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I see the article more as a broader metaphor for the AI hype. Take, I dunno, video recommendation. Sure, YouTube itself proably built insane stuff in their eng

I see the article more as a broader metaphor for the AI hype. Take, I dunno, video recommendation. Sure, YouTube itself proably built insane stuff in their engine you could never replicate with classic methods (ignoring whether the YT algo is any good). However, if we are just talking about the Vlog of your real estate company, you should probably A/B-test whether your viewers prefer order by time or clicks and implement a decent title search bar. And kick the consultant hyping you up about ML out, now. So my takeaway is that is not about whether AI is never useful or about 2D steering, but about using the right tool for the right job. And building on that, I have to give the author probs to demonstrating an alternative solution to I problem wich I would have definitely solved via AI.

mumblemumble 6y ago
Video recommendation is the quintessential machine learning killjoy. YouTube and Netflix were a lot more interesting before they achieved algorithmic homogeneity.
- finnthehuman 6y ago
  They kill exposure to anything fresh, you teach it a couple things you like and then it keeps you swimming in the same pool. Rather than discovering something new, everyone just watches The Office and Parks and Rec., again and again. Now those theme songs make my skin fucking crawl.
  - galangalalgol 6y ago
    On the other hand I taught pandora to only play songs by artists that had done heroin, thats kind of cool, it has tons of variety from ray charles to jhonny cash to alice in chains, and it finds artists I had no idea about like James Taylor. Also, don't try to code while listening to my horses channel... There may be variety but there is also a common quality of alert-sedation.
  - blackbear_ 6y ago
    > ... and then it keeps you swimming in the same pool. This is a consequence of the metrics that are being optimized, it's not a fault of the algorithm per se.
    - mumblemumble 6y ago
      My (wild) guess is that it would be very hard to come up with a universal algorithm that doesn't exhibit this characteristic, due to some sort of effect that's comparable to the class imbalance problem, but with added feedback effects.
    - thotsBgone 6y ago
      It's not a fault at all. If you're going to spend more time watching videos if you're recommended stuff Youtube knows you already like, that's what it's going to do. Youtube just wants you to watch more videos. They don't care whether you are exposed to a variety of content.
      - leobakerhytch 6y ago
        Except I think there’s convincing argument to make that engagement will go down over time, if the algorithm makes no attempt to prioritize or suggest novel content. The rare occasions I discover a new channel, it’s almost always from some source other than the algorithm: a referral from a friend, this site, another YouTuber, etc. My viewership of the same repetitive roster of videos absolutely tails off until I find something new from elsewhere. For example, in months of being subscribed to my mechanics [0] (who does incredibly engrossing and relaxing restorations of mechanical stuff), not once was I suggested a video from Baumgartner Restoration [1], an art conservator who produces videos with a similar attention to detail and high production value. Thematically this should be an easy recommendation for YouTube to make, but evidently the content is just different enough that it scores as a false-negative. After finding the latter channel independently, my viewing time absolutely rose for a while. In theory, YouTube ought to be able to detect and learn from this signal of non-algorithmic discovery of new content. Yet, here we are. [0]: https://www.youtube.com/c/mymechanics https://www.youtube.com/c/mymechanics [1]: https://www.youtube.com/c/BaumgartnerRestoration https://www.youtube.com/c/BaumgartnerRestoration
mathattack 6y ago
You may be overrating what they do. I suspect that 90% of the recommendation weight is based on what other people clicked after watching the same video.
- sidlls 6y ago
  I've seen so many really complex real-time recommendation pipelines that could be replaced by a simple weighted click-rate style algorithm. The application of ML and data science in this industry is quite hilariously bad, really.
  - mathattack 6y ago
    Somehow so much leads to correlation. :-)
martamorena9182 6y ago
> Sure, YouTube itself proably built insane stuff in their engine you could never replicate with classic methods Probably. But pretty much anything they recommend is junk, so... That's where the author may have a point. If you don't understand your AI algorithm anymore, it's hard to improve it or even realize how wrong it is. AI is generally good at steering the masses into a couple of "averaged" directions. At the individual level though, it's often crap, unless you are the perfect stereotype that the algorithms assumes you to be.
- thotsBgone 6y ago
  What's good for you is not the same as what's good for Youtube. Youtube wants to maximize watch time at all costs. Their algorithms is probably very good at that.

