The following is a conversation with Christos Goodrow,

vice president of engineering at Google and head of search and discovery at YouTube,

also known as the YouTube algorithm. YouTube has approximately 1.9 billion users,

and every day people watch over 1 billion hours of YouTube video. It is the second most popular

search engine behind Google itself. For many people, it is not only a source of entertainment,

but also how we learn new ideas from math and physics videos to podcasts to debates,

opinions, ideas from out of the box thinkers and activists on some of the most tense,

challenging and impactful topics in the world today. YouTube and other content platforms

receive criticism from both viewers and creators, as they should. Because the engineering task

before them is hard, and they don't always succeed, and the impact of their work is truly

world changing. To me, YouTube has been an incredible wellspring of knowledge. I've watched

hundreds if not thousands of lectures that change the way I see many fundamentals ideas in math,

science, engineering, and philosophy. But it does put a mirror to ourselves, and keeps the

responsibility of the steps we take in each of our online educational journeys into the hands of

each of us. The YouTube algorithm has an important role in that journey of helping us find new exciting

ideas to learn about. That's a difficult and an exciting problem for an artificial intelligence

system. As I've said in lectures and other forums, recommendation systems will be one of the most

impactful areas of AI in the 21st century, and YouTube is one of the biggest recommendation

systems in the world. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe

on YouTube, give it five stars on Apple Podcasts, follow on Spotify, support it on Patreon, or

simply connect with me on Twitter. Alex Friedman spelled F R I D M A N. I recently started doing

ads at the end of the introduction. I'll do one or two minutes after introducing the episode and

never any ads in the middle that can break the flow of the conversation. I hope that works for you

and doesn't hurt the listening experience. This show is presented by Cash App, the number one

finance app in the App Store. I personally use Cash App to send money to friends, but you can

also use it to buy, sell, and deposit Bitcoin in just seconds. Cash App also has a new investing

feature. You can buy fractions of a stock, say $1 worth, no matter what the stock price is.

Broker services are provided by Cash App Investing, a subsidiary of Square and member SIPC.

I'm excited to be working with Cash App to support one of my favorite organizations called First,

best known for their first robotics and Lego competitions. They educate and inspire hundreds

of thousands of students in over 110 countries and have a perfect rating and charity navigator,

which means that donated money is used to maximum effectiveness. When you get Cash App from the

App Store, Google Play, and use code LEX Podcast, you'll get $10 and Cash App will also donate to

$10 to First, which again is an organization that I've personally seen inspire girls and boys

to dream of engineering a better world. And now here's my conversation with Christos Godro.

YouTube is the world's second most popular search engine behind Google, of course.

We watch more than 1 billion hours of YouTube videos a day, more than Netflix and Facebook

video combined. YouTube creators upload over 500,000 hours of video every day. Average lifespan

of a human being just for comparison is about 700,000 hours. So what's uploaded every single date

is just enough for a human to watch in a lifetime. So let me ask an absurd philosophical question.

If from birth, when I was born, and there's many people born today with the internet,

I watched YouTube videos nonstop. Do you think there are trajectories through

YouTube video space that can maximize my average happiness or maybe education or

my growth as a human being? I think there are some great trajectories through YouTube videos,

but I wouldn't recommend that anyone spend all of their waking hours or all of their hours

watching YouTube. I mean, I think about the fact that YouTube has been really great for my kids,

for instance. My oldest daughter, you know, she's been watching YouTube for several years,

she watches Tyler Oakley and the vlogbrothers. And I know that it's had a very profound and

positive impact on her character. And my younger daughter, she's a ballerina and her teachers tell

her that YouTube is a huge advantage for her because she can practice a routine and watch

like professional dancers do that same routine and stop it and back it up and rewind and all

that stuff, right? So it's been really good for them. And then even my son is a sophomore in

college. He got through his linear algebra class because of a channel called Three Blue One Brown,

which, you know, helps you understand linear algebra, but in a way that would be very hard

for anyone to do on a whiteboard or a chalkboard. And so I think that those experiences, from my

point of view, were very good. And so I can imagine really good trajectories through YouTube. Yes.

Have you looked at, do you think of broadly about that trajectory over a period because

YouTube has grown up now. So over a period of years, you just kind of gave a few anecdotal

examples. But, you know, I used to watch certain shows on YouTube. I don't anymore. I've moved on

to other shows. And ultimately, you want people to, from YouTube's perspective, to stay on YouTube,

to grow as human beings on YouTube. So you have to think not just what makes them engage

today or this month, but also over a period of years. Absolutely. That's right. I mean,

if YouTube is going to continue to enrich people's lives, then, you know, then it has to grow with

them. And people's interests change over time. And so I think we've been working on this problem.

And I'll just say it broadly as like, how to introduce diversity and introduce people who

are watching one thing to something else they might like. We've been working on that problem

all the eight years I've been at YouTube. It's a hard problem because, I mean, of course,

it's trivial to introduce diversity that doesn't help. Yeah, just had a random video.

I could just randomly select a video from the billions that we have. It's likely not to even

be in your language. So the likelihood that you would watch it and develop a new interest is

very, very low. And so what you want to do when you're trying to increase diversity is find something

that is not too similar to the things that you've watched, but also something that you might be

likely to watch. And that balance, finding that spot between those two things is quite challenging.

So the diversity of content, diversity of ideas, it's a really difficult,

it's the thing like that's almost impossible to define, right? Like what's different?

So how do you think about that? So two examples is I'm a huge fan of Three Blue One Brown, say,

and then one diversity, I wasn't even aware of a channel called Veritasium,

which is a great science, physics, whatever channel. So one version of diversity is showing me

Derek's Veritasium channel, which I was really excited to discover actually and now watch a lot

of his videos. Okay, so you're a person who's watching some math channels and you might be

interested in some other science or math channels. So like you mentioned, the first kind of diversity

is just show you some, some things from other channels that are related, but not just, you know,

not all the Three Blue One Brown channel throw in a couple others. So that's the, maybe the first

kind of diversity that we started with many, many years ago. Taking a bigger leap is about,

I mean, the mechanisms we do, we use for that is, is we basically cluster videos and channels

together, mostly videos, we do every, almost everything at the video level. And so we'll,

we'll make some kind of a cluster via some embedding process. And then, and then measure,

you know, what is the likelihood that a, that users who watch one cluster might also watch

another cluster that's very distinct. So we may come to find that, that people who watch science

videos also like jazz. This is possible, right? And so, and so because of that relationship that

we've identified through the measure, through the embeddings and then the measurement of the

people who watch both, we might recommend a jazz video once in a while. So there's this

cluster in the embedding space of jazz videos and science videos. And so you kind of try to look at

aggregate statistics where if a lot of people that jump from science cluster to the jazz

cluster tend to remain as engaged or become more engaged, then that's, that means those two

are, they should hop back and forth and they'll be, they'll be happy.

Right. There's a higher likelihood that a person from who's watching science would like jazz than

the person watching science would like, I don't know, backyard railroads or, or something else,

right? And so we can try to measure these likelihoods and use that to make the best recommendation we

can. So, okay. So we'll talk about the machine learning of that, but I have to linger on things

that neither you or anyone have an answer to. There's gray areas of truth, which is, for example,

now I can't believe I'm going there, but politics, it, it, it happens so that certain people believe

certain things and they're very certain about them. Let's move outside the red versus blue

politics of today's world. But there's different ideologies. For example, in college, I read quite

a lot of Vyn Rand I studied and that's a particular philosophical ideology I find, I found it interesting

to explore. Okay. So that was that kind of space. I've kind of moved on from that cluster,

intellectually, but it nevertheless is an interesting cluster. There's, I was born in

the Soviet Union, socialism, communism is a certain kind of political ideology that's

really interesting to explore. Again, objectively, just there's a set of beliefs about how the economy

should work and so on. And so it's hard to know what's true or not in terms of people within

those communities are often advocating that this is how we achieve utopia in this world.

