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
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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 one 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 day 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, 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 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
helps you understand linear algebra, but in a way that would be very hard for anyone to do on a white
board 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 I used to watch certain shows on YouTube. I don't anymore. I've moved on
to other shows. And ultimately, you want people 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 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, is 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 a thing like that's almost impossible to define. 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 use for that is we basically cluster videos and channels
together, mostly videos. We do almost everything at the video level. And so we'll make some kind
of a cluster via some embedding process. And then, and then measure, you know, what is the
likelihood that users who watch one cluster might also watch another cluster that's very
distinct. So we may come to find 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 clustering, the embedding space of
jazz videos and science videos. And so you kind of try to look at aggregates, just kind of
days 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 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 rail
roads 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, no, I can't believe I'm going there, but politics. 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 vine 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
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 possibly any kind of ideas, in terms of filtering what people
should watch next, in terms of also not letting certain things be on YouTube. This is exceptionally
difficult responsibility. 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. 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 border
line. 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. So we're not trying to... I mean, you mentioned Iran and communism.
You know, 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.
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 it 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. 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 now. 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, what do you mean limiting sort of curating speech? I mean, I think that that
overall is our whole project here at YouTube. 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. 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 writing 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 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 are the ideals that
we should be promoting? That kind of thing. So algorithm versus human 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,
you know, 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
things have a bias in some kind of direction? Sort of, 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? You know, even in the sense that they're
better than, 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
the cement, 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, like 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, you know, 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 observed 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
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. 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 more number of cat 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 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 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?
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
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, right? 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 move from views 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 2 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 of those things, all of 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 subscribe 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 subscribe 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. But 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. So 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 and 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 478,
you know, A team versus B team. And World of Warcraft wasn't in the title. I just like, come on,
give me. But being literal, being literal on the internet is actually very uncool.
Which is the problem. Oh, is that right? Well, I mean, in some sense,
well, some of the greatest videos, I mean, there's a humor to just being indirect,
being witty and so on. And actually, being, you know, machine learning algorithms want you to
be, you know, literal, right? 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, right?
So, but you're saying that for now sort of the content of the title, the content of the description,
the actual text is one of the best ways to, for the, for the argument to find your video and put
them in the right cluster. That's right. And, 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, 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, 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? Like, 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 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, 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 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. Yes. Right? Yeah. 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
Pierro de Freddy, 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 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 it's all caps with too many exclamation points,
we observe that users are kind of 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. Just saying, I didn't, something like I don't want to see this video
anymore or something like this is, like there's certain videos that just cut me the wrong way,
like just jump out at me and 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. 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. Cause 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, you know,
I must 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
through all together creating a beautiful thing. YouTube is doesn't always remind me of that.
It's, 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, 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 there's a, I don't know if that's human nature, but you want
the products you love. And I certainly love YouTube. Like you want to help it get smarter,
smarter, smarter. Cause there's some kind of coupling between our lives together being better.
If YouTube was better than I will, 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, 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 from for you than it is for me, right?
And 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. 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 watch 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 worth anybody seeing it really, right? And so, you know, there are some creators who say,
like, I wouldn't have this opportunity 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 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 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, 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 recommendation
is 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. And so that has made me realize that YouTube is really about the
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 Ray Dalio video that the video itself is incredible. But there are some
people who are just creators that I love. One of the cool things about people who call themselves
YouTubers or whatever is they have a journey. They usually almost all of them 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,
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, you know, some other thing happening, then it's
hard for me to, to when I'm not watching YouTube say, gosh, I really, you know, would like to see
something from someone or about something, right? And so that's why I think this connection between
fans and creators so important for both because it's a way of sort 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 community. But some people
feel like this momentum and even when there's times in their life when they don't feel, you know,
that for some reason don't feel like creating. So how do you think about burnout, 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 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
seconds? 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 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 that 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 is not the single equation. It's, 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, you know, 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 know, you start trying to solve a problem and the first way to solve a
problem is often with a simple heuristic, right? And, you know, 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 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, 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 across which time scale, across how many
users, 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? First off, 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 something's going viral, it's good to just let people ride the wave of its
virulence. 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 of months back.
In fact, 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, he 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, 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 in the, 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 going to
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 and 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 and like the coolest points and actually
have explicit videos. Like I'm re-uploading 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 has that just done 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 or 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 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 do you know if from your recollection, do you know if the thumbnail reflects, like,
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
emotional 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 very
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,
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. Yeah. 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 the autonomous 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, understanding 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 know, 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, right? Like, you know, 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, you know, the logo
on their chest in order to know, like, 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 Likung, I'm not sure if you're familiar sort of with his 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 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, will 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 air 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 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, 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.
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. 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
on YouTube eight years, and it wasn't until last year that I learned that I could watch
USC football games from the 1970s. I didn't even know that was possible until last year, and I've
been working here quite some time. So what was broken 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, 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,
I'm talking crazy now, but sort of virtual reality and entering that space. There's 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
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