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| public:t-720-atai:atai-20:understanding [2020/10/18 16:23] – [Understanding & Creativity] thorisson | public:t-720-atai:atai-20:understanding [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| | Understanding | To consistently solve problems regarding a phenomenon <m>X</m> requires //understanding // <m>X</m>. \\ Understanding <m>X</m> means the ability to extract and analyze the //meaning// of any phenomena <m>\phi</m> related to <m>X</m>. | | | Understanding | To consistently solve problems regarding a phenomenon <m>X</m> requires //understanding // <m>X</m>. \\ Understanding <m>X</m> means the ability to extract and analyze the //meaning// of any phenomena <m>\phi</m> related to <m>X</m>. | |
| | Meaning | Meaning is closely coupled with understanding -- the two cannot exist without the other. Are they irreducible? | | | Meaning | Meaning is closely coupled with understanding -- the two cannot exist without the other. Are they irreducible? | |
| | Bottom Line | Can't talk about creativity without talking about understanding, and can't talk about understanding without talking about meaning. No good scientific theory of meaning exists. | | | Bottom Line | Can't talk about creativity without talking about understanding, and can't talk about understanding without talking about meaning. No good scientific theory of meaning (but some philosophical ones) exists. | |
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| | Analogy | The ability to find similarity between even the most disparate phenomena. | | | Analogy | The ability to find similarity between even the most disparate phenomena. | |
| | \\ Why This Matters | Logic is one of the most effective ways to compress information. Reasoning is the process of applying logic to information according to rules. Because of the high ratio of possible states in the physical world to the storage capacity of the human (and machine) mind/memory, it is not conceivable that understanding ("//true// knowledge" - i.e. useful, reliable knowledge) of a large amount of phenomena in the physical world can be achieved without the use of reasoning. | | | \\ Why This Matters | Logic is one of the most effective ways to compress information. Reasoning is the process of applying logic to information according to rules. Because of the high ratio of possible states in the physical world to the storage capacity of the human (and machine) mind/memory, it is not conceivable that understanding ("//true// knowledge" - i.e. useful, reliable knowledge) of a large amount of phenomena in the physical world can be achieved without the use of reasoning. | |
| | \\ How is Reasoning Applied in Understanding & Creativity? | Based on knowledge about objects, parts and relations between these, as well as transformation rules by which these behave and interact, one can construct predictions for what will happen next (predictive control), abduce what may have happened before (how the world got to where it is - constructing explanations), determine for what to do next (make plans), and make analogies - drawing parallels to create hypotheses about novel things. \\ It is in particular this last item (not in isolation with the others but in tandem with them) that is a very useful tool for producing novel insights (i.e. "being creative"). | | | \\ How is Reasoning Applied in Understanding & Creativity? | Based on knowledge about objects, parts and relations between these, as well as transformation rules by which these behave and interact, one can \\ \\ - construct predictions for what will happen next (predictive control), \\ - abduce what may have happened before (how the world got to where it is - constructing explanations), \\ - determine what to do next (make plans), and \\ - make analogies - drawing parallels to create hypotheses about novel things. \\ \\ It is in particular this last item (not in isolation with the others but in tandem with them) that is a very useful tool for producing novel insights (i.e. "being creative"). | |
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| ====In the Vernacular==== | ====In the Vernacular==== |
| | What It Is | A concept that people use all the time about each other's cognition. With respect to achieving a task, given that the target of the understanding is all or some aspects of the task, more of it is generally considered better than less of it. | | | What It Is | A concept that people use all the time about each other's cognition. With respect to achieving a task, given that the target of the understanding is all or some aspects of the task, more of it is generally considered better than less of it. | |
