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| public:t-720-atai:atai-22:agents_and_control [2022/09/17 09:52] – [Goal] thorisson | public:t-720-atai:atai-22:agents_and_control [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| ====Inferred GMI Architectural Features ==== | ====Inferred GMI Architectural Features ==== |
| | \\ Large architecture | From the above we can readily infer that if we want GMI, an architecture that is considerably more complex than systems being built in most AI labs today is likely unavoidable. In a complex architecture the issue of concurrency of processes must be addressed, a problem that has not yet been sufficiently resolved in present software and hardware. This scaling problem cannot be addressed by the usual “we’ll wait for Moore’s law to catch up” because the issue does not primarily revolve around //speed of execution// but around the //nature of the architectural principles of the system and their runtime operation//. || | | \\ Large architecture | From the above we can readily infer that if we want GMI, an architecture that is considerably more complex than systems being built in most AI labs today is likely unavoidable. In a complex architecture the issue of concurrency of processes must be addressed, a problem that has not yet been sufficiently resolved in present software and hardware. This scaling problem cannot be addressed by the usual “we’ll wait for Moore’s law to catch up” because the issue does not primarily revolve around //speed of execution// but around the //nature of the architectural principles of the system and their runtime operation//. || |
| | Predictable Robustness in Novel Circumstances | The system must have a robustness in light of all kinds of task-environment and embodiment perturbations, otherwise no reliable plans can be made, and thus no reliable execution of tasks can ever be reached, no matter how powerful the learning capacity. This robustness must be predictable a-priori at some level of abstraction -- for a wide range of novel circumstances it cannot be a complete surprise that the system "holds up". (If this were the case then the system itself would not be able to predict its chances of success in face of novel circumstances, thus eliminating an important part of the "G" from its "AGI" label.) || | | Predictable Robustness in Novel Circumstances | The system must have a robustness in light of all kinds of task-environment and embodiment perturbations, otherwise no reliable plans can be made, and thus no reliable execution of tasks can ever be reached, no matter how powerful the learning capacity. This robustness must be predictable a-priori at some level of abstraction -- for a wide range of novel circumstances it cannot be a complete surprise that the system "holds up" (halts). (If this were the case then the system itself would not be able to predict its chances of success in face of novel circumstances, thus eliminating an important part of the "G" from its "AGI" label.) || |
| | \\ Graceful Degradation | Part of the robustness requirement is that the system be constructed in a way as to minimize potential for catastrophic (and upredictable) failure. A programmer forgets to delimit a command in a compiled program and the whole application crashes; this kind of brittleness is not an option for cognitive systems operating in partially stochastic environments, where perturbations may come in any form at any time (and perfect prediction is impossible). || | | \\ Graceful Degradation | Part of the robustness requirement is that the system be constructed in a way as to minimize potential for catastrophic (and unpredictable) failure. A programmer forgets to delimit a command in a compiled program and the whole application crashes; this kind of brittleness is not an option for cognitive systems operating in partially stochastic environments, where perturbations may come in any form at any time (and perfect prediction is impossible): It cannot not be hyper-sensitive to tiny deviations or details, quite the contrary, it must be super-robust. || |
| | Transversal Functions | The system must have pan-architectural characteristics that enable it to operate consistently as a whole, to be highly adaptive (yet robust) in its own operation across the board, including metacognitive abilities. Some functions likely to be needed to achieve this include attention, learning, analogy-making capabilities, and self-inspection. || | | Transversal Functions | The system must have pan-architectural characteristics that enable it to operate consistently as a whole, to be highly adaptive (yet robust) in its own operation across the board, including metacognitive abilities. Some functions likely to be needed to achieve this include //attention, learning, analogy-making capabilities//, and //self-inspection// (reflection). || |
| | | \\ Transversal Time | Ignoring (general) temporal constraints is not an option if we want AGI. (Move over Turing!) Time is a semantic property, and the system must be able to understand – and be able to //learn to understand// – time as a real-world phenomenon in relation to its own skills and architectural operation. Time is everywhere, and is different from other resources in that there is a global clock which cannot, for many task-environments, be turned backwards. Energy must also be addressed, but may not be as fundamentally detrimental to ignore as time while we are in the early stages of exploring methods for developing auto-catalytic knowledge acquisition and cognitive growth mechanisms. \\ Time must be a tightly integrated phenomenon in any AGI architecture - managing and understanding time cannot be retrofitted to a complex architecture! | | | | \\ Transversal Time | Ignoring (general) temporal constraints is not an option if we want AGI. (Move over Turing!) Time is a semantic property, and the system must be able to understand – and be able to //learn to understand// – time as a real-world phenomenon in relation to its own skills and architectural operation. Time is everywhere, and is different from other resources in that there is a global clock which cannot, for many task-environments, be turned backwards. Energy must also be addressed, but may not be as fundamentally detrimental to ignore as time while we are in the early stages of exploring methods for developing auto-catalytic knowledge acquisition and cognitive growth mechanisms. \\ Time must be a tightly integrated phenomenon in any AGI architecture in its very design - management and understanding of time **cannot be retrofitted** into a complex architecture! | |
| | | \\ Transversal Learning | The system should be able to learn anything and everything, which means learning probably not best located in a particular "module" or "modules" in the architecture. \\ Learning must be a tightly integrated phenomenon in any AGI architecture, and must be part of the design from the beginning - implementing general learning into an existing architecture is out of the question: Learning cannot be retrofitted to a complex architecture! | | | | \\ Transversal Learning | The system should be able to learn anything and everything, which means learning probably not best located in a particular "module" or "modules" in the architecture. \\ Learning must be a tightly integrated phenomenon in any AGI architecture, and must be part of the design from the beginning - implementing general learning into an existing architecture is out of the question: Learning cannot be retrofitted to a complex architecture! | |
| | | Transversal Resource Management | Resource management - //attention// - must be tightly integrated.\\ Attention must be part of the system design from the beginning - retrofitting resource management into a architecture that didn't include this from the beginning is next to impossible! | | | | Transversal Resource Management | Resource management - //attention// - must be tightly integrated.\\ Attention must be part of the system design from the beginning - retrofitting resource management into a architecture that didn't include this from the beginning is next to impossible! | |
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| ====Learning RPR Controllers Must Also Classify==== | ====Learning RPR Controllers Must Also Classify==== |
| | Classification | To act, one needs to know what to act on. \\ To know what to act on, one needs to classify. \\ To classify, one needs to sense. \\ To sense, one needs measurement devices. | | | \\ Classification | To act, one needs to know what to act on. \\ To know what to act on, one needs to classify. \\ To classify, one needs to sense. \\ To sense, one needs measurement devices. | |
| | Learning to Classify | In a world with a lot of variation, a learning controller must also learn to classify. \\ To learn to classify, one must learn what to control to classify appropriately. \\ Learning to control, therefore, requires learning two kinds of classification (at the very least). | | | Learning to Classify | In a world with a lot of variation, a learning controller must also learn to classify. \\ To learn to classify, one must learn what to control to classify appropriately. \\ Learning to control, therefore, requires learning two kinds of classification (at the very least). | |
| | ANNs | Contemporary ANNs (e.g. Deep Neural Networks, Double-Deep Q-Learners, etc.) can only do classification. They can only do classification by going through a long continuous training session, after which the learning is turned off. | | | ANNs | Contemporary ANNs (e.g. Deep Neural Networks, Double-Deep Q-Learners, etc.) can only do classification. They can only do classification by going through a long continuous training session, after which the learning is turned off. | |
