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| public:t-720-atai:atai-18:lecture_notes_w1 [2018/08/20 15:14] – [AI is a Broad Field] thorisson | public:t-720-atai:atai-18:lecture_notes_w1 [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| ====Important Concepts in This Course==== | ====Important Concepts in This Course==== |
| | Methodology | Present methods in AI will not suffice for addressing the full scope of the phenomenon of intelligence, as see in nature. | | | Methodology | Present methods in AI will not suffice for addressing the full scope of the phenomenon of intelligence, as see in nature. | |
| | Attention | The resulting state after a successful change. | | | Attention | The ability to manage resources, including computational (thought), information from the external environment, energy, and time. | |
| | Meta-Cognition | The ability of a system to reason about itself. | | | Meta-Cognition | The ability of a system to reason about itself. | |
| | Reasoning | The application of logical rules to knowledge. | | | Reasoning | The application of logical rules to knowledge. | |
| | AI spans many fields | Psychology, mathematics and computation, neurology, philosophy. | | | AI spans many fields | Psychology, mathematics and computation, neurology, philosophy. | |
| | Is AI a subfield of computer science? | Yes and No. Yes, because this is the field that has the best and most tools for studying it as a phenomenon. No, because the field does not address important concepts and features of intelligence. | | | Is AI a subfield of computer science? | Yes and No. Yes, because this is the field that has the best and most tools for studying it as a phenomenon. No, because the field does not address important concepts and features of intelligence. | |
| | Terminology | 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. \\ One tendency for creating multiple meanings for terms is the habit of, in the beginning of a new research field to use terms to refer to general concepts in nature, and then as time passes to use the same terms to refer to work already done in the field (e.g. expert system, machine learning, neural net). \\ Needless to say, this regularly makes for some lively but useless debates on many subjects. | | | Terminology | 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 tendency for creating multiple meanings for terms is the habit of, in the beginning of a new research field to use terms to refer to general concepts in nature, and then as time passes to use the same terms to refer to work already done in the field (e.g. expert system, machine learning, neural net). \\ Needless to say, this regularly makes for some lively but useless debates on many subjects. | |
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| | ==== How To Study (and not fail) ==== |
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| | **As you read papers from each of the following categories I want you ask yourself a few questions:** |
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| | * For each paper on a topic X, ask yourself: |
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| | | 1 | What is X? | |
| | | 2 | How does the human mind do X? | |
| | | 3 | Do current computers do X? | |
| | | 4 | Do we need (to replicate or capture the principles of) what the human mind does to achieve X to create a machine that rivals the ability of humans to do X? | |
| | | Result | If you can answer them satisfactorily when you're done reading you're good! Even if you can't you'll be fine if you: Write down the discrepancies and //bring them to class in the form of questions//. There is no such thing as a 'stupid question' when you're learning something new. | |
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| 2018(c)K. R. Thórisson | |
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| | 2020(c)K.R.Thórisson |
| // EOF // | |