| | Why It Is Important | Seems to be connected to "real intelligence" - when a machine does X reliably and repeatedly we say that it is "capable" of doing X qualify it with "... but it doesn't 'really' understand what it's doing". | | | Why It Is Important | Seems to be connected to "real intelligence" - when a machine does X reliably and repeatedly we say that it is "capable" of doing <m>X</m> qualify it with "... but it doesn't 'really' understand what it's doing". | |
| | What Does It Mean? | No well-known scientific theory exists. \\ Normally we do not hand control of anything over to anyone who doesn't understand it. All other things being equal, this is a recipe for disaster. | | | What Does It Mean? | No well-known scientific theory exists. \\ Normally we do not hand control of anything over to anyone who doesn't understand it. All other things being equal, this is a recipe for disaster. | |
| | Evaluating Understanding | Understanding any X can be evaluated along four dimensions: 1. Being able to predict X, 2. being able to achieve goals with respect to X, 3. being able to explain X, and 4. being able to "re-create" X ("re-create" here means e.g. creating a simulation that produces X and many or all its side-effects.) | | | Evaluating Understanding | Understanding any <m>X</m> can be evaluated along four dimensions: \\ 1. Being able to predict <m>X</m>, \\ 2. being able to achieve goals with respect to <m>X</m>, \\ 3. being able to explain <m>X</m>, and \\ 4. being able to "re-create" <m>X</m> \\ ("re-create" here means e.g. creating a simulation that produces X and many or all its side-effects.) | |
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| ====It Used To Be "Common Sense"==== | ====It Used To Be Called "Common Sense"==== |
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| | Status of Understanding in AI | Since the 70s the concept of //understanding// has been relegated to the fringes of research. The only AI contexts it regularly appears in are "language understanding", "scene understanding" and "image understanding". | | | Status of Understanding in AI | Since the 70s the concept of //understanding// has been relegated to the fringes of AI research. The only AI contexts the term regularly appears in are "language understanding", "scene understanding" and "image understanding". | |
| | What Took Its Place | What took the place of understanding in AI is //common sense//. Unfortunately the concept of common sense does not capture at all what we generally mean by "understanding". | | | What Took Its Place | What took the place of understanding in AI is //common sense//. Unfortunately the concept of common sense does not necessarily capture at all what we generally mean by "understanding". | |
| | Projects | The best known project on common sense is the CYC project, which started in the 80s and is apparently still going. It is the best funded, longest running AI project in history. | | | Projects | The best known project on common sense is the CYC project, which started in the 80s and is apparently still going. It is the best funded, longest running AI project in history. | |
| | Main Methodology | The foundation of CYC is formal logic, represented in predicate logic statements and structures. | | | Main Methodology | The foundation of CYC is formal logic, represented in predicate logic statements and compound structures. | |
| | Key Results | Results from the CYC project are similar to the expert systems of the 80s - these systems are brittle and unpredictable. \\ Apparently the CYC system is being commercialized by a company called Lucid [[https://www.technologyreview.com/s/600984/an-ai-with-30-years-worth-of-knowledge-finally-goes-to-work/|REF]]. | | | Key Results | Results from the CYC project are similar to the expert systems of the 80s - these systems are brittle and unpredictable. \\ The state of the CYC system in 2016 is provided in this nicely written essay: [[https://www.technologyreview.com/s/600984/an-ai-with-30-years-worth-of-knowledge-finally-goes-to-work/|REF]]. | |
| | | \\ Two Problems | Upon further scrutiny, no good analysis or arguments exist of why 'understanding' should be equated with 'common sense'. The two are simply not the same thing. \\ Furthermore, progress under the rubric of 'common sense' in AI has neither produced any grand results nor evidence that the methodology followed is a promising one. | |
