our relationship with technology. That's right,
Speaker 1: which is the topic of his book Daus. And he
Speaker 1: has this phrase data is m to describe how we've
Speaker 1: kind of come to worship the data we create and
Speaker 1: our own technological creations. So what happens when based on
Speaker 1: all of our past behavior, AI starts to know us
Speaker 1: better than we know ourselves. Here's a clip from You
Speaker 1: are talking about exactly that. When we talk about AI,
Speaker 1: we tend to greatly exaggerate the potential abilities, but at
Speaker 1: the same time we also tend to exaggerate the abilities
Speaker 1: of humans. People say that AI is not going to
Speaker 1: take over our lives because it's very imperfect and it
Speaker 1: won't be able to know us perfectly. But what people
Speaker 1: forget is that humans often have a very poor understanding
Speaker 1: of themselves, of the desires, of their emotions, of their
Speaker 1: mental states. For AI to take over your life, it
Speaker 1: doesn't need to know you perfectly, just need to know
Speaker 1: you better than you know yourself. And that's not very
Speaker 1: difficult because we often don't know the most important things
Speaker 1: about ourselves. So, but let's say you could turn back
Speaker 1: the clock to being fifteen, would you have wanted to
Speaker 1: live in a world where there was sufficiently good sensors
Speaker 1: to monitor your eyes, your eye movement, you're breathing, you know,
Speaker 1: while you're going about your daily life, and then to
Speaker 1: interpret that and say to you you have all more
Speaker 1: likely than not you're gay. That's a very good question
Speaker 1: which will become very practical questions in a few years.
Speaker 1: And the way that I grew up and developed. It
Speaker 1: would have been a very bad idea. I wouldn't like
Speaker 1: to receive this kind of insight from form a machine.
Speaker 1: I'm not sure how I would have dealt with it
Speaker 1: when I was fifteen, you know, in Israel in the
Speaker 1: nineteen eighties, and maybe partly it was you know, a
Speaker 1: defense mechanism. In the future too. It it depends where
Speaker 1: you live. Brunei has instituted the death penalty for gay people,
Speaker 1: at least for people engaged in homosexual sex. So if
Speaker 1: I'm a teenager in Brunei, I don't want to be
Speaker 1: told by the computer that I'm gay, because the computer
Speaker 1: will then be able to tell that to the police
Speaker 1: and to the authorities as well. So the apps we use,
Speaker 1: the product we buy, the number of steps we take,
Speaker 1: the delivery I ordered last night, that will becomes data,
Speaker 1: and that data can feed into neural networks to create
Speaker 1: statistical models of us and what we might do next,
Speaker 1: sometimes in order to diagnose a medical condition, and other
Speaker 1: times to sell us a product. Here's V again, looking
Speaker 1: to the future, say ten twenty years. The danger is
Speaker 1: if I still don't know that I'm gay, but the
Speaker 1: government and Coca Cola and and Amazon and Google. They
Speaker 1: already know it. I'm at a huge disadvantage. So it
Speaker 1: could be something as frightening as the secret police coming
Speaker 1: and taking me to a concentration camp. But it could
Speaker 1: also be something Coca Cola knowing that I'm gay, they
Speaker 1: want to sell me a new drink, and they choose
Speaker 1: the advertisement with the shirtless guy and not the advertisement
Speaker 1: with the girl in the bikini. And next day morning
Speaker 1: I go and I buy this soft drink and I
Speaker 1: don't even know why, and they have this huge advantage
Speaker 1: over me and can manipulate me in all kinds of ways. Well,
Speaker 1: as you've all brought up soda, I was not allowed
Speaker 1: to drink soda as a child. My parents tricked me
Speaker 1: into thinking that Seltzer was soda. I later found out
Speaker 1: that soda is soda and Seltzer is water. And somehow
Speaker 1: the Seltzer of it all is the perfect segue because
Speaker 1: AI is not only being used to sell a product,
Speaker 1: it's also being used to create products like Seltzer in
Speaker 1: the R and D research and development phase. And for
Speaker 1: this bombs episode, Julian remember Juliana, a lovely producer, And
Speaker 1: I went to meet the company behind the gastrograph app,
Speaker 1: which is using consumer preference data to make predictions about
Speaker 1: new flavors. People might like, I think you hate Nettle,
Speaker 1: think against Analytical Flavor Systems or a f S is
Speaker 1: tucked away down a side street in Chinatown, up in
Speaker 1: a third story walk up. It does actually and when
Speaker 1: we were there, the office was still waiting for furniture.
Speaker 1: You know, it had this We're going to disrupt the
Speaker 1: industry vibe. We moved in like a month or two ago.
Speaker 1: But it turns out you can't just buy office furniture.
