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| public:t-713-mers:mers-25:concepts_terms [2025/08/19 09:12] – [Science, Technology, Philosophy, Mathematics] thorisson | public:t-713-mers:mers-25:concepts_terms [2025/08/19 12:27] (current) – [Correlation, Knowledge, Causation] thorisson |
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| ====Correlation, Knowledge, Causation==== | ====Correlation, Knowledge, Causation==== |
| | Correlation | Some factors/variables co-vary when changes in one variable are related with changes in the other, negative or positive. | | | Correlation | Some factors/variables co-vary when changes in one variable are related with changes in the other, negative or positive. | |
| | Correlation: Powerful source of information | Any variables in the world can be measured for correlation (i.e. to see if they are correlated). Only two variables are needed (independent and dependent) for doing correlation studies. | | | Correlation: Powerful source of information | Any variables in the world can be measured for correlation (i.e. to see if they are correlated). Only two variables are needed (independent and dependent) for doing correlation studies. | |
| | Main operating principle behind correlation | There is no causation without correlation. \\ BUT: It is not guaranteed to be measurable. | | | Main operating principle behind correlation | There is no causation without correlation. \\ BUT: It is not guaranteed to be measurable. | |
| | Correlation: Pitfall | Correlation does not imply causation between the variables measured! \\ BUT: ALL correlation that is NOT a coincidence has a cause. | | | Correlation: Pitfall | Correlation does not imply causation between the variables measured! \\ BUT: ALL correlation that is NOT a coincidence has a cause. | |
| | Quasi-experimental designs | Purpose: Where true experimental design is not possible, approximate it. \\ If direct control over dependent/independent variables is not possible. | | | Modeling Correlation | A model that makes use of observed correlation between A and B is only good for prediction, not for manipulation; to be useful for manipulation (goal-directed behavior and planning) it must include the causal direction of the relationship between A and B. | |
| | How it works | 1. One-shot case study (no control group) \\ 2. Single group pre- and post-test (minimal control) \\ 3. ABAB: Single-group repeated measures (slightly less minimal control) | | | Causal Relations are Invisible | Causal relations do not jump out at us when we observe something unfamiliar because the cause and effect are "spread out" over time -- cause-effect happens over time, as the cause must happen before the effect. | |
| | Limitations | Much greater uncertainty as to the internal and external validity of the quasi-experiments than true experimental designs | | | Modeling Cause-Effect | Intelligent agents can model cause and effect with many methods; two of them are invention (coming up with a wild idea for the relationship betweeen A and B, e.g. that spirits make people sick) and discovery (through observation and experimentation with A and B, e.g. that unclean surgical knives can bring disease-carrying material between people). | |
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| | \\ Philosophy | A systematic investigation into the deeper nature of any phenomenon. \\ Fundamental motivation: Deepened understanding of our place in the universe. \\ Fundamental driving principle: Human reasoning and creativity. \\ Fundamental organizing principle: Schools of thought; methods of reasoning. \\ //When you hardly know anything about a phenomenon, yet insist on getting to the bottom of it, philosophizing gets you started.// | | | \\ Philosophy | A systematic investigation into the deeper nature of any phenomenon. \\ Fundamental motivation: Deepened understanding of our place in the universe. \\ Fundamental driving principle: Human reasoning and creativity. \\ Fundamental organizing principle: Schools of thought; methods of reasoning. \\ //When you hardly know anything about a phenomenon, yet insist on getting to the bottom of it, philosophizing gets you started.// | |
| | \\ Science | A systematic investigation into phenomena in the natural world susceptible to physical experimentation. \\ Fundamental motivation: Reliable knowledge of the world. \\ Fundamental driving principle: Induction. \\ Fundamental organizing principle: Controlled comparative experiment. \\ //When you embark on improving your understanding of a phenomenon with measurable/quantifiable variables, through comparative experiments, you are applying the **scientific method** ("doing science").// | | | \\ Science | A systematic investigation into phenomena in the natural world susceptible to physical experimentation. \\ Fundamental motivation: Reliable knowledge of the world. \\ Fundamental driving principle: Induction. \\ Fundamental organizing principle: Controlled comparative experiment. \\ //When you embark on improving your understanding of a phenomenon with measurable/quantifiable variables, through comparative experiments, you are applying the **scientific method** ("doing science").// | |
| | Induction | The cognitive act of generalizing from experience. \\ //Example: Socrates is a man. Socrates died. Hence, all men will die.// | | | Induction | The cognitive act of generalizing from experience. \\ //Example: Socrates is a man. Socrates died. Theory: All men die.// | |
