January 24, 2020 • 24 mins
In this special bonus episode, Oz and Karah share their trip to the Consumer Electronics Show. They present excerpts from a conversation with Matt Monahan of The Washington Post about how to best harness the power of AI, while avoiding common pitfalls. Matt is head of product for Arc Publishing, which began life as The Post's internal publishing suite, and is now licensed by hundreds of partner sites. Oz and Karah also discuss their highlights from the CES floor, including a device to track dogs' emotions.
Learn more about your ad-choices at https://www.iheartpodcastnetwork.com
See omnystudio.com/listener for privacy information.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:04):
Sleepwalkers is a production of I Heart Radio and Unusual Productions.
I'm Lain and I'm Kara Price. Welcome to a special
bonus episode of Sleepwalkers from the Consumer Electronic show, So Kara,
(00:35):
I'd never been to Las Vegas before, which is the
difference between us. I've been to Vegas too many times. Well,
I could tell, and it did feel good to be
in good hands with an old Vegas hand like you.
One of the new things though for me was slats,
which I don't normally play. I think so consciously. I
was thinking about what Tristan Harris talked about in the
first season Sleepwalkers. You know he was that former googler
(00:57):
who told us that Instagram is actually supposed to feel
a lot like slot machines. Well, that's right. Tristan studied
at the Stanford Persuasion Lab and told us about how
casino architecture has influenced the development of highly addictive type
products like Instagram. So it's interesting for me to actually
see Vegas and the bright lights and the impossibility of
escape firsthand, not to mention the replicas of the Empire
(01:20):
State Building, the Canals of Venice, the Colisseum of Rome,
and you know, I was lucky enough to see the
Seattle Space Needle for the first time. I just didn't
know that it was in Las Vegas. But that's not
why we were there. We were there for CEES, the
Consumer Electronics Show, and this episode we're actually going to
talk about some of the coolest things we saw there,
but we're going to focus more on the innovations that
(01:42):
are at the intersection of technology and humanity rather than
talk about you know, infamous toilet paper dispensers. One of
the big reasons we went is because we were invited
by WaveMaker, which is an agency part of w p P,
to do an interview on stage, a live podcast, so
to speak with Matt and on a hand, who is
head of product at ARC Publishing and ARC Publishing is
(02:04):
part of the Washington Post. Yeah, and ARC is also
an interesting case of AI and action because they're forward
thinking in terms of increasing the visibility of content through
personalization and optimizing everything from headlines to photo selection, all
using machine learning, and those are things that really matter
for journalists and readers. Yeah, and this use of AI
stands out to me because it provides a solution to
(02:27):
real problem. How do you get eyeballs on the right
content when there's just so much. That said, the issue
of personalization does also raise questions about what happens when
machines start to know us better than we know ourselves.
Not to mention, what are the appropriate limits of how
companies use AI and dature about us. Yeah, AI can
definitely streamline processes by detecting patterns that you know, human
(02:51):
beings cannot see, or it can allow you to do
things at the scale like tag hundreds of thousands of
articles that again, human beings just cannot do. So greater
efficiency is on one side of the spectrum and extremely
attractive to people, but on the other side you have
issues of taking humans out of the loop, like the
black box problem and authenticity in a world of deep fakes.
(03:14):
So a question for businesses and users of technology is
sort of when does AI add to our experience and
when does it maybe hold us back or take advantage
of us. You know, for example, from seeing news stories
that we should see, but maybe the algorithm doesn't think
we want to see it or that we won't click
on it. Right in the old days when everyone received
(03:34):
a print use of paper on their doorstep, everyone has
the same front page and the same headlines. Nowadays, when
you log onto a news website or onto social media,
everybody has a different version of the world, and that
is obviously positive for driving engagement, but may not be
so positive in terms of having conversations with the same
facts about the same stories. Equally, we have to ask
(03:56):
do we want articles where the headline has been written
by an algorithm or do we prefer headlines written by
a person. And that's something we talked about with Matt
because ARC actually tested the headline writing technology. Let's talk
to Matt seriously. Let's cut to the chase. Are really
came out of a collaboration trying to better understand what
(04:18):
actual journalists needed. Can you talk a little bit more. Yeah,
at the very beginning, you know, we were just trying
to solve problems for ourselves seven or eight years ago.
You know, we knew and he had to make some
pretty fundamental transformation to the post and to really prepare
ourselves for the digital future. We didn't have the right
tools to do it, and we couldn't really find the
right tools on the market either. What we did was
(04:39):
spent a lot of time with the journalists and the
editors trying to figure out what it was that make
their lives easier. It's trying to figure out how do
you make journalists work better, how can they publish faster?
What are the little things you can do inside of
a product to make it easier for them to write
stories or publish From there? About four years ago is
when we started evolving it into a commercial offering. Today
we're running hundreds of websites around the world. We're in
(05:00):
about twenty different countries. We're running companies like BP their
internal communications as well as some of their marketing. We're
running large broadcasters and all their live video and body
and of course we're still running a lot of newspapers
and news publishers like The Post and many others around
the world, Lucky and publishing. You know that AI and
artificial intelligence are made in headlines, and there was a
(05:22):
story in the Financial Times last year we said fort
of AI startups use no AI whatsoever. So I bet
it's probably higher. But when we talk about using AI,
or when you talk about using AI, what do we
actually mean? So it can span the range of technologies
from something like machine learning, which is basically a way
(05:43):
to use algorithms, to take large sets of data and
either uncovered patterns in it or try to model a
way to predict a certain outcome to technologies like computer vision,
which you can use to look at images or video
and extract information about them by recognizing patterns and trying
to identify objects inside of them, and so a lot
of those technologies, then when you put them together, you
can form some really interesting workflows that you know in
(06:04):
the past you might have had to use humans to
do that, you can actually do much more simple automatically.
