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From my recent post: "The thing that could bring the most change to the world is a system that finds everybody a thousand or more best friends based on psycholo

From my recent post: "The thing that could bring the most change to the world is a system that finds everybody a thousand or more best friends based on psychological proximity and compatibility. Don’t tell me it’s impossible to predict whether two people are likely to get along or not based on a bunch of factors that can be analyzed with various techniques including AI. What would the world look like if every single person had a bunch of people who get them because they are almost exactly like them or are highly compatible although different? Imagine having a huge support group that roots for each cause of yours because it’s also theirs. What could you all do together, pulling in the same direction, inspiring each other and contributing your best? I want all of the existing social networks to create a version of this. Create groups out of thin air. You have the data, so use it for something better than serving ads with efficient precision." I've been developing ideas on how to match people, but I can't code. I also want a drawing app that hides all my attempts at a stroke and lets me pick the best one AFTER I'm done generating them: http://zeroprecedent.com/lore/flipmark.html http://zeroprecedent.com/lore/flipmark.html martin dot nenov at gmail dot com

minihat 5y ago
Have you seen the movie Ron's Gone Wrong? Not trying to make fun of your idea. I just expect you may find this movie especially enjoyable given the problem you are interested in.
- EZ-Cheeze 5y ago
  I'll watch it!
erikerikson 5y ago
Why would you limit it to friendships? Why not to find colleagues? Game theoretic strategy compatibility? The problem comes in how you'd make it real (i.e. implement) and find adoption.
- EZ-Cheeze 5y ago
  It wouldn't be just friends, but that's how I phrased it there. I called it "supporters" in these 280 characters: "Some people just get along. They are compatible. They mesh. I think it's possible to predict whether any two people will hit it off, and to create groups composed entirely of highly compatible, highly synergistic people. What will happen when EVERYONE has a THOUSAND SUPPORTERS?" And it wouldn't just be friends or supporters, either: "Just as the system can find those who are your mirror reflection by minimizing adjacency distances, so it can find your very antithesis by maximizing them, as well as anything in-between." That's from this post which explores implementation: https://zeroprecedent.substack.com/p/a-technique-for-generating-endless?s=r https://zeroprecedent.substack.com/p/a-technique-for-generat... I think after implementation, adoption is inevitable since the value is truly insane, better than anything else online (unless you're antisocial, etc.)
  - erikerikson 5y ago
    Okay. The problem is something like "social knowing doesn't naturally scale" but the entirety of the problem is how to make it do so effectively without ill side effect. I'm curious if you've gone the further step on how the system may simply iterate the current state. What ends do you seek? Most important... What have you done to start solving this problem?
    - EZ-Cheeze 5y ago
      emailed, plus see reply to skywal_l
webmaven 5y ago
You might find the novel The Affinities by Robert Charles Wilson interesting: https://books.google.com/books/about/The_Affinities.html?id=zPd-BwAAQBAJ&source=kp_book_description https://books.google.com/books/about/The_Affinities.html?id=...
- EZ-Cheeze 5y ago
skywal_l 5y ago
What would be the kind of data you would need for that? Also, how would you kickstart it? This kind of site needs a huge amount of data to start becoming useful so there is always the problem of recruiting users while at the same time not providing any useful service.
- EZ-Cheeze 5y ago
  -start with some rough seed data -make matches based on our initial hypotheses of data vs action vs outcome -learn from success/failure of matches -adjust matching strategy & request more or different data as needed -search for best predictors by babble and prune [1] with extremely prolific babblers and extremely discerning pruners. GANs & transformers & hashtables, we'll grow em quickly out of the right SUBSTRATE; I think representation efficacy makes for the biggest performance jumps in machine learning training and inference speed. Constant and well-directed experimentation is key in innovation, let's get all the best people spazzing out constructively on a constant basis Let a thousand distributed "Bell Labs" type bloom. (If you don't know, Bell Labs is known for many innovations and was ran with a spirit of collaboration, tinkering, and interdisciplinary work. And also it had all the right people in the same spot!) [1] https://www.lesswrong.com/s/pC6DYFLPMTCbEwH8W https://www.lesswrong.com/s/pC6DYFLPMTCbEwH8W "Babble creates a constant stream of coarse material, while Prune cuts these ideas harshly without the slightest hint of remorse."

minihat 5y ago

Have you seen the movie Ron's Gone Wrong? Not trying to make fun of your idea. I just expect you may find this movie especially enjoyable given the problem you are interested in.

EZ-Cheeze 5y ago
I'll watch it!

erikerikson 5y ago

Why would you limit it to friendships? Why not to find colleagues? Game theoretic strategy compatibility? The problem comes in how you'd make it real (i.e. implement) and find adoption.

EZ-Cheeze 5y ago
It wouldn't be just friends, but that's how I phrased it there. I called it "supporters" in these 280 characters: "Some people just get along. They are compatible. They mesh. I think it's possible to predict whether any two people will hit it off, and to create groups composed entirely of highly compatible, highly synergistic people. What will happen when EVERYONE has a THOUSAND SUPPORTERS?" And it wouldn't just be friends or supporters, either: "Just as the system can find those who are your mirror reflection by minimizing adjacency distances, so it can find your very antithesis by maximizing them, as well as anything in-between." That's from this post which explores implementation: https://zeroprecedent.substack.com/p/a-technique-for-generating-endless?s=r https://zeroprecedent.substack.com/p/a-technique-for-generat... I think after implementation, adoption is inevitable since the value is truly insane, better than anything else online (unless you're antisocial, etc.)
- erikerikson 5y ago
  Okay. The problem is something like "social knowing doesn't naturally scale" but the entirety of the problem is how to make it do so effectively without ill side effect. I'm curious if you've gone the further step on how the system may simply iterate the current state. What ends do you seek? Most important... What have you done to start solving this problem?
  - EZ-Cheeze 5y ago
    emailed, plus see reply to skywal_l

webmaven 5y ago

You might find the novel The Affinities by Robert Charles Wilson interesting: https://books.google.com/books/about/The_Affinities.html?id=zPd-BwAAQBAJ&source=kp_book_description https://books.google.com/books/about/The_Affinities.html?id=...

EZ-Cheeze 5y ago

skywal_l 5y ago

What would be the kind of data you would need for that? Also, how would you kickstart it? This kind of site needs a huge amount of data to start becoming useful so there is always the problem of recruiting users while at the same time not providing any useful service.

EZ-Cheeze 5y ago
-start with some rough seed data -make matches based on our initial hypotheses of data vs action vs outcome -learn from success/failure of matches -adjust matching strategy & request more or different data as needed -search for best predictors by babble and prune [1] with extremely prolific babblers and extremely discerning pruners. GANs & transformers & hashtables, we'll grow em quickly out of the right SUBSTRATE; I think representation efficacy makes for the biggest performance jumps in machine learning training and inference speed. Constant and well-directed experimentation is key in innovation, let's get all the best people spazzing out constructively on a constant basis Let a thousand distributed "Bell Labs" type bloom. (If you don't know, Bell Labs is known for many innovations and was ran with a spirit of collaboration, tinkering, and interdisciplinary work. And also it had all the right people in the same spot!) [1] https://www.lesswrong.com/s/pC6DYFLPMTCbEwH8W https://www.lesswrong.com/s/pC6DYFLPMTCbEwH8W "Babble creates a constant stream of coarse material, while Prune cuts these ideas harshly without the slightest hint of remorse."