Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. This season,
Speaker 1: non Smart Talks with IBM, Malcolm Glabwell is back, and
Speaker 1: this time he's taking the show on the road. Malcolm
Speaker 1: is stepping outside the studio to explore how IBM clients
Speaker 1: are using artificial intelligence to solve real world challenges and
Speaker 1: transform the way they do business, from accelerating scientific breakthroughs
Speaker 1: to reimagining education. It's a fresh look at innovation in action,
Speaker 1: where big ideas meet cutting edge solutions. You'll hear from
Speaker 1: industry leaders, creative thinkers, and of course, Malcolm Glabwell himself
Speaker 1: as he guides you through each story. New episodes of
Speaker 1: Smart Talks with IBM drop every month on the iHeartRadio app,
Speaker 1: Apple Podcasts, or wherever you get your podcasts. Learn more
Speaker 1: at IBM dot com slash smart Talks.
Speaker 2: To understand why the cosmetics supergiant Lorel Group is teaming
Speaker 2: up with IBM, you must first take a closer look
Speaker 2: at its products. Take lipstick, for example. It's one of
Speaker 2: those things that seems straightforward, a waxy cylinder that you
Speaker 2: rub on your lips to turn them a different color. Easy, right,
Speaker 2: Well maybe not. As my colleague Lucy Sullivan found out
Speaker 2: when I sent her an assignment to Loreel's North America
Speaker 2: Research and Innovation Center.
Speaker 3: All right, I'm reporting live from the Loreal visitor's parking lot.
Speaker 3: Malcolm told me that he would be sending me to Paris,
Speaker 3: France for this Looreal excursion, but instead I am in Clark,
Speaker 3: New Jersey. Pass a lot of strip malls on the
Speaker 3: way here. But to be fair to Clark, New Jersey
Speaker 3: and Lorel, this is a beautiful compound. It kind of
Speaker 3: looks like a spa.
Speaker 2: Lucy went into the center and was blown away. The
Speaker 2: facility houses about six hundred scientists and experts across skincare, makeup, fragrance,
Speaker 2: hair care, innovative packaging, and tech. It is one of
Speaker 2: the largest formulation lab spaces in the industry. It's the
Speaker 2: size of six basketball courts. The reason Loreel's facility is
Speaker 2: so big and has so many people is that everything
Speaker 2: Loriel does to bring a product to market happens here,
Speaker 2: from molecule discovery and product development to consumer testing. The
Speaker 2: center even has its own mini factory. My conception of
Speaker 2: lipstick that it's just a waxy stick was plain wrong.
Speaker 2: Lipstick is a high performance product born from years of research,
Speaker 2: consumer insights and precision science. Lipstick isn't simple. It's incredibly complex,
Speaker 2: and one of the main reasons it's so complex is
Speaker 2: just a nature of fashion trends. The kind of lipstick
Speaker 2: consumers want is constantly changing.
Speaker 4: A lot of our consumer insights with Loril is like,
Speaker 4: where are consumers going in the future.
Speaker 2: This is Nadine Gomez, She's vice president for Loreel's research
Speaker 2: and innovation development.
Speaker 4: Team Park Chemist are working out five six years down
Speaker 4: the line. We predicted that consumers wanted more of a
Speaker 4: softer look on their lips as well.
Speaker 3: So how do you predict something like that.
Speaker 4: We see slow signals from fashion houses and social media
Speaker 4: and things like that. We kind of see that trend
Speaker 4: evolving a little bit, and then we know at five
Speaker 4: six years it's going.
Speaker 5: To become big.
Speaker 2: Lucy talked with her about the origins of one of
Speaker 2: their products, Mabe Lene matt Inc Liquid lipstick.
Speaker 4: Our competitors had two steps. The first step is a
Speaker 4: base coat. It's super opaque. You get the color and
Speaker 4: you get the maddy, but it's very very drying ellips.
Speaker 4: You cannot wear that, honestly more than ten minutes. It
Speaker 4: feels like your lips are like aching at one point,
Speaker 4: So we had to develop a top coat, and you'll
Speaker 4: see many of our competitors did the same thing. It's
Speaker 4: like a bomb. You put it on top, it's super comfortable.
