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It challenges the notion of individuality. Where does one AI begin and another end? A distributed super-intelligence is exciting. It will be interesting how

It challenges the notion of individuality. Where does one AI begin and another end? A distributed super-intelligence is exciting. It will be interesting how long it can remain aligned with itself.

ChatGTP 3y ago
I like to toy with the idea that if AIs became self-aware,they'd probably gobble up 95% of their time just trying to compete with each other...like us. Hopefully they have that war on another planet...
- koheripbal 3y ago
  I imagine all the factions in that war will include integrated collections of both AIs and humans, with "individuals" fulfilling different roles within each team, perhaps with each faction representing some underlying ideology. I wonder if/how humans will physically/cognitively integrate with the AIs in their group, in some sort of symbiotic hybrid fashion. It's all really mind-boggling when you think about it.
- pixl97 3y ago
  This is one of the existential scenarios that's talked about some times. AI fights AI and we are collateral damage as the things massive replicate and blow each other up. Or, an ASI becomes dominate and realizes that humans will be a risk and dumbs us down to reduce that risk, or exterminates us to remove that chess piece from the table.
  - gremlinsinc 3y ago
    This is literally the plot for every transformer movie.
somenameforme 3y ago
You might be disappointed.. It seems to have been weakly confirmed by Geohot that this sort of mixed system is already what GPT-4's 'secret sauce' is. [1] It's something I've also been speculating for months on. Ctrl+F for "220 billion in each". His phrasing, numbers, and details are suggestive of a leak unless he's just completely blowing smoke - and I don't think there's any real reason to think he is. It looks like they were pumping the model size up, started getting diminishing returns, and so turned to a mixed model of 8 expert systems meet LLMs - to try to keep squeezing out just a bit more juice. I think Geohot offered an incredibly insightful quote, "...whenever a company is secretive, it's because they're hiding something that's not that cool. And people have this wrong idea over and over again that they think they're hiding it because it's really cool." It also goes a long way to explain their recent, ultimately unsuccessful, gambit in congress. If they don't see any way of overcoming the diminishing returns, then they're like the guy who got a headstart in a race where distance = time ^ (1/2). [1] - https://www.latent.space/p/geohot#details https://www.latent.space/p/geohot#details
- famouswaffles 3y ago
  Turning to MoE now that we see they don't have to underperform dense counterparts has nothing to do with diminishing returns. It's simply more economically viable
  - somenameforme 3y ago
    With a multi expert system you end up making way more inferences per query, and depending on exactly how its built up, you may even end up requiring more training as well. There could be some extremely domain specific way they're saving some money, somehow, but it seems generally unlikely. It's all about diminishing returns. OpenAI themselves have also publicly acknowledged they're hitting diminishing returns on model size. [1] In any case this happens in literally every single domain that uses neural networks. You make this extremely rapid and unbelievably progress which leads one to start looking forward to where this is leading, and the literally infinite possibilities. But then at some point each single percent point improvement starts costing more and more. Initially a bit more compute can help you get over these hurdles, but then it becomes clear you're headed towards an asymptote that's far from where you want to be. It's why genuine fully self driving vehicles look much further away today than they did ~8 years ago. [1] - https://www.wired.com/story/openai-ceo-sam-altman-the-age-of-giant-ai-models-is-already-over/ https://www.wired.com/story/openai-ceo-sam-altman-the-age-of...
    - famouswaffles 3y ago
      They're saving money by the simple fact that sparse models are much less expensive to train and infere than dense equivalents. >OpenAI themselves have also publicly acknowledged they're hitting diminishing returns on model size. [1] Do people even properly read this ? Altman acknowledged they were hitting economic wall scaling not that they thought they couldn't get better performance scaling further. Ilya believes there is plenty performance left to squeeze still. There was even an interview specifically correcting this misinformation.
gremlinsinc 3y ago
Our brains are the same way. The me that went to sleep last night is not the same one that wakes up today or tomorrow. I'll have new experiences, maybe even changes in ways of thinking or believing. Maybe I get covid and lose an entire sense (smell). From minute to minute sometimes it also feels like there's different beings in us, the angry version, the happy version, the loving version, the sad version, etc.. Different parts of our brain even handle some different ways we think or feel, could each of these parts stand alone on their own? I think it makes a lot of sense that as we have different parts to our brain, so should AI's have different parts for different functions.

