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> Current language models are trained on datasets fast approaching “all the text, ever”. What happen when it runs out? Robots. To reduce hallucinations our AI

> Current language models are trained on datasets fast approaching “all the text, ever”. What happen when it runs out? Robots. To reduce hallucinations our AI models need more grounding in the real world. No matter how smart an AI is it won't be able to magically come up with answer to any possible question just by sitting and thinking about it. AIs will need to do experiments and science just as we do. To maximize the amount of data AIs can train on, we need robots to enable AIs to do their own science in the physical world. Then there is no limit to the data they can gather.

SketchySeaBeast 2y ago
Doesn't this then turn into a problem of sample quantity? You would need to shift into a quality mindset because with a robot you can't perform a billion iterations, you're locked into much more complex world with unavoidably real time interactions. Failure is suddenly very costly.
- modeless 2y ago
  With a million robots you can perform a billion iterations. We won't need a billion iterations on every task; we will start to see generalization and task transfer just as we did for LLMs once we have LLM-scale data. You are right that failure is costly with today's robots. We need to reduce the cost of failure. That means cheaper and more robust robots. Robots that, like a toddler, can jump off a couch and fall over and still be OK. Tying back to the article, this is the real evolutionary advantage that humans have over AIs. Not innate language skills or anything about the brain. It's our highly optimized, perceptive, robust, reliable, self-repairing, fail-safe, and efficient bodies, allowing us to experiment and learn in the real physical world.
  - dingnuts 2y ago
    > robust, reliable, self-repairing, fail-safe, and efficient bodies you must be young and healthy because I cannot imagine using any of these words to describe this continuously decaying mortal coil in which we are all trapped and doomed
    - modeless 2y ago
      I wish! Hopefully AI can help with that too, but (contra Kurzweil) I fear medicine moves too slowly and it is already too late to save our generation from aging. Hopefully our kids can reap the benefits.
- guitheeengineer 2y ago
  AI's advantage would be that their learning can be shared For example if Robot 0002 learns that trying to move a pan without using the handle is a bad idea, Robot 0001 would get that update (even if it came before)
  - SketchySeaBeast 2y ago
    But that ends up with weirdly dogmatic rules because it's not always a bad idea to move a pan without using the handle, it's just in some situations. It still takes a ton of potentially destructive iterations to be sure of something.
    - guitheeengineer 2y ago
      Yea its tricky and costly. I believe we should bet on specificity to make this more optimal. I know the trend with AI is to keep the scope generic so it can tackle different domains and look more like us, but I believe that even if we reach that, we'll always come back to make it better for a specific skill set, because we also do that as humans. No reason for an AI driver to know how to cook. If we narrow the domain as much as possible it will cut the number of experiments it needs to do significantly Edit: I wonder if its even going to be useful to devote so much resources into making a machine as similar as us as possible. We don't want a plane to fly like a bird, even if we could build it.
    - kridsdale3 2y ago
      Then we will continue to have a Temperature variable in the Action Models.
Mistletoe 2y ago
My roomba can't do the whole room without screwing up or getting stuck, it feels like we are eons away from a robot being able to do what you describe autonomously.
- modeless 2y ago
  A few short years ago we were eons away from passing the Turing test.
  - jcgrillo 2y ago
    Are we not still? How can you tell?
  - marcosdumay 2y ago
    What? There has been some odd people fooled by computers since the 70s, and 10 years ago we had competitions where the best hint people claimed they had was if their interlocutors were good at math or not. It was clear 10 years ago that the Turing test would be beat soon.
    - modeless 2y ago
      My point is really that a lot of people thought we were eons away. "It was clear" to you, but your opinion was not shared by the majority probably until at least GPT-2 at the very earliest (2019). (Note that the sibling comment to yours still doesn't believe we are close...) Similarly, today a lot of people think we are eons away from useful general purpose robots, but it is clear to me that they are coming soon.
      - marcosdumay 2y ago
        That we'd beat the Turing test soon was a majoritarian opinion around here. Honestly, I wasn't even that well informed to make my own. I kept postponing looking at the chatbots until after it was beat.
AStonesThrow 2y ago
Imagine if NASA-JPL had an LLM connected to all their active spacecraft, and at the terminal you could just type, "Hey V'Ger, how are conditions on Phobos over the past Martian Year?"
- kevin_thibedeau 2y ago
  But then you'd have to have a NASA department of machine psychology to ensure it isn't hallucinating or going crazy.
- skydhash 2y ago
  And then it replies: "Cold" The human language is great, but it fails utterly on some tasks. Which is why we have all the jargon in specialized environment. I'd take any system with a reduced command interface that works well than one that takes generic commands and tries to infer what I mean (meaning it will get it wrong most of the time),especially for vocal interface.
  - AStonesThrow 2y ago
    1966 called, and they're asking to speak to DWIM... https://en.wikipedia.org/wiki/DWIM https://en.wikipedia.org/wiki/DWIM
dimatura 2y ago
I think it's reasonable to argue that data acquired via a sensorimotor loop in an embodied agent will go beyond what you can learn passively from a trove of internet data, but this argument goes beyond that - the "data" in evolution is "learned" (in a fashion) not just from a single agent, but from millions of agents, even those that didn't survive to replicate (the "selection", of course, being a key part of evolution). A neat thing about the kind of artificial robots we build now is that the process can be massively sped up compared to the plodding trial and error of natural evolution.
- modeless 2y ago
  Exactly. We have huge advantages over evolution in some regards. All of the experience from every robot can be combined into a single agent, so even if AI is not as sample efficient as human brains it could still far surpass us. And honestly the jury is still out on sample efficiency. We haven't yet attempted to train models on the same kind of data a human child gets, and once we do we may find that we are not as far away from the brain's sample efficiency as we thought.
  - jcgrillo 2y ago
    > All of the experience from every robot can be combined into a single agent I'm not so sure. It's not obvious that experience combines linearly, so you'll have to somehow figure out how to make the combination work in such a way that it doesn't mess up the receiver too badly--you still want some individuality among the robot fleet right?
    - dimatura 2y ago
      That's interesting to think about. I'm not familiar with the literature on this but I'm 100% sure there's some interesting work on it (and related fields such as distributed and federated learning). I guess the simplest solution would be "centralized" - periodically aggregate all the raw data from all robots, train a model with all the data, redistribute the model. In that case there wouldn't be any "individuality", but (maybe again, by analogy with evolution) one could think it'd be advantageous to have some. But even if all the models all the same, the robots might be different types and operate in different environments, which raises issues of generalizability, transferability and specialization. Either way the centralized would have some scaling problems, naturally - some way to transfer/aggregate experience (possibly "peer to peer") without resorting to training from raw data then becomes attractive, and I'm sure something people are working on. It does turn out that at least in some recent LLMs, weights appear to be sort of linear and people have been using that to merge them with fairly naive methods with good results.
seunosewa 2y ago
Additional answer: we generate more text every day.