And they're pretty certain about it. So how do you try to manage politics in this chaotic,

divisive world, not positive or any kind of ideas in terms of filtering what people should

watch next and in terms of also not letting certain things be on YouTube? This is exceptionally

difficult responsibility. Right. Well, the responsibility to get this right is our top

priority. And the first comes down to making sure that we have good, clear rules of the road.

Just because we have freedom of speech doesn't mean that you can literally say anything. Like

we as a society have accepted certain restrictions on our freedom of speech. There are things like

libel laws and things like that. And so where we can draw a clear line, we do and we continue to

evolve that line over time. However, as you pointed out, wherever you draw the line, there's going to

be a borderline. And in that borderline area, we are going to maybe not remove videos, but we will

try to reduce the recommendations of them or the proliferation of them by demoting them. And then

alternatively, in those situations, try to raise what we would call authoritative or credible

sources of information. You mentioned Iran and communism. Those are two valid points of view

that people are going to debate and discuss. And of course, people who believe in one or the other

of those things are going to try to persuade other people to their point of view. And so

we're not trying to settle that or choose a side or anything like that. What we're trying

to do is make sure that the people who are expressing those point of view and offering

those positions are authoritative and credible. So let me ask a question about people I don't like

personally. You heard me. I don't care if you leave comments on this. But sometimes they're

brilliantly funny, which is trolls. So people who kind of mock, I mean, the internet is full,

the Reddit of mock style comedy, where people just kind of make fun of, point out that the emperor

has no clothes. And there's brilliant comedy in that. But sometimes you can get cruel and mean.

So on that, on the mean point, and sorry to linger on these things that have no good answers,

but actually, I totally hear you that this is really important that you're trying to solve it.

But how do you reduce the meanness of people on YouTube?

I understand that anyone who uploads YouTube videos has to become resilient to a certain

amount of meanness. I've heard that from many creators. And we are trying in various ways,

comment ranking, allowing certain features to block people to reduce or make that meanness or

that trolling behavior less effective on YouTube. And so, I mean, it's very important. But it's

something that we're going to keep having to work on. And as we improve it, maybe we'll get

to a point where people don't have to suffer this sort of meanness when they upload YouTube videos.

I hope we do. But it just does seem to be something that you have to be able to deal with

as a YouTube creator nowadays. Do you have a hope that, so you mentioned two things that

kind of agree with this. So there's like a machine learning approach of ranking

comments based on whatever, based on how much they contribute to the healthy conversation.

Let's put it that way. And the other is almost an interface question of how do you,

how does the creator filter, so block or, how do humans themselves, the users of

YouTube manage their own conversation? Do you have hope that these two tools will

create a better society without limiting freedom of speech too much? Without sort of

attacking, even like saying that, people like, what do you mean limiting sort of curating speech?

I mean, I think that that overall is our whole project here at YouTube.

Right. Like, we fundamentally believe and I personally believe very much that YouTube can

be great. It's been great for my kids. I think it can be great for society. But it's absolutely

critical that we get this responsibility part right. And that's why it's our top priority.

Susan Wojcicki, who's the CEO of YouTube, she says something that I personally find very inspiring,

which is that we want to do our jobs today in a manner so that people 20 and 30 years from now

will look back and say, you know, YouTube, they really figured this out. They really found a way

to strike the right balance between the openness and the value that the openness has, and also

making sure that we are meeting our responsibility to users in society.

So the burden on YouTube actually is quite incredible. And the one thing that people don't

give enough credit to the seriousness and the magnitude of the problem, I think.

So I personally hope that you do solve it because a lot is in your hand. A lot is riding on your

success or failure. So it's besides, of course, running a successful company, you're also curating

the content of the internet and the conversation on the internet. That's a powerful thing.

So one thing that people wonder about is how much of it can be solved with pure machine learning?

So looking at the data, studying the data and creating algorithms that curate the comments,

curate the content, and how much of it needs human intervention, meaning people here at

YouTube in a room sitting and thinking about what is the nature of truth? What is, what are the

ideals that we should be promoting, that kind of thing? So algorithm versus human input,

input. What's your sense? I mean, my own experience has demonstrated that you need both of those

things. Algorithms, I mean, you're familiar with machine learning algorithms. And the thing they

need most is data. And the data is generated by humans. And so for instance, when we're building

a system to try to figure out which are the videos that are misinformation or borderline

policy violations, well, the first thing we need to do is get human beings to make decisions about

which of those videos are in which category. And then we use that data and basically take

that information that's determined and governed by humans and extrapolate it or apply it to the

entire set of billions of YouTube videos. And we couldn't get to all the videos on YouTube well

without the humans. And we couldn't use the humans to get to all the videos of YouTube.

So there's no world in which you have only one or the other of these things. And just as you said,

a lot of it comes down to people at YouTube spending a lot of time trying to figure out what

are the right policies? What are the outcomes based on those policies? Are they the kinds of

things we want to see? And then once we kind of get an agreement or build some consensus around

what the policies are, well, then we've got to find a way to implement those policies across all

of YouTube. And that's where both the human beings, we call them evaluators or reviewers,

come into play to help us with that. And then once we get a lot of training data from them,

then we apply the machine learning techniques to take it even further.

Do you have a sense that these human beings have a bias in some kind of direction?

I mean, that's an interesting question. We do sort of in autonomous vehicles and computer vision

in general, a lot of annotation. And we rarely ask what bias do the annotators have? Even in the

sense that they're better at annotating certain things than others. For example, people are much

better at for annotating segmentation at segmenting cars in a scene versus segmenting bushes or trees.

You know, there's specific mechanical reasons for that, but also because it's semantic gray area.

And just for a lot of reasons, people are just terrible at annotating trees.

Okay. So in the same kind of sense, do you think of in terms of people reviewing videos or annotating

the content of videos, is there some kind of bias that you're aware of or seek out in that human input?

Well, we take steps to try to overcome these kinds of biases or biases that we think would be

problematic. So for instance, we ask people to have a bias towards scientific consensus. That's

something that we instruct them to do. We ask them to have a bias towards demonstration of

expertise or credibility or authoritativeness. But there are other biases that we want to

make sure to try to remove. And there's many techniques for doing this. One of them is you

send the same thing to be reviewed to many people. And so that's one technique. Another is that you

make sure that the people that are doing these sorts of tasks are from different backgrounds and

different areas of the United States or of the world. But then even with all of that, it's possible

for certain kinds of what we would call unfair biases to creep into machine learning systems,

primarily as you said, because maybe the training data itself comes in in a biased way. And so

we also have worked very hard on improving the machine learning systems to remove and reduce

unfair biases when it's when it goes against or is involved some protected class, for instance.

