Speaker 1: Pushkin. It feels like searching the web is a problem
Speaker 1: that's been solved. You know, it's ridiculously easy for me
Speaker 1: to say, find out when Alexander Hamilton was shot eighteen
Speaker 1: oh four, or whether they are making Sing three. Not yet,
Speaker 1: but Matthew McConaughey has expressed interest and yet. And maybe
Speaker 1: this is not surprising. The people who spend their lives
Speaker 1: working on search do not think search is solved. This
Speaker 1: is partly because the people at the frontier of search
Speaker 1: don't just want to search the web. They want to
Speaker 1: answer every question that might cross your mind, even questions
Speaker 1: you can't put into words. I'm Jacob Goldstein, and this
Speaker 1: is What's your problem? The show where on entrepreneurs and
Speaker 1: engineers talk about how they're going to change the world
Speaker 1: once they solve a few problems. My guest today is
Speaker 1: Kathy Edwards, vice president and GM of Search at Google.
Speaker 1: Cathy's problem is this, how do you teach computers to
Speaker 1: tell people what they want to know, even if they
Speaker 1: don't know how to ask. Later in the conversation we
Speaker 1: get to the frontier of what Kathy and Google are
Speaker 1: working on now, but we started with the problem they
Speaker 1: have largely solved in the six years Kathy has been
Speaker 1: at Google, the jump from search results based on keywords
Speaker 1: to search results based on natural language, the way people
Speaker 1: talk in everyday life. So one of the problems that
Speaker 1: we were working on around six years ago is this
Speaker 1: problem of natural language queries. So, if you're old enough
Speaker 1: to remember the early days of search on the Internet,
Speaker 1: there was this idea of keywordies, right, that you had
Speaker 1: to sort of take this idea you had in your
Speaker 1: mind of what you what you needed to know and
Speaker 1: figure out what were the exact right keywords to enter
Speaker 1: into the search engine to get your results back right.
Speaker 1: I mean an example as an example, very early in
Speaker 1: the you know, I remember being taught how to query
Speaker 1: back in you know, nineteen ninety nine and being told
Speaker 1: never used the word and never used the word because
Speaker 1: the word and or the word there is in almost
Speaker 1: every document on the Internet. And the way it worked
Speaker 1: back then is you did this word matching, right, and
Speaker 1: so if you had a word that was in your
Speaker 1: query and there was that same word in the document,
Speaker 1: then that document would be returned and potentially scored right.
Speaker 1: And that was very helpful if it was a word
Speaker 1: like genetics, right, which was highly specific and wasn't in
Speaker 1: a heap of documents on the internet. But the word
Speaker 1: and not very specific. And you know, in the very
Speaker 1: early days the Internet, these words weren't even weighted particularly right,
Speaker 1: The word and count for as much as the word genetics,
Speaker 1: and so a document might have a ton of the
Speaker 1: uses of the word and and one use of the
Speaker 1: word genetics, and it would score really highly, even though
Speaker 1: it wasn't particularly genetics. Folk. Now, by the time you
Speaker 1: get to Google, that part is solved, right Google buy
Speaker 1: that part is years ago? Is waiting genetics more more
Speaker 1: heavily than it's waiting the But but what what part
Speaker 1: six years ago was not solved? That's solved now or
Speaker 1: solved ish now. But we were still seeing people do
Speaker 1: these very keyword oriented queries. So they weren't saying things
Speaker 1: like what wine pairs best with chicken? Or if they were,
Speaker 1: they were doing those queries and getting not the best results,
Speaker 1: because not only is there a question of word matching
Speaker 1: and how much each word counts for, there's also the
Speaker 1: question of like does the word what appear at all? Right?
Speaker 1: Like are the answers to that question actually just documents
Speaker 1: that talk about the best wine to pair with chicken
Speaker 1: is you know, chardonnay, right, and not so much talking.