| | \\ Engineering | Effort to construct things using relevant knowledge (often state-of-the-art scientific models/theories - see below), systematic methods, and relevant technology. \\ Fundamental motivation: Control of human environment. \\ Fundamental principle: Design. \\ Fundamental organizing principle: Methodical application of known procedures and methods. \\ //When you embark on changing or improving any aspect of your environment, working towards the implementation of a well-defined end product, through an application of best known practices, you are doing engineering.// | | | \\ Engineering | Effort to construct things using relevant knowledge (often state-of-the-art scientific models/theories - see below), systematic methods, and relevant technology. \\ Fundamental motivation: Control of human environment. \\ Fundamental principle: Design. \\ Fundamental organizing principle: Methodical application of known procedures and methods. \\ //When you embark on changing or improving any aspect of your environment, working towards the implementation of a well-defined end product, through an application of best known practices, you are doing engineering.// | |
| | Technology | Tools and techniques for getting things done. \\ Fundamental principle: Composition, design, engineering. | | | Technology | Tools and techniques for getting things done. \\ Fundamental principle: Composition, design, engineering. | |
| | How it relates to logic | If causal relations are rules, and the world has regularity, then the world is rules-based. Reasoning is the method of following logic when working with rules. It means we can reason about the world. | | | How it relates to logic | If causal relations are rules, and the world has regularity, then the world is rules-based. Reasoning is the method of following logic when working with rules. It means we can reason about the world. | |
| | Types of \\ (basic) \\ causal relations | A->B & A->C : A causes B and C. \\ A->B->C : A causes B and B causes C \\ [A+B]->C : A and B together cause C. \\ A->C, B->C : Both A and B are sufficient to cause B. | | | Types of \\ (basic) \\ causal relations | A->B & A->C : A causes B and C. \\ A->B->C : A causes B and B causes C \\ [A+B]->C : A and B together cause C. \\ A->C, B->C : Both A and B are sufficient to cause B. | |
| | Time and Causation | The temporal relation between cause and effect is strict on time: Effects cannot happen before causes. | | | Time and Causation | The temporal relation between cause and effect is strict on time: Effects cannot happen before causes! | |
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| ====Reasoning==== | ====Reasoning==== |
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| | What is Reasoning? | A systematic way of considering implications. | | | What is Reasoning? | A systematic way of considering statements and their implications. | |
| | How is it done? | Via processes that observe rules. | | | How is it done? | Via processes that observe rules, called "reasoning processes". | |
| | What are \\ the main reasoning \\ process types? | **Deduction**: All men are mortal. Socrates is a man. Hence, Socrates is mortal \\ **Abduction**: How did this come about? (Sherlock Holmes) \\ **Induction**: What is the general rule? \\ **Analogy**: 'This' is like 'that' (in 'this' way). | | | What are \\ the main reasoning \\ process types? | **Deduction**: All men are mortal. Socrates is a man. Hence, Socrates is mortal \\ **Abduction**: How did this come about? (Sherlock Holmes) \\ **Induction**: What is the general rule? \\ **Analogy**: 'This' is like 'that' (in 'this' way). | |
| | How are they used \\ in science? | In empirical science to unearth the "rules of the universe". \\ In mathematics as axioms. \\ In philosophy as a way to construct arguments. \\ In computer science to write code. | | | How are they used \\ in science? | In empirical science to unearth the "rules of the universe". \\ In mathematics as axioms. \\ In philosophy as a way to construct arguments. \\ In computer science to write code. | |
| | Why does it work? | Because the world's behavior is/seems rule-based. | | | Why does it work? | Because the world's behavior is/seems rule-based. | |
| | | Deduction | Mathematical deduction is axiomatic - **always** Boolean (true or false). \\ Scientific deduction is **never** axiomatic - it can always (potentially) be disproven. \\ // IRL we do deduction (of the latter kind) all the time, e.g. when picking option B to meet when you already said you cannot do option A.// | |
| | | Abduction | Mathematical abduction can be completely verified through its axioms. \\ Scientific abduction can always be challenged. \\ //IRL: Choosing the side-entrance when we see that the front door is locked (which might still be wrong).// | |
| | | Induction | The best method for verifying induction in mathematics is //mathematical proof.// \\ In science, induction can never be proven. This is one reason why scientific theories must instead be formulated in a way that they can be //disproven// (cf. Karl Popper). \\ // IRL: Generalizations are made in the form of rules of thumb all the time; mostly we don't care whether they are always true or not, only how useful they are in particular situations for particular purposes.// | |
| | | Analogy | Used by scientists, mathematicians, and average Joe and Jane to come up with new ideas, compare and contrast, and analyze anything the heart desires. | |
| | | Reasoning in \\ Maths vs. Science | Reasoning is different in maths than in science because to do math we //must know all the rules completely up front//. The main role of maths is to inspect what the rules imply. \\ In science //all rules are hidden.// The main role of science is to //uncover the rules//. | |
| | | Axiomatic vs. \\ Non-axiomatic | In axiomatic reasoning we know for a fact \\ 1. we have the complete set of rules \\ 2. all the rules are true, and that \\ 3. if we strictly follow the rules we will have absolute truth every time. \\ In non-axiomatic reasoning, none of the above holds. \\ //So how is it then that we can reason?// | |