SketchySeaBeast 2y ago

Doesn't this then turn into a problem of sample quantity? You would need to shift into a quality mindset because with a robot you can't perform a billion iterations, you're locked into much more complex world with unavoidably real time interactions. Failure is suddenly very costly.

modeless 2y ago
With a million robots you can perform a billion iterations. We won't need a billion iterations on every task; we will start to see generalization and task transfer just as we did for LLMs once we have LLM-scale data. You are right that failure is costly with today's robots. We need to reduce the cost of failure. That means cheaper and more robust robots. Robots that, like a toddler, can jump off a couch and fall over and still be OK. Tying back to the article, this is the real evolutionary advantage that humans have over AIs. Not innate language skills or anything about the brain. It's our highly optimized, perceptive, robust, reliable, self-repairing, fail-safe, and efficient bodies, allowing us to experiment and learn in the real physical world.
- dingnuts 2y ago
  > robust, reliable, self-repairing, fail-safe, and efficient bodies you must be young and healthy because I cannot imagine using any of these words to describe this continuously decaying mortal coil in which we are all trapped and doomed
  - modeless 2y ago
    I wish! Hopefully AI can help with that too, but (contra Kurzweil) I fear medicine moves too slowly and it is already too late to save our generation from aging. Hopefully our kids can reap the benefits.
guitheeengineer 2y ago
AI's advantage would be that their learning can be shared For example if Robot 0002 learns that trying to move a pan without using the handle is a bad idea, Robot 0001 would get that update (even if it came before)
- SketchySeaBeast 2y ago
  But that ends up with weirdly dogmatic rules because it's not always a bad idea to move a pan without using the handle, it's just in some situations. It still takes a ton of potentially destructive iterations to be sure of something.
  - guitheeengineer 2y ago
    Yea its tricky and costly. I believe we should bet on specificity to make this more optimal. I know the trend with AI is to keep the scope generic so it can tackle different domains and look more like us, but I believe that even if we reach that, we'll always come back to make it better for a specific skill set, because we also do that as humans. No reason for an AI driver to know how to cook. If we narrow the domain as much as possible it will cut the number of experiments it needs to do significantly Edit: I wonder if its even going to be useful to devote so much resources into making a machine as similar as us as possible. We don't want a plane to fly like a bird, even if we could build it.
  - kridsdale3 2y ago
    Then we will continue to have a Temperature variable in the Action Models.

