Speaker 1: To understand why the cosmetics supergiant Lorel Group is teaming
Speaker 1: up with IBM, you must first take a closer look
Speaker 1: at its products. Take lipstick, for example, It's one of
Speaker 1: those things that seems straightforward, a waxy cylinder that you
Speaker 1: rub on your lips to turn them a different color. Easy, right,
Speaker 1: Well maybe not, as my colleague Lucy Sullivan found out
Speaker 1: when I sent her an assignment to Lorel's North America
Speaker 1: Research and Innovation Center.
Speaker 2: All right, I am reporting live from the Looreal visitor's
Speaker 2: parking lot. Malcolm told me that he would be sending
Speaker 2: me to Paris, France for this Looreal excursion, but instead
Speaker 2: I am in Clark, New Jersey. Pass a lot of
Speaker 2: strip malls on the way here. But to be fair
Speaker 2: to Clark, New Jersey and Lorel, this is a beautiful compound.
Speaker 2: It kind of looks like a spa.
Speaker 1: Lucy went into the center and was blown away. The
Speaker 1: facility houses about six hundred scientists and experts across skincare, makeup, fragrance,
Speaker 1: hair care, innovative packaging, and tech. It is one of
Speaker 1: the largest formulation lab spaces in the industry. It's the
Speaker 1: size of six basketball courts. The reason Loreel's facility is
Speaker 1: so big and has so many people is that everything
Speaker 1: Loriel does to bring a product to market happens here,
Speaker 1: from molecule discovery and product development to consumer testing. The
Speaker 1: center even has its own mini factory. My conception of lipstick,
Speaker 1: that it's just a waxy stick was plain wrong. Lipstick
Speaker 1: is a high performance product born from years of research,
Speaker 1: consumer insights, and precision science. Lipstick isn't simple. It's incredibly complex,
Speaker 1: and one of the main reasons it's so complex is
Speaker 1: just a nature of fashion trends. The kind of lipstick
Speaker 1: consumers want is constantly changing.
Speaker 3: A lot of our consumer insights with Floreal is like,
Speaker 3: where are consumers going in the future.
Speaker 1: This is Nadine Gomez. She's vice president for Loreel's research
Speaker 1: and innovation development team.
Speaker 3: Our chemists are working on five six years down the line.
Speaker 3: We predicted that consumers wanted more of a softer look
Speaker 3: on their lips as well.
Speaker 2: So how do you predict something like that.
Speaker 3: We see slow signals from fashion houses and social media
Speaker 3: and things like that. We kind of see that trend
Speaker 3: evolving a little bit, and then we know at five
Speaker 3: six years it's going to become.
Speaker 1: Big Lucy talked with her about the origins of one
Speaker 1: of their products, Mabelne matt Inc Liquid lipstick.
Speaker 3: Our competitors have two steps. The first step is a
Speaker 3: base go it's super opaque. You get the color and
Speaker 3: you get the maddy, but it's very, very drying ellips.
Speaker 3: You cannot wear that, honestly more than ten minutes. It
Speaker 3: feels like your lips are like aching at one point.
Speaker 3: So we had to develop a top cot and you'll
Speaker 3: see many of our competitors did the same thing. It's
Speaker 3: like a bomb. You put it on top, it's super comfortable,
Speaker 3: but we also noticed that consumers kind of get tired
Speaker 3: reapplying a bomb. So we're like, what can we do
Speaker 3: to create this two step into one step?
