Philosophy Dictionary of Arguments

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Knowledge: Knowledge is a conscious relationship to sentences or propositions, which legitimately attributes to them truth or falsehood. What is known is true. Conversely, it does not apply that everything that is true is also known. See also knowledge how, propositional knowledge, realism, abilities, competence, truth, facts, situations, language, certainty, beliefs, omniscience, logical knowledge, reliability

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Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments.

 
Author Item Summary Meta data
Brockman I 102
Knowledge/Pinker: Knowledge can be explained as patterns in matter or energy that stand in systematic relations with states of the world, with mathematical and logical truths, and with one another.
Brockman I 109
Omniscience/Pinker: Knowledge is acquired by formulating explanations and testing them against reality, not by running an algorithm faster and faster. Devouring the information on the Internet will not confer omniscience either: Big Data is still finite data, and the universe of knowledge is infinite. >Artificial intelligence/Pinker, >Omniscience/Pinker, >Big Data.


Pinker, S. “Tech Prophecy and the Underappreciated Causal Power of Ideas” in: Brockman, John (ed.) 2019. Twenty-Five Ways of Looking at AI. New York: Penguin Press.


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Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution.
The note [Author1]Vs[Author2] or [Author]Vs[term] is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition.

Pi I
St. Pinker
How the Mind Works, New York 1997
German Edition:
Wie das Denken im Kopf entsteht München 1998

Brockman I
John Brockman
Possible Minds: Twenty-Five Ways of Looking at AI New York 2019


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Ed. Martin Schulz, access date 2020-09-28
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