Speaker 4: But we also noticed that consumers kind of get tired
Speaker 4: of reapplying a bomb. So we're like, what can we
Speaker 4: do to create this two step into one step?
Speaker 2: So Loriel had a challenge, how do you make a
Speaker 2: comfortable liquid map lipstick that doesn't require consumers to reapply
Speaker 2: a top layer of bomb. Solving this type of problem
Speaker 2: takes a lot of resources and a lot of expertise,
Speaker 2: and crucially, it takes time. Remember Nadine said that working
Speaker 2: on a breakthrough product such as matt inc can take
Speaker 2: years before it comes out. But can this process be accelerated,
Speaker 2: taken further, be even more sustainable. That's what IBM and
Speaker 2: Laurel are hoping to find out. My name is Malcolm Gladwell.
Speaker 2: You're listening to the latest episode of Smart Talks with IBM,
Speaker 2: where we offer our listeners a glimpse behind the curtain
Speaker 2: of the world of technology. In our last episode, we
Speaker 2: talked about how an AI assistant created with IBM Watson
Speaker 2: X helps future teachers practice responsive teaching by simulating classroom
Speaker 2: interactions with students. In this episode, we take you on
Speaker 2: an even more unexpected journey into the world of cosmetics,
Speaker 2: hair care, skincare, fragrance, makeup, and how a custom AI
Speaker 2: model could help Lorel's researchers shape the future of what
Speaker 2: we put on our faces every morning. I want to
Speaker 2: stay on lipstick a moment longer to help illustrate what
Speaker 2: goes into Loriel's product development, and let's focus on matt
Speaker 2: inc lipstick. Loreel wanted to create something that was comfortable
Speaker 2: and could be applied in one step.
Speaker 6: So to go from two step to one step, we
Speaker 6: had to look cross functionally and try to figure out
Speaker 6: what can we bring into the product to make it
Speaker 6: more comfortable, And luckily we have many different types of
Speaker 6: products at Lorel.
Speaker 2: That's Alex Good, a senior chemist who leads the lip
Speaker 2: products team in North America. She says the trick to
Speaker 2: making matt incwork was finding an elastomer, a substance they
Speaker 2: were already using in foundation.
Speaker 6: We have this elastomer that can give you like more
Speaker 6: comfortable and make it feel like there's like something on
Speaker 6: your lips like a cushion.
Speaker 2: She handed Lucy two jars. The first jar contained the
Speaker 2: former version of the product that was used in super
Speaker 2: State twenty four. By the way, this is exactly why
Speaker 2: I sent Lucy to the lab in my place the samples.
Speaker 6: And I actually have something for you to try here,
Speaker 6: so you can try this is what was the initial product.
Speaker 3: Okay, So this is like it sort of looks like Okay,
Speaker 3: it is clay. It looks like vacline that has like
Speaker 3: a more of a color. It's kind of a beige,
Speaker 3: looks like some skin. Okay. So this was from the
Speaker 3: two steps this would go on after oh okay, right violet.
Speaker 6: Okay, So it feels like very wet as you can see,
Speaker 6: it's kind of it's gonna absorb into your skin and
Speaker 6: leave and then you're gonna feel the dryness.
Speaker 5: Of the product once it's not okay. So we're gonna move.
Speaker 6: From the clay product that you have on your hand
Speaker 6: now to the last summer like you try half.
Speaker 2: One or This jar held the elastomer that Lareel has
Speaker 2: spent years developing in the lab.
Speaker 3: This one is a clear looks like aqua for a
Speaker 3: much player.
Speaker 6: And you can pay a physical layer that you're putting.
Speaker 7: On your aid.
Speaker 3: Yeah, so that's much thicker. It kind of like clumps together. Yeah,
Speaker 3: it was more of a cloudy it's less shimmery though
Speaker 3: that's intended.
Speaker 6: Yes, So this is a like a powder. This dispersed
Speaker 6: in dimethicone and it creates like a comfort on your lips.
