|Artificial intelligence: is the ability to recognize artificial systems, patterns and redundancies, to complete incomplete sequences, to re-formulate and solve problems, and to estimate probabilities. This is not an automation of human behavior, since such an automation could be a mechanical imitation. Rather, artificial systems are only used by humans to make decisions, when these systems have already made autonomous decisions._____________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. |
|Brockman I 148
Artificial intelligence/Chris Anderson: Traditional software (…) follows deterministic trees of hard logic: lf this, do that.” But software that interacts with the physical world tends to work more like the physical world. That means dealing with noisy inputs (sensors or human behavior) and providing probabilistic, not deterministic, results. And that, in turn, means more gradient descent. >Universe/Anderson, >Local minimum/Anderson, >Neural networks/Anderson.
Anderson, Chris “Gradient Descent” in: Brockman, John (ed.) 2019. Twenty-Five Ways of Looking at AI. New York: Penguin Press._____________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.
The Long Tail: Why the Future of Business is Selling Less of More New York 2006
Possible Minds: Twenty-Five Ways of Looking at AI New York 2019