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> best solution is to simply use actual humans Gee, how many humans might it take to review the >400 hours of video uploaded every minute? (source: https://www

> best solution is to simply use actual humans Gee, how many humans might it take to review the >400 hours of video uploaded every minute? (source: https://www.tubefilter.com/2015/07/26/youtube-400-hours-content-every-minute/ https://www.tubefilter.com/2015/07/26/youtube-400-hours-cont...)

Hussell 8y ago
400 hours/minute is 24,000 minutes of video per minute, or 241,920,000 minutes of video per week. Assuming a human can review 8 hours of video per day, 5 days a week (too high, but not by much), and that YouTube engineers can create an algorithm that automatically marks 95% of video (probably low, 95% correct labelling is easy to get; 99% is usually necessary to outdo humans), then you would need 5,040 humans. Automatically labelling 97.5% would halve that. If employees can only review 6 hours of video per work-day, then it would increase by a third. Both, you'd need 3,360 humans.
- mdorazio 8y ago
  And multiple that by let's say 50K/year fully burdened cost per employee (assuming locating them in low-cost areas) and we've now added a minimum of $125 million per year in overhead for a service that already probably doesn't make much money.
  - sinatra 8y ago
    > we've now added a minimum of $125 million per year in overhead Which is <1% of the revenue that YouTube generated last year? Still a meaningful number. But, not unmanageable.
    - mdorazio 8y ago
      At what profit margin, though? I've never seen actual YouTube numbers broken out in a financial statement.
- anonymousab 8y ago
  >and that YouTube engineers can create an algorithm that automatically marks 95% of video (probably low, 95% correct labelling is easy to get; 99% is usually necessary to outdo humans) I think a crucial question is whether the numbers quoted are before or after the current set of automated filtering (content id and any existing mechanisms to catch bad, illegal or offensive content.) They may already be catching the vast majority of easy and fingerprintable things. They may be at the barrier where false positives increase at a high rate.
- canada_dry 8y ago
  > you'd need 3,360 humans Likely this number would decline rapidly over time - i.e. months vs. years - as the ML would use the human tagging to improve itself to the point of reaching the 99% threshold fairly quickly.
JudgeWapner 8y ago
why are you falsely assuming it all needs to be reviewed? it only needs to be reviewed if it's flagged. each additional flag on the same vid should double the likelihood that the material is objectionable. The question is how much video is actually flagged, and what percentage of those reports are false? Even that's an easy problem to solve, because if the same accounts keep flagging clearly-not-objectionable content they can just be muted.
- canada_dry 8y ago
  > why are you falsely assuming JudgeWapner: 8 days and -2 karma. Nice track record so far. Edit: I got down voted for calling out a troll? Oy.

Hussell 8y ago

400 hours/minute is 24,000 minutes of video per minute, or 241,920,000 minutes of video per week. Assuming a human can review 8 hours of video per day, 5 days a week (too high, but not by much), and that YouTube engineers can create an algorithm that automatically marks 95% of video (probably low, 95% correct labelling is easy to get; 99% is usually necessary to outdo humans), then you would need 5,040 humans. Automatically labelling 97.5% would halve that. If employees can only review 6 hours of video per work-day, then it would increase by a third. Both, you'd need 3,360 humans.

mdorazio 8y ago
And multiple that by let's say 50K/year fully burdened cost per employee (assuming locating them in low-cost areas) and we've now added a minimum of $125 million per year in overhead for a service that already probably doesn't make much money.
- sinatra 8y ago
  > we've now added a minimum of $125 million per year in overhead Which is <1% of the revenue that YouTube generated last year? Still a meaningful number. But, not unmanageable.
  - mdorazio 8y ago
    At what profit margin, though? I've never seen actual YouTube numbers broken out in a financial statement.
anonymousab 8y ago
>and that YouTube engineers can create an algorithm that automatically marks 95% of video (probably low, 95% correct labelling is easy to get; 99% is usually necessary to outdo humans) I think a crucial question is whether the numbers quoted are before or after the current set of automated filtering (content id and any existing mechanisms to catch bad, illegal or offensive content.) They may already be catching the vast majority of easy and fingerprintable things. They may be at the barrier where false positives increase at a high rate.
canada_dry 8y ago
> you'd need 3,360 humans Likely this number would decline rapidly over time - i.e. months vs. years - as the ML would use the human tagging to improve itself to the point of reaching the 99% threshold fairly quickly.

JudgeWapner 8y ago

why are you falsely assuming it all needs to be reviewed? it only needs to be reviewed if it's flagged. each additional flag on the same vid should double the likelihood that the material is objectionable. The question is how much video is actually flagged, and what percentage of those reports are false? Even that's an easy problem to solve, because if the same accounts keep flagging clearly-not-objectionable content they can just be muted.

canada_dry 8y ago
> why are you falsely assuming JudgeWapner: 8 days and -2 karma. Nice track record so far. Edit: I got down voted for calling out a troll? Oy.