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| public:t720-atai-2012:nars [2012/10/03 09:27] – thorisson | public:t720-atai-2012:nars [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| Because we need to implement mechanisms that are capable of building models of the causal chains at work in the world, reasoning cannot be avoided (random search is categorically out of the question, as it will take more time than the age of the universe to come up with anything useful). If reasoning is a necessary part of the only approach, what kinds of technologies allow us to implement reasoning? To some extent one can think of reasoning as particular sequences of pattern matching. The implementation task therefore revolves around how to efficiently implement sequential pattern matching mechanisms. But first we need to get an idea of what kinds of pattern matching we are talking about. Humans use several types of reasoning, chief among them being **deduction**, **abduction**, and **induction**. Collectively these have been called **ampliative reasoning**. NARS implements ampliative reasoning methods via a kind of **term logic**, which is different from propositional logic in important ways -- a key one being that abduction and induction, two forms of reasoning considered extremely challenging to implement in AI systems, become much easier to do. The catch is that deduction becomes a bit less obvious in this approach. But the benefits far outweigh the negatives, because no AGI can be envisioned without abduction and induction. Why is that? | Because we need to implement mechanisms that are capable of building models of the causal chains at work in the world, reasoning cannot be avoided (random search is categorically out of the question, as it will take more time than the age of the universe to come up with anything useful). If reasoning is a necessary part of the only approach, what kinds of technologies allow us to implement reasoning? To some extent one can think of reasoning as particular sequences of pattern matching. The implementation task therefore revolves around how to efficiently implement sequential pattern matching mechanisms. But first we need to get an idea of what kinds of pattern matching we are talking about. Humans use several types of reasoning, chief among them being **deduction**, **abduction**, and **induction**. Collectively these have been called **ampliative reasoning**. NARS implements ampliative reasoning methods via a kind of **term logic**, which is different from propositional logic in important ways -- a key one being that abduction and induction, two forms of reasoning considered extremely challenging to implement in AI systems, become much easier to do. The catch is that deduction becomes a bit less obvious in this approach. But the benefits far outweigh the negatives, because no AGI can be envisioned without abduction and induction. Why is that? |
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| Abduction is the act of inferring causes from effects: If you see that the ground is wet, you may infer that it recently rained. It is very difficult to imagine how a cognitive agent that does not posses at least //some// ability to do abductive reasoning. Abduction is the kind of intelligence that the detective Sherlock Holmes had in such plentiful amounts. Induction is the act of generalizing from observed instances -- to invent a global rule for a particular kind of phenomena. Einstein's theory of relativity, E=mc^2, is the ultimate example of inductive reasoning: In its extreme forms it is essentially scientific discovery, and starts to resemble very closely what professional inventors are so good at. (Some philosophers argue that all scientific discoveries are inventions, because until we have a complete theory of anything and everything in the universe, scientific "discoveries" are actually not really true but rather approximations to something we //hope// is the truth; approximations are not discoveries at all -- they are inventions.) It is difficult to imagine an AGI that doesn't have at least //some// form of inductive reasoning capabilities. Without inductive reasoning one would be hard pressed to see a system create anything more complex than that which is informed by trial and error, or some form of inductive bias imparted to the system at birth by its designer. Needless to say, no one has suggested how either deduction or induction can be implemented in a statistical framework or in an ANN framework. We are thus forced to fall back on known methods from logical programming for implementing these methods. | Abduction is the act of inferring causes from effects: If you see that the ground is wet, you may infer that it recently rained. Abduction is the kind of intelligence that the detective Sherlock Holmes had in such plentiful amounts. It is very difficult to imagine how a cognitive agent that does not posses at least //some// ability to do abductive reasoning could reach any sort of general intelligence. Induction is the act of generalizing from observed instances -- to invent a global rule for a particular kinds of phenomena. Einstein's theory of relativity, E=mc^2, is the ultimate example of inductive reasoning: In its extreme forms it is essentially scientific discovery, and starts to resemble very closely what professional inventors are so good at. (Some philosophers argue that all scientific discoveries are inventions, because until we have a complete theory of anything and everything in the universe, scientific "discoveries" are actually only approximations to something we //hope// is the truth; approximations are not discoveries at all -- they are inventions.) Like abduction, it is difficult to imagine an AGI that doesn't have at least //some// form of inductive reasoning capabilities. Without inductive reasoning one would be hard pressed to see a system create anything more complex than that which is informed by trial and error, or some form of inductive bias imparted to the system at birth by its designer. Needless to say, no one has suggested how either deduction or induction can be implemented in a statistical framework or in an ANN framework. We are thus forced to fall back on known methods from logical programming for implementing these methods. |
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| 2012(c)Kristinn R. Thórisson | 2018(c)Kristinn R. Thórisson |
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