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>But I fail to see how an AI whose entire domain of knowledge is limited to board games, or financial trading, would qualify as artificial general intelligence.

>But I fail to see how an AI whose entire domain of knowledge is limited to board games, or financial trading, would qualify as artificial general intelligence. Let me recast this: >I fail to see how an I whose entire domain of knowledge is limited to low energy physics, or the large mass limit, would ever qualify as general intelligence. You see the problem. You can extrapolate just fine beyond the reality of Newtonian physics and Euclidean geometry which is native to you. A trading bot AGI could talk about things that aren't money, it would just be using money related objects with money related properties to tell this narrative picture of a world which isn't money. Not too different from the sort of analogies we use all the time to map hard concepts back to the space we find natural. So let me answer your question with a question. What makes a substrate of properties like "size" and "color" and "texture" a better reality to be grounded in than properties like "buy price", "sell price", "dividend"? Is the former uniquely suited for intelligence to generalize beyond that given domain, or is it possible that all groundings are good as any other?

r_hoods_ghost 4y ago
Very interesting question! I would argue that concepts like "size", "colour", "texture" are grounded in a more base level of reality than concepts such as "buy price", "sell price", "dividend" etc. The former set of concepts relate to aspects of the physical world as perceived by creatures that inhabit that world, while the latter refer to social constructs of the human socio-economic world that are themselves reliant on abstract human concepts of utility, value, the market etc. This thought is not fully developed but I'm drawn to the idea that if an intelligence is grounded in an understanding of a more base level of reality it will find it easier to generalize beyond any one given domain. I could be entirely wrong of course.
- IIAOPSW 4y ago
  But how do you determine the basicness level of reality? And how do you know there isn't a layer lower than our own?
  - r_hoods_ghost 4y ago
    I'm philosophically comfortable with the notion that some things are unknowable and that eventually one just has to take certain cornerstone propositions about the nature of reality on trust and / or faith. Ultimately though the question is irrelevant to the discussion at hand, as the claim I'm making is that physical reality is at a lower level than socio economic reality (which I'll give you is a contested claim these days)
    - IIAOPSW 4y ago
      Yes, the systems we construct within this reality are obviously not the bottom of the stack because we built the stack. But suppose the trading bots figure out how to manipulate the rounding mechanics of high frequency trading to jerry rig a Turing machine within their reality. Now fast forward from this and suppose they've all obtained an equivalent of a home PC. For fun they all start playing a "sim city" like game version of our reality. So far as they can tell, money is real reality (rather than socio-economic construct), and the "physical" reality is an abstract game constructed at a higher level. So who's reality is who's construct in the end?
bravura 4y ago
I get your point, but you're making my head spin here from the sea-sickness involved in being asked to acknowledge that all viewpoints are equally valid. People care about human-like intelligence. That's the point. We also care about trading and physics. But when we talk about the specific question of human-like intelligence (and not other kinds of intelligence), the human reality is the appropriate substrate. Acknowledging, of course, that for non-human intelligence there might be other substrates and also acknowledging that non-human intelligence can be valuable to people. But let me take your argument to the extreme. In classical machine learning theory, there's a proof of the value of bias. Bias plays a crucial role in guiding the learning process. If all hypotheses are considered equally possible, learning becomes infeasible due to the lack of any preference or constraint on the hypothesis space. Bias, in this context, refers to the inherent assumptions a learning algorithm makes about the data's underlying structure. Introducing some bias into the model allows it to favor certain types of solutions, thus narrowing down the hypothesis space and making learning feasible. The logical extreme of your argument---if I understand it correctly---is that all machine learners are equally valuable. For example, an AI that learns a domain that is completely removed from all human values and thus we would be completely agnostic to and ignorant of, ipso facto, because it does not pertain or relate to us at all. In which case, who cares? By "who", I mean people. No one. By construction. [edit: I'll give an example here. Let's say I randomly pick a corpus of images that are pure noise. I induce an algorithm to model that noise. That model will learn something valid but in a domain completely divorced from anything with human impact.] So if we can acknowledge that some forms of learning are more important, merely by virtue of the fact that humans have preference, then perhaps we can perhaps wend our way back to agreeing that human-like intelligence is one valuable kind of intelligence, in addition to other valuable forms of intelligence like trading or board games. We can play the game all day of "everything is subjective" and "up is down and black is white", but at the end of the day, for human beings, it's night.
- IIAOPSW 4y ago
  The "general" in "general intelligence" is when you can hack together a picture of other domains by stitching together objects in your own domain. Our reasoning system only evolved to talk about 3 physical space dimensions and 1 time dimension and Newtonian physics and euclidean geometry. Things like trades and ownership and train lines aren't basic objects in our domain and the abstract rules governing their relations are completely unlike those rules of object properties like heavy and bright and loud. Can an intelligence stuck in a different substrate also come up with abstractions and generalize its intelligence? Maybe >The logical extreme of your argument---if I understand it correctly---is that all machine learners are equally valuable. That's not quite it. My argument is that general intelligence can in principle be grounded in the semantics of pretty much any baseline of objects and relations. The specific base line properties such as size, shape, color etc is orthogonal to the property of intelligence. We don't need to give the machine familiar sensory input for general intelligence, and general intelligence would not imply that it can do well at conversation in our domain (merely that it can talk about our domain in a very contorted way if it had to). I'll admit if the goal is to make an intelligence "in our image" then sensory modalities will get us there faster. It will also lead people to erroneously believe that the key ingredient to general intelligence is physical grounding in our reality. My counter example to that belief is general intelligence embedded in a pure language model, which is perfectly recognizable as "in our image" AGI just so long as you stick to topics like transit lines and chess and whatever else can be grounded in words alone.