Speaker 1: You know, they didn't include the question, and so we
Speaker 1: sort of saw these like SEO documents that would spring
Speaker 1: up that would have the questions kind of baked in
Speaker 1: and an attempt to match. But those documents weren't necessarily
Speaker 1: the best answers. And so this is when we started
Speaker 1: to go just that next level deeper in our language
Speaker 1: understanding with these AI models, their language models that really
Speaker 1: can start to map out in a concept space, things
Speaker 1: like this sort of translation between how you might ask
Speaker 1: a query and then what that might look like in
Speaker 1: the document. So, to take the example you gave of
Speaker 1: what wine pairs best with chicken, even as late as
Speaker 1: six years ago when you got to Google, you're saying
Speaker 1: Google wasn't great at delivering the best results to a
Speaker 1: query like that because it was written as speech, not
Speaker 1: written as a series of keywords. So six years ago,
Speaker 1: I would have been better off typing chicken wine pairing.
Speaker 1: I would have got better results if I did that,
Speaker 1: you're saying, because that's kind of the way. That's the
Speaker 1: way Google had mapped the web. It was like a
Speaker 1: series of important words and what sites are reliable and
Speaker 1: they it just the technology wasn't there to actually try
Speaker 1: and understand the way people ask questions in real life. Absolutely,
Speaker 1: And it was this idea of bringing AI into search
Speaker 1: and having these like large scale language models. That first
Speaker 1: one was called Bert. We now use one called mom,
Speaker 1: which is get to mom. But let's talk about Let's
Speaker 1: try talking about Bert for a second. So how do
Speaker 1: you get from search results that are fundamentally keyword based
Speaker 1: to search results that are fundamentally you know, answering questions
Speaker 1: that are posed in a more natural way, like how
Speaker 1: do you make that leap? So the fundamental insight is
Speaker 1: you go from looking at these words as tokens that
Speaker 1: get matched against each other to suddenly you look at
Speaker 1: all the words in all the documents on the Internet
Speaker 1: and you create what's code an embedding space, which is
Speaker 1: essentially you can think of it as a map of
Speaker 1: the concepts that these documents know about. And suddenly, by
Speaker 1: being able to say, okay, you can take a query,
Speaker 1: map that into this concept embedding space. You'd take these documents,
Speaker 1: map that into the content embedding space. You can start
Speaker 1: to actually match together not these words, but what people
Speaker 1: actually mean what they actually mean when they ask these questions,
Speaker 1: and what they actually mean when they write these web
Speaker 1: pages on the Internet. That seems I mean, A, it
Speaker 1: seems super hard, right, and B As I'm parsing that,
Speaker 1: I'm tempted to use a lot of anthropomorphic language, right,
Speaker 1: I'm tempted to say, like, you have to go from
Speaker 1: the computer just sort of having a list of words
Speaker 1: and kind of weights around those words to a computer
Speaker 1: understanding what people mean. Like, am I right to say that?
Speaker 1: Or is that just my like layperson intuition getting in
Speaker 1: the way of what's going on? I Mean, the first
Speaker 1: thing I'll say is I think we're very far away
Speaker 1: from the computer having any sort of sentience and truly understanding.
Speaker 1: But I think it is true. It is fair to
Speaker 1: say that there is a level of deeper understanding that
Speaker 1: you're not just looking at these words as as you know,
Speaker 1: bits in a computer, but you're actually starting to model
Speaker 1: in a way that a human might, a brain might
Speaker 1: model what the concepts are. And I do think that's
Speaker 1: a first step of getting closer to this sort of
Speaker 1: natural human understanding. So is there a way to talk
Speaker 1: about how that works? It's it's pattern matching effectively right,
Speaker 1: And it just so happens that if you magnify pattern
Speaker 1: matching on a very large scale, that can be a
Speaker 1: pretty compelling understanding. And so that's the sort of big idea,
Speaker 1: the theory of how it works. I'm sure in actually
Speaker 1: building the thing in building Burt, which was this big
Speaker 1: model that did work, it wasn't that easy, right, I mean,
Speaker 1: is there a is there a story version of how
Speaker 1: you built it? So I think there were two hard
Speaker 1: points along the journey. The first hard point was just
Speaker 1: these models were being built at a scale that was
Speaker 1: unprecedented the amount of information. You know, traditional neural networks
Speaker 1: would run on thousands, maybe millions of training examples. Suddenly
Speaker 1: you're trying to model all the words on the Internet
Speaker 1: and this scale. Firstly, this scale is what gets you
Speaker 1: the amount of training to actually get the concepts model
Speaker 1: to be compelling. But frankly, the computers just couldn't process.
