000			03466nam a22003975i 4500
988			_aSpringer_Engineering_2020
999			_c116911 _d116911 _x1
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003			ES-MaUEC
005			20240111050158.0
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008			191014s2020 gw | s |||| 0|eng d
020			_a9783662597170
024	7		_a10.1007/978-3-662-59717-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ335 _b2020 EB
100	1		_aMainzer, Klaus _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9671991
245	1	0	_aArtificial intelligence - When do machines take over? _cby Klaus Mainzer.
250			_a1st ed. 2020.
264		1	_aBerlin, Heidelberg _bSpringer Berlin Heidelberg _c2020.
300			_a1 recurso en línea (XI, 279 páginas) _b57 ilustraciones, 10 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aTechnik im Fokus _x2194-0770
490	0		_aEngineering (Springer-11647)
505	0		_aIntroduction: What is AI? -- A brief history of AI -- Logical thinking becomes automatic -- Systems become experts -- Computers learn to speak -- Algorithms simulate evolution -- Neural networks simulate brains -- Robots become social -- Automobiles become autonomous -- Factories become intelligent -- From natural to artificial to super intelligence?.
520			_aEverybody knows them. Smartphones that talk to us, wristwatches that record our health data, workflows that organize themselves automatically, cars, airplanes and drones that control themselves, traffic and energy systems with autonomous logistics or robots that explore distant planets are technical examples of a networked world of intelligent systems. Machine learning is dramatically changing our civilization. We rely more and more on efficient algorithms, because otherwise we will not be able to cope with the complexity of our civilizing infrastructure. But how secure are AI algorithms? This challenge is taken up here: Complex neural networks are fed and trained with huge amounts of data (big data). The number of necessary parameters explodes exponentially. Nobody knows exactly what is going on in these "black boxes". In machine learning we need more explainability and accountability of causes and effects in order to be able to decide ethical and legal questions of responsibility (e.g. in autonomous driving or medicine). Besides causal learning, we also analyze procedures of tests and verification to get certified AI-programs. Since its inception, AI research has been associated with great visions of the future of mankind. It is already a key technology that will decide the global competition of social systems. "Artificial Intelligence and Responsibility" is another central supplement to this book: How should we secure our individual liberty rights in the AI world? This book is a plea for technology design: AI must prove itself as a service in society.
650		7	_2embne _aInteligencia artificial _9413115
776	0	8	_iPrinted edition: _z9783662597163
776	0	8	_iPrinted edition: _z9783662597187
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-662-59717-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b01/2020 _eIG _zSI