Thank you for exploring with me some of the more challenging things. I'm sure there's a few more

that we'll jump back to. But let me jump into the fun part, which is maybe the basics

of the quote unquote YouTube algorithm. What does the YouTube algorithm look at to make

recommendation for what to watch next from a machine learning perspective? Or when you

search for a particular term, how does it know what to show you next? Because it seems to, at

least for me, do an incredible job of both. Well, that's kind of you to say. It didn't

used to do a very good job. But it's gotten better over the years. Even I observe that it's

improved quite a bit. Those are two different situations. Like when you search for something,

YouTube uses the best technology we can get from Google to make sure that the YouTube search system

finds what someone's looking for. And of course, the very first things that one thinks about is,

okay, well, does the word occur in the title? For instance, but there are much more sophisticated

things where we're mostly trying to do some syntactic match or maybe a semantic match based on

words that we can add to the document itself. For instance, maybe is this video

watched a lot after this query? That's something that we can observe. And then as a result,

make sure that that document would be retrieved for that query. Now, when you talk about what kind

of videos would be recommended to watch next, that's something, again, we've been working on for

many years. And probably the first real attempt to do that well was to use collaborative filtering.

So you can describe what collaborative filtering is?

Sure. It's just basically what we do is we observe which videos get watched close together

by the same person. And if you observe that, and if you can imagine creating a graph where the videos

that get watched close together by the most people are sort of very close to one another in this

graph and videos that don't frequently get watched close to close together by the same person or

the same people are far apart, then you end up with this graph that we call the related

graph that basically represents videos that are very similar or related in some way. And

what's amazing about that is that it puts all the videos that are in the same language together,

for instance. And we didn't even have to think about language. It just doesn't, right? And it

puts all the videos that are about sports together, and it puts most of the music videos together,

and it puts all of these sorts of videos together just because that's sort of the way the people

using YouTube behave. So that already cleans up a lot of the problem. It takes care of the

lowest hanging fruit, which happens to be a huge one of just managing these millions of videos.

That's right. I remember a few years ago, I was talking to someone who was

trying to propose that we do a research project concerning people who are bilingual. And this

person was making this proposal based on the idea that YouTube could not possibly be good

at recommending videos well to people who are bilingual. And so she was telling me

about this, and I said, well, can you give me an example of what problem do you think we have on

YouTube with the recommendations? And so she said, well, I'm a researcher in the U.S., and when I'm

looking for academic topics, I want to see them in English. And so she searched for one,

found a video, and then looked at the watch next suggestions, and they were all in English.

And so she said, oh, I see. YouTube must think that I speak only English. And so she said,

now I'm actually originally from Turkey, and sometimes when I'm cooking, let's say I want to

make some baklava, I really like to watch videos that are in Turkish. And so she searched for a

video about making the baklava, and then selected it, and it was in Turkish. And the watch next

recommendations were in Turkish. And she just couldn't believe how this was possible. And how

is it that you know that I speak both these two languages and put all the videos together? And

it's just sort of an outcome of this related graph that's created through collaborative filtering.

So for me, one of my huge interests is just human psychology, right? And that's such a powerful

platform on which to utilize human psychology to discover what individual people want to watch

next. But it's also be just fascinating to me. You know, I've Google search has ability to look

at your own history. And I've done that before. Just, just what I've searched three years for

many, many years. And it's fascinating picture of who I am actually. And I don't think anyone's

ever summarized. I personally would love that a summary of who I am as a person on the internet

to me, because I think it reveals, I think it puts a mirror to me or to others, you know,

that's actually quite revealing and interesting. You know, just maybe the number of it's a joke,

but not really is the number of cap videos I've watched or videos of people falling,

you know, stuff that's absurd, that kind of stuff. It's really interesting. And of course,

it's really good for the machine learning aspect to, to show, to figure out what to show next.

But it's interesting. Have you just as a tangent played around with the idea of giving a map to

people sort of as opposed to just using this information to show us next, showing them here

are the clusters you've loved over the years kind of thing. Well, we do provide the history of all

the videos that you've watched. Yes. So you can definitely search through that and look through

it and search through it to see what it is that you've been watching on YouTube. We have actually,

in various times, experimented with this sort of cluster idea, finding ways to demonstrate or show

people what topics they've been interested in or what what clusters they've watched from.

It's interesting that you bring this up because in some sense, the way the recommendation system

of YouTube sees a user is exactly as the history of all the videos they've watched on YouTube.

And so you can think of yourself or any user on YouTube as kind of like a DNA strand of all

your videos, right? That sort of represents you. You can also think of it as maybe a vector in

the space of all the videos on YouTube. And so now, once you think of it as a vector in the

space of all the videos on YouTube, then you can start to say, okay, well, which other vectors

are close to me and to my vector? And that's one of the ways that we generate some diverse

recommendations is because you're like, okay, well, these people seem to be close with respect

to the videos they've watched on YouTube. But here's a topic or a video that one of them has

watched and enjoyed, but the other one hasn't. That could be an opportunity to make a good

recommendation. I gotta tell you, I'm gonna ask for things that are impossible, but I would love

to cluster than human beings. I would love to know who has similar trajectories as me,

because you probably would want to hang out. There's a social aspect there.

Like actually finding some of the most fascinating people I find on YouTube have like no followers,

and I start following them and they create incredible content. And you know, and on that topic,

I just love to ask, there's some videos that just blow my mind in terms of quality and depth.

And just in every regard are amazing videos and they have like 57 views. Okay. How do you get

videos of quality to be seen by many eyes? So the measure of quality, is it just something?

Yeah. How do you know that something is good? Well, I mean, I think it depends initially on

what sort of video we're talking about. So in the realm of let's say, you mentioned politics and news,

in that realm, quality news or quality journalism relies on having a journalism

department, right? Like you have to have actual journalists and fact checkers and people like

that. And so in that situation, and in others, maybe science or in medicine, quality has a lot

to do with the authoritativeness and the credibility and the expertise of the people who make the video.

Now, if you think about the other end of the spectrum,

you know, what is the highest quality prank video? Or what is the highest quality

Minecraft video, right? That might be the one that people enjoy watching the most and watch to the

end. Or it might be the one that when we ask people the next day after they watched it,

were they satisfied with it? And so we, especially in the realm of entertainment,

have been trying to get at better and better measures of quality or satisfaction or enrichment

since I came to YouTube. And we started with, well, you know, the first approximation is the one that

gets more views. But, you know, we both know that things can get a lot of views and not really be

that high quality, especially if people are clicking on something and then immediately

realizing that it's not that great and abandoning it. And that's why we moved from views to thinking

to thinking about the amount of time people spend watching it with the premise that like,

you know, in some sense, the time that someone spends watching a video is related to the value

that they get from that video. It may not be perfectly related, but it has something to say

about how much value they get. But even that's not good enough, right? Because I myself have spent

time clicking through channels on television late at night and ended up watching under siege too,

for some reason, I don't know. And if you were to ask me the next day, are you glad that you

watched that show on TV last night? I'd say, yeah, I wish I would have gone to bed or read a book or

almost anything else really. And so that's why some people got the idea a few years ago to try

to survey users afterwards. And so we get feedback data from those surveys and then use that in the

machine learning system to try to not just predict what you're going to click on right now,

what you might watch for a while, but what when we ask you tomorrow, you'll give four or five stars

to. So just to summarize, what are the signals from the machine learning perspective that the

user can provide? So you mentioned just clicking on the video views, the time watch, maybe the

relative time watch, the clicking like and dislike on the video, maybe commenting on the video,

all those things, all those things. And then the one I wasn't actually quite aware of,

even though I might have engaged in it is a survey afterwards, which is a brilliant idea.