| | | Empirical Reasoning | For reasoning in the physical world, we must assume that we possibly got things wrong. But //some things are more wrong than others.// To ensure that we use the best knowledge we have for any task X, we must do bookkeeping about //what works and what doesn't in what situations//. | |
| | | Defeasible Reasoning | Reasoning that is based on assumptions and conclusions that can be refuted is called //defeasible reasoning//. | |
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| | A scientific theory gives us the big picture | A good scientific theory relates together, in a coherent way, some part of the world -- in general the bigger the part, the better the theory. | | | A scientific theory gives us the big picture | A good scientific theory relates together, in a coherent way, some part of the world -- in general the bigger the part, the better the theory. | |
| | Occam's Razor | A good scientific theory cannot be simplified; it is the shortest and most accurate explanation of a phenomenon. Einstein is quoted as saying: "A theory should be as simple as possible, but not simpler". | | | Occam's Razor | A good scientific theory cannot be simplified; it is the shortest and most accurate explanation of a phenomenon. Einstein is quoted as saying: "A theory should be as simple as possible, but not simpler". | |
| | A scientific theory can be **disproven** | A scientific theory or hypothesis is a statement that is //disprovable//. To count as "scientific" a theory //must// be disprovable. For this there must exist some measures and actions that are //possible// (in theory, but better yet, practic) whose results would possibly - should the measurements come out a particular way - disprove the theory. \\ Applying this criterion strictly means that //all scientific theories to date have been disproven - i.e. proven incorrect.// \\ This is not a bug but a //**feature!**:// Exposing the limits of our theories by demonstrating in which contexts they are incorrect allows us to come up with better theories. | | | \\ A scientific theory can be **disproven** | A scientific theory or hypothesis is a statement that is //disprovable//. To count as "scientific" a theory //must// be disprovable. For this there must exist some measures and actions that are //possible// (in theory, but better yet, practic) whose results would possibly - should the measurements come out a particular way - disprove the theory. \\ Applying this criterion strictly means that //all scientific theories to date have been disproven - i.e. proven incorrect.// \\ This is not a bug but a //**feature!**:// Exposing the limits of our theories by demonstrating in which contexts they are incorrect allows us to come up with better theories. | |
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| | What it is | A model is a "cartoon" of a phenomenon -- an information structure that captures the most important (preferably all the important) aspects of a phenomenon in question. | | | What it is | A model is a "cartoon" of a phenomenon -- an information structure that captures the most important (preferably all the important) aspects of a phenomenon in question. | |
| | All scientific theories present a model | No matter how explicit or implicit, all scientific theories are models of the world. //Best known example: E=mc^2// | | | All scientific theories present a model | No matter how explicit or implicit, all scientific theories are models of the world. //Best known example: E=mc^2// | |
| | Science vs. Mathematics | Mathematics is **axiomatic**: Some a-priori premises are (and must be) assumed. \\ Science is non-axiomatic: We do not know the full set of rules that govern the universe, and we will never know (for sure). | | |
| | Science vs. Engineering | In science we look for the model; \\ in engineering we mold the world to behave like our model. \\ \\ // Example for science: // \\ [phenomenon] // We see something interesting and call it "**intelligence**". // \\ [model] // We come up with theories for how it works. // \\ [empirical research] // We test the models through systematic creation and evaluation of hypotheses. // \\ [create new models] // We revise the theory. // \\ \\ // Example for engineering:// \\ [model] // We have a theory of intelligence. // \\ [engineering] // We want to build an intelligent system. // \\ [implementation] // We use the theory as a blueprint. // \\ [requirements and theory] // We make sure it behaves to its specifications. // | | | Science vs. Engineering | In science we look for the model; \\ in engineering we mold the world to behave like our model. \\ \\ // Example for science: // \\ [phenomenon] // We see something interesting and call it "**intelligence**". // \\ [model] // We come up with theories for how it works. // \\ [empirical research] // We test the models through systematic creation and evaluation of hypotheses. // \\ [create new models] // We revise the theory. // \\ \\ // Example for engineering:// \\ [model] // We have a theory of intelligence. // \\ [engineering] // We want to build an intelligent system. // \\ [implementation] // We use the theory as a blueprint. // \\ [requirements and theory] // We make sure it behaves to its specifications. // | |
| | Why AI is special | Most research fields rely primarily on either the scientific research approach or the engineering approach. \\ //**AI is special in that it is committed to doing both.**// \\ (Although it does not always operate accordingly.) \\ AI is also the only field of science explicitly committed to the phenomenon of intelligence. | | | Why AI is special | Most research fields rely primarily on either the scientific research approach or the engineering approach. \\ //**AI is special in that it is committed to doing both.**// \\ (Although it does not always operate accordingly.) \\ AI is also the only field of science explicitly committed to the phenomenon of intelligence. | |
