LEADER 04011nam 22005775 450 001 9910366619303321 005 20200702075309.0 010 $a3-662-59717-9 024 7 $a10.1007/978-3-662-59717-0 035 $a(CKB)4100000009522832 035 $a(DE-He213)978-3-662-59717-0 035 $a(MiAaPQ)EBC5945761 035 $a(PPN)258863692 035 $a(EXLCZ)994100000009522832 100 $a20191014d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial intelligence - When do machines take over? /$fby Klaus Mainzer 205 $a1st ed. 2020. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2020. 215 $a1 online resource (XI, 279 p. 57 illus., 10 illus. in color.) 225 1 $aTechnik im Fokus,$x2194-0770 311 $a3-662-59716-0 320 $aIncludes bibliographical references. 327 $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?. 330 $aEverybody knows them. Smartphones that talk to us, wristwatches that record our health data, workflows that organize themselves automatically, cars, airplanes and drones that control themselves, traffic and energy systems with autonomous logistics or robots that explore distant planets are technical examples of a networked world of intelligent systems. Machine learning is dramatically changing our civilization. We rely more and more on efficient algorithms, because otherwise we will not be able to cope with the complexity of our civilizing infrastructure. But how secure are AI algorithms? This challenge is taken up here: Complex neural networks are fed and trained with huge amounts of data (big data). The number of necessary parameters explodes exponentially. Nobody knows exactly what is going on in these "black boxes". In machine learning we need more explainability and accountability of causes and effects in order to be able to decide ethical and legal questions of responsibility (e.g. in autonomous driving or medicine). Besides causal learning, we also analyze procedures of tests and verification to get certified AI-programs. Since its inception, AI research has been associated with great visions of the future of mankind. It is already a key technology that will decide the global competition of social systems. "Artificial Intelligence and Responsibility" is another central supplement to this book: How should we secure our individual liberty rights in the AI world? This book is a plea for technology design: AI must prove itself as a service in society. 410 0$aTechnik im Fokus,$x2194-0770 606 $aTechnology 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aPopular Science in Technology$3https://scigraph.springernature.com/ontologies/product-market-codes/Q36000 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aTechnology. 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aPopular Science in Technology. 615 24$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a600 700 $aMainzer$b Klaus$4aut$4http://id.loc.gov/vocabulary/relators/aut$045836 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910366619303321 996 $aArtificial intelligence - When do machines take over$91990778 997 $aUNINA