Speaker 1: Sleepwalkers is a production of I Heart Radio and Unusual Productions.
Speaker 1: I'm Lain and I'm Kara Price. Welcome to a special
Speaker 1: bonus episode of Sleepwalkers from the Consumer Electronic show, So Kara,
Speaker 1: I'd never been to Las Vegas before, which is the
Speaker 1: difference between us. I've been to Vegas too many times. Well,
Speaker 1: I could tell, and it did feel good to be
Speaker 1: in good hands with an old Vegas hand like you.
Speaker 1: One of the new things though for me was slats,
Speaker 1: which I don't normally play. I think so consciously. I
Speaker 1: was thinking about what Tristan Harris talked about in the
Speaker 1: first season Sleepwalkers. You know he was that former googler
Speaker 1: who told us that Instagram is actually supposed to feel
Speaker 1: a lot like slot machines. Well, that's right. Tristan studied
Speaker 1: at the Stanford Persuasion Lab and told us about how
Speaker 1: casino architecture has influenced the development of highly addictive type
Speaker 1: products like Instagram. So it's interesting for me to actually
Speaker 1: see Vegas and the bright lights and the impossibility of
Speaker 1: escape firsthand, not to mention the replicas of the Empire
Speaker 1: State Building, the Canals of Venice, the Colisseum of Rome,
Speaker 1: and you know, I was lucky enough to see the
Speaker 1: Seattle Space Needle for the first time. I just didn't
Speaker 1: know that it was in Las Vegas. But that's not
Speaker 1: why we were there. We were there for CEES, the
Speaker 1: Consumer Electronics Show, and this episode we're actually going to
Speaker 1: talk about some of the coolest things we saw there,
Speaker 1: but we're going to focus more on the innovations that
Speaker 1: are at the intersection of technology and humanity rather than
Speaker 1: talk about you know, infamous toilet paper dispensers. One of
Speaker 1: the big reasons we went is because we were invited
Speaker 1: by WaveMaker, which is an agency part of w p P,
Speaker 1: to do an interview on stage, a live podcast, so
Speaker 1: to speak with Matt and on a hand, who is
Speaker 1: head of product at ARC Publishing and ARC Publishing is
Speaker 1: part of the Washington Post. Yeah, and ARC is also
Speaker 1: an interesting case of AI and action because they're forward
Speaker 1: thinking in terms of increasing the visibility of content through
Speaker 1: personalization and optimizing everything from headlines to photo selection, all
Speaker 1: using machine learning, and those are things that really matter
Speaker 1: for journalists and readers. Yeah, and this use of AI
Speaker 1: stands out to me because it provides a solution to
Speaker 1: real problem. How do you get eyeballs on the right
Speaker 1: content when there's just so much. That said, the issue
Speaker 1: of personalization does also raise questions about what happens when
Speaker 1: machines start to know us better than we know ourselves.
Speaker 1: Not to mention, what are the appropriate limits of how
Speaker 1: companies use AI and dature about us. Yeah, AI can
Speaker 1: definitely streamline processes by detecting patterns that you know, human
Speaker 1: beings cannot see, or it can allow you to do
Speaker 1: things at the scale like tag hundreds of thousands of
Speaker 1: articles that again, human beings just cannot do. So greater
Speaker 1: efficiency is on one side of the spectrum and extremely
Speaker 1: attractive to people, but on the other side you have
Speaker 1: issues of taking humans out of the loop, like the
Speaker 1: black box problem and authenticity in a world of deep fakes.