Was there a particular business challenge or challenge at the
Washington Post that you couldn't have solved if you hadn't
been using AI. Any story that we write on Washington Post,
we're mapping to a set of two or three hundred topics.
Maybe an example of one of those might be like
(06:25):
congressional policy or narcotics crime. What you're trying to do
is say, if I look at all this content, I'm
not just pulling specific words out of it. I'm actually
trying to figure out what is this content about, what
is the fundamental concept of this content. So you pick
a set of articles, let's say a hundred thousand news
articles in the case of this example for the post,
and at first you use humans it's called micro labor
(06:47):
to do this training set. And the goal is you're
building an algorithm based on a set of real data.
And so the humans are going there and saying this article, yeah,
this is about congressional policy. Why because I know it is,
I read it, that's what it's about. This one's about
narcotics time, and this one's about soccer. And so you
train all these articles against that algorithm until finally the
algorithm is basically sufficiently advanced to predict a new article
(07:10):
that you put into it and determine an outcome with
the same high probability of success that you were able
to with humans training it. Now, every time a journalist
saves or publishes a story, we're able to parse over
all the content inside that story. Then we can predict
the strength at which it's likely to belong to that topic.
How do you create a better user experience, in your case,
(07:32):
news experience for an individual or consumer With that metadat
you can do a lot of interesting things. We can
figure out that hey, this is something that they're interested
in reading, perhaps they'd like to read more, and it
actually serves the signal into our recommendation algorithms. From your perspective,
where can businesses sort of harness the power of machine
learning to really hone in on who their customer is
(07:55):
and what that customer wants. We want to deliver more
content to our readers who want to help them find
more content that we've created. We have about nine journalists
at the Washington Post. We write something like, you know,
three or four hundred original stories a day, so there's
a lot of content there. To get readers to all
that different content and have them continue moving through your
content you spend a lot of money to produce is
(08:17):
really challenging, and so that's a great use case for personalization.
But where you can make it really come alive is
by having more sophisticated metadata, more sophisticated information about that content.
It's more likely to bring readers to it, and so
that's where these machine learning models really come in handy.
I think part of what's fun about this conversation is
there's a lot of cases out there where average users,
(08:38):
you know, they imagine they see something like that. You
see the boots on Instagram and you think, oh my god,
like these companies must crazy, like you know, indiscernible for magic, right, like,
there must be some crazy model out there doing this,
and perhaps there is. But in a lot of ways,
you know, your users aren't necessarily as aware of the
advertising ecosystem, the data ecosystem, and how these things tied
(09:01):
together between platforms and sites, and I think, as like
industry professionals, we always kind of underestimate that fact. And
so the net effect is that users are completely surprised
by this. They think you must be doing something completely
unheard of to achieve it, when in fact, you know
it could be really simple data sharing. And so the
reason I think that's important is then when you do
build technologies that actually utilize some of these more sophisticated
methods to build data sets, you have to be aware
(09:24):
that your users. You know, first of all, your users
aren't gonna necessarily anticipate the outcomes that you can create,
and if you don't do a good job on the
product side of making sure that you really think through
the use case and how you're leveraging technology to solve it,
you can generate unexpected outcomes. You know, there was the
example of a retailer who produced advertising flyers that were
able to predict folks who are pregnant, right, even if
(09:47):
some of those folks didn't necessarily know that themselves yet
or hadn't shared it with with their family or their spouses.
And so that was a case really of both the
company and the consumer being shocked by outcomes we're generating
exactly right. I mean, the you know, the algorithm doesn't
do anything magic, but that's a case of you know,
putting together in that case, like a marketing program, where
(10:08):
you don't really think through what's the possible data that
this could produce and what are my users? What do
they already know about this data? You know, you need
to think really hard about your users and what they
want and what they're trying to achieve, and what the
dangers are and leveraging this technology. It's no different than
in that way than any previous technology solutions you might
have used to build a great product for people, and
it can be misused just as easily. Funny enough, the
(10:28):
first episode of Sleepwalkers season one open with a story
of Washington Post employee Gillian Brockell, who, to your point
about pregnancy and data stuffered a miscarriage but continue to
receive targeted ads for pregnancy goods after a miscarriage, and
she wrote this openless is the technology companies saying please
(10:49):
stop targeting me. But that raised a big question for us,
which is what happens when the algorithms go wrong? Yeah,
I'd almost be more specific with the way that you
say that, and like the algorithm didn't go wrong, right,
but like the implementation of it and the product that
they built around it did because it wasn't really correctly conceived.
