[maksimum]

Facebook (and Google and others) can easily identify themes in natural language both written and spoken. They're really good at those problems because they're used as inputs for ad sales.

Identifying themes in video is harder than natural language. But fundamentally it requires the same kind of ML tools as natural language, which these companies have already mastered. I think the bigger issue is that Facebook doesn't have a business purpose for understanding video as compelling as it has for understanding natural language. They also don't have a business purpose for building scalable content censoring workflows since there's no serious regulation.

[o10449366]

I don't know how you came to these conclusions, but they're fundamentally incorrect. Video has been one of Facebook's fastest growing advertisement categories in recent years, particularly after their acquisition of Instagram. They absolutely have a business purpose for understanding video and if they didn't they wouldn't have invested so heavily in it with features like livestreaming. Furthermore, I don't know where the idea of government regulation as an incentive came from in this thread, but it's illogical and ridiculous. Facebook doesn't need an incentive to try and prevent objectionable content like mass murders from appearing on their platform because they're well-aware of the damage they can cause their brand. That's incentive enough.

[manfredo]

Yes they can identify themes, but not nearly with enough accuracy to make policy enforcement decisions. IIRC the preemptive video blocks were based on fingerprinting previous video uploads. They're still a long way away from being able to automate policy enforcement dynamically.

[profmonocle]

Would sample size be an issue? An algorithm can recognize that a video is about someone discussing the latest Marvel movie because there are a lot of those types of videos.

But can an algorithm recognize that a video is of someone committing a real-life atrocity? Those are comparatively rare.

[antidesitter]

It’s clear you have very little understanding of what is feasible with our current state of knowledge.