Speaker 1: So you're you're building this model and saying, okay, now
Speaker 1: to learn what you need to learn, read literally every
Speaker 1: word on the Internet, is that right, yes, and not
Speaker 1: read at once, because every layer of the neuronet needs
Speaker 1: to read it and reprocess it. Right. So you're reading
Speaker 1: every word, you know, a massive number of times. And
Speaker 1: at the time we didn't really have the compute power.
Speaker 1: You just needed more more computers, essentially more and more chips,
Speaker 1: more more engines to just process and process and process.
Speaker 1: So our research team had developed these these chips that
Speaker 1: these processes that were really optimized for doing a sort
Speaker 1: of deep learning work. And it was that these chips
Speaker 1: and the way that we could sort of put all
Speaker 1: the chips together at a work in concert to solve
Speaker 1: this problem that really unlocked the amount of processing power
Speaker 1: and needed to even build these models in the festival.
Speaker 1: So the binding constraint wasn't like the theory or the
Speaker 1: ideas of it, like you knew how to do it,
Speaker 1: you just didn't have enough enough horsepower to actually make
Speaker 1: it happen. Well, we knew that we could do it,
Speaker 1: we didn't know offer to be any good, right, it
Speaker 1: wasn't it, and you couldn't even try, right, yeah? Right?
Speaker 1: And so then we tried it and we found out, actually,
Speaker 1: this thing is pretty compelling. It can understand things that
Speaker 1: our models previously have never understood. You know. But I
Speaker 1: will say the second and this gets to the second
Speaker 1: hard part. We once we had these large scale language models,
Speaker 1: we didn't quite know how to put them into search ranking.
Speaker 1: This was not something that had been done before. So
Speaker 1: we have in search this incredibly rigorous methodology for testing
Speaker 1: any given change to our algorithm, and it's it's based
Speaker 1: in statistics, and it's statistically samples queries, and we look
Speaker 1: at the before and they after, and there's a scoring
Speaker 1: system to say is it better or not? And I
Speaker 1: remember looking at the early experiments from this burst integration
Speaker 1: into our search engine, and the queries that it was
Speaker 1: impacting were just queries that, honestly before we would have said,
Speaker 1: we don't know how we can solve this query. And
Speaker 1: suddenly the model was just able to figure out these
Speaker 1: sort of unspoken concepts that just our previous technology just
Speaker 1: would not have even been able to come close to.
Speaker 1: Like give me an example, like what kind of thing?
Speaker 1: So here's a really great example. This is directly from
Speaker 1: the one of the very first bit evaluations that we
Speaker 1: did internally, and the query is can you get medicine
Speaker 1: for someone? Pharmacy? Right? And so what's interesting about this
Speaker 1: question is the users looking for something very specific. They're
Speaker 1: looking like maybe my partner is sick. Can I go
Speaker 1: and pick up their prescription at the pharmacy for them?
Speaker 1: Or do they have to go and get it? Right?
Speaker 1: It's also a goodly jankie where it's half in natural
Speaker 1: language can you get medicine for someone? And half in
Speaker 1: like keyword ease, they're just typing pharmacy at the end, right,
Speaker 1: it's a weird exactly yeah. And so previously we didn't
Speaker 1: know how to pause out this intent right, this idea.
Speaker 1: You know, we could tell that it was about getting
Speaker 1: a prescription from a pharmacy, but this notion of force
Speaker 1: someone was a concept that was just slightly too complex. Oh,
Speaker 1: I didn't even understand it until now. What they mean
Speaker 1: is can I pick up someone else's prescription? That's what
Speaker 1: they're actually asking, But it's very it's poorly worded, frankly,
Speaker 1: and they'refore hard to figure out exactly right. And so previously,
Speaker 1: before Bert, we would turn these wonderful web pages saying
Speaker 1: this is how you get a prescription filled, which you
Speaker 1: can imagine if you're this user doing this query, you're like, yeah,
Speaker 1: I already know how to get a prescription that filled.