Speaker 6: It feels like there's something there for a barrier to
Speaker 6: keep the film form on. And that's like the key
Speaker 6: ingredient that came from Foundation that we transferred into lipstick
Speaker 6: to give us this innovative product ahead of the market. Yeah,
Speaker 6: this is what gives it comfort. So the difference between
Speaker 6: super State twenty four and matt Inc is really the comfort.
Speaker 6: They both last a long time, but this matt Inc
Speaker 6: you don't have to apply the bomb over over again,
Speaker 6: so you can fly matting once for the day and
Speaker 6: you're good.
Speaker 2: For Alex Good is underselling it here, once for the
Speaker 2: day and you're good. That's a liquid lipstick revolution. Literally
Speaker 2: millions of loreal consumers around the world have worn matt ink.
Speaker 2: It's a blockbuster. It's also a marvel of science. The
Speaker 2: world's first liquid lipstick was developed in the nineteen thirties
Speaker 2: and it was actually just a stain for your lips,
Speaker 2: barely counts as lipstick. Then came another wave of liquid lipstick.
Speaker 2: When they were able to make it matt that was
Speaker 2: a two step version. It felt heavy on your lips.
Speaker 2: You had to keep reapplying the top coat. It was inconvenient.
Speaker 2: Loreel tackled that challenge in the lab with chemists like
Speaker 2: Alex and Nadine leaving the charge their breakthrough matt Inc.
Speaker 2: But creating matt Inc took a long time, trial and error,
Speaker 2: the hard work of scientific experimentation. As Nadean told Lucy,
Speaker 2: the lipstick team had to put the new product to
Speaker 2: extensive tests.
Speaker 4: We do a very robustability system here. You know, we
Speaker 4: have color odor appearance. We monitor this in extreme conditions.
Speaker 4: We simulate a forty five degrees celsius and that can
Speaker 4: be something like a three year shelf life. I'm saying
Speaker 4: we simulate your real life product. Like if you leave
Speaker 4: your lip gloss in the car in Arizona's one hundred
Speaker 4: and twelve degrees or three days, is it still going
Speaker 4: to perform? Is it gonna smell? Is it gonna look granted?
Speaker 4: Is it gonna change colors we.
Speaker 5: Do all that.
Speaker 2: See what I mean. Lipstick is complex. Most people would
Speaker 2: never consider it a piece of technology, but one lip
Speaker 2: product has millions of data points.
Speaker 4: So much science behind. And you can see here how
Speaker 4: many scientists we have. You know, some of them have PhDs,
Speaker 4: some of them have master's degrees to chemistry, biology, psychology.
Speaker 2: Also, when I first heard about this collaboration between LORI
Speaker 2: L and IBM, I was surprised. I thought, these are
Speaker 2: two very different companies. What do they really have in common?
Speaker 7: Pleasure, you guys, treasure. Yeah.
Speaker 2: To find out, I went to the IBM Research Center
Speaker 2: outside New York City, which I have to say is
Speaker 2: one of the coolest buildings I've ever been in, A
Speaker 2: semi circular modernist masterpiece with a long curving wall of windows,
Speaker 2: looks like something out of a Stanley Kubrick movie. I
Speaker 2: was there to talk with two experts from research and
Speaker 2: innovation at LOREL, Metheu Cassier and gabriel Bertoli. Matthew is
Speaker 2: VP for Digital and Transformation, Gabrielle is a Chief Digital
Speaker 2: Transformation Officer for Formulation. These are the people whose jobs
Speaker 2: are to oversee big changes within the company, and Methu
Speaker 2: told me to try on some lipstick.
Speaker 8: I'm gonna make you try this one.
Speaker 7: Okay, this is superstay vinitin final in. Yeah, so that's
Speaker 7: a glosse. Never in my life put on lipsey. I've
Speaker 7: no idea what I'm doing. You don't have to put it.
Speaker 8: You can try it virtually.
Speaker 2: Oh this may not be news to people who buy makeup,
Speaker 2: but it was news to me. You can try on
Speaker 2: loreal products virtually. They call it augmented beau.
Speaker 7: Oh my goodness.