Is there other signals? I mean, that's already a really rich space of signals to learn from.

Is there something else? Well, you mentioned commenting, also sharing the video. If you

think it's worthy to be shared with someone else, you know, within YouTube or outside of YouTube

as well. Either. Let's see, you mentioned like, dislike. Like and dislike. How important is that?

It's very important, right? We want, it's predictive of satisfaction. But it's not, it's

not perfectly predictive. Subscribe. If you subscribe to the channel of the person who

made the video, then that also is a piece of information that signals satisfaction. Although

over the years, we've learned that people have a wide range of attitudes about what it means to

subscribe. We would ask some users who didn't subscribe very much, but they watched a lot from

a few channels. We'd say, well, why didn't you subscribe? And they would say, well, I can't

afford to pay for anything. And, you know, we tried to let them understand like, actually,

it doesn't cost anything. It's free. It just helps us know that you are very interested in this

creator. But then we've asked other people who subscribed to many things and don't really watch

any of the videos from those channels. And we say, well, why did you subscribe to this if you

weren't really interested in any more videos from that channel? And they might tell us,

well, I just, you know, I thought the person did a great job and I just want to kind of

give them a high five. Yeah. And so, yeah, that's where I said, I actually subscribed to channels

where I just, this person is amazing. I like this person. But then I like this person,

I really want to support them. That's how I click subscribe, even though I mean, never actually want

to click on their videos when they're releasing it. I just love what they're doing. And it's maybe

outside of my interest area and so on, which is probably the wrong way to use the subscribe button.

Well, I just want to say congrats. This is a great work. Well, so you have to deal with all the

space of people that see the subscribe button is totally different. That's right. And so, you know,

we, we can't just close our eyes and say, sorry, you're using it wrong. You know, we're not going

to pay attention to what you've done. We need to embrace all the ways in which all the different

people in the world use the subscribe button or the like and the dislike button. So in terms of

signals of machine learning, using for the search and for the recommendation, you've mentioned title,

so like metadata, like text data that people provide description and title and maybe keywords.

So maybe you can speak to the value of those things in search and also this incredible,

fascinating area of the content itself. So the video content itself, trying to understand what's

happening in the video. So YouTube released a data set that, you know, in the machine learning

computer vision world, this is just an exciting space. How much is that currently? How much are

you playing with that currently? How much is your hope for the future of being able to analyze the

content of the video itself? Well, we have been working on that also since I came to YouTube.

Analyzing the content. Analyzing the content of the video, right. And what I can tell you is that

our ability to do it well is still somewhat crude. We can, we can tell if it's a music video. We can

tell if it's a sports video. We can probably tell you that people are playing soccer. We probably

can't tell whether it's Manchester United or my daughter's soccer team. So these things are kind

of difficult and using them, we can use them in some ways. So for instance, we use that kind of

information to understand and inform these clusters that I talked about. And also maybe to add some

words like soccer, for instance, to the video if it doesn't occur in the title or the description,

which is remarkable that often it doesn't. One of the things that I ask creators to do is please

help us out with the title in the description. For instance, we were a few years ago having a

live stream of some competition for World of Warcraft on YouTube. And it was a very important

competition. But if you typed World of Warcraft in search, you wouldn't find it. World of Warcraft

wasn't in the title? World of Warcraft wasn't in the title. It was match four, seven, eight, you know,

A team versus B team. And World of Warcraft wasn't in the title. Just like, come on, give me.

Being literal on the internet is actually very uncool, which is the problem.

Oh, is that right?

Well, I mean, in some sense, some of the greatest videos, I mean, there's a humor to just being

indirect, being witty and so on. And actually, machine learning algorithms want you to be

literal. You just want to say what's in the thing, be very, very simple. And in some sense,

that gets away from wit and humor. So you have to play with both. But you're saying that for

now, the content of the title, the content of the description, the actual text is one of the

best ways for the algorithm to find your video and put them in the right cluster.

That's right. And I would go further and say that if you want people, human beings, to select

your video in search, then it helps to have, let's say, World of Warcraft in the title. Because

why would a person's, you know, if they're looking at a bunch, they type World of Warcraft,

and they have a bunch of videos, all of whom say World of Warcraft, except the one that you uploaded,

well, even the person is going to think, well, maybe this isn't somehow search made a mistake.

This isn't really about World of Warcraft. So it's important not just for the machine

learning systems, but also for the people who might be looking for this sort of thing. They get a

clue that it's what they're looking for by seeing that same thing prominently in the title of the

video. Okay, let me push back on that. So I think from the algorithm perspective, yes, but if they

typed in World of Warcraft and saw a video that with the title simply winning, and the thumbnail

has like a sad orc or something, I don't know, right? Like, I think that's much, it gets your

curiosity up. And then if they could trust that the algorithm was smart enough to figure out somehow

that this is indeed a World of Warcraft video, that would have created the most beautiful

experience. I think in terms of just the wit and the humor and the curiosity that we human beings

naturally have, but you're saying, I mean, realistically speaking, it's really hard for

the algorithm to figure out that the content of that video will be a World of Warcraft video.

And you have to accept that some people are going to skip it. Yeah, right. I mean, and so you're

right. The people who don't skip it and select it are going to be delighted. Yeah. But other people

might say, yeah, this is not what I was looking for. And making stuff discoverable, I think,

is what you're really working on and hoping. So yeah, so from your perspective, put stuff in

the title of the description. And remember, the collaborative filtering part of the system

starts by the same user watching videos together, right? So the way that they're probably going

to do that is by searching for them. That's a fascinating aspect of it. It's like ant colonies.

That's how they find stuff. So I mean, what degree for collaborative filtering in general

is one curious ant, one curious user essential? So just a person who is more willing to click on

random videos and sort of explore these cluster spaces. In your sense, how many people are just

like watching the same thing over and over and over and over? And how many are just like the

explorers that just kind of like click on stuff and then help the other ant in the ants colony

discover the cool stuff. Do you have a sense of that at all? I really don't think I have a sense

for the relative sizes of those groups. But I would say that people come to YouTube with

some certain amount of intent. And as long as they, to the extent to which they try to satisfy

that intent, that certainly helps our systems, right? Because our systems rely on kind of a

faithful amount of behavior, right? And there are people who try to trick us, right? There are people

and machines that try to associate videos together that really don't belong together,

but they're trying to get that association made because it's profitable for them. And so we have

to always be resilient to that sort of attempt at gaming the systems. So speaking to that,

there's a lot of people that in a positive way, perhaps, I don't know, I don't like it, but

like to gain, want to try to gain the system to get more attention. Everybody, creators,

in a positive sense, want to get attention, right? So how do you, how do you work in this space when

people create more and more sort of click baity titles and thumbnails? Sort of very

to ask him, Derek has made a video where basically describes that it seems what works is to create

a high quality video, really good video where people would want to watch and wants to click on it,

but have click baity titles and thumbnails to get them to click on it in the first place.

And he's saying, I'm embracing this fact, I'm just going to keep doing it. And I hope

you forgive me for doing it. And you will enjoy my videos once you click on them.

So in what sense do you see this kind of click bait style attempt to manipulate to get people in

the door to manipulate the algorithm or play with the algorithm or game the algorithm?