Speaker 1: So a question for businesses and users of technology is
Speaker 1: sort of when does AI add to our experience and
Speaker 1: when does it maybe hold us back or take advantage
Speaker 1: of us. You know, for example, from seeing news stories
Speaker 1: that we should see, but maybe the algorithm doesn't think
Speaker 1: we want to see it or that we won't click
Speaker 1: on it. Right in the old days when everyone received
Speaker 1: a print use of paper on their doorstep, everyone has
Speaker 1: the same front page and the same headlines. Nowadays, when
Speaker 1: you log onto a news website or onto social media,
Speaker 1: everybody has a different version of the world, and that
Speaker 1: is obviously positive for driving engagement, but may not be
Speaker 1: so positive in terms of having conversations with the same
Speaker 1: facts about the same stories. Equally, we have to ask
Speaker 1: do we want articles where the headline has been written
Speaker 1: by an algorithm or do we prefer headlines written by
Speaker 1: a person. And that's something we talked about with Matt
Speaker 1: because ARC actually tested the headline writing technology. Let's talk
Speaker 1: to Matt seriously. Let's cut to the chase. Are really
Speaker 1: came out of a collaboration trying to better understand what
Speaker 1: actual journalists needed. Can you talk a little bit more. Yeah,
Speaker 1: at the very beginning, you know, we were just trying
Speaker 1: to solve problems for ourselves seven or eight years ago.
Speaker 1: You know, we knew and he had to make some
Speaker 1: pretty fundamental transformation to the post and to really prepare
Speaker 1: ourselves for the digital future. We didn't have the right
Speaker 1: tools to do it, and we couldn't really find the
Speaker 1: right tools on the market either. What we did was
Speaker 1: spent a lot of time with the journalists and the
Speaker 1: editors trying to figure out what it was that make
Speaker 1: their lives easier. It's trying to figure out how do
Speaker 1: you make journalists work better, how can they publish faster?
Speaker 1: What are the little things you can do inside of
Speaker 1: a product to make it easier for them to write
Speaker 1: stories or publish From there? About four years ago is
Speaker 1: when we started evolving it into a commercial offering. Today
Speaker 1: we're running hundreds of websites around the world. We're in
Speaker 1: about twenty different countries. We're running companies like BP their
Speaker 1: internal communications as well as some of their marketing. We're
Speaker 1: running large broadcasters and all their live video and body
Speaker 1: and of course we're still running a lot of newspapers
Speaker 1: and news publishers like The Post and many others around
Speaker 1: the world, Lucky and publishing. You know that AI and
Speaker 1: artificial intelligence are made in headlines, and there was a
Speaker 1: story in the Financial Times last year we said fort
Speaker 1: of AI startups use no AI whatsoever. So I bet
Speaker 1: it's probably higher. But when we talk about using AI,
Speaker 1: or when you talk about using AI, what do we
Speaker 1: actually mean? So it can span the range of technologies
Speaker 1: from something like machine learning, which is basically a way
Speaker 1: to use algorithms, to take large sets of data and
Speaker 1: either uncovered patterns in it or try to model a
Speaker 1: way to predict a certain outcome to technologies like computer vision,
Speaker 1: which you can use to look at images or video
Speaker 1: and extract information about them by recognizing patterns and trying
Speaker 1: to identify objects inside of them, and so a lot
Speaker 1: of those technologies, then when you put them together, you
Speaker 1: can form some really interesting workflows that you know in
Speaker 1: the past you might have had to use humans to
Speaker 1: do that, you can actually do much more simple automatically.
Speaker 1: Was there a particular business challenge or challenge at the
Speaker 1: Washington Post that you couldn't have solved if you hadn't
Speaker 1: been using AI. Any story that we write on Washington Post,
Speaker 1: we're mapping to a set of two or three hundred topics.
Speaker 1: Maybe an example of one of those might be like
Speaker 1: congressional policy or narcotics crime. What you're trying to do
Speaker 1: is say, if I look at all this content, I'm
Speaker 1: not just pulling specific words out of it. I'm actually
Speaker 1: trying to figure out what is this content about, what
Speaker 1: is the fundamental concept of this content. So you pick
Speaker 1: a set of articles, let's say a hundred thousand news
Speaker 1: articles in the case of this example for the post,
Speaker 1: and at first you use humans it's called micro labor
Speaker 1: to do this training set. And the goal is you're
Speaker 1: building an algorithm based on a set of real data.