Speaker 2: That is the strangest thing I've ever said. I look
Speaker 2: quite fetching.
Speaker 7: That's amazing. And I can just.
Speaker 8: Hit you can choose your color absolutely.
Speaker 7: So I'm on a little app.
Speaker 2: It's looking at me and it's just showing me exactly
Speaker 2: how I would look with different shades of lipstick. So
Speaker 2: the odd idea of going into a store and trying
Speaker 2: on each one, you cannot do that from home, if
Speaker 2: you're not even at the store.
Speaker 8: Yeah, absolutely, that's all purpose. If you want to manage
Speaker 8: a trend, I would go for something more like pitch.
Speaker 7: You think I'm a peach person.
Speaker 2: I don't know that looks I have to say, that
Speaker 2: looks kind of natural. It just is enhanced. It's given
Speaker 2: me a boys share I would not otherwise have. This
Speaker 2: is why Loreel says it creates beauty products and beauty experiences.
Speaker 2: Loriel is a beauty tech company. Over the last decade,
Speaker 2: Loreel has seized the power of AI and more recently,
Speaker 2: generative AI technology has become a driving force alongside science
Speaker 2: and creativity. And while some of this digital technology is
Speaker 2: relatively new, Matthew helped me see that IBM and Lorel
Speaker 2: have always had a lot in common.
Speaker 8: So the original creator of Loyal Legentulier was a chemist
Speaker 8: in nineteen or nine, so one hundred and sixteen years ago,
Speaker 8: and he created this new air color type for the
Speaker 8: market in France, and then little by little, it has
Speaker 8: been always a very scientific company. So if you look
Speaker 8: a little bit at key facts, we invented sun filters
Speaker 8: in the nineteen thirties, there was a very very big
Speaker 8: milestone where we also invented not only product, but a
Speaker 8: reconstructed skin. So if you look at nineteen seventeen nine,
Speaker 8: we've been the created this reconstructed skin that helped us
Speaker 8: to go out of animal testing very fast, and by
Speaker 8: the way, before the law even asked it to cosmetic companies,
Speaker 8: and then more recently, because it's a history of innovation,
Speaker 8: we'll launch on new molecules like one that you can
Speaker 8: find in laroche pose Melabi three, which is really helping
Speaker 8: people to find again some you know, spots they could
Speaker 8: have on their skin. It's all about like big mountation,
Speaker 8: how to regulate it.
Speaker 2: Loreel and IBM were both started in the early twentieth century,
Speaker 2: Loreel in nineteen oh nine and IBM in nineteen eleven.
Speaker 2: Both companies have long standing histories of innovation, of using
Speaker 2: trial and error to improve everything they do. The two
Speaker 2: companies have been doing that in parallel for more than
Speaker 2: a century until recently. When does it start? When do
Speaker 2: Lorel and IBM start working together?
Speaker 9: So we started in twenty twenty three, at the end
Speaker 9: of the year. But you know, really the discussion is
Speaker 9: really recent, absolutely, absolutely, it's really recent in reality. You know,
Speaker 9: I would say the first really interaction happened at the
Speaker 9: beginning of twenty twenty four.
Speaker 2: This is Gabriel Bertoli, who I spoke to alongside Matthew.
Speaker 9: What really played a key role here is we wanted
Speaker 9: to bring from a logic perspective to R and D together,
Speaker 9: which Normally, you know companies like us, you just go
Speaker 9: to a provider. You know, it's a customer and a
Speaker 9: supplier and new work they delivered to you. Here, the
Speaker 9: concept was totally different.
Speaker 2: Mid two said that the collaboration began with simple conversations.
Speaker 8: So if you look at the way IBM entered into
Speaker 8: Loreal Labs, it's started by interviewing people, what would help
Speaker 8: you to do your job? What is your business need?
Speaker 8: So it was, by the way, two months ago, a
Speaker 8: long series of interviews and from all the people around
Speaker 8: the world we have in research in Brazil, in India,
Speaker 8: in China, Japan, US, France of course, So we really
Speaker 8: want to make sure that at the end of the day,
Speaker 8: this new model, this new tool that we will give
Speaker 8: to people is really people c trick in the way
Speaker 8: that it selves their daily need.
