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Ai overview

Ai overview






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  • In artificial intelligence, the labels neats and scruffies are used to refer to one of the continuing philosophical disputes in artificial intelligence research. This conflict is over a serious concern: what is the best way to design an intelligent system?http://artificial-intuition.com/index.htmlNeats consider that solutions should be elegant, clear and provably correct. Scruffies believe that intelligence is too complicated (or computationally intractable) to be solved with the sorts of homogeneous system such neat requirements usually mandate.
  • Depending on the model that AI development takes a AI may or may not be able to transfer its knowledge to another AI. But the entire AI is likely much easier to fully copy.http://artificial-intuition.com/index.html
  • Cyc project attempted to input millions of “common sense” facts to support AI common sense. Project largely failed in main goal but did produce a corpus for possible future work.A deep weakness of this approach is encoding millions of factoids in formal logic instead of an organic understanding of interrelated concepts. It is not clear that attempting to introspect this interrelation and encode it in such formal terms is possible.
  • General important categories include Events, Times, Physical Objects, Beliefs. The general field of creating representations for these sorts of things is sometimes called Ontological Engineering.
  • Modal logic is a type of formal logic that includes modalities like possibility and belief and frequency. Difference between “John is happy” and “John is usually happy”.
  • Inference is reaching conclusions from what is known that are not present in the known facts. It is also a basis for abstraction / concept formation.Inductive reasoning, also known as induction or inductive logic, or educated guess in colloquial English, is a kind of reasoning that constructs or evaluates inductive arguments. The premises of an inductive logical argument indicate some degree of support (inductive probability) for the conclusion but do not entail it; that is, they suggest truth but do not ensure it.http://en.wikipedia.org/wiki/Deductive_reasoninghttp://en.wikipedia.org/wiki/Inductionhttp://en.wikipedia.org/wiki/Abduction_(logic)Of the candidate systems for an inductive logic, the most influential is Bayesianism[citation needed]. As a logic of induction rather than a theory of belief, Bayesianism does not determine which beliefs are a priori rational, but rather determines how we should rationally change the beliefs we have when presented with evidence. We begin by committing to a (really any) hypothesis, and when faced with evidence, we adjust the strength of our belief in that hypothesis in a precise manner using Bayesian logic.http://en.wikipedia.org/wiki/Abductive_reasoning#Deduction.2C_induction.2C_and_abductionInductive reasoning allows for the possibility that the conclusion is false, even where all of the premises are true.[1] For example:All of the swans we have seen are white.All swans are white.
  • http://en.wikipedia.org/wiki/Inductive_bias
  • http://en.wikipedia.org/wiki/Decision_tree_learninghttp://en.wikipedia.org/wiki/Association_rule_learninghttp://en.wikipedia.org/wiki/Artificial_neural_networkhttp://en.wikipedia.org/wiki/Genetic_programminghttp://en.wikipedia.org/wiki/Inductive_logic_programminghttp://en.wikipedia.org/wiki/Support_vector_machineshttp://en.wikipedia.org/wiki/Cluster_analysishttp://en.wikipedia.org/wiki/Bayesian_networkhttp://en.wikipedia.org/wiki/Reinforcement_learningDecision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value.Association rule learning is a method for discovering interesting relations between variables in large databases.

Ai overview Ai overview Presentation Transcript