Speaker 1: And so the humans are going there and saying this article, yeah,
Speaker 1: this is about congressional policy. Why because I know it is,
Speaker 1: I read it, that's what it's about. This one's about
Speaker 1: narcotics time, and this one's about soccer. And so you
Speaker 1: train all these articles against that algorithm until finally the
Speaker 1: algorithm is basically sufficiently advanced to predict a new article
Speaker 1: that you put into it and determine an outcome with
Speaker 1: the same high probability of success that you were able
Speaker 1: to with humans training it. Now, every time a journalist
Speaker 1: saves or publishes a story, we're able to parse over
Speaker 1: all the content inside that story. Then we can predict
Speaker 1: the strength at which it's likely to belong to that topic.
Speaker 1: How do you create a better user experience, in your case,
Speaker 1: news experience for an individual or consumer With that metadat
Speaker 1: you can do a lot of interesting things. We can
Speaker 1: figure out that hey, this is something that they're interested
Speaker 1: in reading, perhaps they'd like to read more, and it
Speaker 1: actually serves the signal into our recommendation algorithms. From your perspective,
Speaker 1: where can businesses sort of harness the power of machine
Speaker 1: learning to really hone in on who their customer is
Speaker 1: and what that customer wants. We want to deliver more
Speaker 1: content to our readers who want to help them find
Speaker 1: more content that we've created. We have about nine journalists
Speaker 1: at the Washington Post. We write something like, you know,
Speaker 1: three or four hundred original stories a day, so there's
Speaker 1: a lot of content there. To get readers to all
Speaker 1: that different content and have them continue moving through your
Speaker 1: content you spend a lot of money to produce is
Speaker 1: really challenging, and so that's a great use case for personalization.
Speaker 1: But where you can make it really come alive is
Speaker 1: by having more sophisticated metadata, more sophisticated information about that content.
Speaker 1: It's more likely to bring readers to it, and so
Speaker 1: that's where these machine learning models really come in handy.
Speaker 1: I think part of what's fun about this conversation is
Speaker 1: there's a lot of cases out there where average users,
Speaker 1: you know, they imagine they see something like that. You
Speaker 1: see the boots on Instagram and you think, oh my god,
Speaker 1: like these companies must crazy, like you know, indiscernible for magic, right, like,
Speaker 1: there must be some crazy model out there doing this,
Speaker 1: and perhaps there is. But in a lot of ways,
Speaker 1: you know, your users aren't necessarily as aware of the
Speaker 1: advertising ecosystem, the data ecosystem, and how these things tied
Speaker 1: together between platforms and sites, and I think, as like
Speaker 1: industry professionals, we always kind of underestimate that fact. And
Speaker 1: so the net effect is that users are completely surprised
Speaker 1: by this. They think you must be doing something completely
Speaker 1: unheard of to achieve it, when in fact, you know
Speaker 1: it could be really simple data sharing. And so the
Speaker 1: reason I think that's important is then when you do
Speaker 1: build technologies that actually utilize some of these more sophisticated
Speaker 1: methods to build data sets, you have to be aware
Speaker 1: that your users. You know, first of all, your users
Speaker 1: aren't gonna necessarily anticipate the outcomes that you can create,
Speaker 1: and if you don't do a good job on the
Speaker 1: product side of making sure that you really think through
Speaker 1: the use case and how you're leveraging technology to solve it,
Speaker 1: you can generate unexpected outcomes. You know, there was the
Speaker 1: example of a retailer who produced advertising flyers that were
Speaker 1: able to predict folks who are pregnant, right, even if
Speaker 1: some of those folks didn't necessarily know that themselves yet
Speaker 1: or hadn't shared it with with their family or their spouses.