I think that that you can look at it as an attempt to game the algorithm. But

even if you were to take the algorithm out of it and just say, okay, well, all these videos

happen to be lined up, which the algorithm didn't make any decision about which one to

put at the top or the bottom, but they're all lined up there, which one are the people going to

choose? And I'll tell you the same thing that I told Derek is, you know, I have a bookshelf

and they have two kinds of books on them, science books. I have my math books from when I was a

student and they all look identical except for the titles on the covers. They're all yellow,

they're all from Springer, and they're every single one of them. The cover is totally the same.

Right? On the other hand, I have other more pop science type books, and they all have very

interesting covers, right? And they have provocative titles and things like that. I mean, I wouldn't

say that they're click baity because they are indeed good books. And I don't think that they

cross any line, but that's just a decision you have to make, right? Like the people who write

classical recursion theory by P. R. O. D. Freddie, he was fine with the yellow title and nothing

more. Whereas I think other people who wrote a more popular type book understand that they need

to have a compelling cover and a compelling title. And, you know, I don't think there's anything

really wrong with that. We do take steps to make sure that there is a line that you don't cross.

And if you go too far, maybe your thumbnail is especially racy or, you know, it's all caps with

too many exclamation points. We observe that users are kind of, you know, sometimes offended

by that. And so for the users who are offended by that, we will then depress or suppress those

videos. And which reminds me, there's also another signal where users can say, I don't know if I was

recently added, but I really enjoy it. I'm just saying I didn't, something like I don't want to

see this video anymore or something like, like this is a, like there's certain videos that just

cut me the wrong way. Like just jump out at me. It's like, I don't want this. And it feels really

good to clean that up. To be like, I don't, that's not, that's not for me. I don't know. I think

that might have been recently added, but that's also a really strong signal. Yes, absolutely.

Right. We don't want to make a recommendation that people are unhappy with. And that makes me,

that particular one makes me feel good as a user in general, and as a machine learning person,

because I feel like I'm helping the algorithm. My interaction on YouTube don't always feel like

I'm helping the algorithm. Like I'm not reminded of that fact. Like for example, Tesla and Autopilot

and, you know, Musk create a feeling for their customers, for people that own Tesla's, that

they're helping the algorithm of Tesla. Like they're all like a really proud, they're helping

the fleet learn. I think YouTube doesn't always remind people that you're helping the algorithm

get smarter. And for me, I love that idea. Like we're all collaboratively, like Wikipedia gives

that sense that we're all together creating a beautiful thing. YouTube doesn't always remind

me of that. This conversation is reminding me of that, but... Well, that's a good tip. We should

keep that fact in mind when we design these features. I'm not sure I really thought about it

that way, but that's a very interesting perspective. It's an interesting question of personalization

that I feel like when I click like on a video, I'm just improving my experience.

It would be great. It would make me personally, people are different, but make me feel great

if I was helping also the YouTube algorithm broadly say something. You know what I'm saying?

Like I don't know if that's human nature, but the products you love, and I certainly love

YouTube, you want to help it get smarter and smarter and smarter because there's some kind

of coupling between our lives together being better. If YouTube was better, then my life

will be better. And that's that kind of reasoning. I'm not sure what that is. And I'm not sure how

many people share that feeling. That could be just a machine learning feeling. But on that point,

how much personalization is there in terms of next video recommendations? So is it kind of

all really boiling down to clustering? Like if I'm in your clusters to me and so on and

that kind of thing, or how much is personalized to me, the individual completely?

It's very, very personalized. So your experience will be quite a bit different from anybody else's

who's watching that same video, at least when they're logged in. And the reason is that we found

that users often want two different kinds of things when they're watching a video. Sometimes

they want to keep watching more on that topic or more in that genre. And other times they just

are done and they're ready to move on to something else. And so the question is,

well, what is the something else? And one of the first things one can imagine is, well,

maybe something else is the latest video from some channel to which you've subscribed. And

that's going to be very different for you than it is for me, right? And even if it's not something

that you subscribe to, it's something that you watch a lot. And again, that'll be very different

on a person by person basis. And so even the watch next, as well as the homepage, of course,

is quite personalized. So what we mentioned some of the signals, but what does success look like?

What does success look like in terms of the algorithm creating a great long term experience

for a user? Or put another way, if you look at the videos I've watched this month,

how do you know the algorithm succeeded for me? I think, first of all, if you come back and watch

more YouTube, then that's one indication that you found some value from it. So just the number of

hours is a powerful indicator? Well, I mean, not the hours themselves, but the fact that you return

on another day. So that's probably the most simple indicator. People don't come back to things that

they don't find value in, right? There's a lot of other things that they could do. But like I said,

I mean, ideally, we would like everybody to feel that YouTube enriches their lives and that every

video they watched is the best one they've ever watched since they've started watching YouTube.

And so that's why we survey them and ask them, like, is this one to five stars? And so our version

of success is every time someone takes that survey, they say it's five stars. And if we ask them,

is this the best video you've ever seen on YouTube? They say yes, every single time. So it's hard to

imagine that we would actually achieve that. Maybe asymptotically, we would get there. But

that would be what we think success is. It's funny. I've recently said somewhere, I don't know,

maybe tweeted, but that Ray Dalio has this video on the economic machine. I forget what it's called,

but it's a 30 minute video. And I said, it's the greatest video I've ever watched on YouTube.

It's like, I watched the whole thing and my mind was blown as a very crisp, clean description of

how at least the American economic system works. It's a beautiful video. And I was just, I wanted

to click on something to say, this is the best thing ever. Please let me, I can't believe I

discovered it. I mean, the views and the likes reflect its quality. But I was almost upset that

I haven't found it earlier and wanted to find other things like it. I don't think I've ever felt

that this is the best video I've ever watched. And that was that. And to me, the ultimate Utopia,

the best experiences were every single video where I don't see any of the videos I regret and

every single video I watched is one that actually helps me grow, helps me enjoy life, be happy,

and so on. Well, so that's, that's, that's a heck of a, that's a, that's one of the most beautiful

and ambitious, I think, machine learning tasks. So when you look at a society as opposed to an

individual user, do you think of how YouTube is changing society when you have these millions

of people watching videos, growing, learning, changing, having debates? Do you have a sense

of, yeah, what the big impact on society is? Because I think it's huge, but do you have a

sense of what direction we're taking this world? Well, I mean, I think, you know, openness has had

an impact on society already. There's a lot of... What do you mean by openness? Well, the fact that

unlike other mediums, there's not someone sitting at YouTube who decides before you can upload your

video, whether it's worth having you upload it, or, or worth anybody seeing it really, right? And so,

you know, there are some creators who say, like, I wouldn't have this opportunity to, to reach an

audience. Tyler Oakley often said that, you know, he wouldn't have had this opportunity to reach this

audience if it weren't for YouTube. And, and so I think that's one way in which YouTube has changed

society. I know that there are people that I work with from outside the United States, especially

from places where literacy is low. And they think that YouTube can help in those places because

you don't need to be able to read and write in order to learn something important for your life,

maybe, you know, how to do some job or how to fix something. And so that's another way in which I

think YouTube is possibly changing society. So I've, I've worked at YouTube for eight, almost nine

years now. And it's fun because I meet people and, you know, you tell them where they, where you work,

you say you work on YouTube, and they immediately say, I love YouTube. Yeah. Right. Which is great,

makes me feel great. But then, of course, when I ask them, well, what is it that you love about

YouTube? Not one time ever has anybody said that the search works outstanding or that the recommendations

are great. What they always say when I ask them, what do you love about YouTube is they immediately

start talking about some channel or some creator or some topic or some community that they found on

