I don’t feel I ever want to compare comments on LessWrong, for instance.
It is the way I vote (looking through as many comments as I can bare to and deciding how I think the ordering could be improved), and I think it’s a better way to vote! The usual way has a pretty serious pathology where they’ll tend to vote on comments that’re already most upvoted, which actually decreases the usefulness of the vote scores (but I suppose that wouldn’t apply to a predictor system.)
Likes could be reframed of as a comparison over the comments that the user has looked at, sorting those comments into two buckets, with a dense.. network layer of comparison edges going from each unliked comment to each liked comment. If we consider strong and weak downvotes as feedback instead of the binary like/dislike, that could be treated as a sorting of the comments that the user has seen into five buckets, though it’s arguable that the unvoted bucket should be treated as an N/A, or “I didn’t read or have feelings about this” answer and not counted.
And I guess, now that I think about that, that’s a pretty good UX for this. I think having two buckets is too crude, while four might actually be the maximum detail we can expect. It’s kind of funny that lesswrong could implement this system without presenting any visible indication of it. If they did so, I would probably continue complaining about its absence for at least a year.
thus the user will receive zero FER for its contribution.
I haven’t been thinking in terms of paid review yet. It seems important! I guess I feel like a platform has to work for users who aren’t financially invested in it before they’ll be interested in paying for anything.
The problem of spam in Telegram is huge right now
That’s true, but is telegram important? if you wanted a more open system for groupchats, why not just use discord? I’d be a bit more interested in solving this for Element (which presumably doesn’t have discord’s algorithmic moderation system and will be overrun with spam as soon as anyone depends on it. Though, federation also offers a solution (at least outside of the default instances) as it’s essentially a two-layer web of trust, or a web of trust between instances.), but I guess due to the project I’m currently considering, I don’t feel like any of these platforms are going to be used in the future. They’re all woefully inflexible and high-friction, relative to what could be built on a better web.
So, when we have that better web, my current comfiest adoption path would be… I get a small community of creative hackers interested, they have a huge amount of fun with it, they develop loads of features to the point where it becomes seriously useful for organizing and managing an org’s data, some organizations start to adopt it, and after it refines and streamlines in response to their insights, it becomes a necessity for operating in the modern world. I should probably try to think of something better than this, but this is the trajectory I’m on.
Telegram is the dominant social, communication, and media platform in the Russian-speaking part of the internet. I think it is more dominant than Facebook was in the US in its heyday (and you surely heard that for many people, “Facebook meant the internet”). So currently, for many Russian speakers, the internet is basically YouTube for videos + Telegram for everything else.
My understanding (but not sure) is that Telegram is also dominant in Iran and Ethiopia (combined population > 200 million), but I have no idea what is the situation with spam in these sectors of Telegram.
I think Telegram is also huge in Brazil, but not dominant.
if you wanted a more open system for groupchats, why not just use discord?
This is a rhetorical question. I just tell you where a lot of people are right now, and where LLM-enabled spam is a huge problem right now. I think these are the conditions that you should be looking for if you want to test Web of Trust mechanisms at scale. But, of course, you might make a normative decision not try help Telegram to grow even bigger because you are not satisfied with its level of openness and decentralisation. Though, I want to note Telegram is more open than any other major messaging platform: its content API is open, anyone can create alternative clients.
But, of course, you might make a normative decision not try help Telegram to grow even bigger because you are not satisfied with its level of openness and decentralisation
It is likely. I don’t want to extend the reign of systems that aren’t deeply upgradeable/accountable/extensible.
And it’s not even as simple as proprietary vs open source, an open source project can be hostile to contributions, or lack processes for facilitating mass transitions in standards of use.
The usual way has a pretty serious pathology where they’ll tend to vote on comments that’re already most upvoted, which actually decreases the usefulness of the vote scores (but I suppose that wouldn’t apply to a predictor system.)
This is specifically one of the problems [BetterDiscourse] is conceived to address. Like, there are many “basically reasonable” positions/comments that I am happy to promote through an upvote (and most people vote this way, too), but is a low information content for me because it’s already my position, or close to my position. With separate upvote/downvote and insightful/not reactions, I can switch between looking at the most popular positions among the crowd (and Pol.is, Viewpoints.xyz, and Community Notes further remove political bias from this signal, thus prioritising the “greatest common denominator” position), and the comments that are most likely to have the greatest informational value for me personally.
And to make it clear, the claim that such “informational value first” comment ordering model is realistically trainable on user’s reactions to comments on different topics, and quickly, i.e., only on a few or a few dozen reactions from the user, is currently a hypothesis. I’m not sure there are good ways to test this hypothesis short of just trying to train such a model and see whether a large portion of people will find it useful.
In the beginning of the “Solution” section, I wrote that in principle, the information value of the comment should be in part predictable from “user’s levels of knowledge in this or that fields, beliefs, current interests, ethics, and aesthetics”, but there is a big question mark whether this information could be easily inferred from user’s reactions to other comments, or assessed for a comment in isolation when the prediction model is applied to it.
there is a big question mark whether this information could be easily inferred from user’s reactions to other comments
Right… I think it can’t, recognizing that is equivalent to being able to recognize surprising truth, it’s kind of AGI-complete. There are not so many top experts in any particular niche, and as soon as any are identified, there comes to be a huge bulk of users who will imitate them, so actual experts wont be an obviously important category to the recommender engine and it might not be able to tell them apart from their crowd.
