Social value of computer science research - Revision history

Vipul at 21:38, 23 March 2014

2014-03-23T21:38:36Z

← Older revision		Revision as of 21:38, 23 March 2014
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			{{social value\|computer science research}}

	Computer science is a discipline that, ''in principle'', looks attractive for doing research, because it lies at the intersection of high epistemic standards, intellectually fascinating content, and significant practical utility (given that constructs from computer science, such as algorithms and theories of computation, underpin computing, which runs the world).		Computer science is a discipline that, ''in principle'', looks attractive for doing research, because it lies at the intersection of high epistemic standards, intellectually fascinating content, and significant practical utility (given that constructs from computer science, such as algorithms and theories of computation, underpin computing, which runs the world).

Vipul at 02:21, 14 February 2014

2014-02-14T02:21:27Z

← Older revision		Revision as of 02:21, 14 February 2014
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	Computer science is a discipline that, ''in principle'', looks attractive for doing research, because it lies at the intersection of high epistemic standards, intellectually fascinating content, and significant practical utility (given that constructs from computer science, such as algorithms and theories of computation, underpin computing, which runs the world).		Computer science is a discipline that, ''in principle'', looks attractive for doing research, because it lies at the intersection of high epistemic standards, intellectually fascinating content, and significant practical utility (given that constructs from computer science, such as algorithms and theories of computation, underpin computing, which runs the world).

	It seems that there is considerable variation ''within'' computer science in the expected social impact. Some subdisciplines, such as machine learning and other subdisciplines of artificial intelligence, are becoming increasingly important in the real world, and their importance is likely to increase over the next few years. Research in these can have practical relevance today or in the near future.		It seems that there is considerable variation ''within'' computer science in the expected social impact. Some subdisciplines, such as machine learning and other subdisciplines of artificial intelligence, are becoming increasingly important in the real world, and their importance is likely to increase over the next few years. Research in these can have practical relevance today or in the near future. For more, see [[social value of artificial intelligence research]].

	Algorithms is an area where continued improvement is useful, but the utility again depends on the specific algorithm. For instance, improved algorithms for matrix multiplication represent important theoretical advances, but their practical implementation has been slow. Also, making improvements on the best algorithms is quite hard.		Algorithms is an area where continued improvement is useful, but the utility again depends on the specific algorithm. For instance, improved algorithms for matrix multiplication represent important theoretical advances, but their practical implementation has been slow. Also, making improvements on the best algorithms is quite hard.

	Separation problems in theoretical computer science (such as <math>P \ne NP</math>) are profound questions about the nature of reality. Their answers have deep relevance to philosophy and, depending on the nature of the answer, may have practical utility. But these problems are extremely hard, and most progress so far has been in the direction of showing that various classes of methods ''cannot'' be used to resolve the problem. At the margin, working in these areas is unlikely to yield huge social value.		Separation problems in theoretical computer science (such as <math>P \ne NP</math>) are profound questions about the nature of reality. Their answers have deep relevance to philosophy and, depending on the nature of the answer, may have practical utility. But these problems are extremely hard, and most progress so far has been in the direction of showing that various classes of methods ''cannot'' be used to resolve the problem. At the margin, working in these areas is unlikely to yield huge social value.

Vipul: Created page with "Computer science is a discipline that, ''in principle'', looks attractive for doing research, because it lies at the intersection of high epistemic standards, intellectually f..."

2014-02-14T02:21:00Z

Created page with "Computer science is a discipline that, ''in principle'', looks attractive for doing research, because it lies at the intersection of high epistemic standards, intellectually f..."

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Computer science is a discipline that, ''in principle'', looks attractive for doing research, because it lies at the intersection of high epistemic standards, intellectually fascinating content, and significant practical utility (given that constructs from computer science, such as algorithms and theories of computation, underpin computing, which runs the world).

It seems that there is considerable variation ''within'' computer science in the expected social impact. Some subdisciplines, such as machine learning and other subdisciplines of artificial intelligence, are becoming increasingly important in the real world, and their importance is likely to increase over the next few years. Research in these can have practical relevance today or in the near future.

Algorithms is an area where continued improvement is useful, but the utility again depends on the specific algorithm. For instance, improved algorithms for matrix multiplication represent important theoretical advances, but their practical implementation has been slow. Also, making improvements on the best algorithms is quite hard.

Separation problems in theoretical computer science (such as <math>P \ne NP</math>) are profound questions about the nature of reality. Their answers have deep relevance to philosophy and, depending on the nature of the answer, may have practical utility. But these problems are extremely hard, and most progress so far has been in the direction of showing that various classes of methods ''cannot'' be used to resolve the problem. At the margin, working in these areas is unlikely to yield huge social value.