Speaker 1: Thanks for me. What I need is filled for somebody
Speaker 1: else exactly, and with Bert we were able to understand
Speaker 1: pick up this idea of the force someone and put
Speaker 1: the appropriate weight on it, that that was the sort
Speaker 1: of you know, discriminating thing in the query, that that
Speaker 1: was the key thing that the query turned on. And
Speaker 1: then we were able to show this this web page
Speaker 1: that talked about can I have a friend or family
Speaker 1: member pick up a prescription for me? And that was
Speaker 1: the sort of like aha moment where we could all
Speaker 1: just sit around and be like, Wow, this is a
Speaker 1: new level of understanding that we haven't got to previously.
Speaker 1: So with birth, Google got to the point where it
Speaker 1: was very very good at dealing with words in a deep,
Speaker 1: complex way. But words make up less and less of
Speaker 1: the Internet. Pictures and videos are a whole other story
Speaker 1: that's coming up in a minute. Now back to the show.
Speaker 1: So you have got to this place now where you
Speaker 1: have you Google have made the leap from keyword based
Speaker 1: searches to intention based search is what do people mean? Right?
Speaker 1: Which is this big interesting leap? And so I'm interested
Speaker 1: in kind of the next leap, like what's the next
Speaker 1: big hard problem you're trying to solve? What's really interesting
Speaker 1: to me is this idea of how many questions you
Speaker 1: don't ask because you don't even know the words, right,
Speaker 1: Like this is a bit of a sad story. But
Speaker 1: I have at my house this oak tree, and the
Speaker 1: oak tree I think is dead, and it's very sad
Speaker 1: for me because a very beautiful oak tree. And what's
Speaker 1: interesting is, you know, I looked at the oak tree,
Speaker 1: I'm like, wow, those leaves are kind of brown, Like,
Speaker 1: that's not it doesn't seem right to me. I wonder
Speaker 1: if there's something's wrong with the oak tree, right, But
Speaker 1: I can't necessarily right now really articulate that to a
Speaker 1: computer this fundamental question of is this oak tree dead?
Speaker 1: And if not, what can I do to save it? Right?
Speaker 1: So what I do is I go and type in
Speaker 1: some queries, I say, you know, oak tree dead? How
Speaker 1: do I know if my oak tree is dead? You know?
Speaker 1: And I get back results. But those results aren't necessarily
Speaker 1: taking what they're not taking in any context of this
Speaker 1: particular tree and what do the leaves look like? And
Speaker 1: so this idea of how can you start to ask
Speaker 1: these questions using all of the information around you, using
Speaker 1: your camera to actually capture this particular oak tree, using
Speaker 1: your location to know, you know, what are the native
Speaker 1: oaks in this area? And what's the current incidence of
Speaker 1: sudden oak death syndrome, which is a thing that I
Speaker 1: have recently learned exists. Okay, so I get why this
Speaker 1: is a hard thing to search in a text box, right,
Speaker 1: And so the thing that's interesting to me is how
Speaker 1: can we facilitate asking those types of questions where it's
Speaker 1: a mix of here's something that you're looking at, Here's
Speaker 1: something that you're saying with your words that adds to
Speaker 1: the picture. You know, here's a lemon tree that's got
Speaker 1: some black spots on it? What's wrong with it? Like?
Speaker 1: How can you help me understand what I should do
Speaker 1: about this? You know, these sorts of questions I think
Speaker 1: are right now. We have to do a tremendous amount
Speaker 1: of work to try and translate these questions into text
Speaker 1: that we would issue to a search engine. And yeah,
Speaker 1: we use that. Yeah, yeah, normal people. Yes, we're all
Speaker 1: doing it. And when you think it's you know, sometimes
Speaker 1: it's very easy, right, but sometimes you're like really having
Speaker 1: to work hard to come up with a query that
Speaker 1: will actually get you the answers that you need. And
Speaker 1: I think that's really the next frontier for us is
Speaker 1: how do we on the query side help users just
Speaker 1: naturally intuitively express whatever information need they have. And then
Speaker 1: how do we understand the whole universe of information, not
Speaker 1: just the web pages, that all the images and video
Speaker 1: and audio out there, and take that next level of
Speaker 1: like concept understanding to match those together so that we
Speaker 1: can get users even more precise answers that really help them. Great.