Speaker 1: So Loriel had a challenge, how do you make a
Speaker 1: comfortable liquid matt lipstick that doesn't require consumers to reapply
Speaker 1: a top layer of bomb. Solving this type of problem
Speaker 1: takes a lot of resources and a lot of expertise,
Speaker 1: and crucially, it takes time. Remember, Nadine said that working
Speaker 1: on a breakthrough product such as matt Inc can take
Speaker 1: years before it comes out. But can this process be
Speaker 1: accelerated taken further? Be even more sustainable. That's what IBM
Speaker 1: and Loreel are hoping to find out. My name is
Speaker 1: Malcolm Gladwell. You're listening to the latest episode of Smart
Speaker 1: Talks with IBM, where we offer our listeners a glimpse
Speaker 1: behind the curtain of the world of technology. In our
Speaker 1: last episode, we talked about how an AI assistant created
Speaker 1: with IBM Watson X helps future teachers practice responsive teaching
Speaker 1: by simulating class interactions with students. In this episode, we
Speaker 1: take you on an even more unexpected journey into the
Speaker 1: world of cosmetics, hair care, skincare, fragrance, makeup, and how
Speaker 1: a custom AI model could help Loriel's researchers shape the
Speaker 1: future of what we put on our faces every morning.
Speaker 1: I want to say on lipstick a moment longer to
Speaker 1: help illustrate what goes into Loriel's product development, and let's
Speaker 1: focus on matt inc lipstick. Loriel wanted to create something
Speaker 1: that was comfortable and could be applied in one step.
Speaker 4: So to go from two step to one step, we
Speaker 4: had to look cross functionally and try to figure out
Speaker 4: what can we bring into the product to make it
Speaker 4: more comfortable, and luckily we have many different types of
Speaker 4: products at Lorel.
Speaker 1: That's Alex Good, a senior chemist who leads the lip
Speaker 1: products team in North America. She says the trick to
Speaker 1: making matt incwork was finding an elastomer, a substance they
Speaker 1: were already using in foundation.
Speaker 4: We have this elastomer that can give you like more
Speaker 4: comfortable and make it feel like there's like something on
Speaker 4: your lips, like a cushion.
Speaker 1: She handed Lucy two jars. The first jar contained the
Speaker 1: former version of the product that was used in super
Speaker 1: State twenty four. By the way, this is exactly why
Speaker 1: I sent Lucy to the lab in my place the.
Speaker 4: Samples, and I actually have something for you to try here,
Speaker 4: so you can try this is what was the initial product.
Speaker 2: Okay, so this is like it sort of looks like okay,
Speaker 2: it is. It looks like vacline that has like more
Speaker 2: of a color. It's kind of a beige, looks like
Speaker 2: some skin.
Speaker 5: Okay.
Speaker 2: So this was from the two steps this would go
Speaker 2: on after Oh okay, right islet Okay, So.
Speaker 4: It feels like very wet. As you can see, it's
Speaker 4: kind of it's gonna absorb into your skin and leaves
Speaker 4: and then you're gonna feel the dryness.
Speaker 2: Of the product. Okay, so we're gonna move.
Speaker 4: From the clay product that you have on your hand
Speaker 4: now to the elastimmer or.
Speaker 6: You try half hour.
Speaker 1: This jar held the elastomer that Laurel had spent years
Speaker 1: developing in the lab.
Speaker 2: This one is it clear, looks like aqua for a
Speaker 2: much bligger and you.
Speaker 4: Can pay a physical layer that you're putting on your aid.
Speaker 2: Yeah, so that's much thicker. It kind of like clumps together. Yeah,
Speaker 2: it's more of a cloudy. It's less shimmery though that's intended.
Speaker 4: Yes, so this is a like a powder, this dispersed
Speaker 4: in dimetic code, and it creates like a comfort on
Speaker 4: your lips, a field like there's something there for a
Speaker 4: barrier to keep the film form on. And that's like
Speaker 4: the key ingredient that came from Foundation that we transferred
Speaker 4: into lipstick to give us this innovative product ahead of
Speaker 4: the market. Yeah, this is what gives it comfort. So
Speaker 4: the difference between super State twenty four and matt Inc
Speaker 4: is really the comfort. They both last a long time,
Speaker 4: but this matt Inc you don't have to apply the
Speaker 4: bomb over and over again, So you can apply matt
Speaker 4: ink once for the day and.
