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My main concern with things like this and autocomplete is can they actually influence how the user thinks and what they say? It would be interesting to have use

by nimos 8y ago

My main concern with things like this and autocomplete is can they actually influence how the user thinks and what they say? It would be interesting to have users write about a subject with a "good adjective"/"bad adjective" biased AI and see if there is a measurable difference in opinion on the subject afterwards between groups.

vl 8y ago
For Google, Facebook and others, as a companies, best users to maximize long-term revenue are people that have income and choose to spend all their disposable income (and thus generate ad and lead conversion profits). As such, Google should show/suggest/autoplay search results/ads/YouTube videos/smart replies/assistant answers/News/Google Now cards to increase life-time likelihood for the given user to become working professional and mindless spender. For specific hypothetical example, it's not hard to imagine, that training users to prefer things like "experiences" or entertainment that need to be re-purchased is better that promoting physical goods, like board games or bicycles.
- TheDong 8y ago
  This has got to be some sort of fallacy. It rings similar to the "if evolution selected for good traits, why can't we fly and shoot lasers out of our eyes?" Yes, its to their benefit if their users have money. No, I don't think there's any chance that any company is able to optimize for someone gaining a middle-class life that meaningfully. Companies do small-scale user studies and measure things like engagement and increased clicks on a small scale. They do not perfectly capture their user's income and spending level for many years and correlate that well enough with the company's auto-suggestions that said suggestions optimize for that very macro goal over the very micro measurements they make. I think it's very hard to imagine Google successfully optimizing itself to meaningfully lead do that outcome for users, just as it's very hard to imagine humans evolving the ability to fly.
  - vl 8y ago
    Current recommendations systems for FB, Google/YouTube have switched from models that try to increase immediate engagement to models that try to increase retention because it turned out that optimizing for immediate engagement decreases retention. (Latter is obviously much harder ML task requiring Q-learning, longer data series, etc.) In other words companies choose to sacrifice immediate revenue in order to increase long-term (optimally life-time) revenue from each particular user. You can see it an example of early precursor of ML that shapes user behavior: learning what motivates user to return and then displaying it.

by nimos 8y ago

vl 8y ago

For Google, Facebook and others, as a companies, best users to maximize long-term revenue are people that have income and choose to spend all their disposable income (and thus generate ad and lead conversion profits). As such, Google should show/suggest/autoplay search results/ads/YouTube videos/smart replies/assistant answers/News/Google Now cards to increase life-time likelihood for the given user to become working professional and mindless spender. For specific hypothetical example, it's not hard to imagine, that training users to prefer things like "experiences" or entertainment that need to be re-purchased is better that promoting physical goods, like board games or bicycles.

TheDong 8y ago
This has got to be some sort of fallacy. It rings similar to the "if evolution selected for good traits, why can't we fly and shoot lasers out of our eyes?" Yes, its to their benefit if their users have money. No, I don't think there's any chance that any company is able to optimize for someone gaining a middle-class life that meaningfully. Companies do small-scale user studies and measure things like engagement and increased clicks on a small scale. They do not perfectly capture their user's income and spending level for many years and correlate that well enough with the company's auto-suggestions that said suggestions optimize for that very macro goal over the very micro measurements they make. I think it's very hard to imagine Google successfully optimizing itself to meaningfully lead do that outcome for users, just as it's very hard to imagine humans evolving the ability to fly.
- vl 8y ago
  Current recommendations systems for FB, Google/YouTube have switched from models that try to increase immediate engagement to models that try to increase retention because it turned out that optimizing for immediate engagement decreases retention. (Latter is obviously much harder ML task requiring Q-learning, longer data series, etc.) In other words companies choose to sacrifice immediate revenue in order to increase long-term (optimally life-time) revenue from each particular user. You can see it an example of early precursor of ML that shapes user behavior: learning what motivates user to return and then displaying it.