And we have to make sure that like what you're
trying to do automatically fits really well with what your
users are trying to accomplish, doesn't happen in a way
(11:11):
that's not expected. Is a well designed product, you know,
So in that specific case, yeah, I mean it always
starts and ends with kind of good product design. If
you're not doing that, just like any other tool, you
can misuse it. One of the other things we did
on the show was we used a language generator to
can't with pickup lines based on a data set of
all none of them were actually I don't have kind
(11:36):
of things like you are a thing and I love you,
you know, which is now the name of the book
by the woman who. Yeah, woman Jell Shane her wrote
a book about it, and then she also did these
things like AI recipes, like one was for chocolate chocolate
chocolate chicken cake. So there is funny things and Shakespeare
on it, and I didn't revealed two things. One is
when you turn these deep learning algorithms onto big data sets,
(12:00):
they reveal passions you might not necessarily be aware of,
like with a lot of chicken and quite a lot
of chocolates. On the other hand, like these were clearly
not something human would ever make. So how do you
think about the line between doing fun things in AI
and doing stuff which is valuable for business and also
not getting lost in the uncanny valley. So a good
example of this for instances. We spent some time at
(12:21):
the Post trying to build a headline generation algorithm we
could automatically create headlines for stories. And you know, the
idea I think at the beginning wasn't necessarily that, you know,
journalists are never read headlines again, but we'd be able
to create some alternative headlines in different ways to think
about a story. Our intention was, let's see if we
can come up with something so that we can create
several different variants of a headline. Part of our software
(12:42):
platform we include content testing framework. So one of the
things that we can do is say, for a given story,
let's have three different headlines for it, Let's run a
test as soon as it publishest of the audience is
going to get each variant, and then as people start
to click one more than the other, we're gonna shift
the burden of traffic to the most successful variant. And that,
I would them, by itself works really well. If you know,
folks in the audience here were to look at the
(13:04):
homepage of our site right now, there's probably two or
three stories that are running those types of tests where
different people would se different headlines or different images, or
in fact maybe actually just complutely different stories, and those
tests will resolve in like fifteen or twenty minutes. So
that works well enough by itself. But then we realized, well,
we could probably create more of these tests if only
we could automatically create headlines for them. We could just
be running these tests all the time for every single story.
(13:26):
But what we found was, you know, not exactly so
if the idea was to save journalists time and doing
that in the end, I mean, you'd have to come
up with something that's fairly solid and ready to publish out.
We were able to create something that allowed you know,
journalists basically have different formulations that they could play with
and maybe gave them some ideas of what to create,
but it still require people to look at it. In
the end, how can businesses work better with their engineers,
(13:48):
with their tech teams to sort of create and not
stay siloed in a way that like somebody who works
in marketing feels like, well, you know, there's actually this
need that I have, but I don't know who to
talk to about it, and that don't really know what
to do. It's an awesome question to me, Like one
of the best things that you can do as a
business is to put those people together, sometimes even physically.
(14:10):
So when we started this project, you know, we literally
co located engineers, product people directly inside the news room
to sit with the folks who are doing this work. Now,
when it comes to a I M L, you remember,
these are just tools. These are tools to make work easier.
Their tools in a lot of cases for automation and efficiency.
There's some problems that can't be solved without it. In
the end, though, you know, you're still trying to solve
(14:32):
some business problem, and most of those involved some sort
of users that you need to get to know. So
you know, even at the post we had data sciencests
who were on those teams embedded in the news room
as well. You know, they weren't kind of seated somewhere
else thinking of problems on their own. There's a time
and a place for creating room for prototyping sometimes that
has to happen to especially with really advanced technologies. But
beyond prototyping, putting those teams together is super crucial. So
(14:54):
how do you make sure, speaking metaphorically, you write a
good brief to your AI team, well, your engineering team.
I still think, you know, start with the problem that
you're trying to solve. Like, if you're going in thinking
let's use AI to solve something, I think you're probably
starting the problem the wrong way, and start by framing
up the problem in business terms. For people, you'd be surprised,
I think how much you know engineers and product folks
(15:15):
really actually prefer to get that first before they start
diving into what's the technology that I'm getting used to
solve this problem? With a buzz around AI, especially right now.
You know, people tend to go into it. I think
thinking this is kind of something that's pretty close to magic.
We just use AI. It's going to solve these problems
that we haven't been able to solve some other way.
And that's not really the right way to approach it.
But a I will be transformative, I think for organizations
(15:37):
that apply it the right way, with the product mindset,
with a good knowledge of the problem that they're trying
to solve, with empathy for their users. When we're doing
our research for this panel, as an article in Bloomberg
News saying that Jeff Bezos is pestimally very invested in
the product, and then you called him Jeff in conversation,
I found very impress So we will unders people sending
(16:01):
me an email already without obviously telling us the contented
your meetings. How does his vision imbue what you do?
So certainly for us it's boon to have him owning
the company. I think that's one of the greatest things
is you know, obviously at the Washington Post were known
for an amazing newsroom, but we've also spent a lot
of time investing in our engineering team that started to
(16:21):
some extent before you know, we were purchased by Jeff,
but certainly after we purchased us. It opened a lot
of new doors for us, and it gets people excited
to come and work for us and some of the
problems that we're trying to solve. It really inspires people
to be able to build like a platform like we built,
you know, within a newspaper company. I think would have
been hired to Fathom probably ten years ago, but I
mean today we really can say that, you know, we're
a content company and we're a technology company. And I
(16:44):
think part of that starts with him in the leadership
that he provides. More Sleepwalkers after the break, So, Kara,
(17:05):
that was our conversation on stage with Matt monahantan CS
in early January. It was interesting because we hear so
much about tech companies becoming publishers, whether it's Facebook, YouTube,
or Twitter, but we hear less about publishers becoming tech companies.
I guess that's where Jeff Basos as an owner is
what we might call a differentiator. So I was personally
(17:25):
struck with Matt's experiments with the headline generator. You know,
for the time being, it doesn't work well enough to
be a commercial product, but I think it will soon.
