|Brockman I 164
Noise/symbols/Neumann/Gershenfeld: Von Neumann presented in 1952(1) a result corresponding to Shannon’s for computation (they had met at the Institute for Advanced Study in Princeton), showing that it was possible to compute reliably with an unreliable computing device by using symbols rather than continuous quantities. This was, again, a scaling argument, with a linear increase in the physical resources representing the symbol resulting in an exponential reduction in the error rate as long as the noise was below a threshold.
That’s what makes it possible to have a billion transistors in a computer chip, with the last one as useful as the first one. This relationship led to an exponential increase in computing performance, which solved a second problem in AI: how to process exponentially increasing amounts of data. >Artificial intelligence/Gershenfeld.
1. No source indicated.
Gershenfeld, Neil „Scaling”, 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.
J. v. Neumann
The Computer and the Brain New Haven 2012
Possible Minds: Twenty-Five Ways of Looking at AI New York 2019