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Within the history of Artificial Intelligence there is a trend towards including [[linguistics]] and probability theory into existing software frameworks.{{sfn|Seising |2013}} The highly successful innovation of [[deep learning]] is based on the idea, that input data is transformed into abstract representation.


==Beyond Turing machines==
[[File:BQP complexity class diagram.svg|thumb|BQP complexity class diagram]]
In classical so called hard computing there is a bottleneck available<ref>{{cite conference |doi=10.1007/978-1-4471-1599-1_111 |year=1998 |publisher=Springer London |pages=725--730 |author=B. Sick and M. Keidl and M. Ramsauer and S. Seltzsam |title=A Comparison of Traditional and Soft-Computing Methods in a Real-Time Control Application |conference=ICANN 98 }}</ref> which has to do how a turing machine is working. For solving a task, a turing machine needs a certain amount of steps, which are executed by the algorithm. If an algorithm needs to much steps to solve a problem, it's called an np hard problem, which means, that every turing machine fails to solve this sort of problem.

Soft computing claims to bypass the Turing limit by introducing a different kind of computational paradigm, which is called a super-turing machine.<ref>{{cite conference |doi=10.1109/wcica.2006.1713000 |year=2006 |publisher=IEEE |author=Yongming Li |title=Some Results of Fuzzy Turing Machines |conference=2006 6th World Congress on Intelligent Control and Automation }}</ref><ref>{{cite journal |doi=10.1016/j.biosystems.2004.05.032 |year=2004 |publisher=Elsevier BV |volume=77 |number=1-3 |pages=175--194 |author=Cristian S. Calude and Gheorghe P\uaun |title=Bio-steps beyond Turing |journal=Biosystems }}</ref> Super Turing machines are able to solve all the np hard problems by grounding the algorithm to the outside world.<ref>{{cite journal |doi=10.1016/s0921-8890(03)00021-6 |year=2003 |publisher=Elsevier BV |volume=43 |number=2-3 |pages=85--96 |author=Silvia Coradeschi and Alessandro Saffiotti |title=An introduction to the anchoring problem |journal=Robotics and Autonomous Systems }}</ref> Grounding means to connect the numerical values in a fuzzy set with natural language terms which are describing the problem.<ref>{{cite journal |doi=10.1142/s1793351x10001061 |year=2010 |publisher=World Scientific Pub Co Pte Lt |volume=04 |number=03 |pages=331--356 |author=YINGXU WANG |title=ON CONCEPT ALGEBRA FOR COMPUTING WITH WORDS (CWW) |journal=International Journal of Semantic Computing }}</ref> The discipline of granular computing<ref>{{cite conference |title=The roots of granular computing |author=Bargiela, Andrzej and Pedrycz, Witold |conference=2006 IEEE International Conference on Granular Computing |pages=806--809 |year=2006 |publisher=IEEE }}</ref> is equal to soft computing and can be interpreted as the opposite to classical hard computing.⏎
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==References==
{{reflist}}

==Bibliography==
*{{cite book |last1=Samir |first1=Rov |last2=Chakraborty |first2=Udit |title=Introduction to soft computing : neuro-fuzzy and genetic algorithms |date=3 June 2013 |publisher=Pearson |isbn=978-8131792469 |ref=harv }}
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* {{cite journal |last1=Zadeh |first1=Lotfi A. |url=https://go.galegroup.com/ps/i.do?id=GALE%7CA15061349&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=00010782&p=AONE&sw=w |title=Fuzzy Logic, Neural Networks, and Soft Computing| journal= Communications of the ACM|date=March 1994 |volume=37 |number=3| pages =77-84 |ref=harv}}

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[[:Category:Scientific modeling]]
[[:Category:Artificial intelligence]]
[[:Category:Semantic Web]]
[[:Category:Soft computing]]