Speaker 1: And so that was a case really of both the
Speaker 1: company and the consumer being shocked by outcomes we're generating
Speaker 1: exactly right. I mean, the you know, the algorithm doesn't
Speaker 1: do anything magic, but that's a case of you know,
Speaker 1: putting together in that case, like a marketing program, where
Speaker 1: you don't really think through what's the possible data that
Speaker 1: this could produce and what are my users? What do
Speaker 1: they already know about this data? You know, you need
Speaker 1: to think really hard about your users and what they
Speaker 1: want and what they're trying to achieve, and what the
Speaker 1: dangers are and leveraging this technology. It's no different than
Speaker 1: in that way than any previous technology solutions you might
Speaker 1: have used to build a great product for people, and
Speaker 1: it can be misused just as easily. Funny enough, the
Speaker 1: first episode of Sleepwalkers season one open with a story
Speaker 1: of Washington Post employee Gillian Brockell, who, to your point
Speaker 1: about pregnancy and data stuffered a miscarriage but continue to
Speaker 1: receive targeted ads for pregnancy goods after a miscarriage, and
Speaker 1: she wrote this openless is the technology companies saying please
Speaker 1: stop targeting me. But that raised a big question for us,
Speaker 1: which is what happens when the algorithms go wrong? Yeah,
Speaker 1: I'd almost be more specific with the way that you
Speaker 1: say that, and like the algorithm didn't go wrong, right,
Speaker 1: but like the implementation of it and the product that
Speaker 1: they built around it did because it wasn't really correctly conceived.
Speaker 1: And we have to make sure that like what you're
Speaker 1: trying to do automatically fits really well with what your
Speaker 1: users are trying to accomplish, doesn't happen in a way
Speaker 1: that's not expected. Is a well designed product, you know,
Speaker 1: So in that specific case, yeah, I mean it always
Speaker 1: starts and ends with kind of good product design. If
Speaker 1: you're not doing that, just like any other tool, you
Speaker 1: can misuse it. One of the other things we did
Speaker 1: on the show was we used a language generator to
Speaker 1: can't with pickup lines based on a data set of
Speaker 1: all none of them were actually I don't have kind
Speaker 1: of things like you are a thing and I love you,
Speaker 1: you know, which is now the name of the book
Speaker 1: by the woman who. Yeah, woman Jell Shane her wrote
Speaker 1: a book about it, and then she also did these
Speaker 1: things like AI recipes, like one was for chocolate chocolate
Speaker 1: chocolate chicken cake. So there is funny things and Shakespeare
Speaker 1: on it, and I didn't revealed two things. One is
Speaker 1: when you turn these deep learning algorithms onto big data sets,
Speaker 1: they reveal passions you might not necessarily be aware of,
Speaker 1: like with a lot of chicken and quite a lot
Speaker 1: of chocolates. On the other hand, like these were clearly
Speaker 1: not something human would ever make. So how do you
Speaker 1: think about the line between doing fun things in AI
Speaker 1: and doing stuff which is valuable for business and also
Speaker 1: not getting lost in the uncanny valley. So a good
Speaker 1: example of this for instances. We spent some time at
Speaker 1: the Post trying to build a headline generation algorithm we
Speaker 1: could automatically create headlines for stories. And you know, the
Speaker 1: idea I think at the beginning wasn't necessarily that, you know,
Speaker 1: journalists are never read headlines again, but we'd be able
Speaker 1: to create some alternative headlines in different ways to think
Speaker 1: about a story. Our intention was, let's see if we
Speaker 1: can come up with something so that we can create
Speaker 1: several different variants of a headline. Part of our software
Speaker 1: platform we include content testing framework. So one of the
Speaker 1: things that we can do is say, for a given story,
Speaker 1: let's have three different headlines for it, Let's run a
Speaker 1: test as soon as it publishest of the audience is
Speaker 1: going to get each variant, and then as people start
Speaker 1: to click one more than the other, we're gonna shift
Speaker 1: the burden of traffic to the most successful variant. And that,
Speaker 1: I would them, by itself works really well. If you know,
Speaker 1: folks in the audience here were to look at the
Speaker 1: homepage of our site right now, there's probably two or
Speaker 1: three stories that are running those types of tests where
Speaker 1: different people would se different headlines or different images, or
Speaker 1: in fact maybe actually just complutely different stories, and those
Speaker 1: tests will resolve in like fifteen or twenty minutes. So
Speaker 1: that works well enough by itself. But then we realized, well,
Speaker 1: we could probably create more of these tests if only
Speaker 1: we could automatically create headlines for them. We could just
Speaker 1: be running these tests all the time for every single story.