Speaker 1: You're good, right, Alex Good is under selling it here
Speaker 1: once for the day, and you're good. That's a liquid
Speaker 1: lipstick revolution. Literally millions of Loreal consumers around the world
Speaker 1: have worn matt ink. It's a blockbuster. It's also a
Speaker 1: marvel of science. The world's first liquid lipstick was developed
Speaker 1: in the nineteen thirties, and it was actually just a
Speaker 1: stain for your lips, barely counts as lipstick. Then came
Speaker 1: another wave of liquid lipstick when they were able to
Speaker 1: make it matt That was a two step version. It
Speaker 1: felt heavy on your lips. You had to keep reapplying
Speaker 1: the top coat. It was inconvenient. Lorel tackled that challenge
Speaker 1: in the lab, with chemists like Alex and Nadine leaving
Speaker 1: the charge their breakthrough matt Inc. But creating matt Inc
Speaker 1: took a long time, trial and error, the hard work
Speaker 1: of scientific experimentation. As Nadine told Lucy, the lipstick team
Speaker 1: had to put the new product to extensive tests.
Speaker 3: We do a very robustability system here. You know, we
Speaker 3: have color odor appearance. We monitor this in extreme conditions.
Speaker 3: We simulate a forty five degrees celsius and that can
Speaker 3: be something like a three year shelf life. I'm saying,
Speaker 3: we simulate your real life product. Like if you leave
Speaker 3: your lip gloss in the car in Arizona's one hundred
Speaker 3: and twelve degrees for three days, is it still going
Speaker 3: to perform? Is it gonna smell? Is it gonna look granted?
Speaker 3: Is it gonna change colors?
Speaker 7: We do all that.
Speaker 1: See what I mean. Lipstick is complex. Most people would
Speaker 1: never consider it a piece of technology, but one lip
Speaker 1: product has millions of data.
Speaker 3: Points, so much science behind. And you can see here
Speaker 3: how many scientists we have. You know, some of them
Speaker 3: have PhDs, some of them have masters degrees chemistry, biology, psychology.
Speaker 1: Also, when I first heard about this collaboration between LORI
Speaker 1: L and IBM, I was surprised. I thought, these are
Speaker 1: two very different companies. What do they really have in common?
Speaker 1: Meet you guys, Yeah. To find out, I went to
Speaker 1: the IBM Research Center outside New York City, which I
Speaker 1: have to say is one of the coolest buildings I've
Speaker 1: ever been in, A semi circular modernist masterpiece with a
Speaker 1: long curving wall of windows, looks like something out of
Speaker 1: a Stanley Kubrick movie. I was there to talk with
Speaker 1: two experts from research and innovation at Lorel, Methu Cassier
Speaker 1: and Gabriel Bertoli metthew is VP for Digital and Transformation.
Speaker 1: Gabriel is the Chief Digital Transformation Officer for Formulation. These
Speaker 1: are the people whose jobs are to oversee big changes
Speaker 1: within the company. And Methu told me to try on
Speaker 1: some lipstick.
Speaker 7: I'm gonna make you try this one.
Speaker 1: Okay, this is super stay viniting final inc.
Speaker 7: Yeah, so that's a glosse.
Speaker 1: Never in my life put on lipsey. You have no
Speaker 1: idea what I'm doing.
Speaker 7: You don't have to put it. You can try it virtu.
Speaker 1: Oh this may not be news to people who buy makeup,
Speaker 1: but it was news to me. You can try on
Speaker 1: Loreal products virtually. They call it augmented beauty. Oh my goodness.
Speaker 1: That is the strangest thing I've ever said. I look
Speaker 1: quite fetching, that's the way.
Speaker 2: It amazing.
Speaker 7: And I can just hit you can choose your color absolutely.
Speaker 1: So I'm on a little app it's looking at me
Speaker 1: and it's just showing me exactly how I would look
Speaker 1: with different shades of lipstick. So the odd idea of
Speaker 1: going into a store and trying on each one, you
Speaker 1: cannot do that from home, if you're not even at
Speaker 1: the store.