YouTube and that they just love. Yeah. And so that has made me realize that YouTube is really about

the video and connecting the people with the videos and then everything else kind of gets out of the

way. So beyond the video, it's an interesting, because you kind of mentioned creator. What about

the connection with just the individual creators as opposed to just individual video? So like,

I gave the example of Dalya video that the video itself is incredible. But there's some people

who are just creators that I love that they're one of the cool things about people who call

themselves YouTubers or whatever is they have a journey. They usually almost all of them are

or they suck horribly in the beginning and then they kind of grow and then there's that

genuineness in their growth. So YouTube clearly wants to help creators connect with their audience

in this kind of way. So how do you think about that process of helping creators grow,

helping them connect with their audience, develop not just individual videos, but the

entirety of a creator's life on YouTube? Well, I mean, we're trying to help creators find the

biggest audience that they can find. And the reason why that's you brought up creator versus

video, the reason why creator channel is so important is because if we have a hope of people

coming back to YouTube, well, they have to have in their minds some sense of what they're going to

find when they come back to YouTube. If YouTube were just the next viral video, and I have no

concept of what the next viral video could be one time it's a cat playing a piano and the next day

it's some children interrupting a reporter and the next day it's some other thing happening,

then it's hard for me to when I'm not watching YouTube say, gosh, I really would like to see

something from someone or about something. And so that's why I think this connection between

fans and creators is so important for both because it's a way of fostering a relationship

that can play out into the future. Let me talk about kind of a dark and interesting question

in general. And again, a topic that you or nobody has an answer to, but social media has a sense of

you know, it gives us highs and it gives us lows in the sense that sort of creators often speak

about having sort of burn out and having psychological ups and downs and challenges

mentally in terms of continuing the creation process. There's a momentum. There's a huge

excited audience that makes everybody feel that makes creators feel great. And I think it's more

than just financial. I think it's literally just they love that sense of community. It's part of

the reason I upload to YouTube. I don't care about money. Never will. What I care about is the the

community, but some people feel like this momentum and even when there's times in their life when

they don't feel, you know, for some reason don't feel like creating. So how do you think about

burn out this mental exhaustion that some YouTube creators go through? Is that something we have

an answer for? Is it something? How do we even think about that? Well, the first thing is we

want to make sure that the YouTube systems are not contributing to this sense, right? And so

we've done a fair amount of research to demonstrate that you can absolutely take a break. If you are

if you are a creator and you've been uploading a lot, we have just as many examples of people who

took a break and came back more popular than they were before as we have examples of going the other

way. Yeah, can we pause on that for a second? So the feeling that people have, I think, is if I take

a break, everybody, the party will leave, right? So if you could just linger on that. So in your

sense that taking a break is okay. Yes, taking a break is absolutely okay. And the reason I say

that is because we have we can observe many examples of being of creators coming back very

strong and even stronger after they have taken some sort of break. And so I just want to dispel the

myth that this somehow necessarily means that your channel is going to go down or lose views.

That is not the case. We know for sure that this is not a necessary outcome. And so we want to

encourage people to make sure that they take care of themselves. That is job one, right? You have

to look after yourself and your mental health. And you know, I think that it probably in some of

these cases contributes to better videos once they come back, right? Because a lot of people,

I know myself, if I burn out on something, then I'm probably not doing my best work,

even though I can keep working until I pass out. And so I think that the taking a break

may even improve the creative ideas that someone has.

Okay, I think it's a really important thing to dispel. I think it applies to all of social media.

Like literally, I've taken a break for a day every once in a while. Sorry, sorry if that sounds

like a short time. But even like email, just taking a break from email or only checking email once a

day, especially when you're going through something psychologically in your personal life or so on,

or really not sleeping much because of work deadlines, it can refresh you in a way that's

profound. And so the same applies. It was there when you came back, right? It's there. And it looks

different actually when you come back. You're sort of brighter eyed with some coffee, everything,

the world looks better. So it's important to take a break when you need it.

So you've mentioned kind of the the YouTube algorithm isn't, you know, e equals mc squared.

It's not the single equation. It's potentially sort of more than a million lines of code.

Sort of is it more akin to what autonomous successful autonomous vehicles today are,

which is they're just basically patches on top of patches of heuristics and human experts really

tuning the algorithm and have some machine learning modules? Or is it becoming more and more a giant

machine learning system with humans just doing a little bit of tweaking here and there? What's

your sense? First off, do you even have a sense of what is the YouTube algorithm at this point?

And whichever, however much you do have a sense, what does it look like?

Well, we don't usually think about it as the algorithm because it's a bunch of systems that

work on different services. The other thing that I think people don't understand is that

what you might refer to as the YouTube algorithm from outside of YouTube is actually

a bunch of code and machine learning systems and heuristics, but that's married with the behavior

of all the people who come to YouTube every day. So the people part of the code essentially.

Exactly, right? Like if there were no people who came to YouTube tomorrow, then the algorithm

wouldn't work anymore, right? So that's a critical part of the algorithm. And so when people talk

about, well, the algorithm does this, the algorithm does that, it's sometimes hard to understand.

Well, you know, it could be the viewers are doing that and the algorithm is mostly just keeping track

of what the viewers do and then reacting to those things in sort of more fine grained situations.

And I think that this is the way that the recommendation system and the search system and

probably many machine learning systems evolve is you start trying to solve a problem and the

first way to solve a problem is often with a simple heuristic, right? And you want to say,

what are the videos we're going to recommend? Well, how about the most popular ones?

Right? And that's where you start. And over time, you collect some data and you refine

your situation so that you're making less heuristics and you're building a system that can

actually learn what to do in different situations based on some observations of those situations

in the past. And you keep chipping away at these heuristics over time. And so I think that

that just like with diversity, you know, I think the first diversity measure we took was, okay,

not more than three videos in a row from the same channel, right? It's a pretty simple heuristic

to encourage diversity, but it worked, right? Who needs to see four, five, six videos in a row

from the same channel? And over time, we try to chip away at that and make it more fine grained

and basically have it remove the heuristics in favor of something that can react to individuals

and individual situations. So how do you, you mentioned, you know, we know that something

worked. How do you get a sense when decisions are the kind of A B testing that this idea was a good

one, this was not so good? What's, how do you measure that? And across which time scale, across

how many users that kind of, that kind of thing? Well, you mentioned that A B experiments. And so

just about every single change we make to YouTube, we do it only after we've run a A B experiment.

And so in those experiments, which run from one week to months, we measure hundreds, literally

hundreds of different variables and, and measure changes with confidence intervals in all of them,

because we really are trying to get a sense for ultimately, does this improve the experience

for viewers? That's the question we're trying to answer. And an experiment is one way because we

can see certain things go up and down. So for instance, if we noticed in the experiment, people

are dismissing videos less frequently, or they're saying that they're more satisfied,

they're giving more videos five stars after they watch them, then those would be indications of

that the experiment is successful, that it's improving the situation for viewers.

But we can also look at other things, like we might do user studies where we invite some

people in and ask them, like, what do you think about this? What do you think about that? How do

you feel about this? And other various kinds of user research. But ultimately, before we launch

something, we're going to want to run an experiment. So we get a sense for what the impact is going to

be, not just to the viewers, but also to the different channels and all of that.