You know, look at autocomplete when you send a Gmail
like Sincerely Comma. You know I get those all the time,
and it works. You know, in an apocalyptic reading, that
means that machines will take over our lives and there
(17:47):
will be no work left for humans. We won't have
to come up with smart headlines. But I think in
a more optimistic reading, using algorithms to generate writing suggestions
could actually enable originality. Reminds me of that Chinese science
fiction writer who you and I have talked about named
Chen show Fund, who actually used an algorithm to create
(18:08):
ideas for his own work, and he used it when
he had writer's block. He wasn't using it to replace
his creative skill. He was using it as an enhancement tool.
And I think that's really interesting. Yeah, And in season
one of sleep Walkers, we spoke to a filmmaker called
Oscar Shop who actually shot a whole film written by
Ai called Sunspring. Oscar and Chen turned the technology into
(18:28):
a tool that actually serves their purposes. You know, you
can develop all kinds of technology in a vacuum. About
the technology that really serves people and fills a need.
Is the technology that sticks around, you know, speaking of
technology that really sticks around, and then some technology that
might not stick around. There's so much stuff on the
floor of CS you and I have never been to
(18:49):
see us before. I think we were very overwhelmed by
what we saw and excited. It was kind of inspected
gudgets paradise, and obviously, as someone who's obsessed with technology
and consumer technology, I would have bought every single thing
I thought you tried to buy. I did try to
buy that. I mean that keyboard with the mouse burnt
into the keyboard. How much did this cost? I almost
(19:11):
bought a laser cool laser patch for my back, which
placebo or not made me look very hot. But no,
in all seriousness, you know, there are things that were
on the floor that are kind of amazing when you
think about it, Like from this company called Pillow Health.
They've developed this device called Priya that looks like a
(19:33):
little face, a cute little face, as they always do,
and it's basically a pill dispenser that is voice and
face activated, so anybody could have one. I could have one,
You could have one. But I think they've developed it
mostly for elderly people who have many pills that they
have to take throughout the day, and who's children or
(19:54):
health aids want to be able to control when their
medicine is dispensed. And I think for someone who might
have memory impairment, physical impairment, the idea that someone who
isn't in the room with that person could control when
they're getting you know, vital medicine is really amazing. And
(20:15):
you know, you say what you will about privacy. I
think being able to do something like take care of
your elderly parent with a device is you know, the
perfect intersection of technology and humanity, right. I spoke to
the founder about exactly that. And you know, we have
a lot of concerns about facial recognition that we've discussed
at length on Sleepwalkers and will continue to discuss in
(20:38):
season two. But in a narrow use case like this,
in a voluntary use case where it can help somebody
out to remember something very important, like what pills are taken,
when it may well be that that's a sacrifice which
is very much worth taking. There was another startup on
the floor that really caught my eye, which was called
in New Pathy, and according to the card which have
(20:59):
in front of me. It's the first device in the
world which is equipped with technology to visualize your dog's
status from his or her heart rate information. And this
is basically a harness that you put on your dog
and it recalls your dog's heart rate and in particular
the variability in your dog's heart rate to tell you
if your dog is happy or sad, or anxious or
(21:19):
excited or curious. And you know, people struggle to know
what that dogs are thinking. And if you can use
data from historical doggie feelings to model what a current
dog is feeling and use that to have better interaction
with your dog, more power to you. I think it's cool.
There was this other piece of technology from a company
called we Labs. They were Japanese, right, and it kind
(21:40):
of blew my mind in the same way that like
thinking about automation of drive through blew my mind. You know.
It was this like would beam that looked like a
beam in a house, and it had a computer that
was inside of it. And the woman who was showcasing
it basically asked ours to stay end up against it
(22:01):
like you would when you're charting at child's height, and
she took a pen or stylus and marked Oz's height,
and then immediately that marking was uploaded into the cloud
and displayed on a device next to this would beam
And it just made me think, like this thing that
millions of families do as their children are growing up
(22:24):
is now being digitized, and again like going back to
the intersection of technology and human behavior, like imagine if
someone moves from the house, those height markings that were
such a part of your child's growing up can be
taken with you in the cloud. It's just I mean,
that's the kind of stuff where I'm like, do I
need it? Does someone need it? Who cares? But the
(22:47):
idea that it's like replicating this very very personal feeling
and you know activity that we do in our childhood
is I don't know, it kind of blew my mind.
All three of the things we ended up talking about,
you know, pillow Health, the doggy heart rate monitor, and
this Japanese WOULD device. You know, they go back to
(23:08):
the most human things are our parents, okay, is our
dog okay? Our children growing up? What does it make
us feel as they grow up? And so technology that
addresses those questions in a sensitive and humanistic way will
always be interesting to us because it really allows us
to think about and tell stories about ourselves. The oldest
stories we tell, the stories that are parts of novels
(23:29):
and films and all other kinds of art. So that's
to me where technology is most interesting and the types
of stories that will continue to tell on Sleepwalkers. So
everything we just talked about is consumer focus and very interesting,
but a I can also help address problems at scale,
you know, issues ranging from climate change to pain management,
and those are all things that we're going to talk
about in our very exciting season two. Thank you for listening,
(23:53):
and we're looking forward to seeing you for Season two
of Sleepwalkers very soon. Und the Roots under the Roots
for the f
Sleepwalkers is a production of I Heart Radio and Unusual Productions.
I'm Lain and I'm Kara Price. Welcome to a special
bonus episode of Sleepwalkers from the Consumer Electronic show, So Kara,
(00:35):
I'd never been to Las Vegas before, which is the
difference between us. I've been to Vegas too many times. Well,
I could tell, and it did feel good to be
in good hands with an old Vegas hand like you.