r_hoods_ghost 4y ago

Very interesting question! I would argue that concepts like "size", "colour", "texture" are grounded in a more base level of reality than concepts such as "buy price", "sell price", "dividend" etc. The former set of concepts relate to aspects of the physical world as perceived by creatures that inhabit that world, while the latter refer to social constructs of the human socio-economic world that are themselves reliant on abstract human concepts of utility, value, the market etc. This thought is not fully developed but I'm drawn to the idea that if an intelligence is grounded in an understanding of a more base level of reality it will find it easier to generalize beyond any one given domain. I could be entirely wrong of course.

IIAOPSW 4y ago
But how do you determine the basicness level of reality? And how do you know there isn't a layer lower than our own?
- r_hoods_ghost 4y ago
  I'm philosophically comfortable with the notion that some things are unknowable and that eventually one just has to take certain cornerstone propositions about the nature of reality on trust and / or faith. Ultimately though the question is irrelevant to the discussion at hand, as the claim I'm making is that physical reality is at a lower level than socio economic reality (which I'll give you is a contested claim these days)
  - IIAOPSW 4y ago
    Yes, the systems we construct within this reality are obviously not the bottom of the stack because we built the stack. But suppose the trading bots figure out how to manipulate the rounding mechanics of high frequency trading to jerry rig a Turing machine within their reality. Now fast forward from this and suppose they've all obtained an equivalent of a home PC. For fun they all start playing a "sim city" like game version of our reality. So far as they can tell, money is real reality (rather than socio-economic construct), and the "physical" reality is an abstract game constructed at a higher level. So who's reality is who's construct in the end?

bravura 4y ago

I get your point, but you're making my head spin here from the sea-sickness involved in being asked to acknowledge that all viewpoints are equally valid. People care about human-like intelligence. That's the point. We also care about trading and physics. But when we talk about the specific question of human-like intelligence (and not other kinds of intelligence), the human reality is the appropriate substrate. Acknowledging, of course, that for non-human intelligence there might be other substrates and also acknowledging that non-human intelligence can be valuable to people. But let me take your argument to the extreme. In classical machine learning theory, there's a proof of the value of bias. Bias plays a crucial role in guiding the learning process. If all hypotheses are considered equally possible, learning becomes infeasible due to the lack of any preference or constraint on the hypothesis space. Bias, in this context, refers to the inherent assumptions a learning algorithm makes about the data's underlying structure. Introducing some bias into the model allows it to favor certain types of solutions, thus narrowing down the hypothesis space and making learning feasible. The logical extreme of your argument---if I understand it correctly---is that all machine learners are equally valuable. For example, an AI that learns a domain that is completely removed from all human values and thus we would be completely agnostic to and ignorant of, ipso facto, because it does not pertain or relate to us at all. In which case, who cares? By "who", I mean people. No one. By construction. [edit: I'll give an example here. Let's say I randomly pick a corpus of images that are pure noise. I induce an algorithm to model that noise. That model will learn something valid but in a domain completely divorced from anything with human impact.] So if we can acknowledge that some forms of learning are more important, merely by virtue of the fact that humans have preference, then perhaps we can perhaps wend our way back to agreeing that human-like intelligence is one valuable kind of intelligence, in addition to other valuable forms of intelligence like trading or board games. We can play the game all day of "everything is subjective" and "up is down and black is white", but at the end of the day, for human beings, it's night.

IIAOPSW 4y ago
The "general" in "general intelligence" is when you can hack together a picture of other domains by stitching together objects in your own domain. Our reasoning system only evolved to talk about 3 physical space dimensions and 1 time dimension and Newtonian physics and euclidean geometry. Things like trades and ownership and train lines aren't basic objects in our domain and the abstract rules governing their relations are completely unlike those rules of object properties like heavy and bright and loud. Can an intelligence stuck in a different substrate also come up with abstractions and generalize its intelligence? Maybe >The logical extreme of your argument---if I understand it correctly---is that all machine learners are equally valuable. That's not quite it. My argument is that general intelligence can in principle be grounded in the semantics of pretty much any baseline of objects and relations. The specific base line properties such as size, shape, color etc is orthogonal to the property of intelligence. We don't need to give the machine familiar sensory input for general intelligence, and general intelligence would not imply that it can do well at conversation in our domain (merely that it can talk about our domain in a very contorted way if it had to). I'll admit if the goal is to make an intelligence "in our image" then sensory modalities will get us there faster. It will also lead people to erroneously believe that the key ingredient to general intelligence is physical grounding in our reality. My counter example to that belief is general intelligence embedded in a pure language model, which is perfectly recognizable as "in our image" AGI just so long as you stick to topics like transit lines and chess and whatever else can be grounded in words alone.