Speaker 1: We met the founder of the company, Jason Cohen, and
Speaker 1: we believe that in the future, in order to be competitive,
Speaker 1: you have to be targeted. That that there won't be
Speaker 1: any billion dollar brands right in ten years if you
Speaker 1: don't have an m b A. Here's what Jason's talking about.
Speaker 1: Think about the coffee you drank this morning, is it
Speaker 1: Third Wave? Did you get it from Starbucks or an
Speaker 1: indie roaster. Food and beverage companies are moving more and
Speaker 1: more towards knee markets. The problem is that they have
Speaker 1: very old school ways of developing new products. But a
Speaker 1: FS is offering another way to reach those customers, and
Speaker 1: that's by allowing companies to formulate more specific products using AI.
Speaker 1: Here's Jason again. Usually the way that things are done
Speaker 1: today is you get some conceptual brief. You might say,
Speaker 1: we want to develop a new fruit flavored beverage for
Speaker 1: Japanese millennial women. Right, you would look at what other
Speaker 1: fruit flavored beverages are out there. You'd look at your
Speaker 1: own product lineup and say, well, we already have a
Speaker 1: lemon flavor, and we're going to send out these briefs,
Speaker 1: and we're gonna send these out to these flavor companies.
Speaker 1: We're gonna see what other fruits we can get, and
Speaker 1: you're gonna wind up with very mainstream things. You're gonna
Speaker 1: wind up with peach and mango and strawberry and grapefruit, right,
Speaker 1: and maybe you'll wind up with something interesting like low
Speaker 1: quad or uzo or dragon fruit. Right. And then you're
Speaker 1: gonna have your own consumer tasting panel internally hopefully uh
Speaker 1: and you're gonna have them taste it, and they're gonna
Speaker 1: have to like some of those more than your current
Speaker 1: offering or more than a competitors. After you've done all
Speaker 1: of this work, which sometimes costs in the tens of
Speaker 1: thousands of dollars in order to have the samples developed.
Speaker 1: How the samples sent to you recruit the consumers, but
Speaker 1: the product in front of the consumers, that data is
Speaker 1: only ever usable once. Right. All you get from that
Speaker 1: as a binary yes no, sixty percent of the population
Speaker 1: liked more than the competitors, and so what we're doing
Speaker 1: is entirely different. Jason's team wants to take product development
Speaker 1: out of the yes no binary. Instead of just saying
Speaker 1: coke or pepsi, they can calculate which parts of each
Speaker 1: soft drink people liked and disliked, and then a f
Speaker 1: s can make entirely new flavors based on what a
Speaker 1: person kind of liked about pepsi and kind of liked
Speaker 1: about coke. And finally, they can transfer those preferences to
Speaker 1: entirely different demographics. So what might someone in Mexico want
Speaker 1: to taste in their cola compared to what a Japanese
Speaker 1: millennial woman might want to taste in her cola? This
Speaker 1: is what Jason believes is truly disruptive, being able to
Speaker 1: say to a brand, if you want to launch in Mexico,
Speaker 1: you should tweak your flavors in this way because we're
Speaker 1: actually able to collect this data, develop a data set,
Speaker 1: and make predictions. So we could take data from say, white,
Speaker 1: twenty to three year old college educated males, and use
Speaker 1: that to predict what every other population in the United
Speaker 1: States is going to perceive in that product. And so
Speaker 1: we're taking an industry that has never seen predictions of
Speaker 1: any kind before and finally being able to actually predict things,
Speaker 1: predict who's gonna like it, how much they're gonna like
Speaker 1: it right, and what we can do to optimize it
Speaker 1: so that they like it more. We can actually create
Speaker 1: products that no one would have ever thought of, and
Speaker 1: no one ever would have thought that a segment of
Speaker 1: the population would have liked. And this is something that
Speaker 1: we now do with the companies that we work with.
Speaker 1: We did talk quite a bit about developing a pine
Speaker 1: flavored beverage in Japan. When we first showed these results
Speaker 1: to a company there, they said, natzu desca, do you
Speaker 1: mean pineapple? Because it was just so out of the yeah,
Speaker 1: out of the ordinary. The way Jason and his team
Speaker 1: are able to get such nuanced data is with an
Speaker 1: app they developed called Gastrograph. Gastrograph looks a lot like
Speaker 1: the flavor wheels they use in coffee and wine tasting
Speaker 1: to help people map out what they taste when they
Speaker 1: try a new product. We think of every flavor, romance,
Speaker 1: texture as a signature. You can have the five basic
Speaker 1: flavors bitter, sweet, salty, sour mommy, and then underneath that
Speaker 1: you're gonna have categorical flavors like fruity earth, the herbaceous nuts,
Speaker 1: and seedge roasted. I mentioned that he's a professional taster, right,
Speaker 1: and then underneath that you can have subcategorical like citrus,
Speaker 1: or specific like lemon are very specific like Meyer lemon
Speaker 1: are very very specific like Meyer lemons as right, So
Speaker 1: all of those signatures exist in some infinite dimensional space
Speaker 1: flavor space. So, car, since you're such a Seltzer fiend,
Speaker 1: we demo gaster graph and got a feel for the
Speaker 1: app by doing a Seltzer tasting. Yeah, we tasted five
Speaker 1: Seltzers that are already on the market that you'd buy,
Speaker 1: like seven eleven, and we rated different components of them.