One of the new things though for me was slats,
which I don't normally play. I think so consciously. I
was thinking about what Tristan Harris talked about in the
first season Sleepwalkers. You know he was that former googler
(00:57):
who told us that Instagram is actually supposed to feel
a lot like slot machines. Well, that's right. Tristan studied
at the Stanford Persuasion Lab and told us about how
casino architecture has influenced the development of highly addictive type
products like Instagram. So it's interesting for me to actually
see Vegas and the bright lights and the impossibility of
escape firsthand, not to mention the replicas of the Empire
(01:20):
State Building, the Canals of Venice, the Colisseum of Rome,
and you know, I was lucky enough to see the
Seattle Space Needle for the first time. I just didn't
know that it was in Las Vegas. But that's not
why we were there. We were there for CEES, the
Consumer Electronics Show, and this episode we're actually going to
talk about some of the coolest things we saw there,
but we're going to focus more on the innovations that
(01:42):
are at the intersection of technology and humanity rather than
talk about you know, infamous toilet paper dispensers. One of
the big reasons we went is because we were invited
by WaveMaker, which is an agency part of w p P,
to do an interview on stage, a live podcast, so
to speak with Matt and on a hand, who is
head of product at ARC Publishing and ARC Publishing is
(02:04):
part of the Washington Post. Yeah, and ARC is also
an interesting case of AI and action because they're forward
thinking in terms of increasing the visibility of content through
personalization and optimizing everything from headlines to photo selection, all
using machine learning, and those are things that really matter
for journalists and readers. Yeah, and this use of AI
stands out to me because it provides a solution to
(02:27):
real problem. How do you get eyeballs on the right
content when there's just so much. That said, the issue
of personalization does also raise questions about what happens when
machines start to know us better than we know ourselves.
Not to mention, what are the appropriate limits of how
companies use AI and dature about us. Yeah, AI can
definitely streamline processes by detecting patterns that you know, human
(02:51):
beings cannot see, or it can allow you to do
things at the scale like tag hundreds of thousands of
articles that again, human beings just cannot do. So greater
efficiency is on one side of the spectrum and extremely
attractive to people, but on the other side you have
issues of taking humans out of the loop, like the
black box problem and authenticity in a world of deep fakes.
(03:14):
So a question for businesses and users of technology is
sort of when does AI add to our experience and
when does it maybe hold us back or take advantage
of us. You know, for example, from seeing news stories
that we should see, but maybe the algorithm doesn't think
we want to see it or that we won't click
on it. Right in the old days when everyone received
(03:34):
a print use of paper on their doorstep, everyone has
the same front page and the same headlines. Nowadays, when
you log onto a news website or onto social media,
everybody has a different version of the world, and that
is obviously positive for driving engagement, but may not be
so positive in terms of having conversations with the same
facts about the same stories. Equally, we have to ask
(03:56):
do we want articles where the headline has been written
by an algorithm or do we prefer headlines written by
a person. And that's something we talked about with Matt
because ARC actually tested the headline writing technology. Let's talk
to Matt seriously. Let's cut to the chase. Are really
came out of a collaboration trying to better understand what
(04:18):
actual journalists needed. Can you talk a little bit more. Yeah,
at the very beginning, you know, we were just trying
to solve problems for ourselves seven or eight years ago.
You know, we knew and he had to make some
pretty fundamental transformation to the post and to really prepare
ourselves for the digital future. We didn't have the right
tools to do it, and we couldn't really find the
right tools on the market either. What we did was
(04:39):
spent a lot of time with the journalists and the
editors trying to figure out what it was that make
their lives easier. It's trying to figure out how do
you make journalists work better, how can they publish faster?
What are the little things you can do inside of
a product to make it easier for them to write
stories or publish From there? About four years ago is
when we started evolving it into a commercial offering. Today
we're running hundreds of websites around the world. We're in
(05:00):
about twenty different countries. We're running companies like BP their
internal communications as well as some of their marketing. We're
running large broadcasters and all their live video and body
and of course we're still running a lot of newspapers
and news publishers like The Post and many others around
the world, Lucky and publishing. You know that AI and
artificial intelligence are made in headlines, and there was a
(05:22):
story in the Financial Times last year we said fort
of AI startups use no AI whatsoever. So I bet
it's probably higher. But when we talk about using AI,
or when you talk about using AI, what do we
actually mean? So it can span the range of technologies
from something like machine learning, which is basically a way
(05:43):
to use algorithms, to take large sets of data and
either uncovered patterns in it or try to model a
way to predict a certain outcome to technologies like computer vision,
which you can use to look at images or video
and extract information about them by recognizing patterns and trying
to identify objects inside of them, and so a lot
of those technologies, then when you put them together, you
can form some really interesting workflows that you know in
(06:04):
the past you might have had to use humans to
do that, you can actually do much more simple automatically.
Was there a particular business challenge or challenge at the
Washington Post that you couldn't have solved if you hadn't
been using AI. Any story that we write on Washington Post,
we're mapping to a set of two or three hundred topics.
Maybe an example of one of those might be like
(06:25):
congressional policy or narcotics crime. What you're trying to do
is say, if I look at all this content, I'm
not just pulling specific words out of it. I'm actually
trying to figure out what is this content about, what
is the fundamental concept of this content. So you pick
a set of articles, let's say a hundred thousand news
articles in the case of this example for the post,
and at first you use humans it's called micro labor
(06:47):
to do this training set. And the goal is you're
building an algorithm based on a set of real data.
