LEADER 03880nam 22006375 450 001 9910404104003321 005 20200629120628.0 010 $a3-030-43582-2 024 7 $a10.1007/978-3-030-43582-0 035 $a(CKB)5280000000218635 035 $a(MiAaPQ)EBC6219747 035 $a(DE-He213)978-3-030-43582-0 035 $a(Au-PeEL)EBL6219747 035 $a(OCoLC)1161874734 035 $a(EXLCZ)995280000000218635 100 $a20200602d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOn the path to AI$b[electronic resource] $eLaw?s prophecies and the conceptual foundations of the machine learning age /$fby Thomas D. Grant, Damon J. Wischik 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Palgrave Macmillan,$d2020. 215 $a1 online resource (XXII, 147 p. 4 illus.) 311 $a3-030-43581-4 327 $aPrologue: Starting with logic -- CHAPTER 1: Two Revolutions -- CHAPTER 2: Getting past logic -- CHAPTER 3: Experience and data as input -- CHAPTER 4: Finding patterns as the path from input to output -- CHAPTER 5: Output as prophecy -- CHAPTER 6: Explanations of machine learning -- CHAPTER 7: Juries and other reliable predictors -- CHAPTER 8: Poisonous datasets, poisonous trees -- CHAPTER 9: From Holmes to AlphaGo -- CHAPTER 10:Conclusion -- EPILOGUE: Lessons in two directions. 330 $aThis open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two ?revolutions? in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age?prediction based on datasets. On the path to AI introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data, and informs current debates amongst scholars, lawyers and policy makers on how it should be used and regulated wisely. Technologists will also find useful lessons learned from the last 120 years of legal grappling with accountability, explainability, and biased data. . 606 $aTechnology?Sociological aspects 606 $aHuman geography 606 $aMass media 606 $aLaw 606 $aArtificial intelligence 606 $aScience and Technology Studies$3https://scigraph.springernature.com/ontologies/product-market-codes/X22270 606 $aHuman Geography$3https://scigraph.springernature.com/ontologies/product-market-codes/X26000 606 $aIT Law, Media Law, Intellectual Property$3https://scigraph.springernature.com/ontologies/product-market-codes/R15009 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aTechnology?Sociological aspects. 615 0$aHuman geography. 615 0$aMass media. 615 0$aLaw. 615 0$aArtificial intelligence. 615 14$aScience and Technology Studies. 615 24$aHuman Geography. 615 24$aIT Law, Media Law, Intellectual Property. 615 24$aArtificial Intelligence. 676 $a303.483 700 $aGrant$b Thomas D$4aut$4http://id.loc.gov/vocabulary/relators/aut$0542863 702 $aWischik$b Damon J$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910404104003321 996 $aOn the path to AI$92022476 997 $aUNINA