Speaker 7: Yeah, absolutely, that's all purpose. If you want to manage
Speaker 7: a trend, I would go for something more like pitch.
Speaker 1: You think I'm a peach person.
Speaker 4: I don't know.
Speaker 1: That looks I have to say that looks kind of natural.
Speaker 1: It just is enhanced. It's given me a boys share
Speaker 1: I would not otherwise have. This is why Loreel says
Speaker 1: it creates beauty products and beauty experiences. Loriel is a
Speaker 1: beauty tech company. Over the last decade, Laurel has seized
Speaker 1: the power of AI and more recently, generative AI technology
Speaker 1: has become a driving force alongside science and creativity. And
Speaker 1: while some of this digital technology is relatively new, Matthew
Speaker 1: helped me see that IBM and Lorel have always had
Speaker 1: a lot in common.
Speaker 7: I saw the original creator of Loyal, Jentulier, was a
Speaker 7: chemist in nineteen or nine, so one hundred and sixteen
Speaker 7: years ago, and he created this new air color type
Speaker 7: for the market in France, and then little by little,
Speaker 7: it has been always a very scientific company. So if
Speaker 7: you look a little bit at key facts, we invented
Speaker 7: sun filters in the nineteen thirties. There was a very
Speaker 7: very big milestone where we also invented not only product,
Speaker 7: but a reconstructed skin. So if you look at nineteen
Speaker 7: seventeen nine, we've been the created this reconstructed kin that
Speaker 7: helped us to go out of animal testing very fast
Speaker 7: and by the way, before the law even asked it
Speaker 7: to cosmetic companies. And then more recently, because it's a
Speaker 7: story of innovation, we launch on new molecules like one
Speaker 7: that you can find in laroche pose Milabi three, which
Speaker 7: is really helping people to find against some you know,
Speaker 7: spots that they could have on their skin. It's all
Speaker 7: about like big mountation, how to regulate it.
Speaker 1: Loreel and IBM were both started in the early twentieth century,
Speaker 1: Lorel in nineteen oh nine and IBM in nineteen eleven.
Speaker 1: Both companies have long standing histories of innovation, of using
Speaker 1: trial and error to improve everything they do. The two
Speaker 1: companies have been doing that in parallel for more than
Speaker 1: a century until recently. When does it start, When do
Speaker 1: Lorel and IBM start working together?
Speaker 8: So we started in twenty twenty three at the end
Speaker 8: of the year. But you know, really the discussion is
Speaker 8: really recent, absolutely, absolutely, it's really recent in reality, you know,
Speaker 8: I would say the first really interaction happened at the
Speaker 8: beginning of twenty twenty four.
Speaker 1: This is Gabriel Bertoli who I spoke to alongside too.
Speaker 8: What really played a key role here is we wanted
Speaker 8: to bring from a logic perspective to R and D together,
Speaker 8: which normally you know companies like us, you just go
Speaker 8: to a provider. You know it's a customer and the
Speaker 8: supplier and your work they delivered to you. Here the
Speaker 8: concept was totally different.
Speaker 1: Mid two said that the collaboration began with simple conversations.
Speaker 7: So if you look at the way IBM entered into
Speaker 7: loyal Labs, it's started by interviewing people what would help
Speaker 7: you to do your job? What is your business need?
Speaker 7: So it was, by the way, two months ago, a
Speaker 7: long series of interviews and from all the people around
Speaker 7: the world we have in research in Brazil, in India,
Speaker 7: in China, Japan, US, France of course, so we really
Speaker 7: want to make sure that at the end of the day,
Speaker 7: this new model, this new tool that we will give
Speaker 7: to people is really people centrick in the way that
Speaker 7: it selves their daily need.
Speaker 1: More. The point has leveraged technology for decades and accumulated
Speaker 1: amounted of scientific knowledge, everything from consumer aspirations and market trends,
Speaker 1: to the results of all the experiments conducted during product development,
Speaker 1: to which formulations melt in a hot car. It's hard
Speaker 1: to get your head around. Looreal isn't just a cosmetics company.