Speaker 1: But what we found was, you know, not exactly so
Speaker 1: if the idea was to save journalists time and doing
Speaker 1: that in the end, I mean, you'd have to come
Speaker 1: up with something that's fairly solid and ready to publish out.
Speaker 1: We were able to create something that allowed you know,
Speaker 1: journalists basically have different formulations that they could play with
Speaker 1: and maybe gave them some ideas of what to create,
Speaker 1: but it still require people to look at it. In
Speaker 1: the end, how can businesses work better with their engineers,
Speaker 1: with their tech teams to sort of create and not
Speaker 1: stay siloed in a way that like somebody who works
Speaker 1: in marketing feels like, well, you know, there's actually this
Speaker 1: need that I have, but I don't know who to
Speaker 1: talk to about it, and that don't really know what
Speaker 1: to do. It's an awesome question to me, Like one
Speaker 1: of the best things that you can do as a
Speaker 1: business is to put those people together, sometimes even physically.
Speaker 1: So when we started this project, you know, we literally
Speaker 1: co located engineers, product people directly inside the news room
Speaker 1: to sit with the folks who are doing this work. Now,
Speaker 1: when it comes to a I M L, you remember,
Speaker 1: these are just tools. These are tools to make work easier.
Speaker 1: Their tools in a lot of cases for automation and efficiency.
Speaker 1: There's some problems that can't be solved without it. In
Speaker 1: the end, though, you know, you're still trying to solve
Speaker 1: some business problem, and most of those involved some sort
Speaker 1: of users that you need to get to know. So
Speaker 1: you know, even at the post we had data sciencests
Speaker 1: who were on those teams embedded in the news room
Speaker 1: as well. You know, they weren't kind of seated somewhere
Speaker 1: else thinking of problems on their own. There's a time
Speaker 1: and a place for creating room for prototyping sometimes that
Speaker 1: has to happen to especially with really advanced technologies. But
Speaker 1: beyond prototyping, putting those teams together is super crucial. So
Speaker 1: how do you make sure, speaking metaphorically, you write a
Speaker 1: good brief to your AI team, well, your engineering team.
Speaker 1: I still think, you know, start with the problem that
Speaker 1: you're trying to solve. Like, if you're going in thinking
Speaker 1: let's use AI to solve something, I think you're probably
Speaker 1: starting the problem the wrong way, and start by framing
Speaker 1: up the problem in business terms. For people, you'd be surprised,
Speaker 1: I think how much you know engineers and product folks
Speaker 1: really actually prefer to get that first before they start
Speaker 1: diving into what's the technology that I'm getting used to
Speaker 1: solve this problem? With a buzz around AI, especially right now.
Speaker 1: You know, people tend to go into it. I think
Speaker 1: thinking this is kind of something that's pretty close to magic.
Speaker 1: We just use AI. It's going to solve these problems
Speaker 1: that we haven't been able to solve some other way.
Speaker 1: And that's not really the right way to approach it.
Speaker 1: But a I will be transformative, I think for organizations
Speaker 1: that apply it the right way, with the product mindset,
Speaker 1: with a good knowledge of the problem that they're trying
Speaker 1: to solve, with empathy for their users. When we're doing
Speaker 1: our research for this panel, as an article in Bloomberg
Speaker 1: News saying that Jeff Bezos is pestimally very invested in
Speaker 1: the product, and then you called him Jeff in conversation,
Speaker 1: I found very impress So we will unders people sending
Speaker 1: me an email already without obviously telling us the contented
Speaker 1: your meetings. How does his vision imbue what you do?