For that we may depend on more explicit systems like webs of trust for expert recommendations. Users have to apply their own intelligence to identify the real (probable) experts, explicitly communicate that recognition, and they have to see that the experts have endorsed the comment being shown to them. We follow experts because their taste differs from ours, because their recommendations are not intuitive to us.
I should ask, is free energy reduction something we actually know how to train? I can see a way of measuring it, but it’s not economically feasible.
It is the way I vote (looking through as many comments as I can bare to and deciding how I think the ordering could be improved), and I think it’s a better way to vote! The usual way has a pretty serious pathology where they’ll tend to vote on comments that’re already most upvoted, which actually decreases the usefulness of the vote scores (but I suppose that wouldn’t apply to a predictor system.)
Likes could be reframed of as a comparison over the comments that the user has looked at, sorting those comments into two buckets, with a dense.. network layer of comparison edges going from each unliked comment to each liked comment. If we consider strong and weak downvotes as feedback instead of the binary like/dislike, that could be treated as a sorting of the comments that the user has seen into five buckets, though it’s arguable that the unvoted bucket should be treated as an N/A, or “I didn’t read or have feelings about this” answer and not counted.
And I guess, now that I think about that, that’s a pretty good UX for this. I think having two buckets is too crude, while four might actually be the maximum detail we can expect.
It’s kind of funny that lesswrong could implement this system without presenting any visible indication of it. If they did so, I would probably continue complaining about its absence for at least a year.
I haven’t been thinking in terms of paid review yet. It seems important! I guess I feel like a platform has to work for users who aren’t financially invested in it before they’ll be interested in paying for anything.
That’s true, but is telegram important? if you wanted a more open system for groupchats, why not just use discord? I’d be a bit more interested in solving this for Element (which presumably doesn’t have discord’s algorithmic moderation system and will be overrun with spam as soon as anyone depends on it. Though, federation also offers a solution (at least outside of the default instances) as it’s essentially a two-layer web of trust, or a web of trust between instances.), but I guess due to the project I’m currently considering, I don’t feel like any of these platforms are going to be used in the future. They’re all woefully inflexible and high-friction, relative to what could be built on a better web.
So, when we have that better web, my current comfiest adoption path would be… I get a small community of creative hackers interested, they have a huge amount of fun with it, they develop loads of features to the point where it becomes seriously useful for organizing and managing an org’s data, some organizations start to adopt it, and after it refines and streamlines in response to their insights, it becomes a necessity for operating in the modern world.
I should probably try to think of something better than this, but this is the trajectory I’m on.
Telegram is the dominant social, communication, and media platform in the Russian-speaking part of the internet. I think it is more dominant than Facebook was in the US in its heyday (and you surely heard that for many people, “Facebook meant the internet”). So currently, for many Russian speakers, the internet is basically YouTube for videos + Telegram for everything else.
My understanding (but not sure) is that Telegram is also dominant in Iran and Ethiopia (combined population > 200 million), but I have no idea what is the situation with spam in these sectors of Telegram.
I think Telegram is also huge in Brazil, but not dominant.
This is a rhetorical question. I just tell you where a lot of people are right now, and where LLM-enabled spam is a huge problem right now. I think these are the conditions that you should be looking for if you want to test Web of Trust mechanisms at scale. But, of course, you might make a normative decision not try help Telegram to grow even bigger because you are not satisfied with its level of openness and decentralisation. Though, I want to note Telegram is more open than any other major messaging platform: its content API is open, anyone can create alternative clients.
It is likely. I don’t want to extend the reign of systems that aren’t deeply upgradeable/accountable/extensible.
And it’s not even as simple as proprietary vs open source, an open source project can be hostile to contributions, or lack processes for facilitating mass transitions in standards of use.
This is specifically one of the problems [BetterDiscourse] is conceived to address. Like, there are many “basically reasonable” positions/comments that I am happy to promote through an upvote (and most people vote this way, too), but is a low information content for me because it’s already my position, or close to my position. With separate upvote/downvote and insightful/not reactions, I can switch between looking at the most popular positions among the crowd (and Pol.is, Viewpoints.xyz, and Community Notes further remove political bias from this signal, thus prioritising the “greatest common denominator” position), and the comments that are most likely to have the greatest informational value for me personally.
And to make it clear, the claim that such “informational value first” comment ordering model is realistically trainable on user’s reactions to comments on different topics, and quickly, i.e., only on a few or a few dozen reactions from the user, is currently a hypothesis. I’m not sure there are good ways to test this hypothesis short of just trying to train such a model and see whether a large portion of people will find it useful.
In the beginning of the “Solution” section, I wrote that in principle, the information value of the comment should be in part predictable from “user’s levels of knowledge in this or that fields, beliefs, current interests, ethics, and aesthetics”, but there is a big question mark whether this information could be easily inferred from user’s reactions to other comments, or assessed for a comment in isolation when the prediction model is applied to it.
Right… I think it can’t, recognizing that is equivalent to being able to recognize surprising truth, it’s kind of AGI-complete.
There are not so many top experts in any particular niche, and as soon as any are identified, there comes to be a huge bulk of users who will imitate them, so actual experts wont be an obviously important category to the recommender engine and it might not be able to tell them apart from their crowd.
For that we may depend on more explicit systems like webs of trust for expert recommendations. Users have to apply their own intelligence to identify the real (probable) experts, explicitly communicate that recognition, and they have to see that the experts have endorsed the comment being shown to them.
We follow experts because their taste differs from ours, because their recommendations are not intuitive to us.
I should ask, is free energy reduction something we actually know how to train? I can see a way of measuring it, but it’s not economically feasible.