6 ms·

Their focus is long running digital intelligences that interact with their environment over long periods of time. I enjoyed reading The Alberta Plan when it was

Their focus is long running digital intelligences that interact with their environment over long periods of time. I enjoyed reading The Alberta Plan when it was released earlier this year https://arxiv.org/abs/2208.11173 https://arxiv.org/abs/2208.11173

JKCalhoun 3y ago
Ethics/privacy aside ... I wondered the other day what the result would be if you strapped a microphone on a baby and every utterance that both the baby and mic heard was fed through speech-to-text and went toward training the baby's personal LLM. By the time the child had grown to an adult I wonder what kind of results their LLM would produce and the degree to which it might compare to what the child (now grown) would answer? An "LLM" that could take in visuals via a baby-mounted camera is of course a whole other discussion. (Though I'm sure there are people training ANN's with video feeds rather than the static images that feed systems like DALL•E.)
- volent 3y ago
  I thought about this too. People say that LLM models are only saying the most common tokens that come after the previous token. And that this makes them incomparable to human intelligence. But Humans are basically long running LLMs that are retrained in real-time. We are the product of our environment.
  - 4death4 3y ago
    What makes you think that? What we can LLMs are a specific architecture that literally does predict the next token, one at a time. This isn’t the only way to generate text. Perhaps humans use a method that synthesizes all the text at once. Or some other, unimaginable way.
    - FrustratedMonky 3y ago
      Not sure about everyone else, but I write one word at a time.
      - JKCalhoun 3y ago
        And often unsure about where I'm going until I get there, ha ha.
      - simondotau 3y ago
        To be fair, I rarely write more than a sentence or two in serial form, and I have often determined the point of a sentence before I write the first few words. Occasionally the act of writing out an idea in words clarifies or changes my mind about that idea, causing me to edit or rewrite what I’ve already written.
      - the_other 3y ago
        You may write one word at a time but the grammar of most languages forces their users to know what they’re going to write several words ahead of the current word.
        FrustratedMonky 3y ago
        Ok. Why do you think GPT isn't doing that? or is different? It might be calculating the next word, because you can only write one word at a time, but you can't say that the current next word isn't influenced by a few words ahead. Don't think the current understanding of what LLM's do internally can rule that out.
      - deleted 3y ago
        [deleted]
      - ofjcihen 3y ago
        But are you writing to write or are you writing to convey a thought?
      - dartos 3y ago
        Yeah but do you write that one word based on the past 1000 or so words that come before it and how each of those words relate to each other with a perfect recollection of each word at the same time? And if you believe you do that subconsciously, then how can you be sure you don’t subconsciously plan a few words ahead?
  - TerrifiedMouse 3y ago
    Using that logic regular computers are AI too. Output is dependent on input - i.e. they are a product of their environment.
  - xwowsersx 3y ago
    The claim that "humans are basically long running LLMs" oversimplifies the complexity of human cognition and experience. Humans don't just process information; they experience emotions, desires, and subjective experiences that are deeply intertwined with their cognition. LLMs don't have feelings, motivations, or consciousness. Humans have inner subjective experience, self-awareness, and the ability to reflect on our own existence. LLMs don't. Humans can adapt to a wide range of environments and situations, drawing from a complex interplay of instincts, learned behaviors, emotions, and rational thought. LLMs are much more limited in their adaptability, since they focus primarily on the tasks they were designed for. Human cognition has evolved over millions of years and is rooted in a complex biological system, the brain. Yes, both LLMs and human brains process information, but the underlying mechanisms, structures, and functions are vastly different. I really wish people would stop this sort of cavalier reductionism of humans by saying we are basically LLMs. It isn't true.
    - JKCalhoun 3y ago
      I believe though a significant part of our mind is very much like an LLM. Not all of it of course.
      - geraldwhen 3y ago
        Yes, the parts of the brain attempting to apply correct grammar to a given idea are likely quite similar. As in, the part with producing language. I.e large language model.