Speaker 1: So certainly for us it's boon to have him owning
Speaker 1: the company. I think that's one of the greatest things
Speaker 1: is you know, obviously at the Washington Post were known
Speaker 1: for an amazing newsroom, but we've also spent a lot
Speaker 1: of time investing in our engineering team that started to
Speaker 1: some extent before you know, we were purchased by Jeff,
Speaker 1: but certainly after we purchased us. It opened a lot
Speaker 1: of new doors for us, and it gets people excited
Speaker 1: to come and work for us and some of the
Speaker 1: problems that we're trying to solve. It really inspires people
Speaker 1: to be able to build like a platform like we built,
Speaker 1: you know, within a newspaper company. I think would have
Speaker 1: been hired to Fathom probably ten years ago, but I
Speaker 1: mean today we really can say that, you know, we're
Speaker 1: a content company and we're a technology company. And I
Speaker 1: think part of that starts with him in the leadership
Speaker 1: that he provides. More Sleepwalkers after the break, So, Kara,
Speaker 1: that was our conversation on stage with Matt monahantan CS
Speaker 1: in early January. It was interesting because we hear so
Speaker 1: much about tech companies becoming publishers, whether it's Facebook, YouTube,
Speaker 1: or Twitter, but we hear less about publishers becoming tech companies.
Speaker 1: I guess that's where Jeff Basos as an owner is
Speaker 1: what we might call a differentiator. So I was personally
Speaker 1: struck with Matt's experiments with the headline generator. You know,
Speaker 1: for the time being, it doesn't work well enough to
Speaker 1: be a commercial product, but I think it will soon.
Speaker 1: You know, look at autocomplete when you send a Gmail
Speaker 1: like Sincerely Comma. You know I get those all the time,
Speaker 1: and it works. You know, in an apocalyptic reading, that
Speaker 1: means that machines will take over our lives and there
Speaker 1: will be no work left for humans. We won't have
Speaker 1: to come up with smart headlines. But I think in
Speaker 1: a more optimistic reading, using algorithms to generate writing suggestions
Speaker 1: could actually enable originality. Reminds me of that Chinese science
Speaker 1: fiction writer who you and I have talked about named
Speaker 1: Chen show Fund, who actually used an algorithm to create
Speaker 1: ideas for his own work, and he used it when
Speaker 1: he had writer's block. He wasn't using it to replace
Speaker 1: his creative skill. He was using it as an enhancement tool.
Speaker 1: And I think that's really interesting. Yeah, And in season
Speaker 1: one of sleep Walkers, we spoke to a filmmaker called
Speaker 1: Oscar Shop who actually shot a whole film written by
Speaker 1: Ai called Sunspring. Oscar and Chen turned the technology into
Speaker 1: a tool that actually serves their purposes. You know, you
Speaker 1: can develop all kinds of technology in a vacuum. About
Speaker 1: the technology that really serves people and fills a need.
Speaker 1: Is the technology that sticks around, you know, speaking of
Speaker 1: technology that really sticks around, and then some technology that
Speaker 1: might not stick around. There's so much stuff on the
Speaker 1: floor of CS you and I have never been to
Speaker 1: see us before. I think we were very overwhelmed by
Speaker 1: what we saw and excited. It was kind of inspected
Speaker 1: gudgets paradise, and obviously, as someone who's obsessed with technology
Speaker 1: and consumer technology, I would have bought every single thing
Speaker 1: I thought you tried to buy. I did try to
Speaker 1: buy that. I mean that keyboard with the mouse burnt
Speaker 1: into the keyboard. How much did this cost? I almost
Speaker 1: bought a laser cool laser patch for my back, which
Speaker 1: placebo or not made me look very hot. But no,
Speaker 1: in all seriousness, you know, there are things that were
Speaker 1: on the floor that are kind of amazing when you
Speaker 1: think about it, Like from this company called Pillow Health.