Speaker 2: More the point, Lorel has leveraged technology for decades and
Speaker 2: accumulated amounted of scientific knowledge, everything from consumer aspirations and
Speaker 2: market trends, to the results of all the experiments conducted
Speaker 2: during product development, to which formulations melt in a hot car.
Speaker 2: It's hard to get your head around. Loreal isn't just
Speaker 2: a cosmetics company. It's a beauty data powerhouse.
Speaker 9: If we have sixteen thousand terabat of data coming from
Speaker 9: consumer insights, coming from market research, coming from sales, well
Speaker 9: with the new technology, maybe by aligning those two and
Speaker 9: using best in class technology you can solve that problem.
Speaker 2: So you say you have sixteen terabytes of data. Put
Speaker 2: that in perspective. How much data is that?
Speaker 7: Give me?
Speaker 9: This is one hundred year of Looreal data based on
Speaker 9: the last you know, forty years of data in the systems.
Speaker 9: So this is really I mean, we're talking about one
Speaker 9: hundred year of data that only Lorial have. Let's take
Speaker 9: the example of the ellipsis. I mean, you know, if
Speaker 9: ellipsex can be between twenty and thirty year ow material,
Speaker 9: each raw material will have I would say ten or
Speaker 9: fifteen way of doing things.
Speaker 2: Gabrielle is talking about how things used to be done.
Speaker 2: Researchers at Loreel needed roughly twenty five ingredients for a
Speaker 2: new lipstick formulation, but they have to choose from a
Speaker 2: pool of hundreds, if not thousands, of raw materials, and
Speaker 2: even after they settle on the ones they want, they
Speaker 2: have to figure out how much of each ingredient they
Speaker 2: need and in what form, what molecular weight, what combination.
Speaker 2: It's not just a math problem. It's a problem that
Speaker 2: requires balancing multiple perspectives safety, performance, quality, compliance standards, sustainability,
Speaker 2: and more. It can take years. But what if you
Speaker 2: could simulate hundreds of cars parked in a sweltering heat.
Speaker 2: What if you could do all those trials and errors
Speaker 2: virtually over and over and over again. What if instead
Speaker 2: of mixing materials together by hand, you could ask AI
Speaker 2: to predict what combinations might work best and then try
Speaker 2: those out first.
Speaker 9: This is ten on the power of twenty five. This
Speaker 9: is one hundred billion of years for a human to
Speaker 9: do a change in the formula or the possibility they have.
Speaker 9: You can only do this by using technology, power of
Speaker 9: technology and data that you have.
Speaker 2: This, Matthew says, is where IBM can come in to
Speaker 2: help take things further. Using artificial intelligence, IBM can help
Speaker 2: Lorio create a custom AI model that helps to crunch
Speaker 2: those numbers, to be a companion to the researchers, to
Speaker 2: give them superpowers.
Speaker 8: We don't want to replace the intuition of the sentis.
Speaker 8: We just want to make sure that this intuition is
Speaker 8: really augmented by some calculation poor that as Gabrielle said,
Speaker 8: then do those ten and the poor of twenty five
Speaker 8: solution and probably try this one, this one, this one,
Speaker 8: it looks like a better solution and Thentimately that's really
Speaker 8: the decision of the chemist to make it happen.
Speaker 7: Well.
Speaker 2: To make a predictive AI model that can give Lorel
Speaker 2: researchers those superpowers, you'd need that mountain of data, years
Speaker 2: worth of laboratory testing and all Loreal's data digitized and
Speaker 2: AI ready. You'd need to train artificial intelligence on everything
Speaker 2: the company has already done in order for it to
Speaker 2: predict what it could do.
Speaker 5: Loriial has one hundred years of course of data, fifty
Speaker 5: years of digitized EGDA.
Speaker 2: This is Mariam Ashuri, Senior director of Product Management for
Speaker 2: IBM Watson X. Loreal has the data and part of
Speaker 2: IBM's job is to help put that data to work,
Speaker 2: which involves ensuring data quality. Mariam talked about the concept
Speaker 2: of AI ready data.