    - specialist 3y ago
      > Humans have inner subjective experience, self-awareness, and the ability to reflect on our own existence. "In the end, we are self-perceiving, self-inventing, locked-in mirages that are little miracles of self-reference." — Douglas Hofstadter, I Am a Strange Loop, p. 363 https://en.wikipedia.org/wiki/I_Am_a_Strange_Loop https://en.wikipedia.org/wiki/I_Am_a_Strange_Loop (Knowing nothing about AI, I have no idea how Hofstadter's philosophies have held up since.)
    - ben_w 3y ago
  - dartos 3y ago
    You’re vastly oversimplifying humans. That’s like saying a physics simulation is basically an entire sub universe on your computer. It sounds true, but it’s just not. It’s a gross oversimplification I think Gödel had a proof for how it’s impossible to fully describe a system from within that system. That’s the nail in the coffin for AGI. No matter how much data we give it, no matter how big it is, it’ll never be “human intelligent” since it’s impossible for us to describe a loss function for being human or describe being human in a dataset. We’ll never be able to evaluate it, since we can’t fully describe what it means to communicate because to do that we’d need to communicate it and that process can’t be fully self describing. Not to say AI isn’t useful or impressive, but it’ll never be comparable to humans, truly.
    - andsoitis 3y ago
      > I think Gödel had a proof for how it’s impossible to fully describe a system from within that system. Gödel's incompleteness theorems https://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_theorems https://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_...
    - nebulousthree 3y ago
      It doesn't have to be perfect. It just has to be convincing. That takes a lot less data and is much more tolerant to simplifications. Just look at the claims that were thrown around GPT's abilities at first.
    - deleted 3y ago
      [deleted]
    - Timon3 3y ago
      > I think Gödel had a proof for how it’s impossible to fully describe a system from within that system. That’s the nail in the coffin for AGI. Gödels theorems are about formal axiomatic theories. To apply them to human intelligence, you'd have to prove that human intelligence springs from formal axiomatic theories. I don't think this is possible, which would mean that you can't apply the theorems. > No matter how much data we give it, no matter how big it is, it’ll never be “human intelligent” since it’s impossible for us to describe a loss function for being human or describe being human in a dataset. How do you know? If we were able to fully record whatever is going on in someones brain, we should be able to build a loss function for it. How do you know that this is fundamentally impossible? > We’ll never be able to evaluate it, since we can’t fully describe what it means to communicate because to do that we’d need to communicate it and that process can’t be fully self describing. Why not? Again, if you argue that this is due to Gödels theorems, you'd have to prove that our communication itself is based on formal axiomatic theories.
  - deleted 3y ago
    [deleted]
  - danShumway 3y ago
    Sometimes people use LLMs very broadly to talk about neural networks overall. But to be clear, humans don't have emergent reasoning from language, we learn language as part of our overall reasoning. The short evidence for that being that children are capable of solving logic and spatial puzzles before they learn how to speak. Humans learn concepts like object permanence before we learn language complicated enough to describe that concept. And obviously people are capable of reasoning without learning how to write or interpret text tokens, there are plenty of illiterate people in the world who are nonetheless indisputably intelligent agents. So ignoring other differences about how prediction works, humans are not similar to LLMs in the sense that LLMs are language models that when large enough either develop (or appear to develop depending on who you ask) reasoning capabilities. And that's not how humans work; we don't learn text tokens before we learn how to reason. But very often when people make this claim they're trying to make a broader claim about neural networks or the role of prediction in learning in general. People might disagree or agree with the broader claim, I still think it oversimplifies how humans work, but the point is -- they're not actually saying something specific about LLMs, even though it sounds that way sometimes. It's just that the terminology gets conflated in people's heads. We can have a debate about the similarities and differences between humans and neural networks, but I don't think anyone would seriously claim that GPT-4 in specific works the same way as a human does. I think people are using LLMs to refer to a broader category of AI research.
    - FrustratedMonky 3y ago
      Definitely, LLM's have gotten so much press, that many people arguing about 'AI', are thinking about LLM. And, LLM's are not all that a human can do. Language is not everything about a human. But there is an argument that there is part of the brain that produces language, and it has some LLM characteristics. It's just that the brain is bigger and does more than an LLM. So the brain is not an LLM. The brain has many components. What happens when you take the problem solving of something like AlphaGo/AlphaStar, with the Vision processing in Cars or DaLLe, and the language processing in LLM. Add in hearing, touch. It starts to look like the components of a brain.