And so the humans are going there and saying this article, yeah,
this is about congressional policy. Why because I know it is,
I read it, that's what it's about. This one's about
narcotics time, and this one's about soccer. And so you
train all these articles against that algorithm until finally the
algorithm is basically sufficiently advanced to predict a new article
(07:10):
that you put into it and determine an outcome with
the same high probability of success that you were able
to with humans training it. Now, every time a journalist
saves or publishes a story, we're able to parse over
all the content inside that story. Then we can predict
the strength at which it's likely to belong to that topic.
How do you create a better user experience, in your case,
(07:32):
news experience for an individual or consumer With that metadat
you can do a lot of interesting things. We can
figure out that hey, this is something that they're interested
in reading, perhaps they'd like to read more, and it
actually serves the signal into our recommendation algorithms. From your perspective,
where can businesses sort of harness the power of machine
learning to really hone in on who their customer is
(07:55):
and what that customer wants. We want to deliver more
content to our readers who want to help them find
more content that we've created. We have about nine journalists
at the Washington Post. We write something like, you know,
three or four hundred original stories a day, so there's
a lot of content there. To get readers to all
that different content and have them continue moving through your
content you spend a lot of money to produce is
(08:17):
really challenging, and so that's a great use case for personalization.
But where you can make it really come alive is
by having more sophisticated metadata, more sophisticated information about that content.
It's more likely to bring readers to it, and so
that's where these machine learning models really come in handy.
I think part of what's fun about this conversation is
there's a lot of cases out there where average users,
(08:38):
you know, they imagine they see something like that. You
see the boots on Instagram and you think, oh my god,
like these companies must crazy, like you know, indiscernible for magic, right, like,
there must be some crazy model out there doing this,
and perhaps there is. But in a lot of ways,
you know, your users aren't necessarily as aware of the
advertising ecosystem, the data ecosystem, and how these things tied
(09:01):
together between platforms and sites, and I think, as like
industry professionals, we always kind of underestimate that fact. And
so the net effect is that users are completely surprised
by this. They think you must be doing something completely
unheard of to achieve it, when in fact, you know
it could be really simple data sharing. And so the
reason I think that's important is then when you do
build technologies that actually utilize some of these more sophisticated
methods to build data sets, you have to be aware
(09:24):
that your users. You know, first of all, your users
aren't gonna necessarily anticipate the outcomes that you can create,
and if you don't do a good job on the
product side of making sure that you really think through
the use case and how you're leveraging technology to solve it,
you can generate unexpected outcomes. You know, there was the
example of a retailer who produced advertising flyers that were
able to predict folks who are pregnant, right, even if
(09:47):
some of those folks didn't necessarily know that themselves yet
or hadn't shared it with with their family or their spouses.
And so that was a case really of both the
company and the consumer being shocked by outcomes we're generating
exactly right. I mean, the you know, the algorithm doesn't
do anything magic, but that's a case of you know,
putting together in that case, like a marketing program, where
(10:08):
you don't really think through what's the possible data that
this could produce and what are my users? What do
they already know about this data? You know, you need
to think really hard about your users and what they
want and what they're trying to achieve, and what the
dangers are and leveraging this technology. It's no different than
in that way than any previous technology solutions you might
have used to build a great product for people, and
it can be misused just as easily. Funny enough, the
(10:28):
first episode of Sleepwalkers season one open with a story
of Washington Post employee Gillian Brockell, who, to your point
about pregnancy and data stuffered a miscarriage but continue to
receive targeted ads for pregnancy goods after a miscarriage, and
she wrote this openless is the technology companies saying please
(10:49):
stop targeting me. But that raised a big question for us,
which is what happens when the algorithms go wrong? Yeah,
I'd almost be more specific with the way that you
say that, and like the algorithm didn't go wrong, right,
but like the implementation of it and the product that
they built around it did because it wasn't really correctly conceived.
And we have to make sure that like what you're
trying to do automatically fits really well with what your
users are trying to accomplish, doesn't happen in a way
(11:11):
that's not expected. Is a well designed product, you know,
So in that specific case, yeah, I mean it always
starts and ends with kind of good product design. If
you're not doing that, just like any other tool, you
can misuse it. One of the other things we did
on the show was we used a language generator to
can't with pickup lines based on a data set of
all none of them were actually I don't have kind
(11:36):
of things like you are a thing and I love you,
you know, which is now the name of the book
by the woman who. Yeah, woman Jell Shane her wrote
a book about it, and then she also did these
things like AI recipes, like one was for chocolate chocolate
chocolate chicken cake. So there is funny things and Shakespeare
on it, and I didn't revealed two things. One is
when you turn these deep learning algorithms onto big data sets,
(12:00):
they reveal passions you might not necessarily be aware of,
like with a lot of chicken and quite a lot
of chocolates. On the other hand, like these were clearly
not something human would ever make. So how do you
think about the line between doing fun things in AI
and doing stuff which is valuable for business and also
not getting lost in the uncanny valley. So a good
example of this for instances. We spent some time at
(12:21):
the Post trying to build a headline generation algorithm we
could automatically create headlines for stories. And you know, the
idea I think at the beginning wasn't necessarily that, you know,
journalists are never read headlines again, but we'd be able
to create some alternative headlines in different ways to think
about a story. Our intention was, let's see if we
can come up with something so that we can create
several different variants of a headline. Part of our software
(12:42):
platform we include content testing framework. So one of the
things that we can do is say, for a given story,
let's have three different headlines for it, Let's run a
test as soon as it publishest of the audience is
going to get each variant, and then as people start
to click one more than the other, we're gonna shift
the burden of traffic to the most successful variant. And that,
I would them, by itself works really well. If you know,
folks in the audience here were to look at the
(13:04):
homepage of our site right now, there's probably two or
three stories that are running those types of tests where
different people would se different headlines or different images, or
in fact maybe actually just complutely different stories, and those
tests will resolve in like fifteen or twenty minutes. So
that works well enough by itself. But then we realized, well,
we could probably create more of these tests if only
we could automatically create headlines for them. We could just
be running these tests all the time for every single story.
