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| public:t_720_atai:atai-19:lecture_notes_methodologies [2019/09/04 17:37] – [Belusov-Zhaboutinsky Reaction] thorisson | public:t_720_atai:atai-19:lecture_notes_methodologies [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| =====T-720-ATAI-2019===== | =====T-720-ATAI-2019===== |
| ==== Lecture Notes, W4: AI Methodologies==== | ==== Lecture Notes: AI Methodologies==== |
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| | Rule 30 | [[https://en.wikipedia.org/wiki/Rule_30|Wikipedia]] | | | Rule 30 | [[https://en.wikipedia.org/wiki/Rule_30|Wikipedia]] | |
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| ====How to Study HeLDs Scientifically==== | ====How to Study HeLDs Scientifically==== |
| | | HeLDs | Heterogeneous, large, densely-coupled systems. | |
| | Reductionism | The method of isolating parts of a complex phenomenon or system in order to simplify and speed up our understanding of it. See also [[https://en.wikipedia.org/wiki/Reductionism|Reductionism]] on Wikipedia. | | | Reductionism | The method of isolating parts of a complex phenomenon or system in order to simplify and speed up our understanding of it. See also [[https://en.wikipedia.org/wiki/Reductionism|Reductionism]] on Wikipedia. | |
| | Occam's Razor | Key principle of reductionism. See also [[https://en.wikipedia.org/wiki/Occam%27s_razor|Occam's Razor]]. | | | Occam's Razor | Key principle of reductionism. See also [[https://en.wikipedia.org/wiki/Occam%27s_razor|Occam's Razor]]. | |
| ====Constructivist AI Methodology (CAIM) ==== | ====Constructivist AI Methodology (CAIM) ==== |
| | What it is | A term for labeling a methodology for AGI based on two main assumptions: (1) The way knowledge is acquired by systems with general intelligence requires the automatic integration, management, and revision of data in a way that //infuses meaning// into information structures, and (2) constructionist approaches do not sufficiently address this, and other issues of key importance for systems with high levels of general intelligence and existential autonomy. || | | What it is | A term for labeling a methodology for AGI based on two main assumptions: (1) The way knowledge is acquired by systems with general intelligence requires the automatic integration, management, and revision of data in a way that //infuses meaning// into information structures, and (2) constructionist approaches do not sufficiently address this, and other issues of key importance for systems with high levels of general intelligence and existential autonomy. || |
| | | \\ Why We Need It | Most AI methodology to date has automatically inherited all standard software methodological principles. This approach assumes that software architectures are hand-coded and that (the majority of) the system's knowledge and skills is hand-fed to it. In sharp contrast, CAIM assumes that the system acquires the vast majority of its knowledge on its own (except for a small seed) and manages its own GROWTH on its own. Also, it may change its own architecture over time, due to experience and learning. || |
| | Why it's important | It is the first and only attempt so far at explicitly proposing an alternative to current methodologies and prevailing paradigm, used throughout AI and computer science. || | | Why it's important | It is the first and only attempt so far at explicitly proposing an alternative to current methodologies and prevailing paradigm, used throughout AI and computer science. || |
| | What it's good for | Replacing present methods in AI, by and large, as these will not suffice for addressing the full scope of the phenomenon of intelligence, as seen in nature. || | | What it's good for | Replacing present methods in AI, by and large, as these will not suffice for addressing the full scope of the phenomenon of intelligence, as seen in nature. || |
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| ==== Constructivist AI ==== | ==== Constructivist AI ==== |
| | Foundation | Constructivist AI is concerned with the operational characteristics that the system we aim to build – the architecture – must have. | | | Foundation | Constructivist AI is concerned with the operational characteristics that the system we aim to build – the AGI architecture – must have. | |
| | \\ \\ Behavioral Characteristics | Refer back to the requirements for AGI systems; it must be able to: \\ - handle novel task-environments. \\ - handle a wide range of task-environments (in the same system, and be able to switch / mix-and-match. \\ - transfer knowledge between task-environmets. \\ - perform reasoning: induction, deduction and abduction. \\ - handle realtime, dynamic worlds. \\ - introspect. \\ - .... and more. | | | \\ \\ Behavioral Characteristics | Refer back to the requirements for AGI systems; it must be able to: \\ - handle novel task-environments. \\ - handle a wide range of task-environments (in the same system, and be able to switch / mix-and-match. \\ - transfer knowledge between task-environmets. \\ - perform reasoning: induction, deduction and abduction. \\ - handle realtime, dynamic worlds. \\ - introspect. \\ - .... and more. | |
