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| public:t-720-atai:atai-22:intelligence [2022/08/11 11:42] – [So What Is Intelligence?] thorisson | public:t-720-atai:atai-22:intelligence [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| ==== Intelligence: A Natural Phenomenon==== | ==== Intelligence: A Natural Phenomenon==== |
| | \\ Intelligence | Phenomenon. Intelligence is a phenomenon with good examples in the natural world, but may have more forms than the examples from nature. \\ The only one that //everyone// agrees on to call 'intelligent': **humans**. | | | \\ Intelligence | A phenomenon encountered in nature. Intelligence is a phenomenon with good examples in the natural world, but may have more forms than the examples from nature. \\ The only one that //everyone// agrees on to call 'intelligent': **humans**. | |
| | Natural Intelligence | Phenomenon. Some kinds of animals are considered "intelligent", or at least some behavior of some individuals of an animal species other than humans are deemed indicators of intelligence. | | | Natural Intelligence | Intelligence as it appears in nature. Some kinds of animals are considered "intelligent", or at least some behavior of some individuals of an animal species other than humans are deemed indicators of intelligence. | |
| | Cognitive Science | The study of natural intelligence, in particular human (and that found in nature). | | | Cognitive Science | The study of natural intelligence, in particular human (and that found in nature). | |
| | Artificial Intelligence | The study of how to make intelligent machines. | | | Artificial Intelligence | The study of how to make intelligent machines. | |
| | Intelligent machines | Systems created by humans to display (some - but not all?) features required of beings encountered in nature to be called 'intelligent'. | | | Intelligent Machines | Systems created by humans intended to display (some - but not all?) features required of beings encountered in nature. | |
| | How to define 'intelligence' | Many definitions have been proposed. \\ See e.g.: [[http://www.vetta.org/documents/A-Collection-of-Definitions-of-Intelligence.pdf|A Collection of Definitions of Intelligence]] by Legg & Hutter. | | | How to define 'intelligence' | Many definitions have been proposed. \\ See e.g.: [[http://www.vetta.org/documents/A-Collection-of-Definitions-of-Intelligence.pdf|A Collection of Definitions of Intelligence]] by Legg & Hutter. | |
| | Definitions: a word of caution | We must be careful when it comes to definitions -- for any complex system there is a world of difference between a decent definition and //good accurate appropriate// definition. | | | Definitions: a word of caution | We must be careful when it comes to definitions -- for any complex system there is a world of difference between a decent definition and //good accurate appropriate// definition. | |
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| ==== Definition of INTELLIGENCE Adopted in This Course ==== | ==== Working Definition of Intelligence ==== |
| | \\ \\ Our Chosen Definition | \\ \\ **Adaptation with insufficient knowledge and resources** \\ -- Pei Wang \\ \\ \\ | | | The (working) \\ Definition of \\ Intelligence \\ Used in This \\ Course | \\ \\ **Adaptation with insufficient knowledge and resources** \\ -- Pei Wang \\ \\ | |
| | 'Adaptation' | means changing strategically in light of new information. | | | 'Adaptation' | means changing strategically in light of new information. | |
| | 'Insufficient' | means that it cannot be guaranteed, and in fact, can never be guaranteed to be sufficient to guarantee that goals are achieved. The reason it cannot is that an agent in the physical world can never know for //sure// that it has everything needed to achieve its goals. | | | 'Insufficient' | means that it cannot be guaranteed, and in fact, can never be guaranteed to be sufficient to guarantee that goals are achieved. The reason it cannot is that an agent in the physical world can never know for //sure// that it has everything needed to achieve its goals. | |
| | 'Knowledge' | means information structures (about target phenomena) that allows an agent to predict, achieve goals, explain, or model (target phenomena). | | | 'Knowledge' | means information structures (about target phenomena) that allows an agent to predict, achieve goals, explain, or model (target phenomena). | |
| | 'Resources' | means that it cannot be guaranteed, and in fact, can never be guaranteed. The reason they cannot is that we don't know the 'axioms' of the physical world, and even if we did we could never be sure of it. | | | 'Resources' | means that it cannot be guaranteed, and in fact, can never be guaranteed. The reason they cannot is that we don't know the 'axioms' of the physical world, and even if we did we could never be sure of it. | |
| | \\ Another way to say \\ 'Adaptation under Insufficient Knowledge & Resources' | \\ "Discretionarily Constrained Adaptation Under Insufficient Knowledge & Resources" \\ -- K. R. Thórisson \\ \\ Or simply **Figuring out how to get new stuff done**. \\ \\ | | | \\ Another way to say \\ 'Adaptation under Insufficient Knowledge & Resources' | \\ "Discretionarily Constrained Adaptation Under Insufficient Knowledge & Resources" \\ -- K. R. Thórisson \\ \\ Or simply **Figuring out how to get new stuff done**. \\ \\ | |
| | 'Discretionarily constrained adaptation' | means that an agent can //choose// particular constraints under which to operate or act (e.g. to not consume chocolate for a whole month) -- that the agent's adaptation can be arbitrarily constrained at the discretion of the agent itself (or someone/something else). Extending the term 'adaptation' with this longer 'discretionarily constrained' has the benefit of separating this use of the term ‘adaptation’ from its more common use in the context of natural evolution, where it describes a process fashioned by uniform physical laws. | | | 'Discretionarily constrained' adaptation | means that an agent can //choose// particular constraints under which to operate or act (e.g. to not consume chocolate for a whole month) -- that the agent's adaptation can be arbitrarily constrained at the discretion of the agent itself (or someone/something else). Extending the term 'adaptation' with this longer 'discretionarily constrained' has the benefit of separating this use of the term ‘adaptation’ from its more common use in the context of natural evolution, where it describes a process fashioned by uniform physical laws. | |