Speaker 5: The sole purpose of this data engineering cuyper is to
Speaker 5: clean the data, and we call them AI ready data
Speaker 5: makes them ready to be consumed by AI. So basically
Speaker 5: looking into biases in the data to fix the distribution,
Speaker 5: looking into guard brains that we are putting into place
Speaker 5: in terms of removing personal information.
Speaker 2: Variam that explained that a custom model like the one
Speaker 2: IBM is creating with Lorel can be more efficient and
Speaker 2: targeted than the larger general purpose AI models.
Speaker 5: You've heard about large language models. The reason that they
Speaker 5: call them large language model is they are exposed into
Speaker 5: really large amount of data. So the larger the model,
Speaker 5: the more cake of all the models are, but also
Speaker 5: the larger computed requires that translate stand increase carbon footprint
Speaker 5: and energy consumption that translates stand, increase latency that's your
Speaker 5: response time that translatestand increase costs. So we started seeing
Speaker 5: that interprise started grabbing a much smaller model customize it
Speaker 5: on their proprietary data that's the data their DOMAINO specific data,
Speaker 5: or the data about their users to create something differentiated
Speaker 5: that is applicable to a real word use case but
Speaker 5: also delivers the performance that they needed for a fraction
Speaker 5: of the costs. And that's why there's been a lot
Speaker 5: of push around using custom models versus very large general
Speaker 5: purpose models.
Speaker 2: So how is a custom model created? Miriam says, you
Speaker 2: start with a base model. Imagine you're buying a car,
Speaker 2: You could get a minivan or a sedan or a
Speaker 2: sports car, and then you get to customize it. You
Speaker 2: could add a sunroof, leather seats, or a rearview camera.
Speaker 2: Turns out you could do the same thing with your
Speaker 2: AI model. You pick a base and then you customize it.
Speaker 2: You tune it on the data unique to your organization.
Speaker 5: We do believe that one model doesn't fit all use cases.
Speaker 5: You want to truly have access to any model anywhere,
Speaker 5: and by any model anywhere, I really mean any model anywhere,
Speaker 5: open source, proprietary, low call out your machine wherever the
Speaker 5: model is. You want to host it yourself, because then
Speaker 5: you would be able to take advantage of the best
Speaker 5: of the technology at any point and pick the right
Speaker 5: model for the target use case.
Speaker 2: So a custom model tuned on Lorel's data would be
Speaker 2: more targeted and efficient than a general purpose model. It
Speaker 2: would understand the researchers world and provide transparency into its workings.
Speaker 2: That's part of the magic. And what could a custom
Speaker 2: AI foundation model do for a company like Lorel if.
Speaker 10: You accord it was just moder is contain the complexity
Speaker 10: of the formulation.
Speaker 7: That's game.
Speaker 2: La Moline an IBM distinguished engineer and one of the
Speaker 2: people working on the AI model.
Speaker 10: And to hyperli the formulate or to go not only faster,
Speaker 10: but also I would say, be able to include more
Speaker 10: complexity or so in the formulation, more personalization, more certain ability,
Speaker 10: better selected ingredient. So it's really a tool to help
Speaker 10: them and to also help them to unniche the creativity.
Speaker 2: THEOMI saying that with its custom AI model, Lorel could
Speaker 2: improve every step of its product development pipeline, make the
Speaker 2: process faster and more sustainable. But he's also saying that
Speaker 2: the model could help Lorel create something that's never been
Speaker 2: done before.
Speaker 7: What could that product be? So I'm mourning you with that.
Speaker 7: All my questions are going to be really dumb.
Speaker 11: Okay, now, please, by all means.
Speaker 2: Right to find out what people at Lorel are dreaming of.
Speaker 2: I spoke with Trisha Iyagari, global general manager at Loriel's
Speaker 2: Abeling brand, and they asked her about her own dreams
Speaker 2: and how technology and science could help bring those dreams
Speaker 2: into the world. Do you have a secret wish list
Speaker 2: of things you think that this partnership could produce, Like,
Speaker 2: is there a product out there that's been technically too difficult?
