Advertisement

Künstliche Intelligenz – die nächste Revolution (The Artificial Intelligence Revolution)

  • Patrick GlaunerEmail author
Chapter

Zusammenfassung

Es vergeht mittlerweile kein Tag, an welchem wir nicht von künstlicher Intelligenz (KI) (Artificial Intelligence (AI)) hören: autonom fahrende Autos, Spamfilter, Siri, Schachcomputer, Killerroboter und vieles mehr. Was genau steckt jedoch hinter KI? In diesem Kapitel bieten wir einen Überblick zu KI und stellen moderne KI-Anwendungen vor. Anschließend stellen wir ein Innovationsökosystem vor, in dem wir momentan ein Forschungsprojekt zur Erkennung von Elektrizitätsdiebstahl in Entwicklungs- und Schwellenländern mit Hilfe von KI betreiben.

Literatur

  1. Bishop CM (2006) Pattern recognition and machine learning. Springer, New YorkGoogle Scholar
  2. Bryson MC (1976) The literary digest poll: making of a statistical myth. Am Stat 30(4):184–185 Google Scholar
  3. Computer History Museum and KQED television: CHM Revolutionaries: The Challenge & Promise of Artificial Intelligence (2012) https://www.youtube.com/watch?v=rtmQ3xlt-4A. Zugegriffen: 19. Febr. 2018
  4. Deng L, Yu D (2014) Deep learning: methods and applications. Found Trends® Sig Process 7(3–4):197–387CrossRefGoogle Scholar
  5. Glauner P, Boechat A, Dolberg L, State R, Bettinger F, Rangoni Y, Duarte D (2016) Large-scale detection of non-technical losses in imbalanced data sets. Innovative Smart Grid Technologies Conference (ISGT), 2016 IEEE Power & Energy Society. IEEEGoogle Scholar
  6. Glauner P, Meira J, Valtchev P, State R, Bettinger F (2017a) The challenge of non-technical loss detection using artificial intelligence: a survey. Int J Comput Intell Syst 10(1):760–775CrossRefGoogle Scholar
  7. Glauner P, Migliosi A, Meira J, Valtchev P, State R, Bettinger F (2017b) Is big data sufficient for a reliable detection of non-technical losses? Proceedings of the 19th International Conference on Intelligent System Application to Power Systems (ISAP)Google Scholar
  8. Hargittai E (2015) Is bigger always better? Potential biases of big data derived from social network sites. Ann Am Acad Polit Soc Sci 659(1):63–76CrossRefGoogle Scholar
  9. Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554CrossRefGoogle Scholar
  10. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRefGoogle Scholar
  11. McCarthy J, Minsky ML, Rochester N, Shannon CE (1955) A proposal for the dartmouth summer research project on artificial intelligence. Google Scholar
  12. Russell S, Norvig P (2009) Artificial Intelligence: a modern approach, 3. Aufl. Prentice Hall, New JerseyGoogle Scholar
  13. Shanahan M (2015) The technological singularity. MIT Press, CambridgeGoogle Scholar
  14. Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D (2016) Mastering the game of go with deep neural networks and tree search. Nature 529(7587):484–489CrossRefGoogle Scholar
  15. Smith TB (2004) Electricity theft: a comparative analysis. Energy Policy 32(18):2067–2076CrossRefGoogle Scholar
  16. Turing AM (1950) Computing machinery and intelligence. Mind 59(236):433–460CrossRefGoogle Scholar
  17. Williams C (2015) AI guru Ng: fearing a rise of killer robots is like worrying about overpopulation on Mars. https://www.theregister.co.uk/2015/03/19/andrew_ng_baidu_ai/. Zugegriffen: 19. Febr. 2018
  18. Wolpert DH (1996) The lack of a priori distinctions between learning algorithms. Neural Comput 8(7):1341–1390CrossRefGoogle Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2018

Authors and Affiliations

  1. 1.Universität LuxemburgLuxemburgLuxemburg

Personalised recommendations