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| public:t-719-nxai:nxai-25:intelligence [2025/04/27 11:37] – [A Working Definition of Intelligence] thorisson | public:t-719-nxai:nxai-25:intelligence [2025/04/27 14:05] (current) – thorisson |
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| ====READINGS==== | ====== INTELLIGENCE: THE PHENOMENON ====== |
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| | //Key concepts: Goals, Behaviors, Generality, Cognition, Perception, Classification// |
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| | ====REQUIRED READINGS==== |
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| * [[https://proceedings.mlr.press/v192/thorisson22b/thorisson22b.pdf | The Future of AI Research: Ten Defeasible 'Axioms of Intelligence']] by K.R.Thórisson and H. Minsky | * [[https://proceedings.mlr.press/v192/thorisson22b/thorisson22b.pdf | The Future of AI Research: Ten Defeasible 'Axioms of Intelligence']] by K.R.Thórisson and H. Minsky |
| * [[https://pdfs.semanticscholar.org/4688/e564f16662838938a2d729685baab068751b.pdf?_ga=2.196493103.2038245345.1566469635-1488191987.1566469635|Cognitive Architecture Requirements for Achieving AGI]] by J.E. Laird et al. | * [[https://pdfs.semanticscholar.org/4688/e564f16662838938a2d729685baab068751b.pdf?_ga=2.196493103.2038245345.1566469635-1488191987.1566469635|Cognitive Architecture Requirements for Achieving AGI]] by J.E. Laird et al. |
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| \\ | Related readings: [[http://cadia.ru.is/wiki/public:t-719-nxai:nxai-25:readings#intelligencethe_phenomenon|Intelligence: The Phenomenon]] |
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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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| ====Historical Concepts==== | ====Historical Concepts==== |
| | \\ Create a Working Definition | It's called a "//working// definition" because it is supposed to be subject to (asap) scrutiny and revision. \\ A good working definition avoids the problem of entrenchment, which, in the worst case, may result in a whole field being re-defined around something that was supposed to be temporary. \\ One great example of that: The Turing Test. | | | \\ Create a Working Definition | It's called a "//working// definition" because it is supposed to be subject to (asap) scrutiny and revision. \\ A good working definition avoids the problem of entrenchment, which, in the worst case, may result in a whole field being re-defined around something that was supposed to be temporary. \\ One great example of that: The Turing Test. | |
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| | ====Goal==== |
| | | What it is | A goal is a set of **steady states** to be achieved. \\ A goal must be described by an abstraction language to be achieved; its desccription must contain sufficient information to verify whether that goal has been achieved or not. \\ A well-defined goal can be assigned to an agent/system/process to be achieved. \\ An //ill-defined goal// is a goal description with missing elements (i.e., due to some part of its description, there is missing information for the process/system/agent that is supposed to perform it. | |
| | | \\ How it may be expressed | Gtop = [Gsub1, Gsub-2, ... Gsub-n, G{-sub1}, G{-sub2}, ...G{-sub-m} ], i.e. a set of zero or more subgoals, where \\ G{-} (meaning "to the power of minus") are "negative goals" (states to be avoided = constraints) and \\ G = [s1, s2, ... s-n, R ], where s-n describes a state s subset S of a (subset) of a World and \\ R are relevant relations between these. | |
| | | Components of s | s= [v1, v2, ... v-n, R] rbrace}: A set of //patterns//, expressed as variables with error/precision constraints, that refer to the world. | |
| | | What we can do with it | Define a task: **task := goal + timeframe + initial world state** | |
| | | Why it is important | Goals are needed for concrete tasks, and tasks are a key part of why we would want AI in the first place. For any complex tasks there will be identifiable sub-goals -- talking about these in compressed manners (e.g. using natural language) is important for learning and for monitoring of task progress. | |
| | | Historically speaking | Goals have been with the field of AI from the very beginning, but definitions vary. | |
| | | \\ What to be aware of | We can assign goals to an AI without the AI having an explicit data structure that we can say matches the goal directly (see e.g. [[/public:t-720-atai:atai-20:agents_and_control#braitenberg_vehicle_examples|Braitenberg Vehicles]] - above). These are called //**implicit goals**//. We may conjecture that if we want an AI to be able to talk about its goals they will have to be -- in some sense -- //**explicit**//, that is, having a discrete representation in the AI's mind (information structures) that can be manipulated, inspected, compressed / decompressed, and related to other data structures for various purposes, in isolation (without affecting in any unnecessary, unwanted, or unforeseen way, other (irrelevant) information structures). | |
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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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| ====Goal==== | |
| | What it is | A goal is a set of **steady states** to be achieved. \\ A goal must be described by an abstraction language to be achieved; its desccription must contain sufficient information to verify whether that goal has been achieved or not. \\ A well-defined goal can be assigned to an agent/system/process to be achieved. \\ An //ill-defined goal// is a goal description with missing elements (i.e., due to some part of its description, there is missing information for the process/system/agent that is supposed to perform it. | | |
| | \\ How it may be expressed | Gtop = [Gsub1, Gsub-2, ... Gsub-n, G{-sub1}, G{-sub2}, ...G{-sub-m} ], i.e. a set of zero or more subgoals, where \\ G{-} (meaning "to the power of minus") are "negative goals" (states to be avoided = constraints) and \\ G = [s1, s2, ... s-n, R ], where s-n describes a state s subset S of a (subset) of a World and \\ R are relevant relations between these. | | |
| | Components of s | s= [v1, v2, ... v-n, R] rbrace}: A set of //patterns//, expressed as variables with error/precision constraints, that refer to the world. | | |
| | What we can do with it | Define a task: **task := goal + timeframe + initial world state** | | |
| | Why it is important | Goals are needed for concrete tasks, and tasks are a key part of why we would want AI in the first place. For any complex tasks there will be identifiable sub-goals -- talking about these in compressed manners (e.g. using natural language) is important for learning and for monitoring of task progress. | | |
| | Historically speaking | Goals have been with the field of AI from the very beginning, but definitions vary. | | |
| | \\ What to be aware of | We can assign goals to an AI without the AI having an explicit data structure that we can say matches the goal directly (see e.g. [[/public:t-720-atai:atai-20:agents_and_control#braitenberg_vehicle_examples|Braitenberg Vehicles]] - above). These are called //**implicit goals**//. We may conjecture that if we want an AI to be able to talk about its goals they will have to be -- in some sense -- //**explicit**//, that is, having a discrete representation in the AI's mind (information structures) that can be manipulated, inspected, compressed / decompressed, and related to other data structures for various purposes, in isolation (without affecting in any unnecessary, unwanted, or unforeseen way, other (irrelevant) information structures). | | |
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| 2025(c)K. R. Thórisson | 2025(c)K. R. Thórisson |