Speaker 1: It's a beauty data powerhouse.
Speaker 8: If we have sixteen thousand terabat of data coming from
Speaker 8: consumer insights, coming from market research coming from sales, well
Speaker 8: with the new technology, maybe by aligning those two and
Speaker 8: using best in class technology you can solve that problem.
Speaker 1: So you say you have sixteen terabytes of data, put
Speaker 1: that in perspective. How much data is that?
Speaker 2: Give me?
Speaker 8: This is one hundred year of Floreal data based on
Speaker 8: the last forty years of data the systems. So this
Speaker 8: is really I mean, we're talking about one hundred years
Speaker 8: of data that only Loreal have. Let's take the example
Speaker 8: of the ellipsis. I mean, you know, if ellipsex can
Speaker 8: be between twenty and thirty year ow material, each raw
Speaker 8: material will have I would say ten or fifteen way
Speaker 8: of doing things.
Speaker 1: Gabrielle is talking about how things used to be done.
Speaker 1: Researchers at Lorel needed roughly twenty five ingredients for a
Speaker 1: new lipstick formulation, but they have to choose from a
Speaker 1: pool of hundreds, if not thousands, of raw materials, And
Speaker 1: even after they settle on the ones they want, they
Speaker 1: have to figure out how much of each ingredient they
Speaker 1: need and in what form, what molecular weight, what combination.
Speaker 1: It's not just a math problem. It's a problem that
Speaker 1: requires balancing multiple perspectives safety, performance, quality, compliance standards, sustainability,
Speaker 1: and more. It can take years. But what if you
Speaker 1: could simulate hundreds of cars parked in a sweltering heat.
Speaker 1: What if you could do all those trials and errors
Speaker 1: virtually over and over and over again. What if instead
Speaker 1: of mixing materials together by hand, you could ask AI
Speaker 1: to predict what combinations might work best and then try
Speaker 1: those out first.
Speaker 8: This is ten on the power of twenty five. This
Speaker 8: is one hundred billion of years for a human to
Speaker 8: do a change in the formula or the possibility they have.
Speaker 8: You can only do this by using technology, power of
Speaker 8: technology and data that you have.
Speaker 1: This, Matthew says, is where IBM can come in to
Speaker 1: help take things further. Using artificial intelligence, IBM can help
Speaker 1: Loriel create a custom AI model that helps to crunch
Speaker 1: those numbers to be a companion to the researchers to
Speaker 1: give them superpowers.
Speaker 7: We don't want to replace the intuition of sent this.
Speaker 7: We just want to make sure that this intuition is
Speaker 7: really augmented by some calculation poor that, as Gabrielle said,
Speaker 7: then does all ten and the poor of twenty five
Speaker 7: solution and se probably try this one, this one, this one,
Speaker 7: it looks like a better solution, and then ultimately that's
Speaker 7: really the decision of the chemist to make it happen.
Speaker 1: Well. To make a predictive AI model that can give
Speaker 1: Loreal researchers those superpowers, you'd need that mountain of data,
Speaker 1: years worth of laboratory testing and all Loreal's data digitized
Speaker 1: and AI ready. You need to train artificial intelligence on
Speaker 1: everything the company has already done in order for it
Speaker 1: to predict what it could do.
Speaker 6: Loriial has one hundred years a course of data, fifty
Speaker 6: years of digitized EGGDA.
Speaker 1: This is Miriam Ashuri, Senior director of Product Management for
Speaker 1: IBM Watson X. Lorel has the data, and part of
Speaker 1: IBM's job is to help put that data to work,
Speaker 1: which involves ensuring data quality. Mariam talked about the concept
Speaker 1: of AI ready data.
Speaker 6: The sole purpose of this data engineering pipeline is to
Speaker 6: clean the data and we call them AI ready data
Speaker 6: makes them ready to be consumed by AI. So basically
Speaker 6: looking into biases and the data to fix the distribution,
Speaker 6: looking into guard brains that we are putting into place
Speaker 6: in terms of removing personal information.