      - danShumway 3y ago
- garblegarble 3y ago
  >I wondered the other day what the result would be if you strapped a microphone on a baby and every utterance that both the baby and mic heard was fed through speech-to-text and went toward training the baby's personal LLM. I read about a paper[1] a while back, it was a rather unpleasant animal study in cats. Using two kittens, one was free to look around, but the other they immobilised in some way, and made it see what the other kitten saw as it looked around. They discovered the immobilised kitten's visual processing did not develop normally whereas the mobile kitten's did, suggesting that it's not just the sensory input, but it being feedback to some internal agency within the brain. I suspect the same thing would be true for attempts to develop AGI by giving them an audio-visual copy of a human's environment growing up: that internal state driving (and getting feedback from) the action to investigate/interact with the world is key. 1: https://arxiv.org/abs/1604.03670 https://arxiv.org/abs/1604.03670 "Interactive Perception: Leveraging Action in Perception and Perception in Action"
  - spockz 3y ago
    I suppose a large part of learning is not just seeing things happen but also deciding to do something, executing on it, and then feeling the result. So not surprising.
  - AJRF 3y ago
    Curious to know how you found this paper? I'd like to be able to find interesting papers but I've not found a place of curation unfortunately.
    - garblegarble 3y ago
      I'm afraid I don't remember how I found this paper, but it might be from following Dr Abeba Birhane[1], she's an anti-AI academic (which is why I followed her originally, to get reality checks / contrasting viewpoints), but she also posts and boosts a lot of interesting cognitive science (her field). I also follow Olivia Guest[2] for the same reason. Many of the papers go over my head at times of course (despite my interest, I'm firmly a layman!) More generally, I've had a lot of success with searching for meta-analysis papers on topics I'm interested in, since the authors have already done the hard work of researching the literature and presenting core takeaways (as well as providing terms for more searches). 1: https://bsky.app/profile/abeba.bsky.social https://bsky.app/profile/abeba.bsky.social or https://twitter.com/Abebab https://twitter.com/Abebab 2: https://bsky.app/profile/olivia.science https://bsky.app/profile/olivia.science or https://twitter.com/o_guest https://twitter.com/o_guest
- shrimp_emoji 3y ago
  Obligatory link: https://qntm.org/lena https://qntm.org/lena
  - HanClinto 3y ago
    This was a delightful read, thank you!
- layer8 3y ago
  You might enjoy this short story: http://hubski.com/pub/78001 http://hubski.com/pub/78001 Published in 1990, by the way.
  - HanClinto 3y ago
    That was a gem. Thank you for sharing!
    - layer8 3y ago
      I recommend the Axiomatic anthology which contains this short story.
- deleted 3y ago
  [deleted]
- zellyn 3y ago
  Like this? https://www.fastcompany.com/1733627/mit-scientist-captures-90000-hours-video-his-sons-first-words-graphs-it https://www.fastcompany.com/1733627/mit-scientist-captures-9...
- beremaki 3y ago
  > By the time the child had grown to an adult I wonder what kind of results their LLM would produce and the degree to which it might compare to what the child (now grown) would answer? It very likely would not compare. Humans are shaped by their subjective experiences not by incoming data and you cannot know what a subject is experiencing solely through the incoming data (except if you have a theory accurately modeling the mind of the subject which is exactly what we are currently missing). Your LLM won't experience the subject's heartbreaks, joy, grief, shame, hope etc. It will have heard and be able to talk about those, but it will not give accurate answers about what it felt like. Also it won't be able to predict/model accurately how the subject has been changed by those experiences so it could make very wrong assumptions about what the subject could/would do in the future.
- AndrewKemendo 3y ago
  This is the long term goal of any Augmented Reality interface IMO This is why I got into AR initially, because a computing system needs the input persistence of a literal parent or in the case of a self learning agent - something like a baby to perfectly observe in order to be able to create the data environment necessary for learning at the rate humans learn. You could do it with a collection of sensors, but I think the idealized implementation is basically a perfect recreation of the sense inputs of a person as well as monitoring the person to infer the precise Markov Decision Process.

