4 ms·

> these are the inputs we use, these are the high level functions of the pipeline, these are the outputs" would be enough You do understand that these are ML m

> these are the inputs we use, these are the high level functions of the pipeline, these are the outputs" would be enough You do understand that these are ML models where there is no way to explain their behaviour in a layman way other than what is bleedingly obvious. That it will try and show you more content that you and the people you follow might like.

beezlewax 4y ago
It isn't entirely about what you like, it is about what will drive interaction. This is why hateful posts or controversial ones rise to the top so frequently. It encourages bad and often anti social behaviour. They've created a monster.
- pixl97 4y ago
  I disagree, the monster always existed. There is a reason "If it bleeds, it leads" was a saying long before social media existed. Also, "A lie is half way around the world before the truth puts its pants on". These are default human behaviors that have always existed. In the past there were particular global limits on the spread of these behaviors base on things like "A horse only goes so fast", or there are a limited number of telegraph lines or radio stations. Controversial, hateful stuff was always going to rise to the top. The difference between now and then is we have pretty much unlimited bandwidth covering the globe to spread the message. If you made FB disappear you're going to see the exact same behavior in mastadon servers, just hoping the scale is limited by lower audience sizes.
mjburgess 4y ago
No ad platform has access to what people "like", this is a psychological property of people that isnt measurable, and at least, isnt being measured. ML is perfectly explainable, and all this mysticism around what are just weightings of features is silly. What all recommendation platforms could be required to provide is which are their most important variables for determining what a user sees, and sensitivity analysis.
- simplotek 4y ago
  > No ad platform has access to what people "like", this is a psychological property of people that isnt measurable, and at least, isnt being measured. You're either commenting on something you are oblivious about, or are pulling a red herring just to be contrarian. Concepts like revealed preferences [1] are nearly a century old, not to mention the fact that the whole consumer behavior field as a whole has plenty of quantitative methods to infer "what people like". Facebook and Twitter support a feature where quite literally users specify in no ambiguous term that they like something. Theirnl platform is so effective that their problem is filtering out bad actors. Then there's also engagement. You can get a pretty good idea of what people are interested in by tracking what they read. You might try to nitpick about the true meaning of "like" but you'd need to be really determined to intentionally miss the point. [1] https://en.wikipedia.org/wiki/Revealed_preference https://en.wikipedia.org/wiki/Revealed_preference
  - mjburgess 4y ago
    What goes to the heart of the entire issue is whether recommendation systems, as used by social networks, are inferring "what people like". That's the whole problem. When shown an outrage-inducing tweet are you being showing "what you like" or something quite different? Eg., what is most likey to addict you to the platform, what elicits the highest emotional response. Do people trapped gambling their lives away in casions "like" being there? The idea that social networks are engaged in a sincere project to estimate people's preferences is on the face of it absurd. And requires such superstitions as "revealed preferences" which are often little more than an observation that people do what their environment most easily affords. To call that an exercise of preference, to say that people like it, is a great misunderstanding of human nature. People are much better than what they happen to do in any given environment, and social media companies bare extreme levels of responsibility for creating environments of so-called "revealed preferences" where, apparently people want eating disorders, conspiracy theories, twitter rage sessions, and so on.
