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{{Short description|Phrase used in computer science}} |
{{Short description|Phrase used in computer science}} |
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In [[computer science]], '''garbage in, garbage out''' ('''GIGO''') is the concept that flawed, or nonsense [[input (computer science)|input]] data produces nonsense [[input/output|output]] |
In [[computer science]], '''garbage in, garbage out''' ('''GIGO''') is the concept that flawed, or nonsense (garbage) [[input (computer science)|input]] data produces nonsense [[input/output|output]]. '''Rubbish in, rubbish out''' ('''RIRO''') is an alternate wording.<ref>{{Cite web|url=https://physicsworld.com/a/machine-learning-collaborations-accelerate-materials-discovery/|title=Machine learning collaborations accelerate materials discovery|date=2019-06-30|website=Physics World|language=en-GB|access-date=2019-09-18}}</ref><ref>{{Cite book|url=https://books.google.com/books?id=tb43AAAAQBAJ&q=RIRO&pg=PP1|title=The Art of Creative Thinking: How to be Innovative and Develop Great Ideas|last=Adair|first=John|date=2009-02-03|publisher=Kogan Page Publishers|isbn=9780749460082|language=en|author-link=John Adair (author)}}</ref><ref>{{Cite book|url=https://books.google.com/books?id=xaAip-1f_DUC&q=RIRO&pg=PA23|title=Survivors: The Animals and Plants that Time has Left Behind (Text Only)|last=Fortey|first=Richard|date=2011-09-01|publisher=HarperCollins UK|isbn=9780007441389|pages=23, 24|language=en|author-link=Richard Fortey}}</ref> |
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The principle also applies more generally to all [[analysis]] and [[logic]], in that [[argument]]s are unsound if their [[premise]]s are flawed. |
The principle also applies more generally to all [[analysis]] and [[logic]], in that [[argument]]s are unsound if their [[premise]]s are flawed. |
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Revision as of 15:30, 28 May 2021
In computer science, garbage in, garbage out (GIGO) is the concept that flawed, or nonsense (garbage) input data produces nonsense output. Rubbish in, rubbish out (RIRO) is an alternate wording.[1][2][3]
The principle also applies more generally to all analysis and logic, in that arguments are unsound if their premises are flawed.
History
It was popular in the early days of computing, but applies even more today, when powerful computers can produce large amounts of erroneous data or information in a short time. The first use of the phrase has been dated to a November 10, 1957, syndicated newspaper article about US Army mathematicians and their work with early computers,[4] in which an Army Specialist named William D. Mellin explained that computers cannot think for themselves, and that "sloppily programmed" inputs inevitably lead to incorrect outputs. The underlying principle was noted by the inventor of the first programmable computing device design:
On two occasions I have been asked, "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?" ... I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.
— Charles Babbage, Passages from the Life of a Philosopher[5]
More recently, the Marine Accident Investigation Branch comes to a similar conclusion:
A loading computer is an effective and useful tool for the safe running of a ship. However, its output can only be as accurate as the information entered into it.
The term may have been derived from last-in, first-out (LIFO) or first-in, first-out (FIFO).[7]
Decision-makers increasingly face computer-generated information and analyses that could be collected and analyzed in no other way. Precisely for that reason, going behind that output is out of the question, even if one has good cause to be suspicious. In short, the computer analysis becomes a credible references point although based on poor data.[8]
Uses
This phrase can be used as an explanation for the poor quality of a digitized audio or video file. Although digitizing can be the first step in cleaning up a signal, it does not, by itself, improve the quality. Defects in the original analog signal will be faithfully recorded, but might be identified and removed by a subsequent step by digital signal processing.
GIGO is commonly used to describe failures in human decision-making due to faulty, incomplete, or imprecise data. This sort of issue predates the computer age, but the term can still be applied.
GIGO was the name of a Usenet gateway program to FidoNet, MAUSnet, e.a.[9]
See also
References
- ^ "Machine learning collaborations accelerate materials discovery". Physics World. 2019-06-30. Retrieved 2019-09-18.
- ^ Adair, John (2009-02-03). The Art of Creative Thinking: How to be Innovative and Develop Great Ideas. Kogan Page Publishers. ISBN 9780749460082.
- ^ Fortey, Richard (2011-09-01). Survivors: The Animals and Plants that Time has Left Behind (Text Only). HarperCollins UK. pp. 23, 24. ISBN 9780007441389.
- ^ "Work With New Electronic 'Brains' Opens Field For Army Math Experts". The Hammond Times. p. 65. Retrieved March 20, 2016 – via Newspapers.com.
- ^ Babbage, Charles (1864). Passages from the Life of a Philosopher. Longman and Co. p. 67. OCLC 258982.
- ^ MAIB (2016-03-17). "SAFETY FLYER" (PDF). MAIB. Retrieved 2016-03-19.
- ^ Quinion, Michael (5 November 2005). "Garbage in, garbage out". World Wide Words. Retrieved 2012-02-26.
- ^ "Computer Applications in Particular Industries: Securities". Computers & The Law, American Bar Association, Section of Science and Technology (Third ed.). 1981. pp. 250, 253.
{{cite book}}: Unknown parameter|authors=ignored (help) - ^ jfesler (2001-01-01). "GIGO History". gigo.com. Retrieved 2014-01-24.