Speaker 1: So that's the like vast dream slash big problem. Can
Speaker 1: we reduce it a little bit so we can talk
Speaker 1: in sort of practical terms about what you're working on.
Speaker 1: I mean, I know there's this new AI model that
Speaker 1: integrates images, like you can, you know, whatever, take a
Speaker 1: picture with the camera on your phone and put in text.
Speaker 1: So like, well, you have this new model, and it,
Speaker 1: like the old one, has this worm fuzzy acronym. Right,
Speaker 1: it's called MUM, which stands for hold on, I gotta
Speaker 1: look at my notes, the multitask unified model. So like,
Speaker 1: tell me about MUM. So MOM is our next level
Speaker 1: model that you know Bert was about language. MOM is
Speaker 1: about all these different modalities of information coming together, particular
Speaker 1: images and language. I mean, is that if really images
Speaker 1: and language and we've got some limited applications of it
Speaker 1: in search today. So for example, you can take the
Speaker 1: take the photo of somebody's handbag and say you want
Speaker 1: to shop it, and that will work today. And that
Speaker 1: is like we were not able to do this previously,
Speaker 1: and that in and of itself is a big breakthrough.
Speaker 1: But there's still so much headroom, right like, this, still
Speaker 1: so much ability to say, you know, you can add
Speaker 1: sort of I would I would classify our current ability
Speaker 1: to process words in this multimodal context as you know,
Speaker 1: kind of like back in the early days days of
Speaker 1: the internet, you can say near me to find where
Speaker 1: you can buy it. Near me, you can say buy,
Speaker 1: but you can't necessarily like ask an incredibly complicated question
Speaker 1: about a picture, right like, so we're kind of back
Speaker 1: to keywords in this new pictures plus words universe. Let
Speaker 1: me ask a dumb question, why why can't you just
Speaker 1: take all of your brilliant intent AI and copy and
Speaker 1: paste it to fit with the image AI. So a
Speaker 1: couple of things. The first is that anytime we develop
Speaker 1: sort of this new technology, we also need to see
Speaker 1: how users start using it, right And so I think
Speaker 1: it's also fed say that we don't have. You know,
Speaker 1: we have a ton of people using this, but we
Speaker 1: haven't yet. There hasn't been time for that new technology
Speaker 1: to really be accepted by the world. And then we
Speaker 1: have this vast set of queries that we're doing poorly on, right.
Speaker 1: So that's the other thing you should know about Google.
Speaker 1: We spend a lot of time looking at the queries
Speaker 1: where we're failing. That's one of the other reasons we
Speaker 1: have a deep appreciation of how search is an unsolved
Speaker 1: problem because we're just constantly looking at queries whether the
Speaker 1: users clearly not getting what they're looking for. And I'll
Speaker 1: use that as a as a siege to figure out
Speaker 1: how to make things better. So do I understand you
Speaker 1: that the fundamental thing you need now is just lots
Speaker 1: of people to use this thing so you can see
Speaker 1: the weird ways people search and the things they sort
Speaker 1: of do that are hard to understand. That's certainly one
Speaker 1: of the things we need. I mean, it is fundamentally
Speaker 1: search works in service of our users, right, and so
Speaker 1: understanding the the failures is critical to how we get better.
Speaker 1: I think there are also just things that we know
Speaker 1: that we need to do on the AI and the
Speaker 1: model side that we'll continue working through, right, the ability
Speaker 1: to really bring together more of the two step process
Speaker 1: of how do you conceptually understand the words, conceptually understand
Speaker 1: the image, and then bring those two things together and
Speaker 1: have that be a bit deeper on both sides rather
Speaker 1: than just the combination together and those sorts of things.
Speaker 1: But yeah, I mean people coming in and using it
Speaker 1: and then having a bad time, we'll then make it better.