(13:26):
But what we found was, you know, not exactly so
if the idea was to save journalists time and doing
that in the end, I mean, you'd have to come
up with something that's fairly solid and ready to publish out.
We were able to create something that allowed you know,
journalists basically have different formulations that they could play with
and maybe gave them some ideas of what to create,
but it still require people to look at it. In
the end, how can businesses work better with their engineers,
(13:48):
with their tech teams to sort of create and not
stay siloed in a way that like somebody who works
in marketing feels like, well, you know, there's actually this
need that I have, but I don't know who to
talk to about it, and that don't really know what
to do. It's an awesome question to me, Like one
of the best things that you can do as a
business is to put those people together, sometimes even physically.
(14:10):
So when we started this project, you know, we literally
co located engineers, product people directly inside the news room
to sit with the folks who are doing this work. Now,
when it comes to a I M L, you remember,
these are just tools. These are tools to make work easier.
Their tools in a lot of cases for automation and efficiency.
There's some problems that can't be solved without it. In
the end, though, you know, you're still trying to solve
(14:32):
some business problem, and most of those involved some sort
of users that you need to get to know. So
you know, even at the post we had data sciencests
who were on those teams embedded in the news room
as well. You know, they weren't kind of seated somewhere
else thinking of problems on their own. There's a time
and a place for creating room for prototyping sometimes that
has to happen to especially with really advanced technologies. But
beyond prototyping, putting those teams together is super crucial. So
(14:54):
how do you make sure, speaking metaphorically, you write a
good brief to your AI team, well, your engineering team.
I still think, you know, start with the problem that
you're trying to solve. Like, if you're going in thinking
let's use AI to solve something, I think you're probably
starting the problem the wrong way, and start by framing
up the problem in business terms. For people, you'd be surprised,
I think how much you know engineers and product folks
(15:15):
really actually prefer to get that first before they start
diving into what's the technology that I'm getting used to
solve this problem? With a buzz around AI, especially right now.
You know, people tend to go into it. I think
thinking this is kind of something that's pretty close to magic.
We just use AI. It's going to solve these problems
that we haven't been able to solve some other way.
And that's not really the right way to approach it.
But a I will be transformative, I think for organizations
(15:37):
that apply it the right way, with the product mindset,
with a good knowledge of the problem that they're trying
to solve, with empathy for their users. When we're doing
our research for this panel, as an article in Bloomberg
News saying that Jeff Bezos is pestimally very invested in
the product, and then you called him Jeff in conversation,
I found very impress So we will unders people sending
(16:01):
me an email already without obviously telling us the contented
your meetings. How does his vision imbue what you do?
So certainly for us it's boon to have him owning
the company. I think that's one of the greatest things
is you know, obviously at the Washington Post were known
for an amazing newsroom, but we've also spent a lot
of time investing in our engineering team that started to
(16:21):
some extent before you know, we were purchased by Jeff,
but certainly after we purchased us. It opened a lot
of new doors for us, and it gets people excited
to come and work for us and some of the
problems that we're trying to solve. It really inspires people
to be able to build like a platform like we built,
you know, within a newspaper company. I think would have
been hired to Fathom probably ten years ago, but I
mean today we really can say that, you know, we're
a content company and we're a technology company. And I
(16:44):
think part of that starts with him in the leadership
that he provides. More Sleepwalkers after the break, So, Kara,
(17:05):
that was our conversation on stage with Matt monahantan CS
in early January. It was interesting because we hear so
much about tech companies becoming publishers, whether it's Facebook, YouTube,
or Twitter, but we hear less about publishers becoming tech companies.
I guess that's where Jeff Basos as an owner is
what we might call a differentiator. So I was personally
(17:25):
struck with Matt's experiments with the headline generator. You know,
for the time being, it doesn't work well enough to
be a commercial product, but I think it will soon.
You know, look at autocomplete when you send a Gmail
like Sincerely Comma. You know I get those all the time,
and it works. You know, in an apocalyptic reading, that
means that machines will take over our lives and there
(17:47):
will be no work left for humans. We won't have
to come up with smart headlines. But I think in
a more optimistic reading, using algorithms to generate writing suggestions
could actually enable originality. Reminds me of that Chinese science
fiction writer who you and I have talked about named
Chen show Fund, who actually used an algorithm to create
(18:08):
ideas for his own work, and he used it when
he had writer's block. He wasn't using it to replace
his creative skill. He was using it as an enhancement tool.
And I think that's really interesting. Yeah, And in season
one of sleep Walkers, we spoke to a filmmaker called
Oscar Shop who actually shot a whole film written by
Ai called Sunspring. Oscar and Chen turned the technology into
(18:28):
a tool that actually serves their purposes. You know, you
can develop all kinds of technology in a vacuum. About
the technology that really serves people and fills a need.
