LEADER 00757nam0-22002651i-450- 001 990006989060403321 035 $a000698906 035 $aFED01000698906 035 $a(Aleph)000698906FED01 035 $a000698906 100 $a19990530g19629999km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $aHistoire du languedoc$fpar Emmanuel Le Roy Ladurie 210 $aParis$cPresses Universitaire de France$d1962 215 $a126 p.$d18 cm 225 1 $aQue sais-je?$v958 700 1$aLe Roy Ladurie,$bEmmanuel$f<1929- >$0120380 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006989060403321 952 $aBIB. BAT.4440$b3277$fBAT 959 $aBAT 996 $aHistoire du languedoc$9698697 997 $aUNINA LEADER 01704nam 2200361zu 450 001 9910764194503321 005 20251116152224.0 010 $a9783732867660 010 $a3732867668 035 $a(CKB)28809871900041 035 $a(BIP)093235347 035 $a(Perlego)4086167 035 $a(EXLCZ)9928809871900041 100 $a20231113|2023uuuu || | 101 0 $aeng 135 $aur||||||||||| 200 10$aBeyond Quantity: Research with Subsymbolic AI 210 $cTranscript Verlag$d2023 215 $a1 online resource (360 p.) 311 08$a9783837667660 311 08$a3837667669 330 8 $aHow do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately? 700 $aJahn-Sudmann$b Andreas$f1974-$01451273 702 $aSudmann$b Andreas$4edt 702 $aEchterho?lter$b Anna$4edt 702 $aRamsauer$b Markus$4edt 702 $aRetkowski$b Fabian$4edt 702 $aSchro?ter$b Jens$4edt 702 $aWaibel$b Alex$4edt 906 $aBOOK 912 $a9910764194503321 996 $aBeyond Quantity: Research with Subsymbolic AI$93651365 997 $aUNINA