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| public:t-720-atai:atai-21:generality [2021/09/27 09:14] – created thorisson | public:t-720-atai:atai-21:generality [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| | Standard Learning Expectation | That the system can learn //a task//. | | | Standard Learning Expectation | That the system can learn //a task//. | |
| | Examples of \\ "Intelligent" Systems | Deep Blue. Watson. Alpha Go. | | | Examples of \\ "Intelligent" Systems | Deep Blue. Watson. Alpha Go. | |
| | \\ What these systems \\ have in common | They can only learn (and do) //one task//. \\ They are really bad at learning temporal tasks. \\ Their learning must be turned off when they leave the lab. \\ The tasks they learn are relatively simple (in that their goal structure can be easily formalized). \\ They are neither "domain-independent" nor "general" - they are not //general learners//. | | | \\ What these systems \\ have in common | They can only learn (and do) //one task// (one form of one task, to be exact). \\ They are really bad at learning temporal tasks. \\ Their learning must be turned off when they leave the lab. \\ The tasks they learn are relatively simple (in that their goal structure can be easily formalized). \\ They are neither "domain-independent" nor "general" - they are not //general learners//. | |
| | We want more general learners | A general learner would not be limited by domain, topic, task-environment, or other such limitations - the more free from such constraints, the more "intelligent" the system. | | | We want more general learners | A general learner would not be limited by domain, topic, task-environment, or other such limitations - the more free from such constraints, the more "intelligent" the system. | |
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| ^Key^What it Means^Why it's Important^ | ^Key^What it Means^Why it's Important^ |
| | \\ Mission | **R1.** The system must fulfill its mission – the goals and constraints it has been given by its designers – with possibly several different priorities. | This is the very reason we built the system. We should have pretty good ideas as to why. Shared by all AI systems. | | | \\ Mission | **R1.** The system must fulfill its mission – the goals and constraints it has been given by its designers – with possibly several different priorities. | This is the very reason we built the system. We should have pretty good ideas as to why. Shared by all AI systems (in fact, all engineered systems). | |
| | \\ AILL \\ "After it Leaves the Lab" | **R2.** The system must be designed to be operational in the long-term, without intervention of its designers after it leaves the lab, as dictated by the temporal scope of its mission. | All machine learning methods today are "before it leaves the lab", meaning that the task-environment must be known and clearly delineated beforehand, and the system cannot handle changes to these assumptions. To be more autonomous we must look at the life of these systems "beyond the lab". | | | \\ AiLL \\ "After it Leaves the Lab" | **R2.** The system must be designed to be operational in the long-term, without intervention of its designers after it leaves the lab, as dictated by the temporal scope of its mission. | All machine learning methods today are "before it leaves the lab", meaning that the task-environment must be known and clearly delineated beforehand, and the system cannot handle changes to these assumptions. To be more autonomous we must look at the life of these systems "beyond the lab". | |
| | \\ Domain-independence | **R3.** The system must be domain- and task-independent – but without a strict requirement for determinism: We limit our architecture to handle only missions for which rigorous determinism is not a requirement. | It is easy to implement domain dependence in software systems: Virtually //all// software today is made this way. Domain independence is necessary if we want to build more autonomous systems. | | | \\ Domain-independence | **R3.** The system must be domain- and task-independent – but without a strict requirement for determinism: We limit our architecture to handle only missions for which rigorous determinism is not a requirement. | It is easy to implement domain dependence in software systems: Virtually //all// software today is made this way. Domain independence is necessary if we want to build more autonomous systems. | |
| | \\ Modeling | \\ **R4.** The system must be able to model its environment to adapt to changes thereof. | A good controller not only reacts to changes in its environment, it anticipates them. Anticipation, or prediction, is only possible with a decent model the system whose behavior we are predicting. A good model allows detailed and long-term prediction. | | | \\ Modeling | \\ **R4.** The system must be able to model its environment to adapt to changes thereof. | A good controller not only reacts to changes in its environment, it anticipates them. Anticipation, or prediction, is only possible with a decent model the system whose behavior we are predicting. A good model allows detailed and long-term prediction. | |