| | Constructivist AI: No particular architecture | Constructivist AI does not rest on, and does not need to rest on, assumptions about the particular //kind of architecture// that exists in the human and animal mind. We assume that many kinds of architectures can achieve the above AGI requirements. | | | Constructivist AI: No particular architecture | Constructivist AI does not rest on, and does not need to rest on, assumptions about the particular //kind of architecture// that exists in the human and animal mind. We assume that many kinds of architectures can achieve the above AGI requirements. | |
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| ====Examples of Task-Environments Targeted by Constructivist AI==== | |
| | Diversity | Earth offers great diversity. This is in large part why intelligence is even needed at all. | | |
| | | Desert | | |
| | | Ocean floor | | |
| | | Air | | |
| | | Interplanetary travel | | |
| | The Same System at the Same Time | \\ These task-environments should be handled by a single system at a single period in time, without the designers coming anywhere close. | | |
| | Baby Machines | While the mechanisms constituting an autonomous learning "baby" machine may not be complex compared to a "fully grown" cognitive system, they are likely to result in what nevertheless will seem large in comparison to the AI systems built today, though this perceived size may stem from the complexity of the mechanisms and their interactions, rather than the sheer number of lines of code. | | |
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| ====Architectural Principles of AGI Systems / CAIM==== | ====Architectural Principles of AGI Systems / CAIM==== |
| | Self-Construction | It is assumed that a system must amass the vast majority of its knowledge autonomously. This is partly due to the fact that it is (practically) impossible for any human or team(s) of humans to construct by hand the knowledge needed for an AGI system, and even if this were possible it would still leave unanswered the question of how the system will acquire knowledge of truly novel things, which we consider a fundamental requirement for a system to be called an AGI system. | | | Self-Construction | It is assumed that a system must amass the vast majority of its knowledge autonomously. This is partly due to the fact that it is (practically) impossible for any human or team(s) of humans to construct by hand the knowledge needed for an AGI system, and even if this were possible it would still leave unanswered the question of how the system will acquire knowledge of truly novel things, which we consider a fundamental requirement for a system to be called an AGI system. | |
| | | Baby Machines | To some extent an AGI capable of growing throughout its lifetime will be what may be called a "baby machine", because relative to later stages in life, such a machine will initially seem "baby like". \\ While the mechanisms constituting an autonomous learning baby machine may not be complex compared to a "fully grown" cognitive system, they are nevetheless likely to result in what will seem large in comparison to the AI systems built today, though this perceived size may stem from the complexity of the mechanisms and their interactions, rather than the sheer number of lines of code. | |
| | Semiotic Opaqueness | No communication between two agents / components in a system can take place unless they share a common language, or encoding-decoding principles. Without this they are semantically opaque to each other. Without communication, no coordination can take place. | | | Semiotic Opaqueness | No communication between two agents / components in a system can take place unless they share a common language, or encoding-decoding principles. Without this they are semantically opaque to each other. Without communication, no coordination can take place. | |
| | Systems Engineering | Due to the complexity of building a large system (picture, e.g. an airplane), a clear and concise bookkeeping of each part, and which parts it interacts with, must be kept so as to ensure the holistic operation of the resulting system. In a (cognitively) growing system in a dynamic world, where the system is auto-generating models of the phenomena that it sees, each which must be tightly integrated yet easily manipulatable and clearly separable, the system must itself ensure the semiotic transparency of its constituents parts. This can only be achieved by automatic mechanisms residing in the system itself, it cannot be ensured manually by a human engineer, or even a large team of them. | | | Systems Engineering | Due to the complexity of building a large system (picture, e.g. an airplane), a clear and concise bookkeeping of each part, and which parts it interacts with, must be kept so as to ensure the holistic operation of the resulting system. In a (cognitively) growing system in a dynamic world, where the system is auto-generating models of the phenomena that it sees, each which must be tightly integrated yet easily manipulatable and clearly separable, the system must itself ensure the semiotic transparency of its constituents parts. This can only be achieved by automatic mechanisms residing in the system itself, it cannot be ensured manually by a human engineer, or even a large team of them. | |