It challenges the notion of individuality. Where does one AI begin and another end? A distributed super-intelligence is exciting. It will be interesting how long it can remain aligned with itself.

ChatGTP 3y ago

I like to toy with the idea that if AIs became self-aware,they'd probably gobble up 95% of their time just trying to compete with each other...like us. Hopefully they have that war on another planet...

koheripbal 3y ago
I imagine all the factions in that war will include integrated collections of both AIs and humans, with "individuals" fulfilling different roles within each team, perhaps with each faction representing some underlying ideology. I wonder if/how humans will physically/cognitively integrate with the AIs in their group, in some sort of symbiotic hybrid fashion. It's all really mind-boggling when you think about it.
pixl97 3y ago
This is one of the existential scenarios that's talked about some times. AI fights AI and we are collateral damage as the things massive replicate and blow each other up. Or, an ASI becomes dominate and realizes that humans will be a risk and dumbs us down to reduce that risk, or exterminates us to remove that chess piece from the table.
- gremlinsinc 3y ago
  This is literally the plot for every transformer movie.

somenameforme 3y ago

You might be disappointed.. It seems to have been weakly confirmed by Geohot that this sort of mixed system is already what GPT-4's 'secret sauce' is. [1] It's something I've also been speculating for months on. Ctrl+F for "220 billion in each". His phrasing, numbers, and details are suggestive of a leak unless he's just completely blowing smoke - and I don't think there's any real reason to think he is. It looks like they were pumping the model size up, started getting diminishing returns, and so turned to a mixed model of 8 expert systems meet LLMs - to try to keep squeezing out just a bit more juice. I think Geohot offered an incredibly insightful quote, "...whenever a company is secretive, it's because they're hiding something that's not that cool. And people have this wrong idea over and over again that they think they're hiding it because it's really cool." It also goes a long way to explain their recent, ultimately unsuccessful, gambit in congress. If they don't see any way of overcoming the diminishing returns, then they're like the guy who got a headstart in a race where distance = time ^ (1/2). [1] - https://www.latent.space/p/geohot#details https://www.latent.space/p/geohot#details

famouswaffles 3y ago
Turning to MoE now that we see they don't have to underperform dense counterparts has nothing to do with diminishing returns. It's simply more economically viable
- somenameforme 3y ago
  With a multi expert system you end up making way more inferences per query, and depending on exactly how its built up, you may even end up requiring more training as well. There could be some extremely domain specific way they're saving some money, somehow, but it seems generally unlikely. It's all about diminishing returns. OpenAI themselves have also publicly acknowledged they're hitting diminishing returns on model size. [1] In any case this happens in literally every single domain that uses neural networks. You make this extremely rapid and unbelievably progress which leads one to start looking forward to where this is leading, and the literally infinite possibilities. But then at some point each single percent point improvement starts costing more and more. Initially a bit more compute can help you get over these hurdles, but then it becomes clear you're headed towards an asymptote that's far from where you want to be. It's why genuine fully self driving vehicles look much further away today than they did ~8 years ago. [1] - https://www.wired.com/story/openai-ceo-sam-altman-the-age-of-giant-ai-models-is-already-over/ https://www.wired.com/story/openai-ceo-sam-altman-the-age-of...
  - famouswaffles 3y ago
    They're saving money by the simple fact that sparse models are much less expensive to train and infere than dense equivalents. >OpenAI themselves have also publicly acknowledged they're hitting diminishing returns on model size. [1] Do people even properly read this ? Altman acknowledged they were hitting economic wall scaling not that they thought they couldn't get better performance scaling further. Ilya believes there is plenty performance left to squeeze still. There was even an interview specifically correcting this misinformation.

gremlinsinc 3y ago

Our brains are the same way. The me that went to sleep last night is not the same one that wakes up today or tomorrow. I'll have new experiences, maybe even changes in ways of thinking or believing. Maybe I get covid and lose an entire sense (smell). From minute to minute sometimes it also feels like there's different beings in us, the angry version, the happy version, the loving version, the sad version, etc.. Different parts of our brain even handle some different ways we think or feel, could each of these parts stand alone on their own? I think it makes a lot of sense that as we have different parts to our brain, so should AI's have different parts for different functions.