Mistletoe 2y ago

My roomba can't do the whole room without screwing up or getting stuck, it feels like we are eons away from a robot being able to do what you describe autonomously.

modeless 2y ago
A few short years ago we were eons away from passing the Turing test.
- jcgrillo 2y ago
  Are we not still? How can you tell?
- marcosdumay 2y ago
  What? There has been some odd people fooled by computers since the 70s, and 10 years ago we had competitions where the best hint people claimed they had was if their interlocutors were good at math or not. It was clear 10 years ago that the Turing test would be beat soon.
  - modeless 2y ago
    My point is really that a lot of people thought we were eons away. "It was clear" to you, but your opinion was not shared by the majority probably until at least GPT-2 at the very earliest (2019). (Note that the sibling comment to yours still doesn't believe we are close...) Similarly, today a lot of people think we are eons away from useful general purpose robots, but it is clear to me that they are coming soon.
    - marcosdumay 2y ago
      That we'd beat the Turing test soon was a majoritarian opinion around here. Honestly, I wasn't even that well informed to make my own. I kept postponing looking at the chatbots until after it was beat.

AStonesThrow 2y ago

Imagine if NASA-JPL had an LLM connected to all their active spacecraft, and at the terminal you could just type, "Hey V'Ger, how are conditions on Phobos over the past Martian Year?"

kevin_thibedeau 2y ago
But then you'd have to have a NASA department of machine psychology to ensure it isn't hallucinating or going crazy.
skydhash 2y ago
And then it replies: "Cold" The human language is great, but it fails utterly on some tasks. Which is why we have all the jargon in specialized environment. I'd take any system with a reduced command interface that works well than one that takes generic commands and tries to infer what I mean (meaning it will get it wrong most of the time),especially for vocal interface.
- AStonesThrow 2y ago
  1966 called, and they're asking to speak to DWIM... https://en.wikipedia.org/wiki/DWIM https://en.wikipedia.org/wiki/DWIM

dimatura 2y ago

I think it's reasonable to argue that data acquired via a sensorimotor loop in an embodied agent will go beyond what you can learn passively from a trove of internet data, but this argument goes beyond that - the "data" in evolution is "learned" (in a fashion) not just from a single agent, but from millions of agents, even those that didn't survive to replicate (the "selection", of course, being a key part of evolution). A neat thing about the kind of artificial robots we build now is that the process can be massively sped up compared to the plodding trial and error of natural evolution.

modeless 2y ago
Exactly. We have huge advantages over evolution in some regards. All of the experience from every robot can be combined into a single agent, so even if AI is not as sample efficient as human brains it could still far surpass us. And honestly the jury is still out on sample efficiency. We haven't yet attempted to train models on the same kind of data a human child gets, and once we do we may find that we are not as far away from the brain's sample efficiency as we thought.
- jcgrillo 2y ago
  > All of the experience from every robot can be combined into a single agent I'm not so sure. It's not obvious that experience combines linearly, so you'll have to somehow figure out how to make the combination work in such a way that it doesn't mess up the receiver too badly--you still want some individuality among the robot fleet right?
  - dimatura 2y ago
    That's interesting to think about. I'm not familiar with the literature on this but I'm 100% sure there's some interesting work on it (and related fields such as distributed and federated learning). I guess the simplest solution would be "centralized" - periodically aggregate all the raw data from all robots, train a model with all the data, redistribute the model. In that case there wouldn't be any "individuality", but (maybe again, by analogy with evolution) one could think it'd be advantageous to have some. But even if all the models all the same, the robots might be different types and operate in different environments, which raises issues of generalizability, transferability and specialization. Either way the centralized would have some scaling problems, naturally - some way to transfer/aggregate experience (possibly "peer to peer") without resorting to training from raw data then becomes attractive, and I'm sure something people are working on. It does turn out that at least in some recent LLMs, weights appear to be sort of linear and people have been using that to merge them with fairly naive methods with good results.

seunosewa 2y ago

Additional answer: we generate more text every day.