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Humans learn by adding symbolic data to knowledge base
Both machines and people learn by adding symbolically encoded information to a knowledge base.
RELATED ARTICLES
Explain
⌅
Artificial Intelligence
Artificial Intelligence☜A collaboratively editable version of Robert Horns brilliant and pioneering debate map Can Computers Think?—exploring 50 years of philosophical argument about the possibility of computer thought.☜F1CEB7
⌃
Can computers think? [1]
Can computers think? [1]☜Can a computational system possess all important elements of human thinking or understanding? ☜FFB597
⌃
Yes: physical symbol systems can think [3]
Yes: physical symbol systems can think [3]☜Thinking is a rule governed manipulation of symbolic representational structures. In humans, symbol systems are instantiated in the brain, but the same symbol systems can also be instantiated in a computer. ☜59C6EF
■
Humans learn by adding symbolic data to knowledge base
Humans learn by adding symbolic data to knowledge base☜Both machines and people learn by adding symbolically encoded information to a knowledge base.☜98CE71
↳
Computers never move beyond explicit rules
Computers never move beyond explicit rules☜In acquiring skills humans initially use explicit rules and then advance through stages in which performance becomes increasingly skilled, fluid and habituated. At the highest level the rules are no longer consulted. Computers stay tied to rules.☜EF597B
↳
Learning is a process of interpretation
Learning is a process of interpretation☜A mind, reasoning dialectically and predicationally, learns by interpreting what it perceives in terms of the meaning of what it perceives; as computers dont work with meanings, interpretation and the relevant kind of learning is impossible.☜EF597B
↳
Motivational factors cause different rates of learning
Motivational factors cause different rates of learning☜Humans learn more efficiently when motivated by supplementary knowledge—e.g. by metaphorically motivated knowledge. For computers the opposites the case: they work less efficiently when forced to deal with supplementary knowledge.☜EF597B
□
Thermostats can have beliefs
Thermostats can have beliefs☜Beliefs can reasonably be ascribed to an entity when its actions are consistent, the result of observation, and in accordance with goals. Because the behaviour of thermostats meet those criteria, it is reasonable to say they have beliefs.☜98CE71
□
SOAR (an implemented model)
SOAR (an implemented model)☜SOAR is a general intelligence system that learns by chunking—ie by collapsing the work of satisfying a subgoal into a single condition-action rule, or production. It searches its explicit representations heuristically by means-ends analysis...☜98CE71
□
The Biological Assumption
The Biological Assumption☜The brain is the hardware (or wetware) on which the software of the mind is run. Thinking is a symbolic process that is implemented in the neurons of the brain and that can also be implemented in the circuits of a digital computer.☜98CE71
□
The Disembodied Mind Assumption
The Disembodied Mind Assumption☜Thinking is an abstract process that does not require the presence of a body.☜98CE71
□
The Heuristic Search Assumption
The Heuristic Search Assumption☜Symbolic data is searched using various methods of estimation or heuristics, which make the search more efficient.☜98CE71
□
The Knowledge Base Assumption
The Knowledge Base Assumption☜Symbolic data can be organized into a knowledge base—written in a representation language and allowing computers to infer conclusions from available information. E.g. Chair: is made of Wood, is used for Sitting On. Wood: comes from Tree.☜98CE71
□
The language of thought
The language of thought☜The language of thoughts a formal language that mental processes operate on; with a combinatorial syntax and semantics. Sentences are generated from combinations of words: complex mental representations from combinations of simpler representations.☜98CE71
□
The Representationalist Assumption
The Representationalist Assumption☜Symbol structures are internal representations of external reality, made up of symbols and operated on by psychological processes (inc. rules and search). Theyre constituent in that their meaning is a function of the meaning of their parts.☜98CE71
□
The Rule-Following Assumption
The Rule-Following Assumption☜Humans, like machines, behave intelligently by following rules, which can, in principle, be spelled out as explicit if-then statements.☜98CE71
□
The Symbolic Data Assumption
The Symbolic Data Assumption☜The elements of thinking are represented in discrete, symbolic form. Beliefs, desires, goals, values, plans, etc are represented by such data and by their combination into higher order symbol structures like knowledge bases. ☜98CE71
□
The Universal Conceptual Framework Assumption
The Universal Conceptual Framework Assumption☜There is a neutral and completely general conceptual structure in which all knowledge can be represented. For a machine to understand natural language, it must translate sentences into this universal conceptual framework.☜98CE71
□
The Chinese Room Argument [4]
The Chinese Room Argument [4]☜Instantiation of a formal program isnt enough to produce semantic understanding or intentionality. A man who doesnt understand Chinese, can answer written Chinese questions using an English rulebook telling him how to manipulate Chinese symbols.☜EF597B
□
AI programs are brittle
AI programs are brittle☜Symbolic AI programs use rigid rules & data structures and cant adapt to fluidly to changing environments & ambiguous circumstances. Symbol structures are brittle—they break apart under the pressure of a novel or ambiguous situation.☜EF597B
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The critique of artificial reason
The critique of artificial reason☜AI is the culmination of a flawed tradition in philosophy that tries to explain human reason in terms of explicit rules, symbols, and calculating procedures. But human activity and being differ from that of calculating machines like computers.☜EF597B
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The Lighthill Report
The Lighthill Report☜Lighthill implies AI research is useful and legitimate insofar as it contributes to industrial applications (category A) and neuroscience (category C)—but argues category B activities, which seek to bridge A and C, are unlikely to succeed.☜EF597B
□
Symbol systems can't think dialectically
Symbol systems can't think dialectically☜Computers cant reason dialectically because they cant synthesize opposing meanings into new meaning. All they can do is just shuffle symbols that are given meaning by a programmer.☜EF597B
□
Graph of this discussion
Graph of this discussion☜Click this to see the whole debate, excluding comments, in graphical form☜dcdcdc
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David Price
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Entry date (GMT):
7/5/2006 4:06:00 PM
Last edit date (GMT):
12/12/2007 11:51:00 AM
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