JKCalhoun 3y ago

Ethics/privacy aside ... I wondered the other day what the result would be if you strapped a microphone on a baby and every utterance that both the baby and mic heard was fed through speech-to-text and went toward training the baby's personal LLM. By the time the child had grown to an adult I wonder what kind of results their LLM would produce and the degree to which it might compare to what the child (now grown) would answer? An "LLM" that could take in visuals via a baby-mounted camera is of course a whole other discussion. (Though I'm sure there are people training ANN's with video feeds rather than the static images that feed systems like DALL•E.)

volent 3y ago
I thought about this too. People say that LLM models are only saying the most common tokens that come after the previous token. And that this makes them incomparable to human intelligence. But Humans are basically long running LLMs that are retrained in real-time. We are the product of our environment.
- 4death4 3y ago
  What makes you think that? What we can LLMs are a specific architecture that literally does predict the next token, one at a time. This isn’t the only way to generate text. Perhaps humans use a method that synthesizes all the text at once. Or some other, unimaginable way.
  - FrustratedMonky 3y ago
    Not sure about everyone else, but I write one word at a time.
    - JKCalhoun 3y ago
      And often unsure about where I'm going until I get there, ha ha.
    - simondotau 3y ago
      To be fair, I rarely write more than a sentence or two in serial form, and I have often determined the point of a sentence before I write the first few words. Occasionally the act of writing out an idea in words clarifies or changes my mind about that idea, causing me to edit or rewrite what I’ve already written.
    - the_other 3y ago
      You may write one word at a time but the grammar of most languages forces their users to know what they’re going to write several words ahead of the current word.
      - FrustratedMonky 3y ago
        Ok. Why do you think GPT isn't doing that? or is different? It might be calculating the next word, because you can only write one word at a time, but you can't say that the current next word isn't influenced by a few words ahead. Don't think the current understanding of what LLM's do internally can rule that out.
    - deleted 3y ago
      [deleted]
    - ofjcihen 3y ago
      But are you writing to write or are you writing to convey a thought?
    - dartos 3y ago
      Yeah but do you write that one word based on the past 1000 or so words that come before it and how each of those words relate to each other with a perfect recollection of each word at the same time? And if you believe you do that subconsciously, then how can you be sure you don’t subconsciously plan a few words ahead?
- TerrifiedMouse 3y ago
  Using that logic regular computers are AI too. Output is dependent on input - i.e. they are a product of their environment.
- xwowsersx 3y ago
  The claim that "humans are basically long running LLMs" oversimplifies the complexity of human cognition and experience. Humans don't just process information; they experience emotions, desires, and subjective experiences that are deeply intertwined with their cognition. LLMs don't have feelings, motivations, or consciousness. Humans have inner subjective experience, self-awareness, and the ability to reflect on our own existence. LLMs don't. Humans can adapt to a wide range of environments and situations, drawing from a complex interplay of instincts, learned behaviors, emotions, and rational thought. LLMs are much more limited in their adaptability, since they focus primarily on the tasks they were designed for. Human cognition has evolved over millions of years and is rooted in a complex biological system, the brain. Yes, both LLMs and human brains process information, but the underlying mechanisms, structures, and functions are vastly different. I really wish people would stop this sort of cavalier reductionism of humans by saying we are basically LLMs. It isn't true.
  - JKCalhoun 3y ago
    I believe though a significant part of our mind is very much like an LLM. Not all of it of course.
    - geraldwhen 3y ago
      Yes, the parts of the brain attempting to apply correct grammar to a given idea are likely quite similar. As in, the part with producing language. I.e large language model.
  - specialist 3y ago
    > Humans have inner subjective experience, self-awareness, and the ability to reflect on our own existence. "In the end, we are self-perceiving, self-inventing, locked-in mirages that are little miracles of self-reference." — Douglas Hofstadter, I Am a Strange Loop, p. 363 https://en.wikipedia.org/wiki/I_Am_a_Strange_Loop https://en.wikipedia.org/wiki/I_Am_a_Strange_Loop (Knowing nothing about AI, I have no idea how Hofstadter's philosophies have held up since.)