Is the technology that sticks around, you know, speaking of
technology that really sticks around, and then some technology that
might not stick around. There's so much stuff on the
floor of CS you and I have never been to
(18:49):
see us before. I think we were very overwhelmed by
what we saw and excited. It was kind of inspected
gudgets paradise, and obviously, as someone who's obsessed with technology
and consumer technology, I would have bought every single thing
I thought you tried to buy. I did try to
buy that. I mean that keyboard with the mouse burnt
into the keyboard. How much did this cost? I almost
(19:11):
bought a laser cool laser patch for my back, which
placebo or not made me look very hot. But no,
in all seriousness, you know, there are things that were
on the floor that are kind of amazing when you
think about it, Like from this company called Pillow Health.
They've developed this device called Priya that looks like a
(19:33):
little face, a cute little face, as they always do,
and it's basically a pill dispenser that is voice and
face activated, so anybody could have one. I could have one,
You could have one. But I think they've developed it
mostly for elderly people who have many pills that they
have to take throughout the day, and who's children or
(19:54):
health aids want to be able to control when their
medicine is dispensed. And I think for someone who might
have memory impairment, physical impairment, the idea that someone who
isn't in the room with that person could control when
they're getting you know, vital medicine is really amazing. And
(20:15):
you know, you say what you will about privacy. I
think being able to do something like take care of
your elderly parent with a device is you know, the
perfect intersection of technology and humanity, right. I spoke to
the founder about exactly that. And you know, we have
a lot of concerns about facial recognition that we've discussed
at length on Sleepwalkers and will continue to discuss in
(20:38):
season two. But in a narrow use case like this,
in a voluntary use case where it can help somebody
out to remember something very important, like what pills are taken,
when it may well be that that's a sacrifice which
is very much worth taking. There was another startup on
the floor that really caught my eye, which was called
in New Pathy, and according to the card which have
(20:59):
in front of me. It's the first device in the
world which is equipped with technology to visualize your dog's
status from his or her heart rate information. And this
is basically a harness that you put on your dog
and it recalls your dog's heart rate and in particular
the variability in your dog's heart rate to tell you
if your dog is happy or sad, or anxious or
(21:19):
excited or curious. And you know, people struggle to know
what that dogs are thinking. And if you can use
data from historical doggie feelings to model what a current
dog is feeling and use that to have better interaction
with your dog, more power to you. I think it's cool.
There was this other piece of technology from a company
called we Labs. They were Japanese, right, and it kind
(21:40):
of blew my mind in the same way that like
thinking about automation of drive through blew my mind. You know.
It was this like would beam that looked like a
beam in a house, and it had a computer that
was inside of it. And the woman who was showcasing
it basically asked ours to stay end up against it
(22:01):
like you would when you're charting at child's height, and
she took a pen or stylus and marked Oz's height,
and then immediately that marking was uploaded into the cloud
and displayed on a device next to this would beam
And it just made me think, like this thing that
millions of families do as their children are growing up
(22:24):
is now being digitized, and again like going back to
the intersection of technology and human behavior, like imagine if
someone moves from the house, those height markings that were
such a part of your child's growing up can be
taken with you in the cloud. It's just I mean,
that's the kind of stuff where I'm like, do I
need it? Does someone need it? Who cares? But the
(22:47):
idea that it's like replicating this very very personal feeling
and you know activity that we do in our childhood
is I don't know, it kind of blew my mind.
All three of the things we ended up talking about,
you know, pillow Health, the doggy heart rate monitor, and
this Japanese WOULD device. You know, they go back to
(23:08):
the most human things are our parents, okay, is our
dog okay? Our children growing up? What does it make
us feel as they grow up? And so technology that
addresses those questions in a sensitive and humanistic way will
always be interesting to us because it really allows us
to think about and tell stories about ourselves. The oldest
stories we tell, the stories that are parts of novels
(23:29):
and films and all other kinds of art. So that's
to me where technology is most interesting and the types
of stories that will continue to tell on Sleepwalkers. So
everything we just talked about is consumer focus and very interesting,
but a I can also help address problems at scale,
you know, issues ranging from climate change to pain management,
and those are all things that we're going to talk
about in our very exciting season two. Thank you for listening,
(23:53):
and we're looking forward to seeing you for Season two
of Sleepwalkers very soon. Und the Roots under the Roots
for the f
kill switch News
Hosts And Creators
Oz Woloshyn
Karah Preiss
Show Links
AboutPopular Podcasts
Are You A Charlotte?
In 1997, actress Kristin Davis’ life was forever changed when she took on the role of Charlotte York in Sex and the City. As we watched Carrie, Samantha, Miranda and Charlotte navigate relationships in NYC, the show helped push once unacceptable conversation topics out of the shadows and altered the narrative around women and sex. We all saw ourselves in them as they searched for fulfillment in life, sex and friendships. Now, Kristin Davis wants to connect with you, the fans, and share untold stories and all the behind the scenes. Together, with Kristin and special guests, what will begin with Sex and the City will evolve into talks about themes that are still so relevant today. "Are you a Charlotte?" is much more than just rewatching this beloved show, it brings the past and the present together as we talk with heart, humor and of course some optimism.
On Purpose with Jay Shetty
I’m Jay Shetty host of On Purpose the worlds #1 Mental Health podcast and I’m so grateful you found us. I started this podcast 5 years ago to invite you into conversations and workshops that are designed to help make you happier, healthier and more healed. I believe that when you (yes you) feel seen, heard and understood you’re able to deal with relationship struggles, work challenges and life’s ups and downs with more ease and grace. I interview experts, celebrities, thought leaders and athletes so that we can grow our mindset, build better habits and uncover a side of them we’ve never seen before. New episodes every Monday and Friday. Your support means the world to me and I don’t take it for granted — click the follow button and leave a review to help us spread the love with On Purpose. I can’t wait for you to listen to your first or 500th episode!
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com