    - simplotek 4y ago
      > What goes to the heart of the entire issue is whether recommendation systems, as used by social networks, are inferring "what people like". That's the whole problem. It really doesn't. You're trying to debate the semantics of the keyword "like", but that discussion is meaningless and irrelevant. The only objective fact about these services is that they use the data provided by users to infer the preferences they reveal. These services then use this information to meet the company's goals, such as meeting the expectations of paying customers. These paying customers include marketing companies, and their expectations include driving up user engagement and associate their programs with specific type of content. You might complain about whether these goals are aligned with what you believe are some user's goals, but you'd be missing the fact that users of services like Facebook or Twitter are the product and not the customers. You're not paying for that click, the marketing company is. If customers pay for that click then this clearly means these services do a good job determining what users "like".
BoorishBears 4y ago
You do understand that there isn't just some "ball of ML" sitting on a server forming your feed, right? That's not how ML works at scale. ML is applied to very specific tasks that are pipelined in an intentional and introspectable way. And even at the level of a given model while you might struggle to explain behavior depending on how pedantically you define that, you still know what your reward function was. - This is like saying "There's no point in knowing the source code if you don't have the schematic and microcode for your CPU to know which transistors each line of code is changing the state of"
ericd 4y ago
For ML models, publish the loss function and sampling of the data it’s trained on? For each of these objections, there exists some level that has been reasoned about at the conceptual level during the design. And an army of observers will be happy to interpret for the less technical.
- threeseed 4y ago
  An army of observers across the political spectrum arguing over the intent of a loss function. Sounds like a conspiracy theorist's dream.
  - ericd 4y ago
    For sure, but that's going to happen in any case.
- spookthesunset 4y ago
  Ironic that you are discussing this in a thread about how orgs can get huge enough that nobody really understands why there are so many people. It would most likely take several teams to push out what you are asking for. You’d need lawyers, support staff, engineers to create a platform other teams can hook into in order to fulfill these requests, you’d need ways to strip training data of any PII, you’d need a team to write the front end for support staff and the external users. Etc… that’s a non trivial amount of headcount!
  - BoorishBears 4y ago
    I don't get how you can miss the forest for the trees this badly. You're raving about how much work it would be to build in introspectability into a system that has an extremely large effect on the population, as if that's not justified by the fact we'd be gaining introspectability into a system that has an extremely large effect on the population You're also overselling it, no one is saying we need every single api request to fetch a feed to also hit some endpoint stating "this is why post id X was shown to user Y". ML again, can be introspected at a higher level than all this seems to imply. OpenAI can't tell you exactly why ChatGPT gives a specific string of text but they can (and have) shown how it works at a high level: https://openai.com/blog/instruction-following/ https://openai.com/blog/instruction-following/ You seem to be pushing the line that, because it's difficult to explain why these systems work how they do, and the responsibility is spread so far and wide, we shouldn't bother. It's the literal opposite: it's exactly why its so needed. Even internally these things should be wanted, and I'd be truly shocked if finely tuned advertising giants don't already have a high degree of these understandings in poorly named suborgs that no one pays much attention to (not because cloak and daggers, but because engineers tend to focus on more glamourous things than "introspection").