Speaker 1: It seems like there have been two main threads of
Speaker 1: AI research. One is basically language and the other is
Speaker 1: basically vision and images. I mean, it is it right
Speaker 1: to think of what you're trying to do as the
Speaker 1: synthesis of those two sort of main AI traditions. Yeah,
Speaker 1: I think so. I think it is clearly the case
Speaker 1: that just like uh, you know, with Bert, we took
Speaker 1: all these words and we got down to concepts. Right,
Speaker 1: It is clearly the case that human beings understand the
Speaker 1: world through concepts, and they do that visually, and they
Speaker 1: do that with language, and ultimately the concepts are the same. Right,
Speaker 1: So being able being able to say, okay, here's here's
Speaker 1: a concept, and we can attach to that what that
Speaker 1: concept looks like or that visual representation of that concept
Speaker 1: as much as it has one and the words surrounding
Speaker 1: that concept. That's when we can really unlock this true
Speaker 1: natural way of understanding the world that we think is
Speaker 1: going to enable people to ask all those questions that
Speaker 1: they have that they're not asking right now. Are there
Speaker 1: applications that go beyond search that come to mind if
Speaker 1: you figure this out? Yeah, I mean I think that
Speaker 1: search has this connotation of kind of find what's out there.
Speaker 1: I think there's something, you know, we're thinking about what
Speaker 1: this looks like in the generative space. So for example,
Speaker 1: if you're looking for you know, I bake birthday cakes,
Speaker 1: and sometimes I for my kids, and sometimes what my
Speaker 1: kids want, and a birthday cake just actually doesn't exist
Speaker 1: on the internet right or there's like only one or two,
Speaker 1: So then I have to come up with it myself.
Speaker 1: And like, what if AI could help us generate as
Speaker 1: sample image just based on these concepts that I could
Speaker 1: then use for inspiration. I think that's a pretty interesting concept.
Speaker 1: There's obviously a lot of things that we need to
Speaker 1: be very thoughtful about in this space as we do it,
Speaker 1: But I think this idea of extending search past the
Speaker 1: notion of connecting you with the information that's out there.
Speaker 1: To actually synthesizing new information for you is pretty interesting
Speaker 1: and something we're talking about a lot. You know. One
Speaker 1: of the things that has become clear to me talking
Speaker 1: with you is clearly, I think too narrowly about search. Right.
Speaker 1: I have this very kind of twenty years ago idea
Speaker 1: of like searching text on the web, and the web
Speaker 1: has become much less text based in that time. Right,
Speaker 1: the web includes Instagram, the web includes TikTok, and those
Speaker 1: are places where, weirdly to me, lots of people go
Speaker 1: to search like people go on TikTok to find whatever,
Speaker 1: where to go out to eat, which would never occur
Speaker 1: to me. So I mean it's that part of the
Speaker 1: sort of motivation on some level for you to figure out, Oh, right, text,
Speaker 1: that's not enough, clue, we got to figure out how
Speaker 1: to search in video and what does that even mean.
Speaker 1: I think we're really driven by what our users are
Speaker 1: telling us, and we just have really robust mechanisms for
Speaker 1: understanding what our users are doing. And it's pretty clear
Speaker 1: that people around the world find image and video content
Speaker 1: to be pretty compelling, right, I Mean that's sort of
Speaker 1: a very obvious statement, but you know the Internet in
Speaker 1: the early days, it was banned with constrained. It was
Speaker 1: technology constrained. It had to be words because that's what
Speaker 1: the technology enabled, not necessarily because that's what human beings
Speaker 1: most enjoy in terms of an information consumption experience. And
Speaker 1: so we really are driven by what we're seeing in
Speaker 1: the user trends, and we're really driven by just this
Speaker 1: mission of how do we keep helping people get the
Speaker 1: best answers to their questions that we can give them.
Speaker 1: In a minute, the Lightning round, including what you learn
Speaker 1: about the Internet when you spend six years working at
Speaker 1: Google Search. Now, let's get back to the show. I
Speaker 1: want to do a lightning round. We close usually with
Speaker 1: a lightning round on this show, just a bunch of
Speaker 1: relatively quick questions. So in this instance, I googled best
Speaker 1: Lightning Round questions, and right at the top of the
Speaker 1: search results page I didn't even have to click through,
Speaker 1: is this bulleted list. I'm just going to give you
Speaker 1: a few from there. Sounds good. Favorite day of the week,
Speaker 1: oh Monday, because I get to go to work and
Speaker 1: not deal with my kids all day. Who I love
Speaker 1: very daily. Good favorite city in US besides the one
Speaker 1: you live in just reading here New York's City. Thank you.
