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> We keep getting ads for the thing we bought yesterday, ads for dating sites after we got married and had kids (continously, for ten years despite my utter lac

by m_ke 7y ago

> We keep getting ads for the thing we bought yesterday, ads for dating sites after we got married and had kids (continously, for ten years despite my utter lack of interest), search results keep getting worse[0], obvious spammers keep on spamming in social media (seriously, it seems a simple regex filter could have done a better job to reduce crypto scamming in replies on Twitter than whatever was there last time I checked.) That's because in a lot of cases they're optimizing for the wrong metrics, as in maximizing their revenue instead of your utility. There's way more content on the web than there was in the early 2000s, most of it in form of "content marketing" and explicitly attempting to game the system. If you look at recent results from TREC, it's pretty clear that machine learning provides a large boost over the traditional retrieval systems, on any metric that you want to optimize.

OnlineGladiator 7y ago
> That's because in a lot of cases they're optimizing for the wrong metrics, as in maximizing their revenue instead of your utility. What? How do they maximize their revenue by spending money on ads the users actually laugh about for how bad their targeting is? Based on what I know about machine learning - it almost always gives great short-term results, and it almost always fails to deliver the expected long-term results. What's worse is this comes with the weirdest most indecipherable bugs that pop up more and more over time. Unless you have a large enough database to show statistical errors that are negligible (something like 99.9999% precision or recall, depending on your metrics), you should assume it will break in ways you cannot possibly predict. And even then, you might be using the wrong training data without even realizing it. I'm not saying ML is bad, although I am saying it is ridiculously overhyped. I'm saying ML is still nascent enough nobody really knows how a lot of edge cases will shake out, simply because there are too many edge cases to test before putting it into production. It's not hard to find examples of ML algorithms gone wrong even for sites like Amazon. https://gizmodo.com/amazon-prime-day-glitch-let-people-buy-13-000-camera-g-1836487919 https://gizmodo.com/amazon-prime-day-glitch-let-people-buy-1...
- m_ke 7y ago
  Recommending something you bought before is probably a much better bet than showing you random items from their huge catalogue. You can have a system that generates them a ton of cash while making mistakes in some cases, outliers are inevitable and feedback loops in recommender systems can lead to such issues. Amazon wouldn't deploy these systems if they didn't move the needle.
  - OnlineGladiator 7y ago
    Nobody is saying the ML algorithm (or any algorithm) is expected to be perfect. > Amazon wouldn't deploy these systems if they didn't move the needle. This is an appeal to authority that Amazon executives are immune to making mistakes. They could have bad metrics. They could have bad incentives encouraging managers to make poor decisions (basically this describes everything wrong with Google today). They could be incompetent. They could be focused on short-term gains at the expense of long-term gains because it maximizes their personal net wealth and they can just jump ship in a few years. Again, nobody is saying ML algorithms are worthless. I just don't believe (and have lots of reasons based on personal experience I won't go into) that they are 10% as useful as the industry wants you to believe.
    - m_ke 7y ago
      I'm saying that they have systems in place to measure the impact of the code that they put in production on their bottom line and if those recommendations didn't move their profit margins in the right direction they wouldn't be using them.
      - OnlineGladiator 7y ago
        I know what you're saying. I don't think you understand what I'm saying, which is those systems can be wrong or they might not be optimizing for the bottom line. Why didn't their systems catch that bug that was discounting expensive equipment for more than 90% that I linked in my previous post? People make mistakes. And for a frame of reference, Google's corporate policies definitely don't optimize for the bottom line, and it took a minimum of 5 years for it to affect them negatively (I'd guess closer to 10+). Anyway, agree to disagree.
smsm42 7y ago
> in maximizing their revenue instead of your utility. I am not sure how these are contradictory. My interest is buying things I want. Their interest is selling me things I'd buy. How showing dating site ads to a married person promotes any of those? Where the revenue would come from? Or do you mean it's ad agency revenue, not advertiser revenue? In that case we clearly have a case of agent problem.

by m_ke 7y ago

OnlineGladiator 7y ago

> That's because in a lot of cases they're optimizing for the wrong metrics, as in maximizing their revenue instead of your utility. What? How do they maximize their revenue by spending money on ads the users actually laugh about for how bad their targeting is? Based on what I know about machine learning - it almost always gives great short-term results, and it almost always fails to deliver the expected long-term results. What's worse is this comes with the weirdest most indecipherable bugs that pop up more and more over time. Unless you have a large enough database to show statistical errors that are negligible (something like 99.9999% precision or recall, depending on your metrics), you should assume it will break in ways you cannot possibly predict. And even then, you might be using the wrong training data without even realizing it. I'm not saying ML is bad, although I am saying it is ridiculously overhyped. I'm saying ML is still nascent enough nobody really knows how a lot of edge cases will shake out, simply because there are too many edge cases to test before putting it into production. It's not hard to find examples of ML algorithms gone wrong even for sites like Amazon. https://gizmodo.com/amazon-prime-day-glitch-let-people-buy-13-000-camera-g-1836487919 https://gizmodo.com/amazon-prime-day-glitch-let-people-buy-1...

m_ke 7y ago
Recommending something you bought before is probably a much better bet than showing you random items from their huge catalogue. You can have a system that generates them a ton of cash while making mistakes in some cases, outliers are inevitable and feedback loops in recommender systems can lead to such issues. Amazon wouldn't deploy these systems if they didn't move the needle.
- OnlineGladiator 7y ago
  Nobody is saying the ML algorithm (or any algorithm) is expected to be perfect. > Amazon wouldn't deploy these systems if they didn't move the needle. This is an appeal to authority that Amazon executives are immune to making mistakes. They could have bad metrics. They could have bad incentives encouraging managers to make poor decisions (basically this describes everything wrong with Google today). They could be incompetent. They could be focused on short-term gains at the expense of long-term gains because it maximizes their personal net wealth and they can just jump ship in a few years. Again, nobody is saying ML algorithms are worthless. I just don't believe (and have lots of reasons based on personal experience I won't go into) that they are 10% as useful as the industry wants you to believe.
  - m_ke 7y ago
    I'm saying that they have systems in place to measure the impact of the code that they put in production on their bottom line and if those recommendations didn't move their profit margins in the right direction they wouldn't be using them.
    - OnlineGladiator 7y ago
      I know what you're saying. I don't think you understand what I'm saying, which is those systems can be wrong or they might not be optimizing for the bottom line. Why didn't their systems catch that bug that was discounting expensive equipment for more than 90% that I linked in my previous post? People make mistakes. And for a frame of reference, Google's corporate policies definitely don't optimize for the bottom line, and it took a minimum of 5 years for it to affect them negatively (I'd guess closer to 10+). Anyway, agree to disagree.

smsm42 7y ago

> in maximizing their revenue instead of your utility. I am not sure how these are contradictory. My interest is buying things I want. Their interest is selling me things I'd buy. How showing dating site ads to a married person promotes any of those? Where the revenue would come from? Or do you mean it's ad agency revenue, not advertiser revenue? In that case we clearly have a case of agent problem.