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| public:t720-atai-2012:emergence [2012/10/21 13:50] – [What is Self-Organization?] thorisson | public:t720-atai-2012:emergence [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| ===== The Relevance of Emergence & Self-Organization to AGI ===== | ===== The Relevance of Emergence & Self-Organization to AGI ===== |
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| While it is clear that these concepts must be relevant to all efficient complex systems, the specifics of how they relate to AGI is not obvious. In fact, it may be argued that self-organization presents no principles of construction because emergentist and self-organization principles present a descriptive view of systems: Given that some particular phenomenon, say the natural phenomenon of intelligence, exhibits some features of self-organization and emergence, while we may be able to describe these features in detail, whether they be the control of cognitive development stages or the recall of the right things at the right time when performing a task such as playing tennis or assembling furniture, they tells us little if anything about what kinds of designs could produce such emergent properties. | While it is clear that these concepts must be relevant to all efficient complex systems, the specifics of how they relate to AGI is not obvious. In fact, it may be argued that the study of self-organization presents no principles of construction because an emergentist view on the world represents a //descriptive// view of systems: Given that some particular phenomenon, say the natural phenomenon of intelligence, exhibits some features of self-organization and emergence, while we may be able to describe these features in detail, whether they be the control of cognitive development stages or the recall of the right things at the right time when performing a task such as playing tennis or assembling furniture, they tells us little if anything about what kinds of designs could produce such emergent properties. |
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| Nevertheless it seems obvious that when we are dealing with a system that //must// implement self-organizing principles, via complex nested feedback loops at many levels of detail, it would seem irresponsible at best, and quite possibly self-defeating, to ignore the research on these phenomena so far. We should anticipate having to extend the current work on emergence and self-organization to a point where these concepts can become highly useful for comparisons with naturally intelligent systems, and possibly help us in our design efforts. They are especially relevant to issues of bootstrapping, and to control of ongoing auto-catalytic processes and management of self-organized architectural structures. | Nevertheless it seems obvious that when we are dealing with a system that //must// implement self-organizing principles, via complex nested feedback loops at many levels of detail, it would seem irresponsible at best, and quite possibly self-defeating, to ignore the research on these phenomena so far. We should anticipate having to extend the current work on emergence and self-organization to a point where these concepts can become highly useful for comparisons with naturally intelligent systems, and possibly help us in our design efforts. They are especially relevant to issues of bootstrapping, and to control of ongoing auto-catalytic processes and management of self-organized architectural structures. |
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| | A key problem with present methodologies, and especially constructionist approaches, is that they blind us to the importance, and perhaps sometimes even the //possibility//, of self-organizing auto-catalytic loops. Going beyond current traditions of constructionist methodologies requires us to identify the //need// for intricate feedback loops and self-organizing principles. A constructionist approach will always try to eliminate complex feedbacks and force our architecture into a linear, pipelined model. Even blackboard architectures do not help us think in loops, as it tends to emphasize the activity of the agents or daemons in using the blackboard and hide the fact that incrementally knowledge builds up via feedback loops between the various types of agents. So, to some extent, the blackboard metaphor augmented with the insight of //types// of agents, occupying various points along at least two dimensions: level of detail and temporal scope. And we could add a third dimension of data types. A spread-spectrum of agents, each occupying some point, or possibly more than one point, along these dimensions, will at runtime implement feedback loops that produce knowledge in opportunistic ways, in accordance with what the world exposes the system to over time. |
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| //2012(c)Kristinn R. Thórisson// | //2012(c)Kristinn R. Thórisson// |