Speaker 1: Marriam that explained that a custom model like the one
Speaker 1: IBM is creating with Loril can be more efficient and
Speaker 1: targeted than the larger general purpose AI models.
Speaker 6: You've heard about large language models. The reason that they
Speaker 6: call them large language model is they are exposed into
Speaker 6: really large amount of data. So the larger the model,
Speaker 6: the more take of all the models are, but also
Speaker 6: the larger computed requires that translates and increase carbon footprint
Speaker 6: and entergy consumption, that translatestan increase latency that's your response
Speaker 6: time that translatestand increased costs. So you started seeing that
Speaker 6: enterprises started grabbing a much smaller model customize it on
Speaker 6: their proprietary data that's the data, their DOMAINO specific data,
Speaker 6: or the data about their users to create something differentiated
Speaker 6: that is applicable to a real world use case but
Speaker 6: also delivers the performance that they needed for a fraction
Speaker 6: of the costs, and that's why there's been a lot
Speaker 6: of push around using custom models versus very large general
Speaker 6: purpose models.
Speaker 1: So how is a custom model created? Miriam says, you
Speaker 1: start with the base model. Imagine you're buying a car.
Speaker 1: You could get a minivan, or a sedan or a
Speaker 1: sports car, and then you get to customize it. You
Speaker 1: could add a sunroof, leather seats, or a rearview camera.
Speaker 1: Turns out you could do the same thing with your
Speaker 1: AI model. You pick a base and then you cut
Speaker 1: customize it. You tune it on the data unique to
Speaker 1: your organization.
Speaker 6: We do believe that one model doesn't fit all use cases.
Speaker 6: You want to truly have access to any model anywhere,
Speaker 6: and by any model anywhere, I really mean any model anywhere,
Speaker 6: open source, proprietary, low call out your machine wherever the
Speaker 6: model is. You want to host it yourself, because then
Speaker 6: you would be able to take advantage of the best
Speaker 6: of the technology at any point and pick the right
Speaker 6: model for the target use case.
Speaker 1: So a custom model tuned on Lorel's data would be
Speaker 1: more targeted and efficient than a general purpose model. It
Speaker 1: would understand the researchers world and provide transparency into its workings.
Speaker 1: That's part of the magic. And what could a custom
Speaker 1: AI foundation model do for a company like lorel.
Speaker 9: Accord with more contain the complexity of the formulation.
Speaker 1: That's Gillomme lais Moline, an IBM distinguished engineer and one
Speaker 1: of the people working on the AI model.
Speaker 9: And to hype ower the formulator to go not only
Speaker 9: past but also I would say, be able to include
Speaker 9: more complexity or so in the formulation, more personalization, more
Speaker 9: certain ability, better selected ingredient. So it's really a tool
Speaker 9: to help them and to also help them to unniche
Speaker 9: the creativity.
Speaker 1: Kayomi is saying that with its custom AI model, Loreel
Speaker 1: can improve every step of its product development pipeline, make
Speaker 1: the process faster and more sustainable. But he's also saying
Speaker 1: that the model could help Loriel create something that's never
Speaker 1: been done before. What could that product be? So I'm
Speaker 1: mourning you with that. All my questions are going to
Speaker 1: be really dumb.
Speaker 5: Okay, no, please by all me.
Speaker 1: To find out what people at Loril are dreaming of.
Speaker 1: I spoke with Trisha Iyagari, global general manager at Loriel's
Speaker 1: Mabeline brand and they asked her about her own dreams
Speaker 1: and how technology and science could help bring those dreams
Speaker 1: into the world. Do you have a secret wish list
Speaker 1: of things you think that this partnership could produce, Like,
Speaker 1: is there a product out there that's been technically too
Speaker 1: difficult that you think could be a worthy target?
Speaker 5: There is one that I think could be really amazing.
Speaker 1: What's that?