mumblemumble 6y ago

Video recommendation is the quintessential machine learning killjoy. YouTube and Netflix were a lot more interesting before they achieved algorithmic homogeneity.

finnthehuman 6y ago
They kill exposure to anything fresh, you teach it a couple things you like and then it keeps you swimming in the same pool. Rather than discovering something new, everyone just watches The Office and Parks and Rec., again and again. Now those theme songs make my skin fucking crawl.
- galangalalgol 6y ago
  On the other hand I taught pandora to only play songs by artists that had done heroin, thats kind of cool, it has tons of variety from ray charles to jhonny cash to alice in chains, and it finds artists I had no idea about like James Taylor. Also, don't try to code while listening to my horses channel... There may be variety but there is also a common quality of alert-sedation.
- blackbear_ 6y ago
  > ... and then it keeps you swimming in the same pool. This is a consequence of the metrics that are being optimized, it's not a fault of the algorithm per se.
  - mumblemumble 6y ago
    My (wild) guess is that it would be very hard to come up with a universal algorithm that doesn't exhibit this characteristic, due to some sort of effect that's comparable to the class imbalance problem, but with added feedback effects.
  - thotsBgone 6y ago
    It's not a fault at all. If you're going to spend more time watching videos if you're recommended stuff Youtube knows you already like, that's what it's going to do. Youtube just wants you to watch more videos. They don't care whether you are exposed to a variety of content.
    - leobakerhytch 6y ago
      Except I think there’s convincing argument to make that engagement will go down over time, if the algorithm makes no attempt to prioritize or suggest novel content. The rare occasions I discover a new channel, it’s almost always from some source other than the algorithm: a referral from a friend, this site, another YouTuber, etc. My viewership of the same repetitive roster of videos absolutely tails off until I find something new from elsewhere. For example, in months of being subscribed to my mechanics [0] (who does incredibly engrossing and relaxing restorations of mechanical stuff), not once was I suggested a video from Baumgartner Restoration [1], an art conservator who produces videos with a similar attention to detail and high production value. Thematically this should be an easy recommendation for YouTube to make, but evidently the content is just different enough that it scores as a false-negative. After finding the latter channel independently, my viewing time absolutely rose for a while. In theory, YouTube ought to be able to detect and learn from this signal of non-algorithmic discovery of new content. Yet, here we are. [0]: https://www.youtube.com/c/mymechanics https://www.youtube.com/c/mymechanics [1]: https://www.youtube.com/c/BaumgartnerRestoration https://www.youtube.com/c/BaumgartnerRestoration

mathattack 6y ago

You may be overrating what they do. I suspect that 90% of the recommendation weight is based on what other people clicked after watching the same video.

sidlls 6y ago
I've seen so many really complex real-time recommendation pipelines that could be replaced by a simple weighted click-rate style algorithm. The application of ML and data science in this industry is quite hilariously bad, really.
- mathattack 6y ago
  Somehow so much leads to correlation. :-)

martamorena9182 6y ago

> Sure, YouTube itself proably built insane stuff in their engine you could never replicate with classic methods Probably. But pretty much anything they recommend is junk, so... That's where the author may have a point. If you don't understand your AI algorithm anymore, it's hard to improve it or even realize how wrong it is. AI is generally good at steering the masses into a couple of "averaged" directions. At the individual level though, it's often crap, unless you are the perfect stereotype that the algorithms assumes you to be.

thotsBgone 6y ago
What's good for you is not the same as what's good for Youtube. Youtube wants to maximize watch time at all costs. Their algorithms is probably very good at that.