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After DigitalComputation as Done by Brains and Machines$
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James A. Anderson

Print publication date: 2017

Print ISBN-13: 9780199357789

Published to Oxford Scholarship Online: February 2018

DOI: 10.1093/acprof:oso/9780199357789.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 03 August 2021

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(p.273) Chapter 16 Return to Cognitive Science
After Digital

James A. Anderson

Oxford University Press

Is ambiguity unavoidable? It is found in vision and everywhere in language. Semantic nets for disambiguation are realized in George Miller’s WordNet, a practical project helping disambiguate search strings using contextual disambiguation. Simple association using traditional passive memory is boring compared to complex association using active memory with multiple associative links active at the same time to perform a clearly defined task. A “mixer” is used to recognize items from a list, and generalization of the mixer is used for disambiguation. The chapter also discusses artificial intelligence, both its origins and currently ignored questions: Are biological intelligence and machine intelligence the same thing? Can digital computers really mimic in digital software a largely analog brain? The important question is not why machines are becoming so smart but why humans are still so good. Artificial intelligence is missing something important probably based on hardware differences.

Keywords:   artificial Intelligence, machine intelligence, AlphaGo, Watson, ambiguity, disambiguation, George Miller, WordNet, search strings, computer chess

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