Speaker 1: They've developed this device called Priya that looks like a
Speaker 1: little face, a cute little face, as they always do,
Speaker 1: and it's basically a pill dispenser that is voice and
Speaker 1: face activated, so anybody could have one. I could have one,
Speaker 1: You could have one. But I think they've developed it
Speaker 1: mostly for elderly people who have many pills that they
Speaker 1: have to take throughout the day, and who's children or
Speaker 1: health aids want to be able to control when their
Speaker 1: medicine is dispensed. And I think for someone who might
Speaker 1: have memory impairment, physical impairment, the idea that someone who
Speaker 1: isn't in the room with that person could control when
Speaker 1: they're getting you know, vital medicine is really amazing. And
Speaker 1: you know, you say what you will about privacy. I
Speaker 1: think being able to do something like take care of
Speaker 1: your elderly parent with a device is you know, the
Speaker 1: perfect intersection of technology and humanity, right. I spoke to
Speaker 1: the founder about exactly that. And you know, we have
Speaker 1: a lot of concerns about facial recognition that we've discussed
Speaker 1: at length on Sleepwalkers and will continue to discuss in
Speaker 1: season two. But in a narrow use case like this,
Speaker 1: in a voluntary use case where it can help somebody
Speaker 1: out to remember something very important, like what pills are taken,
Speaker 1: when it may well be that that's a sacrifice which
Speaker 1: is very much worth taking. There was another startup on
Speaker 1: the floor that really caught my eye, which was called
Speaker 1: in New Pathy, and according to the card which have
Speaker 1: in front of me. It's the first device in the
Speaker 1: world which is equipped with technology to visualize your dog's
Speaker 1: status from his or her heart rate information. And this
Speaker 1: is basically a harness that you put on your dog
Speaker 1: and it recalls your dog's heart rate and in particular
Speaker 1: the variability in your dog's heart rate to tell you
Speaker 1: if your dog is happy or sad, or anxious or
Speaker 1: excited or curious. And you know, people struggle to know
Speaker 1: what that dogs are thinking. And if you can use
Speaker 1: data from historical doggie feelings to model what a current
Speaker 1: dog is feeling and use that to have better interaction
Speaker 1: with your dog, more power to you. I think it's cool.
Speaker 1: There was this other piece of technology from a company
Speaker 1: called we Labs. They were Japanese, right, and it kind
Speaker 1: of blew my mind in the same way that like
Speaker 1: thinking about automation of drive through blew my mind. You know.
Speaker 1: It was this like would beam that looked like a
Speaker 1: beam in a house, and it had a computer that
Speaker 1: was inside of it. And the woman who was showcasing
Speaker 1: it basically asked ours to stay end up against it
Speaker 1: like you would when you're charting at child's height, and
Speaker 1: she took a pen or stylus and marked Oz's height,
Speaker 1: and then immediately that marking was uploaded into the cloud
Speaker 1: and displayed on a device next to this would beam
Speaker 1: And it just made me think, like this thing that
Speaker 1: millions of families do as their children are growing up
Speaker 1: is now being digitized, and again like going back to
Speaker 1: the intersection of technology and human behavior, like imagine if
Speaker 1: someone moves from the house, those height markings that were
Speaker 1: such a part of your child's growing up can be
Speaker 1: taken with you in the cloud. It's just I mean,
Speaker 1: that's the kind of stuff where I'm like, do I
Speaker 1: need it? Does someone need it? Who cares? But the
Speaker 1: idea that it's like replicating this very very personal feeling
Speaker 1: and you know activity that we do in our childhood
Speaker 1: is I don't know, it kind of blew my mind.
Speaker 1: All three of the things we ended up talking about,
Speaker 1: you know, pillow Health, the doggy heart rate monitor, and
Speaker 1: this Japanese WOULD device. You know, they go back to
Speaker 1: the most human things are our parents, okay, is our
Speaker 1: dog okay? Our children growing up? What does it make
Speaker 1: us feel as they grow up? And so technology that
Speaker 1: addresses those questions in a sensitive and humanistic way will
Speaker 1: always be interesting to us because it really allows us
Speaker 1: to think about and tell stories about ourselves. The oldest
Speaker 1: stories we tell, the stories that are parts of novels
Speaker 1: and films and all other kinds of art. So that's
Speaker 1: to me where technology is most interesting and the types
Speaker 1: of stories that will continue to tell on Sleepwalkers. So
Speaker 1: everything we just talked about is consumer focus and very interesting,
Speaker 1: but a I can also help address problems at scale,
Speaker 1: you know, issues ranging from climate change to pain management,
Speaker 1: and those are all things that we're going to talk
Speaker 1: about in our very exciting season two. Thank you for listening,
Speaker 1: and we're looking forward to seeing you for Season two
Speaker 1: of Sleepwalkers very soon. Und the Roots under the Roots
Speaker 1: for the f