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| public:t-713-mers:mers-24:knowledge [2024/09/20 08:43] – [Cumulative Learning Through Reasoning] thorisson | public:t-713-mers:mers-24:knowledge [2024/09/20 10:09] (current) – [What is an "agent"?] thorisson |
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| ====Controlled Experiment==== | ====Controlled Experiment==== |
| | What is it? | A fairly recent research method, historically speaking, for testing hypotheses / theories | | | What is it? | A fairly recent research method (approx. 200 years, so, historically speaking) for testing hypotheses / theories | |
| | Why is it Important? | The most reliable way humanity has found to create reliable sharable knowledge. | | | Why is it Important? | The most reliable way humanity has found to create reliable sharable knowledge. | |
| | Why is it Relevant Here? | Like individual learning, it involves dealing with new phenomena and //figuring them out//. | | | Why is it Relevant Here? | Like individual learning, it involves dealing with new phenomena and //figuring them out//. | |
| | \\ Transfer Learning \\ + | The ability to build new knowledge on top of old in a way that the old knowledge facilitates learning the new. While interference/forgetting should not occur, knowledge should still be defeasible: the physical world is non-axiomatic so **//any//** knowledge could be proven incorrect in light of contradicting evidence. \\ //Subsumed by cumulative learning because new information is //integrated// with old information, which may result in exposure of inconsistencies, missing data, etc., which is then dealt with as a natural part of the cumulative learning operations.// | | | \\ Transfer Learning \\ + | The ability to build new knowledge on top of old in a way that the old knowledge facilitates learning the new. While interference/forgetting should not occur, knowledge should still be defeasible: the physical world is non-axiomatic so **//any//** knowledge could be proven incorrect in light of contradicting evidence. \\ //Subsumed by cumulative learning because new information is //integrated// with old information, which may result in exposure of inconsistencies, missing data, etc., which is then dealt with as a natural part of the cumulative learning operations.// | |
| | \\ Few-Shot Learning | The ability to learn something from very few examples or very little data. Common variants include one-shot learning, where the learner only needs to be told (or experience) something once, and zero-shot learning, where the learner has already inferred it without needing to experience or be told. \\ //Subsumed by cumulative learning because prior knowledge is transferrable to new information, meaning that (theoretically) only the delta between what has been priorly learned and what is required for the new information needs to be learned.// | | | \\ Few-Shot Learning | The ability to learn something from very few examples or very little data. Common variants include one-shot learning, where the learner only needs to be told (or experience) something once, and zero-shot learning, where the learner has already inferred it without needing to experience or be told. \\ //Subsumed by cumulative learning because prior knowledge is transferrable to new information, meaning that (theoretically) only the delta between what has been priorly learned and what is required for the new information needs to be learned.// | |
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| | ====What is an "agent"?==== |
| | | "Agent" | In this course an 'agent' is a "stand-alone" intelligent system. | |
| | | "Intelligent System" | We expect an "intelligent" system to be able to //learn autonomously//. | |
| | | Minimum Learning | That the system can learn //a task//. | |
| | | "Task" | A transformation of a stat (typically in the environment) from one (steady-) state to another, that can be described (and thus verified) by a goal (described in some compressed way via a representation language). | |
| | | Examples of \\ "Intelligent" AI Systems to Date | Deep Blue. Watson. Alpha Go. ChatGPT. | |
| | | 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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| | ====What Do You Mean by "Generality"?==== |
| | | Flexibility: \\ Breadth of task-environments | Enumeration of variety. \\ (By 'variety' we mean the discernibly different states that can be sensed and that make a difference to a controller.) \\ If a system X can operate in more diverse task-environments than system Y, system X is more //flexible// than system Y. | |
| | | Solution Diversity: \\ Breadth of solutions | \\ If a system X can reliably generate a larger variation of acceptable solutions to problems than system Y, system X is more //powerful// than system Y. | |
| | | Constraint Diversity: \\ Breadth of constraints on solutions | \\ If a system X can reliably produce acceptable solutions under a higher number of solution constraints than system Y, system X is more //powerful// than system Y. | |
| | | Goal Diversity: \\ Breadth of goals | If a system X can meet a wider range of goals than system Y, system X is more //powerful// than system Y. | |
| | | Generality | Any system X that exceeds system Y on one or more of the above we say it's more //general// than system Y, but typically pushing for increased generality means pushing on all of the above dimensions. | |
| | | General intelligence... | ...means less is needed to be known up front when the system is created; the system can learn to figure things out and how to handle itself, in light of **LTE**. | |
| | | And yet: \\ The hallmark of an AGI | A system that can handle novel or **brand-new** problems, and be expected to attempt to address //open problems// sensibly. \\ The level of difficulty of the problems it solves would indicate its generality. | |
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| | ====Cognitive Architectures==== |
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| | | What it Is | A software architecture that combines perception, reasoning, and action control in a coherent system. | |
| | | Why it Matters | A multitude of processes are needed for supporting learning and acting in a complex environment. | |
| | | How it Works | Typically implemented as some sort of pipeline or modular system (inadequate for implementing general intelligence in a machine). | |
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