Speaker 5: So? Shine products in general are harder to create, and
Speaker 5: we're unable to create a shiny, long wearing eyeshadow. So
Speaker 5: basically like a shadow that could stay on your eyelids,
Speaker 5: that won't settle into creases, that won't move all over
Speaker 5: your face, that has a glossy effect. It's like the
Speaker 5: holy grail.
Speaker 1: That's the holy grail. Yeah, yeah, you may have seen
Speaker 1: that look in fashion shows, but that look isn't real,
Speaker 1: not for people be and lucy.
Speaker 5: Anyway, if you're walking down a runway, you see a
Speaker 5: lot of makeup artists doing techniques where they put some
Speaker 5: shadow on, they layer vasoline over it, and like slather
Speaker 5: vassaline on somebody's eyes to create this very like glossy look.
Speaker 5: But you know, within five minutes after they walk down
Speaker 5: the runway, I'm sure it's all over their face or
Speaker 5: being washed off. So the look is kind of more
Speaker 5: of like a fashion look that we've been unable to create,
Speaker 5: and real, real consumers can't wear it because it would
Speaker 5: get it everywhere.
Speaker 1: Trisha had another thing on her wish list too.
Speaker 5: The other that we would really like is semi permanence makeup.
Speaker 5: So we've talked a lot about really really comfortable thin
Speaker 5: film makeup that you could wear all over your face
Speaker 5: and that you can sleep in and then it will
Speaker 5: last a couple of days basically, so whether it be
Speaker 5: on your face, on your lashes, on your brows. So
Speaker 5: anything that's like more of a semi permanent meaning lasting
Speaker 5: for three days or more, would be amazing.
Speaker 1: Yeah. Yeah, And you say those two things, they have been
Speaker 1: the whole How long have they been on the wish
Speaker 1: list of lorel Oh.
Speaker 5: My gosh. I have been trying to develop this shiny
Speaker 5: eyeshadow since I started. What year did I start? Like
Speaker 5: twenty ten? And I'm sure many people had asked before me,
Speaker 5: and we tried so many iterations of it and nobody's
Speaker 5: been able to achieve it.
Speaker 1: It's clear that Loreel's experts like Tricia have a lot
Speaker 1: of ideas. I once said what I called a magic
Speaker 1: Wand project, where I called up scientists and technologists in
Speaker 1: as many different fields as possible and asked them what
Speaker 1: they could create if they could just wave a magic
Speaker 1: wand and make it real, and everyone had something they'd
Speaker 1: want to create everyone, that's not the issue. The issue
Speaker 1: is that there are a million different impediments to make
Speaker 1: the ideas on the wish list reel. Lack of resources,
Speaker 1: lack of time, some crucial bit of know how is lacking.
Speaker 1: There's a ga between what we want and what we
Speaker 1: can actually have, and one of the simplest ways to
Speaker 1: think of the promise of AI is that it can
Speaker 1: narrow that gap, not close it, of course, but do
Speaker 1: enough that people with dreams realize there are more things
Speaker 1: within their grasp than they could ever have imagined. Smart
Speaker 1: Talks with IBM is produced by Matt Romano, Amy Gaines, McQuaid,
Speaker 1: Lucy Sullivan, and Jake Harper. Were edited by Lacy Roberts.
Speaker 1: Engineering by Nina Bird Lawrence, mastering by Sarah Brugaier, Music
Speaker 1: by Gramoscope. Special thanks to Tatiana Lieberman and Cassidy Meyer.
Speaker 1: Smart Talks with IBM is a production of Pushkin Industries
Speaker 1: and Ruby's studio at iHeartMedia. To find more Pushkin podcast
Speaker 1: listen on the iHeartRadio app, Apple Podcasts, or wherever you
Speaker 1: get your podcasts. I'm Malcolm Laba. This is a paid
Speaker 1: advertisement from IBM. The conversations on this podcast don't necessarily
Speaker 1: represent IBM's positions, strategies, or opinions.