Speaker 2: That you think could be a worthy target.
Speaker 11: There is one that I think could be really amazing.
Speaker 7: What's that?
Speaker 8: So?
Speaker 11: Shine products in general are harder to create, and we're
Speaker 11: unable to create a shiny, long wearing eyeshadow. So basically
Speaker 11: like a shadow that could stay on your eyelids, that
Speaker 11: won't settle into creases, that won't move all over your face,
Speaker 11: that has a glossy effect. It's like the holy grail.
Speaker 2: That's the holy grail. Yeah, yeah, you may have seen
Speaker 2: that look in fashion shows, but that look isn't real,
Speaker 2: not for people like me and Lucy.
Speaker 11: Anyway, if you're walking down a runway, you see a
Speaker 11: lot of makeup artists doing techniques where they put some
Speaker 11: eyeshadow on, they layer vasoline over it on like slatter
Speaker 11: vasalina somebody's eyes to create this very like glossy look.
Speaker 11: But you know, within five minutes after they walk down
Speaker 11: the runway, I'm sure it's all over their face or
Speaker 11: being washed off. So the look is kind of more
Speaker 11: of like a fashion look that we've been unable to create,
Speaker 11: and real, real consumers can't wear it because it would
Speaker 11: get it everywhere.
Speaker 2: Trisia had another thing on her wish list too.
Speaker 11: The other that we would really like is semi permanent makeup.
Speaker 11: So we've talked a lot about really really comfortable, thin
Speaker 11: film makeup that you could wear all over your face
Speaker 11: and that you can sleep in, and that it will
Speaker 11: last a couple of days basically, so whether it be
Speaker 11: on your face, on your lashes, on your brows. So
Speaker 11: anything that's like more of a semi permanent meaning lasting
Speaker 11: for three days or more, would be amazing.
Speaker 7: Yeah.
Speaker 2: Yeah, And you say those two things have been the
Speaker 2: whole How long have they been on the wish list
Speaker 2: of Loreel?
Speaker 4: Oh?
Speaker 11: My gosh. I have been trying to develop this shiny
Speaker 11: eyeshadow since I started. What did I start, like twenty ten,
Speaker 11: And I'm sure many people had asked before me, and
Speaker 11: we tried so many iterations of it and nobody's been
Speaker 11: able to achieve it.
Speaker 2: It's clear that Loreel's experts like Tricia have a lot
Speaker 2: of ideas. I once said what I called a magic
Speaker 2: wand project, where I called up scientists and technologists in
Speaker 2: as many different fields as possible and asked them what
Speaker 2: they could create if they could just wave a magic
Speaker 2: wand and make it real, And everyone had something they'd
Speaker 2: want to create everyone. That's not the issue. The issue
Speaker 2: is that there are a million different impediments to make
Speaker 2: the ideas on the wish list reel. Lack of resources,
Speaker 2: lack of time, some crucial bit of know how is lacking.
Speaker 2: There's a gap between what we want and what we
Speaker 2: can actually have, and one of the simplest ways to
Speaker 2: think of the promise of AI is that it can
Speaker 2: narrow that gap, not close it, of course, but do
Speaker 2: enough that people with dreams realize there are more things
Speaker 2: within their grasp.
Speaker 7: Than they could ever have imagined.
Speaker 2: Smart Talks with IBM is produced by Matt Romano, Amy Gaines, McQuaid,
Speaker 2: Lucy Sullivan and Jake Harper. Were edited by Lacy Roberts.
Speaker 2: Engineering by Nina Bird Lawrence, mastering by Sarah Brugerer. Music
Speaker 2: by Gramoscope. Special thanks to Tatiana Lieberman and Cassidy Meyer.
Speaker 2: Smart Talks with IBM is a production of Pushkin Industries
Speaker 2: and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,
Speaker 2: listen on the iHeartRadio app, Apple Podcasts, or wherever you
Speaker 2: get your podcasts. I'm Malcolm Gabo. This is a paid
Speaker 2: advertised from IBM. The conversations on this podcast don't necessarily
Speaker 2: represent IBM's positions, strategies, or opinions.