Granted, there have been some issues with people getting banned from Google services for what appear to be wrong reasons and then not being able to get any kind of a human response. Those aren't great, but if you ask people whether they want to pay $10/month for YouTube so human moderators can stop this from happening or get it for free and have the .0001% chance that they get banned purely by accident, they'll take the latter. And that's putting aside the fact that there will still be errors with humans doing it, and there will still need to be the same appeal process to resolve those.
And that means you can't just say "If you don't like it, leave, it's a free service", because it's not that simple, as leaving in reality means "give up on your business", and most of people just can't afford that. Youtube has them cornered and thus needs to be careful, since it's not a game, it affects people's lives. For many accidental blocking of channel can mean they'll be living in their cars until the Gods of Google show mercy and fix the error.... and if they do it, since sometimes you don't even get a meaningful reply on what happened, just a template message.
The cost to a competitor is in getting a user to switch. Think of it in terms of paying users: how much would you have to pay someone to switch from Youtube to your site? Multiply the average by the number of users, and that's the advantage Youtube has over competitors.
The reason I ask is because, despite all its flaws, YouTube is one of the treasures of the internet. And I wonder if we'd lose it by breaking up Alphabet.
I am not pro-Alphabet and 100% believe Alphabet needs more regulation, and quite frankly does need some trust busting, but I'd be really sad to lose YouTube. Yeah, there's vimeo but it's clearly not a direct competitor. I also fondly remember stage6 (https://en.wikipedia.org/wiki/Stage6).
However as I understand it the product itself isn't tied to the Google infrastructure. YouTube's main value is the user base, both of viewers and of publishers. It would be a technical marvel to move it off of Google's infrastructure however I don't think that there is any feature that they wouldn't be able to provide anymore. It would certainly be more expensive, but at YouTube's size you could probably work out similar deals with other providers (or with GCP) so I'm not sure that the prices would rise that much. (IDK, maybe 15% increase in cost between provider cut and raw cost increase?)
> The cost to a competitor is in getting a user to switch.
Only if we're assuming that users and uploaders will only ever use exactly one service at any given time. No reason why that needs to be the case; nothing stopping people from uploading to YouTube and Vimeo and Twitch and DailyMotion and PornHub and LBRY and PeerTube and whatever other platforms, and nothing stopping people from viewing from those platforms, either.
The cost to enter the video sharing market is extremely low, especially in this day and age where you can rent computing and storage capacity on the cheap around the world.
The problem here is in the network effect, not in scarcity.
Suppose YouTube disappeared tomorrow. Where would everyone go? Probably odysee. So why don't they go there now? That's why Youtube is a natural monopoly.
I named multiple said competitors. And they seem to get plenty of traffic themselves.
> So why don't they go there now?
Do people know about odysee? First I've heard of it (to my recollection at least).
I mean , YouTube is huge, but we had moderation-at-scale before ML was a popular phrase to bandy around.
'ML' and 'Algorithms' are just convenient non-human bogeyman that any corporation can pin all their woes on without batting an eye right now -- a convenient explanation tool for when things go wrong.
I am going to take a bold stance with this next statement:
An algorithm cannot be at fault -- it is always the fault of those that implement such algorithms.
YouTube and their community is huge -- YouTube has revenue fifteen times greater than Yahoo. 'Too big to X' should not be a tolerable excuse from any industry -- especially so since YouTube/Google has been pushing the idea for years that being a YTer is an actual profession.
Imagine for a moment coming into work and being told that you're fired because an internal corporate policy checker decided that something you did in your past work history was worth of termination; and then 24 hours later being told to continue coming into work because a mistake had been made, but with no further explanation.
This kind of 'workplace' would be intolerable in any real 'work environment'. Just more evidence that YTers are not really treated like any other partner or employee anywhere else in the world -- and that they should probably curtail that language if they want expectations to match the reality that their 'employees' experience.
>People forget that they get these free services for free,
YouTube is not a free service. This fallacy needs to go.
To anyone still not convinced, YouTube takes 45% of all ad revenue generated on videos that YouTube had no part in creating.
Your work comparison just isn't valid because there is no employer with a number of employees within several orders of magnitude of the number of videos on YouTube.
I feel like it's the exact opposite: we're hearing about them because these are the ones that slipped into public awareness. The inadequacy of automated moderation without any real semblance of human oversight has been rampant and frequent for about as long as the concept has existed.
This would be true if youtube/google created the content as well. Youtube essentially gets the content "for free"... and then decides what to do with.
Also, I'd be really interested to know how much the Youtube team payed to build this classifier and how that compares to paying human moderators.