| | Pan-Architectural Pattern Matching | To enable autonomous //holistic integration// the architecture must be capable of comparing (copies of) itself to parts of itself, in part or in whole, whether the comparison is contrasting structure, the effects of time, or some other aspect or characteristics of the architecture. To decide, for instance, if a new attention mechanism is better than the old one, various forms of comparison must be possible. | | | Pan-Architectural Pattern Matching | To enable autonomous //holistic integration// the architecture must be capable of comparing (copies of) itself to parts of itself, in part or in whole, whether the comparison is contrasting structure, the effects of time, or some other aspect or characteristics of the architecture. To decide, for instance, if a new attention mechanism is better than the old one, various forms of comparison must be possible. | |
| | The "Golden Screw" | An architecture meeting all of the above principles is not likely to be "based on a key principle" or even two -- it is very likely to involve a whole set of //new// and fundamentally foreign principles that make their realization possible! | | | The "Golden Screw" | An architecture meeting all of the above principles is not likely to be "based on a key principle" or even two -- it is very likely to involve a whole set of //new// and fundamentally foreign principles that make their realization possible! | |
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| ==== Some Key Requirements For a Constructivist AGI Architecture ==== | ==== Some Key Requirements For a Constructivist AGI Architecture ==== |
| | Tight Integration | A general-purpose system must tightly and finely coordinate a host of skills, including their acquisition, transitions between skills at runtime, how to combine two or more skills, and transfer of learning between them over time at many levels of temporal and topical detail. | | | \\ Tight Integration | A general-purpose system must tightly and finely coordinate a host of skills, including their acquisition, transitions between skills at runtime, how to combine two or more skills, and transfer of learning between them over time at many levels of temporal and topical detail. | |
| | Holistic Integration | The architecture of an AGI cannot be developed in a way where each of the key requirements (see above) is addressed in isolation, or semi-isolation, due to the resulting system's whole-part semiotic opaqueness: When a system learns new things, to see whether it has learned it before, and use it to improve its understanding, it must relate the new knowledge to its old knowledge, something we call **//integration//**. The same mechanisms needed for integration also enable transfer knowledge; it is these same mechanisms that (in humans) are responsible for what is known as "negative transfer of training", where a priorly learned skill makes it //harder// to learn something new (this happens in humans when the new task is //almost// like the old one, but deviates on some points. The more critical these points are in mastering the skill, the worse the negative transfer of training. | | | \\ Holistic Integration | The architecture of an AGI cannot be developed in a way where each of the key requirements (see above) is addressed in isolation, or semi-isolation, due to the resulting system's whole-part semiotic opaqueness: When a system learns new things, to see whether it has learned it before, and use it to improve its understanding, it must relate the new knowledge to its old knowledge, something we call **//integration//**. The same mechanisms needed for integration also enable transfer knowledge; it is these same mechanisms that (in humans) are responsible for what is known as "negative transfer of training", where a priorly learned skill makes it //harder// to learn something new (this happens in humans when the new task is //almost// like the old one, but deviates on some points. The more critical these points are in mastering the skill, the worse the negative transfer of training. | |
| | 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. | |
| | \\ \\ 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 | 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. | |
| | \\ Large Aarchitecture | 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 rather around the //nature of the architectural principles of the system and their runtime operation//. | | | \\ Architecture Based on New Principles | 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 rather around the //nature of the architectural principles of the system and their runtime operation//. | |
| | Predictable Robustness | 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. | | | Predictable Robustness | 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. | |
| | \\ Graceful Degradation | Part of the robustness requirement is that the system be constructed in a way as to minimize potential for catastrophic failure. A programmer can forget to delimit a command in a compiled program and the whole application crashes; this kind of brittleness is not an option for cognitive systems that operate in stochastic environments, where perturbations can come in any form at any time. | | | \\ Graceful Degradation | Part of the robustness requirement is that the system be constructed in a way as to minimize potential for catastrophic failure. A programmer can forget to delimit a command in a compiled program and the whole application crashes; this kind of brittleness is not an option for cognitive systems that operate in stochastic environments, where perturbations can come in any form at any time. | |