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| | \\ Phenomenon \\ <-> \\ Model | Phenomenon <m>Phi</m>: Any group of inter-related variables in the world, some or all of which can be measured.\\ Models <m>M</m>: A set of information structures that reference the variables of <m>Phi</m> and their relations <m>R</m> such that they can be used, applying processes <m>P</m> that manipulate <m>M</m>, to \\ (a) predict, \\ (b) achieve goals with respect to, \\ (c ) explain, and \\ (d) (re-)create <m>Phi</m>. | | | \\ Phenomenon \\ <-> \\ Model | Phenomenon <m>Phi</m>: Any group of inter-related variables in the world, some or all of which can be measured.\\ Models <m>M</m>: A set of information structures that reference the variables of <m>Phi</m> and their relations <m>R</m> such that they can be used, applying processes <m>P</m> that manipulate <m>M</m>, to \\ (a) predict, \\ (b) achieve goals with respect to, \\ (c ) explain, and \\ (d) (re-)create <m>Phi</m>. | |
| | \\ Definition of Understanding | An agent's **understanding** of a phenomenon <m>Phi</m> to some level <m>L</m> when it posses a set of models <m>M</m> and relevant processes <m>P</m> such that it can use <m>M</m> to \\ (a) predict, \\ (b) achieve goals with respect to, \\ (c ) explain, and \\ (d) (re-)create <m>Phi</m>. \\ \\ Insofar as the nature of relations between variables in <m>Phi</m> determine their behavior, the level <m>L</m> to which the phenomenon <m>Phi</m> is understood by the agent is determined by the //completeness// and the //accuracy// to which <m>M</m> matches the variables and their relations in <m>Phi.</m> | | | \\ Definition of Understanding | An agent's **understanding** of a phenomenon <m>Phi</m> to some level <m>L</m> when it posses a set of models <m>M</m> and relevant processes <m>P</m> such that it can use <m>M</m> to \\ (a) predict, \\ (b) achieve goals with respect to, \\ (c ) explain, and \\ (d) (re-)create <m>Phi</m>. \\ \\ Insofar as the nature of relations between variables in <m>Phi</m> determine their behavior, the level <m>L</m> to which the phenomenon <m>Phi</m> is understood by the agent is determined by the //completeness// and the //accuracy// to which <m>M</m> matches the variables and their relations in <m>Phi.</m> | |
| | \\ Why \\ 'Cumulative' \\ ? | According to the theory, 'understanding' is a learning process: A dynamic process that creates knowledge of a particular functional kind, namely the kind that can be used to predict, achieve goals, explain and (re-)create (see the point above in this table). \\ Unlike 'learning', however, in the vernacular the term 'understanding' is also used to describe a state - the state reached when the process is done (the output of 'learning' is 'knowledge' - the output of the 'understanding' is '//an// understanding'). We can see the difference if we replace 'understanding' with the term 'know': "A understands X" vs. "A //knows// X". \\ The term 'cumulative' emphasizes the 'learning' part of understanding - that it's a //process of creation//. | | | \\ Why \\ 'Cumulative' \\ ? | According to the theory, 'understanding' is a learning process: A dynamic process that creates knowledge of a particular functional kind, namely the kind that can be used to predict, achieve goals, explain and (re-)create (see the point above in this table). \\ Unlike 'learning', however, in the vernacular the term 'understanding' is also used to describe a //state// - the state reached when the process completes (the output of 'learning' is 'knowledge' - the output of the 'understanding' is '//an// understanding'). We can see the difference if we replace 'understanding' with the term 'know': "A understands X" vs. "A //knows// X". \\ The term 'cumulative' emphasizes the 'learning' part of understanding - a //process of creation//. \\ This generation process is key to human understanding because \\ (a) it is used many times a day - every time we learn anything new: It is an integral part of the //learning process// proper, and \\ (b) it a //general// process of knowledge acquisition that builds knowledge graphs incrementally, from experience, relying on reasoning processes as well as existing knowledge, and \\ ( c) the processes that //build// the knowledge and those that //use it// are the //**same **// processes. | |
| | REF | [[http://alumni.media.mit.edu/~kris/ftp/AGI16_understanding.pdf|About Understanding]] by Thórisson et al. | | | REF | [[http://alumni.media.mit.edu/~kris/ftp/AGI16_understanding.pdf|About Understanding]] by Thórisson et al. | |
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