  - ben_w 3y ago
- dartos 3y ago
  You’re vastly oversimplifying humans. That’s like saying a physics simulation is basically an entire sub universe on your computer. It sounds true, but it’s just not. It’s a gross oversimplification I think Gödel had a proof for how it’s impossible to fully describe a system from within that system. That’s the nail in the coffin for AGI. No matter how much data we give it, no matter how big it is, it’ll never be “human intelligent” since it’s impossible for us to describe a loss function for being human or describe being human in a dataset. We’ll never be able to evaluate it, since we can’t fully describe what it means to communicate because to do that we’d need to communicate it and that process can’t be fully self describing. Not to say AI isn’t useful or impressive, but it’ll never be comparable to humans, truly.
  - andsoitis 3y ago
    > I think Gödel had a proof for how it’s impossible to fully describe a system from within that system. Gödel's incompleteness theorems https://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_theorems https://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_...
  - nebulousthree 3y ago
    It doesn't have to be perfect. It just has to be convincing. That takes a lot less data and is much more tolerant to simplifications. Just look at the claims that were thrown around GPT's abilities at first.
  - deleted 3y ago
    [deleted]
  - Timon3 3y ago
    > I think Gödel had a proof for how it’s impossible to fully describe a system from within that system. That’s the nail in the coffin for AGI. Gödels theorems are about formal axiomatic theories. To apply them to human intelligence, you'd have to prove that human intelligence springs from formal axiomatic theories. I don't think this is possible, which would mean that you can't apply the theorems. > No matter how much data we give it, no matter how big it is, it’ll never be “human intelligent” since it’s impossible for us to describe a loss function for being human or describe being human in a dataset. How do you know? If we were able to fully record whatever is going on in someones brain, we should be able to build a loss function for it. How do you know that this is fundamentally impossible? > We’ll never be able to evaluate it, since we can’t fully describe what it means to communicate because to do that we’d need to communicate it and that process can’t be fully self describing. Why not? Again, if you argue that this is due to Gödels theorems, you'd have to prove that our communication itself is based on formal axiomatic theories.
- deleted 3y ago
  [deleted]
- danShumway 3y ago
  Sometimes people use LLMs very broadly to talk about neural networks overall. But to be clear, humans don't have emergent reasoning from language, we learn language as part of our overall reasoning. The short evidence for that being that children are capable of solving logic and spatial puzzles before they learn how to speak. Humans learn concepts like object permanence before we learn language complicated enough to describe that concept. And obviously people are capable of reasoning without learning how to write or interpret text tokens, there are plenty of illiterate people in the world who are nonetheless indisputably intelligent agents. So ignoring other differences about how prediction works, humans are not similar to LLMs in the sense that LLMs are language models that when large enough either develop (or appear to develop depending on who you ask) reasoning capabilities. And that's not how humans work; we don't learn text tokens before we learn how to reason. But very often when people make this claim they're trying to make a broader claim about neural networks or the role of prediction in learning in general. People might disagree or agree with the broader claim, I still think it oversimplifies how humans work, but the point is -- they're not actually saying something specific about LLMs, even though it sounds that way sometimes. It's just that the terminology gets conflated in people's heads. We can have a debate about the similarities and differences between humans and neural networks, but I don't think anyone would seriously claim that GPT-4 in specific works the same way as a human does. I think people are using LLMs to refer to a broader category of AI research.
  - FrustratedMonky 3y ago
    Definitely, LLM's have gotten so much press, that many people arguing about 'AI', are thinking about LLM. And, LLM's are not all that a human can do. Language is not everything about a human. But there is an argument that there is part of the brain that produces language, and it has some LLM characteristics. It's just that the brain is bigger and does more than an LLM. So the brain is not an LLM. The brain has many components. What happens when you take the problem solving of something like AlphaGo/AlphaStar, with the Vision processing in Cars or DaLLe, and the language processing in LLM. Add in hearing, touch. It starts to look like the components of a brain.