Speaker 1: So if I tasted fruit, I'd rate that from zero
Speaker 1: to five, and I could add adjectives like strawberry or mango.
Speaker 1: You know, I'm not into like Seltzer two point oh
Speaker 1: with like kumquat flavored sparkling seltzer. You know, I plane vintage,
Speaker 1: but a f s is Resident chemist Ryan On agreed
Speaker 1: to formulate a seltzer based on just our extremely small
Speaker 1: data set. Um, you'd probably be a pretty fast process.
Speaker 1: So we have tons of seltzer data. What we would
Speaker 1: want to do is um run through a couple of
Speaker 1: different flavors to get an idea of the types of
Speaker 1: things that you like, build a model specifically around that,
Speaker 1: run an optimization, predict a new flavor that you've never
Speaker 1: had before, and then have you tried again. So after
Speaker 1: we did all of that, we went home. We sat
Speaker 1: in our hands, and then we went back to the office,
Speaker 1: which actually a little bit more furniture when we got
Speaker 1: back there to see if they actually could create a
Speaker 1: seltzer that both Julian and I would like. So it
Speaker 1: was a blind tasting. Here we go, all right, drink
Speaker 1: one pair. I don't know why I think such a
Speaker 1: thing against pears. The number two berries, that's delish. Let's
Speaker 1: see what we got here. I wouldn't know huckleberry if
Speaker 1: it hit me in the face, honestly, but whatever, Number
Speaker 1: three honestly, curry. Do you not taste curry like there's spice?
Speaker 1: It's so interesting? One? Oh, I love it like hops
Speaker 1: and I'm si care just taste like grass? What are you?
Speaker 1: We're done? So the first thing you guys should tell
Speaker 1: us is which is your favorite? For me? Is number three?
Speaker 1: Just because I like the complexity of flavor. Three was
Speaker 1: a beverage too. I'm used to and I've probably had before.
Speaker 1: Maybe three. I really enjoyed one I did not like.
Speaker 1: I don't like those flavors. We have to reveal. We're
Speaker 1: almost at the reveal can to clarify. Your job as
Speaker 1: a company is to predict future products that people will
Speaker 1: enjoy and come back to. Yes, So in that regard,
Speaker 1: three was a winner. All right? Oh my god, tes us.
Speaker 1: So we had to do one product that was going
Speaker 1: to be optimal for both of you, and we got it.
Speaker 1: It was number three. They nailed it. You could say
Speaker 1: that we got flavor hacked up. Here. This this blue graph,
Speaker 1: this is saying that there's a you know, a seventy
Speaker 1: percent chance that you would give this a six? Did
Speaker 1: I give it a six? I think I did? You? Did?
Speaker 1: You both gave it a six? Um? So we were
Speaker 1: pretty confident on this, but we didn't have that level
Speaker 1: of confidence that we saw this. What I tasted is
Speaker 1: not something I ever predicted I would have liked, but
Speaker 1: it's absolutely something I will continue to think about. It
Speaker 1: was such a unique flavor and it's actually something I
Speaker 1: would buy. It's just that I had never tasted something
Speaker 1: like it before. So when I first tasted it, I
Speaker 1: was like, this is strange. In the case scenario, gastrographs
Speaker 1: AI can help companies create foods that satisfying more specific
Speaker 1: tastes and even bring people more joy, and that's good
Speaker 1: for business. Instead of making huge bets and trying to
Speaker 1: market a product to an entire country, a f S
Speaker 1: has created a way to make more specialized bets and
Speaker 1: help companies tap into those niches. And this isn't about
Speaker 1: AI reducing our experiences to data. AI is being used
Speaker 1: to change how we experience the world. More sleepwalkers after
Speaker 1: the break So, Karen, I would have been quite nervous
Speaker 1: to stand in the shoes of Jason and Ryan and
Speaker 1: the gastrograph team because I know that you're such a
Speaker 1: connoisseur of Seltzer. How do they do? They did really
Speaker 1: well actually, and I think it's important to mention that
Speaker 1: they weren't trying to create something they thought I would
Speaker 1: already like, like I love cranberry, right. They were trying
Speaker 1: to create something that I hadn't tasted before and also liked.