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| | Terminology is important! | The terms we use for phenomena must be shared to work as an effective means of communication. Obsessing about the definition of terms is a good thing! | | | Terminology is important! | The terms we use for phenomena must be shared to work as an effective means of communication. Obsessing about the definition of terms is a good thing! | |
| | Beware of Definitions! | Obsessing over precise, definitive definitions of terms should not extend to the phenomena that the research targets: These are by definition not well understood. It is impossible to define something that is not understood! So beware of those who insist on such things. | | | Beware of Definitions! | Obsessing over precise, definitive definitions of terms should not extend to the phenomena that the research targets: These are by definition not well understood. It is impossible to define something that is not understood! So beware of those who insist on such things. | |
| | \\ Overloaded Terms | Many key terms in AI tend to be overloaded. Others are very unclear. Examples of the latter include: //intelligence, agent, concept, thought.// \\ Many terms have multiple meanings, e.g. reasoning, learning, complexity, generality, task, solution, proof. \\ Yet others are both unclear and polysemous, e.g. //consciousness//. \\ One source of the multiple meanings for terms is the tendency, in the beginning of a new research field, for founders to use common terms that originally refer to general concepts in nature, and which they intend to study, about the results of their own work. As time passes those concepts then begin to reference work done in the field, instead of their counterpart in nature. Examples include reinforcement learning (originally studied by Pavlov, Skinner, and others in psychology and biology), machine learning (learning in nature different from 'machine learning' in many ways), neural nets (artificial neural nets bear almost no relation to biological neural networks). \\ Needless to say, this regularly makes for some lively but more or less //pointless// debates on many subjects within the field of AI (and others, in fact). | | | \\ Overloaded Terms | Many key terms in AI tend to be overloaded. Others are very unclear. Examples of the latter include: //intelligence, agent, concept, thought.// \\ Many terms have multiple meanings, e.g. reasoning, learning, complexity, generality, task, solution, proof. \\ Yet others are both unclear and polysemous, e.g. //consciousness//. \\ One source of the multiple meanings for terms is the tendency, in the beginning of a new research field, for founders to use common terms that originally refer to general concepts in nature, and which they intend to study, about the results of their own work. As time passes those concepts then begin to reference work done in the field, instead of their counterpart in nature. Examples include reinforcement learning (originally studied by Pavlov, Skinner, and others in psychology and biology), machine learning (learning in nature different from 'machine learning' in many ways), neural nets (artificial neural nets bear almost no relation to biological neural networks). \\ Needless to say, this regularly makes for some lively but more or less //pointless// debates on many subjects within the field of AI (and others, in fact, but especially AI). | |
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| ====So What Is Intelligence?==== | ====So What Is Intelligence?==== |
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| | Why The Question Matters | \\ It is important to know what you're studying and researching! | | | Why The Question Matters | It is important to know what you're studying and researching! \\ ...A researcher selects | |
| | \\ The Challenge | You cannot define something precisely until you understand it! \\ Premature precise definitions may be much worse than loose definitions or even bad-but-rough definitions: You are very likely to end up researching //something other// than what you set out to research. | | | \\ The Challenge | You cannot define something precisely until you understand it! \\ Premature precise definitions may be much worse than loose definitions or even bad-but-rough definitions: You are very likely to end up researching //something other// than what you set out to research. | |
| | \\ \\ What Can We Do? | List the //requirements//. Even a partial list will go a long way towards helping steer the research. \\ Engineers use requirements to guide their building of artifacts. If the artifact doesn't meet the requirements it is not a valid member of the category that was targeted. \\ In science it is not customary to use requirements to guide research questions, but it works just the same (and equally well!): List the features of the phenomenon you are researching and group them into **essential**, **important but non-essential**, and **other**. Then use these to guide the kinds of questions you try to answer. | | | \\ \\ What Can We Do? | List the //requirements//. Even a partial list will go a long way towards helping steer the research. \\ Engineers use requirements to guide their building of artifacts. If the artifact doesn't meet the requirements it is not a valid member of the category that was targeted. \\ In science it is not customary to use requirements to guide research questions, but it works just the same (and equally well!): List the features of the phenomenon you are researching and group them into **essential**, **important but non-essential**, and **other**. Then use these to guide the kinds of questions you try to answer. | |
| | Before Requirements, Look At Examples | To get to a good list it may be necessary to explore the boundaries of your phenomenon. | | | Before Requirements, Look At Examples | To get to a good list it may be necessary to explore the boundaries of your phenomenon. | |
| | | \\ 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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