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Comment by cycrutchfield | Hacker News Reader

If the team can’t even beat random, then I think that says more about your team (or perhaps your features) than about ML as a whole.

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Today ML can solve some problems. In the future it might solve some problems with advances in the field. Yet other problems will likely remain unsolved, such as the stock market, or the weather, or predicting /dev/rand

"Up Next" problem can easily fall into any of the three buckets.

YouTube's "Up Next" recommendations do (significantly) better than random, therefore "Today ML can solve some problems".

>YouTube's "Up Next" recommendations do (significantly) better than random, therefore "Today ML can solve some problems".

IMO YT AI is the opposite of intelligent , it still recommends things I disliked. for some reason this basic rule of not showing something that I explicitly disliked was to hard for it to learn, I am wondering if it is truly an AI behind it or just statistics

It's intentional. Controversy is a strong signal for the youtube algorithm.

I don't think so, the videos I was referring were music videos. I engaged with it in a way but hitting dislike on a music genre I don't want to listen(I normally don't dislike things because is not my genre) in the hope the algorithm will learn but it was even worse, it did not learned that I disliked artist X and genre Y it continue to play the exact video that I disliked,

The bad algorithm will force the unhappy user to use manually created playlists leaving less people to engage with the algorithm and probably have the algorithm getting worse in time as more users will avoid it

And even more interesting trying to google "how to make youtube not show X" it is a complete fail, it will just show you youtube video results.

If the goal is simply to maximize engagement, there is no hard requirement that the algorithm should never show you things that you dislike. Essentially, what I am saying is that your belief of what their objective function is may be different from their actual objective function, and that is in no way an indication that their model is a failure.

Isn't AI statistics?

The modern AI/ML is more like we throw a lot of data and we generate a model, we know that it works but we have no idea how and why.

I'm pretty sure it does better than 'next video = random(from all of YouTube)' would, but would it be much better than 'next video = random(videos with the same subject or tags as the one playing now)'?

Yes. They've published quite a number of papers on their recommendation algorithms. You can take a look and decide for yourself whether it's snake oil or not.

Or not enough communication with the team, discussing what the objectives are, providing them with good, enough, relevant data to work with etc. I guess in many cases a data science team is expected to just "do their magic", build the AI and then come back and meanwhile not bother anyone else. In other cases, nobody really cares anyway, they just want the buzzword label to be includable in the brochures.

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