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How to recognize AI snake oil [pdf]

cs.princeton.edu

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Re: How to recognize AI snake oil [pdf]

#361

I don't have time to read the entire paper but I would like to share an anecdote. I worked at a company with a well staffed/funded machine learning team. They were in charge of recommendation systems - think along the lines of youtube up next videos. My team wanted better recommendations (really, less editorial intensive) so the ML team spent weeks crafting 12 or more variants of their recommendation system for our c…

excuse me for butting into dead conversation, but your team used ml to inapplicable case. (at least they aсquired experiense).

up next recommendations wont work without advanced image recognition and topics gathering - basically titles/tags for most videos are garbage and clickbait, and most of youtube work by watching buzzed videos: some well known (by a big amout of watchers) "influencers" push video of some topic (thing/brand) then it get traction from other content creators - they produce videos about it and watchers tend to stick to buzzed topics. it's like news about news.

if your team used ml to recommend up next on your own video hosting your result simply means your videos are equally not on topic Or non-interesting for your service auditory; or they are garbage.

Re: How to recognize AI snake oil [pdf]

#363
post #285

Earlier quoted context omitted.

Right? Recommenders are almost counter productive for me in most cases. I want a recommender to remain broad, not give me increasingly niche recommendations a la youtube. About the only halfway decent recommender system I've interacted with is the various forms of curated lists on Spotify. They seem to actually take a decent stab at it with related but sufficiently different and interesting content. For all Facebook…

> For all Facebook knows about me they've always been exceptionally bad at advertising to me Because it's not Facebook really, it's the advertisers who choose targeting criteria. You as an advertiser have a myriad of options. For example if you've built a competitor to X, you can target users who've visited X recently, aged N-M, residing in countries A, B and C, and so on. There are options with broader interests too…

But they are also really really bad at curating content I like to see. Like even worse than the advertising actually. My feed is just garbage and has been ever since they switched over from being chronological. But I sometimes get on a wild hair about some particular person, and I'll look at their page directly and see actually interesting content on their page that was never shown to me. Facebook's only real guess is then to just say: oh! You must like this person, let me show them, all the time.". The reality is, I'm interested in stuff like when a biased person I don't like shares a more neutral or inclusive opinion, or someone does something interesting. Facebook is just unuseably bad at selecting for that. Their algorithm just pushes really shallow crap and buries anything with substance or depth. I keep it around just to stay in touch with hard to contact people.

Re: How to recognize AI snake oil [pdf]

#364

Earlier quoted context omitted.

Right? Recommenders are almost counter productive for me in most cases. I want a recommender to remain broad, not give me increasingly niche recommendations a la youtube. About the only halfway decent recommender system I've interacted with is the various forms of curated lists on Spotify. They seem to actually take a decent stab at it with related but sufficiently different and interesting content. For all Facebook…

What I've always assumed, but now this thread has me doubting myself, but what I've assumed is that these systems, even though they appear to suck at specifically targeting me , must somehow be pretty good on the average , still netting big profits overall even though they don't seem to live up to the promise of getting me to buy stuff. But if everyone has this impression, maybe it doesn't work? I mean, I assume comp…

I think you've got the nail on the head. It's sort of the tragedy of the commons, which in terms of recommenders is a really easy thing to accidentally optimize for. For example, pop music is popular. In the early days, recommenders would just recommend pop music because on average that was a decent recommendation. We've come a long ways since then. Well, everyone but Facebook has.
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