beezlewax 4y ago

It isn't entirely about what you like, it is about what will drive interaction. This is why hateful posts or controversial ones rise to the top so frequently. It encourages bad and often anti social behaviour. They've created a monster.

pixl97 4y ago
I disagree, the monster always existed. There is a reason "If it bleeds, it leads" was a saying long before social media existed. Also, "A lie is half way around the world before the truth puts its pants on". These are default human behaviors that have always existed. In the past there were particular global limits on the spread of these behaviors base on things like "A horse only goes so fast", or there are a limited number of telegraph lines or radio stations. Controversial, hateful stuff was always going to rise to the top. The difference between now and then is we have pretty much unlimited bandwidth covering the globe to spread the message. If you made FB disappear you're going to see the exact same behavior in mastadon servers, just hoping the scale is limited by lower audience sizes.

mjburgess 4y ago

No ad platform has access to what people "like", this is a psychological property of people that isnt measurable, and at least, isnt being measured. ML is perfectly explainable, and all this mysticism around what are just weightings of features is silly. What all recommendation platforms could be required to provide is which are their most important variables for determining what a user sees, and sensitivity analysis.

simplotek 4y ago
> No ad platform has access to what people "like", this is a psychological property of people that isnt measurable, and at least, isnt being measured. You're either commenting on something you are oblivious about, or are pulling a red herring just to be contrarian. Concepts like revealed preferences [1] are nearly a century old, not to mention the fact that the whole consumer behavior field as a whole has plenty of quantitative methods to infer "what people like". Facebook and Twitter support a feature where quite literally users specify in no ambiguous term that they like something. Theirnl platform is so effective that their problem is filtering out bad actors. Then there's also engagement. You can get a pretty good idea of what people are interested in by tracking what they read. You might try to nitpick about the true meaning of "like" but you'd need to be really determined to intentionally miss the point. [1] https://en.wikipedia.org/wiki/Revealed_preference https://en.wikipedia.org/wiki/Revealed_preference
- mjburgess 4y ago
  What goes to the heart of the entire issue is whether recommendation systems, as used by social networks, are inferring "what people like". That's the whole problem. When shown an outrage-inducing tweet are you being showing "what you like" or something quite different? Eg., what is most likey to addict you to the platform, what elicits the highest emotional response. Do people trapped gambling their lives away in casions "like" being there? The idea that social networks are engaged in a sincere project to estimate people's preferences is on the face of it absurd. And requires such superstitions as "revealed preferences" which are often little more than an observation that people do what their environment most easily affords. To call that an exercise of preference, to say that people like it, is a great misunderstanding of human nature. People are much better than what they happen to do in any given environment, and social media companies bare extreme levels of responsibility for creating environments of so-called "revealed preferences" where, apparently people want eating disorders, conspiracy theories, twitter rage sessions, and so on.
  - simplotek 4y ago
    > What goes to the heart of the entire issue is whether recommendation systems, as used by social networks, are inferring "what people like". That's the whole problem. It really doesn't. You're trying to debate the semantics of the keyword "like", but that discussion is meaningless and irrelevant. The only objective fact about these services is that they use the data provided by users to infer the preferences they reveal. These services then use this information to meet the company's goals, such as meeting the expectations of paying customers. These paying customers include marketing companies, and their expectations include driving up user engagement and associate their programs with specific type of content. You might complain about whether these goals are aligned with what you believe are some user's goals, but you'd be missing the fact that users of services like Facebook or Twitter are the product and not the customers. You're not paying for that click, the marketing company is. If customers pay for that click then this clearly means these services do a good job determining what users "like".

BoorishBears 4y ago

You do understand that there isn't just some "ball of ML" sitting on a server forming your feed, right? That's not how ML works at scale. ML is applied to very specific tasks that are pipelined in an intentional and introspectable way. And even at the level of a given model while you might struggle to explain behavior depending on how pedantically you define that, you still know what your reward function was. - This is like saying "There's no point in knowing the source code if you don't have the schematic and microcode for your CPU to know which transistors each line of code is changing the state of"

ericd 4y ago

For ML models, publish the loss function and sampling of the data it’s trained on? For each of these objections, there exists some level that has been reasoned about at the conceptual level during the design. And an army of observers will be happy to interpret for the less technical.

threeseed 4y ago
An army of observers across the political spectrum arguing over the intent of a loss function. Sounds like a conspiracy theorist's dream.
- ericd 4y ago
  For sure, but that's going to happen in any case.
spookthesunset 4y ago
Ironic that you are discussing this in a thread about how orgs can get huge enough that nobody really understands why there are so many people. It would most likely take several teams to push out what you are asking for. You’d need lawyers, support staff, engineers to create a platform other teams can hook into in order to fulfill these requests, you’d need ways to strip training data of any PII, you’d need a team to write the front end for support staff and the external users. Etc… that’s a non trivial amount of headcount!
- BoorishBears 4y ago
  I don't get how you can miss the forest for the trees this badly. You're raving about how much work it would be to build in introspectability into a system that has an extremely large effect on the population, as if that's not justified by the fact we'd be gaining introspectability into a system that has an extremely large effect on the population You're also overselling it, no one is saying we need every single api request to fetch a feed to also hit some endpoint stating "this is why post id X was shown to user Y". ML again, can be introspected at a higher level than all this seems to imply. OpenAI can't tell you exactly why ChatGPT gives a specific string of text but they can (and have) shown how it works at a high level: https://openai.com/blog/instruction-following/ https://openai.com/blog/instruction-following/ You seem to be pushing the line that, because it's difficult to explain why these systems work how they do, and the responsibility is spread so far and wide, we shouldn't bother. It's the literal opposite: it's exactly why its so needed. Even internally these things should be wanted, and I'd be truly shocked if finely tuned advertising giants don't already have a high degree of these understandings in poorly named suborgs that no one pays much attention to (not because cloak and daggers, but because engineers tend to focus on more glamourous things than "introspection").