At this point, I view YouTube like I viewed bars that allowed smoking--it was sufficiently popular that nobody could compete against it even though it was harmful. So, we needed legislation to move us to a different local minimum.
And, I find this very painful because I firmly believe that smoking bans have killed live music. But I also understand that nobody was ever going to do the right thing while doing the bad one was so profitable.
What's the connection?
It’s an extreme claim with very little rigor.
By almost all measures, it is. Number of artists, average age of artists, revenue, number of customers, etc.
> as opposed to recorded music becoming way more accessible through technology.
Except that by most measures the general public consumes VASTLY less music than they used to, so it isn't accessibility driving it.
Now, you could suggest that it's because of a bunch of changes: video games, social media, etc. That would at least be plausible.
Compared to when? 1800? 1970? 2010?
You can have a small venue (<100) and it will trundle along. You can have a big venue (>1000) because it probably reached self-supporting.
However, we lost a LOT of 100-1000 size venues from about 1990 to about 2005. And those are the ones that working musicians can make a living off of. But those need the stupidly profitable contingent that goes along with smoking and drinking. The single craft beer drinker isn't going to generate enough money to support such a place.
You could see this even before the smoking bans in areas which had sophisticated dancing (swing, ballroom, etc.) groups. If your venue somehow attracted the young dancing contingent, the venue was going to vaporize within 6 months. Everybody loved them--they tended to be polite, didn't harass the waitstaff, looked good, prevented highly skewed M/F ratios, etc. -- except that they spent next to no money compared to the general public so the owners HATED them. This was in stark contrast to the elder dancing contingent who smoked and drank like fish and could keep clubs alive long beyond their expiration date.
Of course, all venues make sure to have outside areas for smoking, because that's the sensible thing to do.
Although, if you say Nashville or New York, you're not helping--those are mega anomalies and all the musicians are already there which makes the situation untenable.
Side Note: WTF, people? Why downvote this person?
However, I am concerned that a lot of these smaller venues and promoters will struggle to survive the pandemic lockdowns, unless our governments pull themselves together and support cultural venues, instead of focusing so hard on sports. Culture isn't just something you play with a ball :-)
As for the downvotes, probably general disagreement or spillover from other discussions where someone has taken offense to what I wrote.
Same in pubs. The smoking-and-drinking crowd got edged out by parents who for some reason want to bring their kids to the pub. They will have brought their own drinks and snacks for the kids too. The mother will have a small glass of wine and the father will drink half a pint of "craft beer", stretched out over a couple of hours while their kids run around screaming and annoying the few remaining paying drinkers.
I loved pubs in the old days but even before COVID they were dying out because they just don't want their loyal paying customers any more.
I’ve yet to see a case of the hoi poloi get a manual review - you either are a well enough known YouTuber where they have humans dedicated to you, you know someone who works at YouTube and can intercede on your behalf, or you have to whip up a Twitter mob big enough that it hurts YouTube's image or stirs up people with contacts at YouTube.
Even then, sometimes that doesn’t work - look at the Terraria dev.
YouTube effectively has no manual review for 99.9% of people which is why its automated systems accuracy is so important.
As of 2018 youtube had 10,000 manual reviewers (and I imagine it's significantly more than that today), but at their scale, that realistically won't go that far.
As such arguments about using automated systems are meaningless, they can pre-filter without having final decision making authority.
You’re off by orders of magnitude.
500 * 60 * 24 * 365 = 262800000 hours per year / 1.3x playback speed / 2,000 hours of work per person per year = ~101,077 people to watch every uploaded video.
Less if people are watching at higher playback speeds or working significantly more than a 40h week. I would personally be surprised if even 1 in 1,000 videos where flagged by ML at which point you’re talking ~100 people. Though that would be a truly terrible job.
When you have people watch the video at that fast speed and instantly make a decision, you might as well keep the algorithm - the failure rate won't be too different.
Additionally, these people need holidays, managers, infrastructure and an actual flag will probably not instantly be clear - you'll need to point out the specific offence and make a case. You might even need to look things up. Then you'll have to handle appeals and discussions, because changing the AI blackbox to an appeal-less human black box wouldn't improve the situation much[0]. Next, you need people who can understand the specific language of that video - most will be english, but what happens when a Nigerian video is flagged? So you need people from that country or at least familiar with it. Plus infrastructure.
Overall, you're probably looking at something approaching 400 or 500 people, with this very low flag rate. Assuming conservative 30k$/year [1], this is 15 million USD alone. Doable, yes, but its not the no-brainer you make it out to be.