An absurd question. Nobody knows. Well, actually, it's interesting. Maybe there's an answer, but

if I want to make a viral video, how do I do it? I don't know how you make a viral video. I know

that we have, in the past, tried to figure out if we could detect when a video was going to go

viral. And those were, you take the first and second derivatives of the view count and maybe

use that to do some prediction. But I can't say we ever got very good at that. Oftentimes,

we look at where the traffic was coming from. If a lot of the viewership is coming from

something like Twitter, then maybe it has a higher chance of becoming viral than if it

were coming from search or something. But that was just trying to detect a video that might be

viral. How to make one? Like, I have no idea. I mean, you get your kids to interrupt you while

you're on the news or something. Absolutely. But after the fact, on one individual video,

sort of ahead of time predicting is a really hard task. But after the video went viral in analysis,

can you sometimes understand why it went viral from the perspective of YouTube broadly?

Firstly, is it even interesting for YouTube that a particular video is viral? Or does that not

matter for the experience of people? Well, I think people expect that if a video is going

viral and it's something they would be interested in, then I think they would expect YouTube to

recommend it to them. Right. So if someone's going viral, it's good to just let people ride the wave

of it. It's viral. Well, I mean, we want to meet people's expectations in that way, of course.

So like I mentioned, I hung out with Derek Mueller a while ago, a couple months back.

He's actually the person who suggested I talk to you on this podcast.

All right. Well, thank you, Derek. At that time, he just recently posted

an awesome science video titled, Why Are 96 Million Black Balls on This Reservoir?

And in a matter of, I don't know how long, but like a few days, you got 38 million views

and it's still growing. Is this something you can analyze and understand why it happened

this video and you want a particular video like it?

I mean, we can surely see where it was recommended, where it was found, who watched it,

and those sorts of things. So it's actually starting to interrupt. It is the video which

helped me discover who Derek is. I didn't know who he is before. So I remember, you know,

usually I just have all of these technical boring MIT Stanford talks in my recommendation

because that's how I watch. And then all of a sudden there's this Black Balls in Reservoir

video with like an excited nerd and with like just, why is this being recommended to me?

So I clicked on it and watched the whole thing. It was awesome. But, and then a lot of people

had that experience like, why was I recommended this? But they all, of course, watched it and

enjoyed it, which is, what's your sense of this just wave of recommendation that comes

with this viral video that ultimately people get enjoy after they click on it?

Well, I think it's the system, you know, basically doing what anybody who's recommending

something would do, which is you show it to some people and if they like it, you say,

okay, well, can I find some more people who are a little bit like them? Okay, I'm gonna

try it with them. Oh, they like it too. Let me expand the circle some more, find some more

people. Oh, it turns out they like it too. And you just keep going until you get some

feedback that says, no, now you've gone too far. These people don't like it anymore.

And so I think that's basically what happened. Now, you asked me about how to make a video go

viral or make a viral video. I don't think that if you or I decided to make a video about 96

million balls, that it would also go viral. It's possible that Derek made like the canonical video

about those black balls in the lake. Exactly. He did actually. Right. And so I don't know whether

or not just following along is the secret. Yeah, but it's fascinating. I mean, just like you said,

the algorithm sort of expanding that circle and then figuring out that more and more people did

enjoy it. And that sort of phase shift of just a huge number of people enjoying it in the algorithm

quickly, automatically, I assume, figuring that out. That's a, I don't know, the dynamics of

psychology, that is a beautiful thing. And so what do you think about the idea of clipping,

like too many people annoyed me into doing it, which is they were requesting it. They said it

would be very beneficial to add clips in like the coolest points and actually have explicit

videos. Like I'm reuploading a video, like a short clip, which is what the podcasts are doing.

Do you see, as opposed to like I also add timestamps for the topics, you know,

do you want the clip? Do you see YouTube somehow helping creators with that process or helping

connect clips to the original videos? Or is that just on a long list of amazing features to work

towards? Yeah, I mean, it's not something that I think we've done yet. But I can tell you that

I think clipping is great. And I think it's actually great for you as a creator.

And here's the reason. If you think about, I mean, let's, let's say the NBA is uploading

videos of, of its games. Well, people might search for warriors versus rockets,

or they might search for Steph Curry. And so a highlight from the game in which Steph Curry

makes an amazing shot is an opportunity for someone to find a portion of that video. And so I think

that you never know how people are going to search for something that you've created. And so you want

to, I would say you want to make clips and, and add titles and things like that so that they can

find it as easily as possible. Do you have a dream of a future, perhaps a distant future,

when the YouTube algorithm figures that out, sort of automatically detects the parts of the video

that are really interesting, exciting, potentially exciting for people, and sort of clip them out

in this incredibly rich space? Because if you talk about, if you talk, even just this conversation,

we probably covered 30, 40 little topics. And there's a huge space of users that would find,

you know, 30% of those topics really interesting. And that space is very different. It's something

that's beyond my ability to clip out, right? But the algorithm might be able to figure all that

out, sort of expand into clips. Do you have a, do you think about this kind of thing? Do you have

a hope, a dream that one day the algorithm will be able to do that kind of deep content analysis?

Well, we've actually had projects that attempt to achieve this. But it really does depend on

understanding the video well. And our understanding of the video right now is quite crude. And so

I think it would be especially hard to do it with a conversation like this. One might be able to do

it with, let's say, a soccer match more easily, right? You could probably find out where the goals

were scored. And then of course, you need to figure out who it was that scored the goal. And

that might require a human to do some annotation. But I think that trying to identify coherent

topics in a transcript, like the one of our conversation, is not something that we're going

to be very good at right away. And I was speaking more to the general problem, actually, of being

able to do both a soccer match and our conversation without explicit sort of almost, my hope was that

there exists an algorithm that's able to find exciting things in video. So Google now on Google

search will help you find the segment of the video that you're interested in. So if you search for

or something like how to change the filter in my dishwasher, then if there's a long video about

your dishwasher, and this is the part where the person shows you how to change the filter, then

it will highlight that area and provide a link directly to it. And from your recollection,

do you know if the thumbnail reflects? What's the difference between showing the full video and

the shorter clip? Do you know how it's presented in search results? I don't remember how it's

presented. And the other thing I would say is that right now it's based on creator annotations.

Ah, got it. So it's not the thing we're talking about. But folks are working on the more

automatic version. It's interesting, people might not imagine this, but a lot of our systems start

by using almost entirely the audience behavior. And then as they get better,

the refinement comes from using the content. And I wish, I know there's privacy concerns, but

I wish YouTube explored the space, which is sort of putting a camera on the users if they allowed

it, right? To study their, like I did a lot of emotion recognition work and so on, to study

actual sort of richer signal. One of the cool things when you upload 360 like VR video to YouTube,

and I've done this a few times. So I've uploaded myself. It's a horrible idea. Some people enjoyed

it, but whatever. The video of me giving a lecture in 360, 360 camera, and it's cool because YouTube

allows you to then watch, where did people look at? There's a heat map of where, you know,

of where the center of the VR experience was. And it's interesting because that reveals to you,

like, what people looked at. And it's, it's not always what you were expecting. It's not,

in the case of the lecture is pretty boring. It is what we're expecting, but we did a few funny

videos where there's a bunch of people doing things and they, everybody tracks those people.

