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| public:t_720_atai:atai-18:lecture_notes_knowledge_representations [2018/10/06 16:48] – [Problems with Feedback-Only Controllers] thorisson | public:t_720_atai:atai-18:lecture_notes_knowledge_representations [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| | Attention | The management of processing, memory, and sensory resources. || | | Attention | The management of processing, memory, and sensory resources. || |
| | Meta-Cognition | The ability of a system to reason about itself. || | | Meta-Cognition | The ability of a system to reason about itself. || |
| | \\ Understanding | The phenomenon of "understanding" has been neglected in AI and AGI; Modern AI systems do not //understand//. \\ Yet the concept seems crucial when talking about human intelligence; the concept holds explanatory power - we do not assign responsibilities for a task who has a significant lack of understanding of the task. Moreover, the level of understanding can be evaluated. \\ Understanding of a particular phenomenon <m>phi</m> is the potential to perform actions and answer questions with respect to <m>phi</m>. Example: Is an automobile heavier or lighter than a human? || | | \\ Understanding | The phenomenon of "understanding" has been neglected in AI and AGI. Modern AI systems do not //understand//. \\ Yet the concept seems crucial when talking about human intelligence; the concept holds explanatory power - we do not assign responsibilities for a task to someone or something with a demonstrated lack of understanding of the task. Moreover, the level of understanding can be evaluated. \\ Understanding of a particular phenomenon <m>phi</m> is the potential to perform actions and answer questions with respect to <m>phi</m>. Example: Is an automobile heavier or lighter than a human? || |
| | | \\ Explanation | When performed by an agent, the ability to transform knowledge about X from a formulation primarily (or only) good for execution with respect to X to a formulation good for being communicated (typically involving some form of linearization, incremental introduction of concepts and issues, in light of an intended receiving agent with a particular a-priori knowledge). \\ Is it possible to explain something that you don't understand? | | | | \\ Explanation | When performed by an agent, the ability to transform knowledge about X from a formulation primarily (or only) good for execution with respect to X to a formulation good for being communicated (typically involving some form of linearization, incremental introduction of concepts and issues, in light of an intended receiving agent with a particular a-priori knowledge). \\ Is it possible to explain something that you don't understand? | |
| | \\ Learning | Acquisition of information in a form that enables more successful completion of tasks. We call information in such a form "knowledge" or "practical knowledge". (There is also the concept of "impractical knowledge", which sometimes people feel must be the case of "useless trivia" that seems to be useless for anything, but can in fact turn out to be useful at any point, as for instance using such trivia to wow others with one's knowledge of trivia.) || | | \\ Learning | Acquisition of information in a form that enables more successful completion of tasks. We call information in such a form "knowledge" or "practical knowledge". (There is also the concept of "impractical knowledge", which sometimes people feel must be the case of "useless trivia" that seems to be useless for anything, but can in fact turn out to be useful at any point, as for instance using such trivia to wow others with one's knowledge of trivia.) || |
| | Limitation | Because the system designer knows beforehand which signals cause perturbations in <m>o</m> and can hard-wire these from the get-go in the thermostat, there is no motivation to create a model-creating controller (it is much harder!). | | | Limitation | Because the system designer knows beforehand which signals cause perturbations in <m>o</m> and can hard-wire these from the get-go in the thermostat, there is no motivation to create a model-creating controller (it is much harder!). | |
| | Other "state of the art" systems | The same is true for expert systems, subsumption robots, and general game playing machines: their model is to tightly baked into their architecture by the designer. Yes, there are some variables in these that can be changed automatically “after the machine leaves the lab” (without designer intervention), but they are parameters inside a (more or less) already-determined //model//. | | | Other "state of the art" systems | The same is true for expert systems, subsumption robots, and general game playing machines: their model is to tightly baked into their architecture by the designer. Yes, there are some variables in these that can be changed automatically “after the machine leaves the lab” (without designer intervention), but they are parameters inside a (more or less) already-determined //model//. | |
| | What Can We Do? | Feed-forward control! But this calls for a **model**. | | | What Can We Do? | Feed-forward control! Which requires **models**. | |
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