Speaker 1: So it was really difficult, and I think it also
Speaker 1: shows that there's a bit of reversal in the way
Speaker 1: that we do things. Companies have always used market research
Speaker 1: to predict consumer preference, but it's often based on things
Speaker 1: like focus groups or survey research. What we have now
Speaker 1: is massive amounts of data being funneled through an algorithm
Speaker 1: to deliver the perfect product for a very specific type
Speaker 1: of person exactly. That's the or yeah, age, demographic, socio
Speaker 1: economic race. They can target it to all these particular categories,
Speaker 1: and I think this is cool and also a little unsettling.
Speaker 1: I think as humans, we like to be in control,
Speaker 1: you know. I like to think that my preferences are
Speaker 1: just that, my own preferences, and this sort of up
Speaker 1: ends that notion. You know, using pre existing data, I
Speaker 1: can kind of be read they're reading me, and that
Speaker 1: makes me feel a little less special. I do think
Speaker 1: it's cool that companies are trying to service niche markets,
Speaker 1: and I think that's a trend I would definitely get
Speaker 1: on board with as far as AI being used to
Speaker 1: make predictions. And the reason this gastrograph pieces interesting is
Speaker 1: because it's a perfect demonstration that AI is not some
Speaker 1: thing which is going to happen in the future. It's
Speaker 1: here with us today. We can literally taste it already
Speaker 1: AIS in our lives. It's interpreting our data, is analyzing
Speaker 1: our preferences, is predicting our behaviors. But we're just starting
Speaker 1: to respond to what that means culturally. And so there's
Speaker 1: a lot of new technologies and new issues that were
Speaker 1: very excited to get our hands dirty with on season two. Absolutely,
Speaker 1: and one of the important issues we're going to explore
Speaker 1: is bias and technology. It's easy to think that algorithms
Speaker 1: are neutral, but the reality is that technology is built
Speaker 1: by someone, and that person's bias can be built into
Speaker 1: a system. This Princeton professor named Ruha Benjamin has introduced
Speaker 1: a concept directly related to this, called the new Gym code,
Speaker 1: which asks us to consider the inequities encoded in algorithms. Well,
Speaker 1: it's the algorithms and also the data they learned from right.
Speaker 1: I mean AI harnesses the power of processing huge amounts
Speaker 1: of data about things that have happened in the past
Speaker 1: in order to predict a future, and so we have
Speaker 1: to be very careful about what that data contains, or
Speaker 1: we might not like the future. It's bits out. I
Speaker 1: think it's a particularly relevant in the air of medicine.
Speaker 1: We see huge, huge promise about honesting AI for better
Speaker 1: medical outcomes, but we also need to be very careful
Speaker 1: about how the data is being used and who has
Speaker 1: access to it, and how can you prevent your data
Speaker 1: from being used against you. Well, we have to think
Speaker 1: about the potential for data to harm but also to
Speaker 1: provide comfort and to drive innovation, sometimes extraordinary, very unexpected innovation.
Speaker 1: There are two stories I can't wait to dive into
Speaker 1: in season two. One is about a doctor using AI
Speaker 1: to record and optimize conversational strategies with very sick patients.
Speaker 1: What should they say, how and when? Another is about
Speaker 1: using natural language processing to enable immersive conversations with holograms
Speaker 1: of people from history, everyone from astronauts to Holocaust survivors.
Speaker 1: In other words, using technology to bring history into the
Speaker 1: present and ensuring we never forget our past it's wild,
Speaker 1: so we're obviously looking forward to seeing you in the
Speaker 1: next season. We have a lot of amazing stories lined up.
Speaker 1: We'd love to hear from you guys about stories that
Speaker 1: you want to hear or subjects that you want to
Speaker 1: hear about, So tweet us at Sleepwalker's Pod on Twitter
Speaker 1: obviously and on Instagram. We are Sleepwalkers Podcast. Yeah, thank
Speaker 1: you so much. We love your feedback and we're really
Speaker 1: looking forward to seeing you for season two very soon.
Speaker 1: Sleepwalkers is a production of I Heart Radio and Unusual Productions.
Speaker 1: For the latest AI news, live interviews, and behind the
Speaker 1: scenes footage, find us on Instagram, at Sleepwalker's Podcast or
Speaker 1: at Sleepwalker's podcast dot com. Sleepwalkers is hosted by me
Speaker 1: Ozveloshin and co hosted by me Kara Price. Were produced
Speaker 1: by Julian Weller with help from Jacopo Penzo and Taylor Chcogin,
Speaker 1: mixing by Tristan McNeil and Julian Weller. Our story editor
Speaker 1: is Matthew Riddle. Sleepwalkers is executive produced by me Ozveloshin
Speaker 1: and Mangesh hat Together. For more podcasts from my Heart Radio,
Speaker 1: visit the I Heart Radio app Apple podcasts, or wherever
Speaker 1: you listen to your favorite shows.