Speaker 1: Would you rather be able to speak every language in
Speaker 1: the world or be able to talk to animals? Speak
Speaker 1: every language in the world. I'm shocked, to be honest,
Speaker 1: Although I get that like a Google might actually figure
Speaker 1: that out. Does it not seem like you can already
Speaker 1: get a translator for every language. Talking that animals would
Speaker 1: be like a revolution and human understanding of the natural world,
Speaker 1: I guess. But I do not speak any language as really,
Speaker 1: and I constantly feel bad about it. So maybe that
Speaker 1: was just a fair personal feeling of weakness. So okay,
Speaker 1: so we're now we're pivoting out of the Google lightning
Speaker 1: round questions into my own bespoke lightning round questions. What's
Speaker 1: your favorite kind of cake to bake? Oh? Well, so
Speaker 1: I really make these, like quite elaborate cakes for my
Speaker 1: children because I want them to be able to grow
Speaker 1: up and say, wow, I remember you making great cakes
Speaker 1: for us. Mostly so I recently made in one of
Speaker 1: my kids plays Minecraft a lot, and there is a
Speaker 1: character called a slime, which is a sort of jelly
Speaker 1: blob that kind of jumps on top of you and
Speaker 1: kills you if you don't fight it off. And so
Speaker 1: I made a slime cake with a cake embedded in
Speaker 1: the jelly. So big idea one here? What do you
Speaker 1: think you understand about the Internet that most people don't understand? Oh?
Speaker 1: I like this one. I think most people don't understand
Speaker 1: how much it changes every day. And you know, so
Speaker 1: we have this astonishing stat that even I didn't believe
Speaker 1: when I heard it, which is that fifteen percent of
Speaker 1: the queries Google sees every day we have never seen before.
Speaker 1: And that happens every day. That there's fifteen percent, I
Speaker 1: just completely new. And the same happens on the internet side.
Speaker 1: Every day we index a ton of new content we've
Speaker 1: never seen before about ideas that are completely new to
Speaker 1: humanity at that time, right, And you know, we have
Speaker 1: to be able to continually understand that and keep up.
Speaker 1: And I think that people sort of have this idea
Speaker 1: that there's a fixed amount of information out there, but
Speaker 1: actually human beings are astonishingly productive and are constantly coming
Speaker 1: up with new ideas. If everything goes well, what problem
Speaker 1: will you be trying to solve in five years, I
Speaker 1: will still be working on making Google Search better for
Speaker 1: all our users. I think we will I think we
Speaker 1: will be working on this for the next hundred years.
Speaker 1: Is there a narrower answer, like this particular problem you're
Speaker 1: working on now of integrating image and words basically like
Speaker 1: you think you'll obviously it won't be completely solved, but
Speaker 1: you think that'll basically work. And if so, is there
Speaker 1: a next thing? I think the problem of a video
Speaker 1: I think will continue to be hard because there's just
Speaker 1: such a large amount of information in a given video.
Speaker 1: The other problem that I'm really interested in is helping
Speaker 1: people pause information with helpful context. So, like, how you
Speaker 1: know we've unleashed the all of the world's information on people,
Speaker 1: how do you actually help help them sift through that
Speaker 1: and make good decisions, whether it's choosing a reliable merchant
Speaker 1: to buy from or finding reliable medical information? How do
Speaker 1: you help people make those decisions for themselves and be
Speaker 1: literate with their information choices. What's one piece of advice
Speaker 1: you'd give to someone trying to solve a hard problem.
Speaker 1: I would say, find a really great group of people
Speaker 1: to help solve up with you, because generally trying to
Speaker 1: solve hard things by yourself enser being an active fustriction.
Speaker 1: Kathy Edwards is vice president and g M of Search
Speaker 1: at Google. Today's show was edited by Robert Smith, produced
Speaker 1: by Edith Russelo, and engineered by Amanda k Waugh. I'm
Speaker 1: Jacob Goldstein, and we'll be back next week with another
Speaker 1: episode of What's Your Problem.