[0] See this case with FB, which was a human error: https://www.nbcnews.com/tech/social-media/facebook-under-fir... [1] Which is probably optimistic for reviewers, but we'll have managerial infrastructure to pull that average up.
Reading the article, "human error" really isn't a reasonable description of what happened:
> The Facebook furor began when the social network deleted the famous photo from Norwegian author Tom Egeland’s Facebook page, where it was part of a series of memorable wartime imagery.
This could be a human error, sure.
> When Egeland subsequently posted his shocked reaction to the removal of the “napalm girl” photo, he found his account suspended.
But not this.
> Norway’s largest newspaper, Aftenposten, published Egeland’s story on the censorship, only to find that its own Facebook posts were also quickly deleted.
Definitely not this.
> Espen Egil Hansen, Aftenposten’s editor, then took to the front page of his paper to slam Facebook in an open letter to CEO Mark Zuckerberg
> Prime Minister Solberg joined the debate on Friday, only to find that her comments and posts about the suppressed photo were also deleted by Facebook.
This is a (ridiculously severe) problem in several of Facebook's policies, not a classification error.
I'm pretty sure to have read that the initial blocking was done by a contractor - I can find a better source, if you want.
> This is a (ridiculously severe) problem in several of Facebook's policies, not a classification error.
Yes, and that is exactly the point I tried to make:
>> changing the AI blackbox to an appeal-less human black box wouldn't improve the situation much
Said another way, having underpaid contractors with no time for consideration (and possibly strange policies) is exactly as bad. This is a pretty clear case: That photo should not have been blocked and the idea behind having a human do the evaluation is that he recognizes the historical relevance and, if not outright allowing it, at least consults the relevant authorities whether this is exempt. This could've been done by an AI all the way without a different outcome.
Only averaging 1.3x assumed significant overhead and going back to re listen to segments, have an escalation queue for flagged videos, and randomly assigning multiple people to the same videos to ensure people are paying attention not just playing solitaire while the video is on. Plus other random crap, in other words ~30% overhead.
Google already has more than 10k people working on this: https://blog.youtube/news-and-events/expanding-our-work-agai... . If 100 people solved the issue, it would have been solved.
You are incorrect about your assumptions in many respects. Let me list out a few:
1) There is a big difference between watching a 15 min video at 2X for fun vs watching 8 hours of videos a day while having to follow laid down policy with complexity. Videos are not taken down only for one reason and there is a lot of complexity involved with edge cases. Humans are not robots and this is not a task we are inherently good at. Give it a shot yourself for a day and see how easy it is.
2) Your overhead does not consider any additional complexity in terms of languages, specialty, regional expertise etc. This is not a simple problem either. Sure maybe you can hire 1000 reviewers in lets say Indonesia but you cannot find a native Kswahili speaker there. Its not cheap to setup an office for 2 people in Kenya. Scale this to the world which has 193 countries and 6,500 languages.
3) People dont work without any management overhead. You need frontline reviewers. Then you need a layer of experts above them. The you need managers to take care of the operations. The managers to manage those managers. Then recruiters to hire those people. Then HR to deal with their issues.
4) Turnover is a big deal. Very few people in the world can watch beheadings 40 hours a week. An even smaller proportion can handle child safety material. What happens when those people need to take time off? You need additional people. If you want a humane operation then you might have to get them to work only 2 hours a day.
Here is some more reading if you are interested in understanding this from the moderators / reviewers view: https://www.theverge.com/2019/12/16/21021005/google-youtube-...
It continues to amaze me how some of the smartest and technically savvy people on HN either do not recognize or refuse to admit how complicated a problem planet scale moderation is.
It's probably a variation of the trivial-core-problem [0]. When looked at roughly, it seems very easy; only when you start implementing it, you'll see all the edge cases appear. I made pretty similar points above to the ones you made - but on a quick napkin calculation, Retric's math checked out, to be fair. And when you're not in the business, missing the inherent complexity is easy.
“Since we started using machine learning to flag violent and extremist content in June, the technology has reviewed and flagged content that would have taken 180,000 people working 40 hours a week to assess.”
That said, I am surprised Google isn’t using machine translation for obscure languages. Props to them that’s going above and beyond in my personal opinion.
- Ultimately, any supervised technique will depend on some system that can provide ground truth, which in these applications must be from humans. You can't avoid some humans in the loop and still learn a fuzzy human concept like "is hate speech". But if your aren't gathering that ground truth info in a very deliberate way, you can end up with a range of gaps or artifacts.