    - danShumway 3y ago
garblegarble 3y ago
>I wondered the other day what the result would be if you strapped a microphone on a baby and every utterance that both the baby and mic heard was fed through speech-to-text and went toward training the baby's personal LLM. I read about a paper[1] a while back, it was a rather unpleasant animal study in cats. Using two kittens, one was free to look around, but the other they immobilised in some way, and made it see what the other kitten saw as it looked around. They discovered the immobilised kitten's visual processing did not develop normally whereas the mobile kitten's did, suggesting that it's not just the sensory input, but it being feedback to some internal agency within the brain. I suspect the same thing would be true for attempts to develop AGI by giving them an audio-visual copy of a human's environment growing up: that internal state driving (and getting feedback from) the action to investigate/interact with the world is key. 1: https://arxiv.org/abs/1604.03670 https://arxiv.org/abs/1604.03670 "Interactive Perception: Leveraging Action in Perception and Perception in Action"
- spockz 3y ago
  I suppose a large part of learning is not just seeing things happen but also deciding to do something, executing on it, and then feeling the result. So not surprising.
- AJRF 3y ago
  Curious to know how you found this paper? I'd like to be able to find interesting papers but I've not found a place of curation unfortunately.
  - garblegarble 3y ago
    I'm afraid I don't remember how I found this paper, but it might be from following Dr Abeba Birhane[1], she's an anti-AI academic (which is why I followed her originally, to get reality checks / contrasting viewpoints), but she also posts and boosts a lot of interesting cognitive science (her field). I also follow Olivia Guest[2] for the same reason. Many of the papers go over my head at times of course (despite my interest, I'm firmly a layman!) More generally, I've had a lot of success with searching for meta-analysis papers on topics I'm interested in, since the authors have already done the hard work of researching the literature and presenting core takeaways (as well as providing terms for more searches). 1: https://bsky.app/profile/abeba.bsky.social https://bsky.app/profile/abeba.bsky.social or https://twitter.com/Abebab https://twitter.com/Abebab 2: https://bsky.app/profile/olivia.science https://bsky.app/profile/olivia.science or https://twitter.com/o_guest https://twitter.com/o_guest
shrimp_emoji 3y ago
Obligatory link: https://qntm.org/lena https://qntm.org/lena
- HanClinto 3y ago
  This was a delightful read, thank you!
layer8 3y ago
You might enjoy this short story: http://hubski.com/pub/78001 http://hubski.com/pub/78001 Published in 1990, by the way.
- HanClinto 3y ago
  That was a gem. Thank you for sharing!
  - layer8 3y ago
    I recommend the Axiomatic anthology which contains this short story.
deleted 3y ago
[deleted]
zellyn 3y ago
Like this? https://www.fastcompany.com/1733627/mit-scientist-captures-90000-hours-video-his-sons-first-words-graphs-it https://www.fastcompany.com/1733627/mit-scientist-captures-9...
beremaki 3y ago
> By the time the child had grown to an adult I wonder what kind of results their LLM would produce and the degree to which it might compare to what the child (now grown) would answer? It very likely would not compare. Humans are shaped by their subjective experiences not by incoming data and you cannot know what a subject is experiencing solely through the incoming data (except if you have a theory accurately modeling the mind of the subject which is exactly what we are currently missing). Your LLM won't experience the subject's heartbreaks, joy, grief, shame, hope etc. It will have heard and be able to talk about those, but it will not give accurate answers about what it felt like. Also it won't be able to predict/model accurately how the subject has been changed by those experiences so it could make very wrong assumptions about what the subject could/would do in the future.
AndrewKemendo 3y ago
This is the long term goal of any Augmented Reality interface IMO This is why I got into AR initially, because a computing system needs the input persistence of a literal parent or in the case of a self learning agent - something like a baby to perfectly observe in order to be able to create the data environment necessary for learning at the rate humans learn. You could do it with a collection of sensors, but I think the idealized implementation is basically a perfect recreation of the sense inputs of a person as well as monitoring the person to infer the precise Markov Decision Process.