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I don't know that "maximize descendants of self" is necessarily right though. It seems like a better statement might be "maximize the development of complexity

I don't know that "maximize descendants of self" is necessarily right though. It seems like a better statement might be "maximize the development of complexity in the universe". Just as an example, lots of people choose to forgo having children to focus on contributing to the universe in other ways (myself included). This isn't just a self-centered drive for fame/wealth/etc either, as many people pursue their quests in poverty/obscurity, and some even choose anonymity.

deleted 9y ago
[deleted]
mirimir 9y ago
Well, for biological organisms, it's all about reproductive success. I mean, what exits today reflects what managed to reproduce, and how well. Overall, that has created lots of complexity. But that's just because there are so many niches and ways to be successful in them. What you say about people reflects cooperative behavior that drives reproductive success for shared gene complexes.
- TheOtherHobbes 9y ago
  It took nature four billion years to invent humans, who are actually - if we're honest - pretty terrible as an example of workable AGI. In fact what nature invented was a persistent colony organism with external memory. Wild solo humans are only a little smarter than wolves individually, but being able to share and externalise invention and learning created a massive advantage. Humans are successful because although only a tiny minority of individuals are any good at invention, the fact that information persists and is shared means the entire population benefits. The problem for AI is modelling the learning and invention process. Classifiers and recognisers are getting better, but they're not really learning in the human sense, which is a combination of abstraction, mimicry, and occasional invention. IMO there's no chance of AGI developing until there's a persistent, transferable, abstracted model generated as an output from classifier systems and other learning machines which is a symbolic - not just a statistical - summary of the learning.
  - red75prime 9y ago
    > until there's a persistent, transferable, abstracted model [...] It can be compressed to "until AIs can talk".
    - mirimir 9y ago
      ... to other AIs.
      - red75prime 9y ago
        or "... to itself"
  - visarga 9y ago
    Transfer learning is a thing (one NN learns from another or multiple NNs), also, large ontologies representing billions of facts.
- visarga 9y ago
  > for biological organisms, it's all about reproductive success Another way of putting it - the source of meaning is life, or death (prolonging life, avoiding death as much as possible). Reproduction is just the start of life. From this game of life and death come reward signals that teach us how to act in the world (our values).
mannykannot 9y ago
There is no guarantee that pursuing complexity as a goal in itself will lead to intelligence. The one thing going for mannigfaltig's proposal is that it has been known to work, though very inefficiently, and we don't have enough examples to estimate the yield. One might suggest that having the right definition of complexity would produce the desired result, but coming up with that definition takes us right back to CuriouslyC's point.
- visarga 9y ago
  I give you a simple definition: maximize diversity (differentiation) and integration. They are opposites, to a degree - so there is a tradeoff and maximum at the middle in both diversity and integration. This idea comes from the Integrated Information Theory of Consciousness (Giulio Tononi, Christoph Koch). In a neural net we do just that - maximize diversity by splitting the signal over many neurons, each with different weights, computing different things. Integration is maximized by the mixing together of signals from other neurons and training them together with a common loss function. Even the internet as a medium requires diversity and integration to be successful. For example, net neutrality is related to diversity. Integration is related to national firewalls, copyright barriers, filter bubbling effect (where one sees only content from parts of the internet they agree with), walled gardens (like the app stores), and other things that cut the connection between people. You can apply diversity and integration to other fields as well, for example, in politics/governance. We can compare a federal system (more diversity) with a centrally planned system (less diversity) and see the effects. With integration - we can compare free trade with regulated trade. The same principles apply to free speech - where diversity and integration are basically promised by the constitution.
  - mannykannot 9y ago
    Well, maybe, but it looks very speculative to me. I think anything deserving the label 'definition' would have to be much more definite than that.

deleted 9y ago

[deleted]

mirimir 9y ago

Well, for biological organisms, it's all about reproductive success. I mean, what exits today reflects what managed to reproduce, and how well. Overall, that has created lots of complexity. But that's just because there are so many niches and ways to be successful in them. What you say about people reflects cooperative behavior that drives reproductive success for shared gene complexes.

TheOtherHobbes 9y ago
It took nature four billion years to invent humans, who are actually - if we're honest - pretty terrible as an example of workable AGI. In fact what nature invented was a persistent colony organism with external memory. Wild solo humans are only a little smarter than wolves individually, but being able to share and externalise invention and learning created a massive advantage. Humans are successful because although only a tiny minority of individuals are any good at invention, the fact that information persists and is shared means the entire population benefits. The problem for AI is modelling the learning and invention process. Classifiers and recognisers are getting better, but they're not really learning in the human sense, which is a combination of abstraction, mimicry, and occasional invention. IMO there's no chance of AGI developing until there's a persistent, transferable, abstracted model generated as an output from classifier systems and other learning machines which is a symbolic - not just a statistical - summary of the learning.
- red75prime 9y ago
  > until there's a persistent, transferable, abstracted model [...] It can be compressed to "until AIs can talk".
  - mirimir 9y ago
    ... to other AIs.
    - red75prime 9y ago
      or "... to itself"
- visarga 9y ago
  Transfer learning is a thing (one NN learns from another or multiple NNs), also, large ontologies representing billions of facts.
visarga 9y ago
> for biological organisms, it's all about reproductive success Another way of putting it - the source of meaning is life, or death (prolonging life, avoiding death as much as possible). Reproduction is just the start of life. From this game of life and death come reward signals that teach us how to act in the world (our values).

mannykannot 9y ago

There is no guarantee that pursuing complexity as a goal in itself will lead to intelligence. The one thing going for mannigfaltig's proposal is that it has been known to work, though very inefficiently, and we don't have enough examples to estimate the yield. One might suggest that having the right definition of complexity would produce the desired result, but coming up with that definition takes us right back to CuriouslyC's point.

visarga 9y ago
I give you a simple definition: maximize diversity (differentiation) and integration. They are opposites, to a degree - so there is a tradeoff and maximum at the middle in both diversity and integration. This idea comes from the Integrated Information Theory of Consciousness (Giulio Tononi, Christoph Koch). In a neural net we do just that - maximize diversity by splitting the signal over many neurons, each with different weights, computing different things. Integration is maximized by the mixing together of signals from other neurons and training them together with a common loss function. Even the internet as a medium requires diversity and integration to be successful. For example, net neutrality is related to diversity. Integration is related to national firewalls, copyright barriers, filter bubbling effect (where one sees only content from parts of the internet they agree with), walled gardens (like the app stores), and other things that cut the connection between people. You can apply diversity and integration to other fields as well, for example, in politics/governance. We can compare a federal system (more diversity) with a centrally planned system (less diversity) and see the effects. With integration - we can compare free trade with regulated trade. The same principles apply to free speech - where diversity and integration are basically promised by the constitution.
- mannykannot 9y ago
  Well, maybe, but it looks very speculative to me. I think anything deserving the label 'definition' would have to be much more definite than that.