- There's a broad swath of technical approaches which would consider multiple kinds of information, but ensemble them together such that a very strong signal from one can have a determinative impact. It would be easy to have a situation where if the video itself talks about white/black and attack/threat/defend many times, the score from some language model into a top level ensemble model is very high and the other components may not matter.
- But a lot of those other components may often be in agreement because of natural structure. A video says black/white a lot, several other videos on the channel do the same, and a bunch of interested users also look at other channels that have very similar feature vectors, suddenly a content level, channel level and community level signal are all in agreement, and your model can believe it found a subcommunity of hate speech.
Incorporating many kinds of info into an ML model isn't that hard ... But doing so in a way which is tightly integrated (eg I'm likely to use a specific meaning of a term in my video which matches a use in a video of a related channel linked in my video description, and that use of that word is also related to what's visible in the video at around that time) is pretty hard. Doing so in a way which distinguishes predictions from confidences is harder.
All of which is just to say that it's hard to build a really good black box for a complex problem, especially one based around subjective human concepts, and maybe both we and the black boxes would do better by attaching more uncertainty to their predictions.
If it's worth the channel owner spending many hours making videos, and viewers spending many many more watching them, I think it's not crazy to ask that if the model predicts that it's likely disallowed content, that a manual reviewer can take a quick look.
It seems more than likely somebody put in a very crude black box to process text without context. (Or should I say, opaque box, so the horde doesn't flag me?)
Obviously, just watching every video in real-time a single time is not an adequate review process. Things can be missed, misunderstood, or just take more time to process, so it is likely that more review would be necessary for non-obvious cases. But, on the other hand, it is not necessary to review literally every second of every video. If they only reviewed reported videos and reports were required to specify the timestamp, they would probably instantly reduce this total burden by a factor of 10x-100x (assuming they filter out obvious spam reports). This would allow them to allocate more independent reviewers to the actual non-obvious cases resulting in reasonable accuracy with a human appeals process while reducing their overall cost burden significantly lower than the previously stated ~50%. They could then tier their review process based on the amount of views to allowing them to limit how many resources they spend on unpopular and irrelevant videos to focus on more visible and profitable videos while still providing a base level of review. And, all of this is assuming that they do not have any automated mechanism to directly decide on cases that are trivially obvious, thus reducing their burden even lower to only the contentious cases where the automated systems can not create a clear-cut answer. Frankly, I could go on about various other potential mechanisms they could do to reduce costs while increasing efficiency and avoiding bad actors, but what I have already said should already be sufficient to handle the basic problem.
All in all, they could probably institute a reasonable human review process that could handle all reasonable cases for <5% of yearly revenue and frankly I would be shocked if it were anywhere near the high end of that estimate. Even without any automated systems I would put my median cost prediction at 1% of yearly revenue and with even basic automated systems probably close to 0.2%.
In addition you do not factor in significant complexities like the ones below:
You are incorrect about your assumptions in many respects. Let me list out a few:
1) There is a big difference between watching a 15 min video at 2X for fun vs watching 8 hours of videos a day while having to follow laid down policy with complexity. Videos are not taken down only for one reason and there is a lot of complexity involved with edge cases. Humans are not robots and this is not a task we are inherently good at. Give it a shot yourself for a day and see how easy it is.
2) Your overhead does not consider any additional complexity in terms of languages, specialty, regional expertise etc. This is not a simple problem either. Sure maybe you can hire 1000 reviewers in lets say Indonesia but you cannot find a native Kswahili speaker there. Its not cheap to setup an office for 2 people in Kenya. Scale this to the world which has 193 countries and 6,500 languages.
3) People dont work without any management overhead. You need frontline reviewers. Then you need a layer of experts above them. The you need managers to take care of the operations. The managers to manage those managers. Then recruiters to hire those people. Then HR to deal with their issues.
4) Turnover is a big deal. Very few people in the world can watch beheadings 40 hours a week. An even smaller proportion can handle child safety material. What happens when those people need to take time off? You need additional people. If you want a humane operation then you might have to get them to work only 2 hours a day.
Here is some more reading if you are interested in understanding this from the moderators / reviewers view: https://www.theverge.com/2019/12/16/21021005/google-youtube-...
It continues to amaze me how some of the smartest and technically savvy people on HN either do not recognize or refuse to admit how complicated a problem planet scale moderation is. If it was a problem that could be solved by spending a few tens of million dollars a year, it would have been solved.
You actually want the algorithm to be a black box so that people don't know that you can trick it by only commenting between 5pm and 8pm on an IP in the Netherlands from Chrome, and avoid using words like "cactus."
The important part here is a transparent appeal process, not that ML is only pretty good at these problems.