You know, in the beginning, they all look at the main person and they start spreading around and

looking at the other people. It's fascinating. So that kind of, that's a really strong signal

of what people found exciting in the video. I don't know how you get that from people just

watching, except they tuned out at this point. Like, it's hard to measure this moment was super

exciting for people. I don't know how you get that signal. Maybe comment, is there a way to get

that signal where this was like, this is when their eyes opened up and they're like, like,

for me with the Ray Dalio video, right? Like, at first I was like, oh, okay, this is another one

of these like dumb it down for you videos. And then you like start watching, it's like, okay,

there's really crisp, clean, deep explanation of how the economy works. That's where I like set up

and started watching right that moment. Is there a way to detect that moment? The only way I can

think of is by asking people to label it. You mentioned that we're quite far away in terms

of doing video analysis, deep video analysis. Of course, Google, YouTube, you know, we're

quite far away from solving a time was driving problem too. I don't know. I think we're closer

to that. You never know. And the Wright brothers thought they're never they're not going to fly

for 50 years, three years before they flew. So what are the biggest challenges, would you say?

Is it the broad challenge of understanding video, understanding natural language, understand the

challenge before the entire machine learning community or just being able to understand

it? Or is there something specific to video that's even more challenging than understanding

natural language understanding? What's your sense of what the biggest challenge is?

I mean, video is just so much information. And so precision becomes a real problem. It's like

you're trying to classify something and you've got a million classes. And the distinctions

among them, at least from a machine learning perspective, are often pretty small. You need

to see this person's number in order to know which player it is. And there's a lot of players.

Or you need to see the logo on their chest in order to know which team they play for.

And so, and that's just figuring out who's who, right? And then you go further and saying, okay,

well, you know, was that a goal? Was it not a goal? Like, is that an interesting moment,

as you said? Or is that not an interesting moment? These things can be pretty hard.

So, okay, so Yan Likun, I'm not sure if you're familiar sort of with this current thinking

and work. So he believes that self, what is referring to as self supervised learning

will be the solution sort of to achieving this kind of greater level of intelligence. In fact,

the thing he's focusing on is watching video and predicting the next frame. So predicting

the future of video, right? So for now, we're very far from that. But his thought is because

it's unsupervised, or as he refers to as self supervised. You know, if you watch enough video,

essentially, if you watch YouTube, you'll be able to learn about the nature of reality,

the physics, the common sense reasoning required by just teaching a system to predict

the next frame. So he's confident this is the way to go. So for you, from the perspective of just

working with this video, how do you think an algorithm that just watches all of YouTube

stays up all day and night watching YouTube would be able to understand enough of the

physics of the world about the way this world works, be able to do common sense reasoning and

so on? Well, I mean, we have systems that already watch all the videos on YouTube, right?

But they're just looking for very specific things, right? They're supervised learning systems that

are trying to identify something or classify something. And I don't know if predicting the

next frame is really going to get there because I'm not an expert on compression algorithms,

but I understand that that's kind of what compression, video compression algorithms do,

is they basically try to predict the next frame and then fix up the places where they got it

wrong. And that leads to higher compression than if you actually put all the bits for the next frame

there. So I don't know if I believe that just being able to predict the next frame is going to be

enough because there's so many frames and even a tiny bit of error on a per frame basis can lead

to wildly different videos. So the thing is the idea of compression is one way to do compression

is to describe through text what's contained in the video. That's the ultimate high level of

compression. So the idea is a tradition when you think of video image compression, you're trying

to maintain the same visual quality while reducing the size. But if you think of deep learning from

a bigger perspective of what compression is, is you're trying to summarize the video. And the idea

there is if you have a big enough neural network by watching the next, by trying to predict the

next frame, you'll be able to form a compression of actually understanding what's going on in the

scene. If there's two people talking, you can just reduce that entire video into the fact that two

people are talking and maybe the content of what they're saying and so on. That's kind of the open

ended dream. So I just wanted to sort of express that because it's an interesting compelling notion, but

it is nevertheless true that video, our world is a lot more complicated than we get credit for.

I mean, in terms of search and discovery, we have been working on trying to summarize videos

in text or with some kind of labels for eight years, at least. And we're kind of so so.

So if you were to say the problem is 100% solved and eight years ago, was 0% solved?

Where are we on that timeline, would you say?

Yeah, to summarize a video well, maybe less than a quarter of the way.

So on that topic, what does YouTube look like 10, 20, 30 years from now?

I mean, I think that YouTube is evolving to take the place of TV. I grew up as a kid in the 70s

and I watched a tremendous amount of television. And I feel sorry for my poor mom because

people told her at the time that it was going to rot my brain and that she should kill her

television. But anyway, I mean, I think that YouTube is, at least for my family,

a better version of television, right? It's one that is on demand. It's more tailored to the things

that my kids want to watch. And also, they can find things that they would never have found on

television. And so I think that at least from just observing my own family, that's where we're

headed is that people watch YouTube kind of in the same way that I watched television when I was

younger. So from a search and discovery perspective, what are you excited about in the 5, 10, 20,

30 years? It's already really good. I think it's achieved a lot of, of course, we don't know what's

possible. So it's the task of search of typing in the text or discovering new videos by the next

recommendation. I personally am really happy with the experience. Continuously, I rarely watch a video

that's not awesome from my own perspective. But what else is possible? What are you excited about?

Well, I think introducing people to more of what's available on YouTube is not only very important

to YouTube and to creators, but I think it will help enrich people's lives. Because there's a lot

that I'm still finding out is available on YouTube that I didn't even know. I've been working

YouTube eight years, and it wasn't until last year that I learned that, that I could watch

USC football games from the 1970s. Like, I didn't even know that was possible until last year. And

I've been working here quite some time. So, you know, what was broken about, about that, that it

took me seven years to learn that this stuff was already on YouTube, even when I got here. So I

think there's a big opportunity there. And then, as I said before, you know, we want to make sure

that YouTube finds a way to ensure that it's acting responsibly with respect to society and

enriching people's lives. So we want to take all of the great things that it does and make sure

that we are eliminating the negative consequences that might happen. And then lastly, if we could

get to a point where all the videos people watch are the best ones they've ever watched, that would

be outstanding too. Do you see, in many senses, becoming a window into the world for people?

It's, especially with live video, you get to watch events. I mean, it's really, it's the way you

experience a lot of the world that's out there is better than TV in many, many ways. So do you see

becoming more than just video? Do you see creators creating visual experiences and virtual worlds?

So if I'm talking crazy now, but sort of virtual reality and entering that space,

or is that, at least for now, totally outside what YouTube is thinking about?

I mean, I think Google is thinking about virtual reality. I don't think about virtual reality too

much. I know that we would want to make sure that YouTube is there when virtual reality becomes

something or if virtual reality becomes something that a lot of people are interested in. But I

haven't seen it really take off yet. Take off. Well, the future is wide open. Christos, I've been

really looking forward to this conversation. It's been a huge honor. Thank you for answering some

of the more difficult questions I've asked. I'm really excited about what YouTube has in store

for us. It's one of the greatest products I've ever used and continues. So thank you so much for

talking to it. It's my pleasure. Thanks for asking me. Thanks for listening to this conversation.

And thank you to our presenting sponsor, Cash App. Download it, use code LEX Podcast.

You'll get $10 and $10 will go to FIRST, a STEM education nonprofit that inspires hundreds of

thousands of young minds to become future leaders and innovators. If you enjoy this podcast,

subscribe on YouTube, give it five stars on Apple Podcasts, follow on Spotify,

support on Patreon, or simply connect with me on Twitter.

And now, let me leave you with some words of wisdom from Marcel Proust. The real

voyage of discovery consists not in seeking new landscapes, but in having new eyes.

Thank you for listening, and I hope to see you next time.