130:, can "play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens." Similarly, some tasks once considered to be AI-complete, like machine translation, are among the capabilities of
107:, that were popular in the 1980s, were able to solve very simple and/or restricted versions of AI-complete problems, but never in their full generality. When AI researchers attempted to "scale up" their systems to handle more complicated, real-world situations, the programs tended to become excessively
115:
or a rudimentary understanding of the situation: they would fail as unexpected circumstances outside of its original problem context would begin to appear. When human beings are dealing with new situations in the world, they are helped by their awareness of the general context: they know what the
393:
to reach human-level machine performance as AI-complete, while only restricted versions of AI-complete problems can be solved by the current AI systems. For Šekrst, getting a polynomial solution to AI-complete problems would not necessarily be equal to solving the issue of artificial general
245:. By definition, it does not cover problems whose solution is unknown or has not been characterized formally. Since many AI problems have no formalization yet, conventional complexity theory does not enable to formally define AI-completeness.
116:
things around them are, why they are there, what they are likely to do and so on. They can recognize unusual situations and adjust accordingly. Expert systems lacked this adaptability and were
57:, and dealing with unexpected circumstances while solving any real-world problem. AI-complete were notably considered useful for testing the presence of humans, as
373:
353:
329:
305:
274:
1242:
1402:
469:
93:, which formally describes the most famous class of difficult problems. Early uses of the term are in Erik Mueller's 1987 PhD dissertation and in
587:
1380:
415:
608:
675:
1791:
1235:
1040:Šekrst, Kristina (2020), "Chapter 11 - AI-Completeness: Using Deep Learning to Eliminate the Human Factor", in Skansi, Sandro (ed.),
1960:
983:
632:
567:
447:
560:
Mallery, John C. (1988), "Thinking About
Foreign Policy: Finding an Appropriate Role for Artificially Intelligent Computers",
1210:
1172:
1110:
1051:
946:
890:
807:
163:
904:
1701:
1392:
1228:
1075:
23rd
Midwest Artificial Intelligence and Cognitive Science Conference, MAICS 2012, Cincinnati, Ohio, USA, 21-22 April 2012
126:
published a work in May 2022 in which they trained a single model to do several things at the same time. The model, named
1955:
1018:
1562:
500:
1716:
1547:
218:
1991:
1487:
1193:
Bintoro, Ted; Velez, Noah (2022), "AI-Complete: What it Means to Be Human in an
Increasingly Computerized World",
379:
problems, that are solvable in polynomial time by a deterministic Turing machine with an oracle for some problem.
1996:
1904:
1557:
933:, Lecture Notes in Computer Science, vol. 13336, Cham: Springer International Publishing, pp. 567–580,
390:
238:
90:
35:
754:
1552:
1297:
875:
Proceedings of the 45th
International ACM SIGIR Conference on Research and Development in Information Retrieval
619:
problem: if we could solve anyone artificial intelligence problem, we could solve all the others", p. 302)
517:
Bergmair, Richard (January 7, 2006). "Natural
Language Steganography and an "AI-complete" Security Primitive".
190:
151:
54:
46:. Calling a problem AI-complete reflects the belief that it cannot be solved by a simple specific algorithm.
1821:
1542:
1014:
869:
Krestel, Ralf; Aras, Hidir; Andersson, Linda; Piroi, Florina; Hanbury, Allan; Alderucci, Dean (2022-07-06).
1514:
401:
For Kwee-Bintoro and Velez, solving AI-complete problems would have strong repercussions on the society.
1859:
1844:
1816:
1681:
1676:
1251:
480:
17:
1596:
1567:
1345:
1197:, Educational Communications and Technology: Issues and Innovations, Cham: Springer, pp. 257–274,
1439:
1292:
697:
202:
159:
545:
1965:
1889:
1621:
1577:
1462:
1360:
523:
395:
1869:
1839:
1506:
31:
1340:
1726:
1419:
1397:
1387:
1355:
1330:
518:
420:
458:(Second Edition, pp. 54–57). New York: John Wiley. (Section 4 is on "AI-Complete Tasks".)
1586:
602:
532:
112:
1939:
1615:
1591:
1444:
1067:
667:
131:
117:
108:
8:
1919:
1849:
1806:
1762:
1534:
1524:
1519:
1407:
926:
242:
213:
Dealing with unexpected circumstances while solving any real world problem, whether it's
198:
155:
1929:
1801:
1666:
1429:
1412:
1270:
927:"Say It Right: AI Neural Machine Translation Empowers New Speakers to Revitalize Lemko"
896:
851:
615:
PhD dissertation, University of
California, Los Angeles. ("Daydreaming is but one more
358:
338:
314:
290:
259:
208:
184:
66:
969:"Introduction to the special issue on word sense disambiguation: the state of the art"
968:
398:
research being the limiting factor towards achieving artificial general intelligence.
1934:
1646:
1454:
1365:
1206:
1168:
1106:
1047:
942:
900:
886:
855:
803:
762:
705:
629:
581:
561:
62:
444:
1811:
1696:
1671:
1472:
1375:
1198:
1160:
1134:
1098:
1009:
934:
878:
841:
795:
253:
1093:
Yampolskiy, Roman (2013), "Turing Test as a
Defining Feature of AI-Completeness",
871:"3rd Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2022)"
1923:
1884:
1879:
1747:
1477:
1350:
1325:
1307:
1202:
1164:
1157:
Guide to Deep
Learning Basics: Logical, Historical and Philosophical Perspectives
938:
870:
799:
792:
Guide to Deep
Learning Basics: Logical, Historical and Philosophical Perspectives
636:
612:
504:
451:
214:
180:
175:
127:
94:
50:
1125:
Groppe, Sven; Jain, Sarika (2024), "The Way
Forward with AI-Complete Problems",
1102:
1631:
1611:
1335:
1152:
1138:
787:
78:
1220:
1004:
729:
1985:
1894:
1706:
1686:
1467:
846:
829:
766:
709:
649:
425:
226:
169:
104:
882:
1874:
1492:
497:
410:
1005:"Elon Musk talks Twitter, Tesla and how his brain works — live at TED2022"
1831:
1711:
1424:
1317:
1041:
383:
194:
147:
98:
82:
1434:
1097:, Studies in Computational Intelligence, vol. 427, pp. 3–17,
1302:
1068:"AI-Complete, AI-Hard, or AI-Easy – Classification of Problems in AI"
1000:
222:
1153:"AI-Completeness: Using Deep Learning to Eliminate the Human Factor"
788:"AI-Completeness: Using Deep Learning to Eliminate the Human Factor"
1777:
1757:
1742:
1721:
1691:
1636:
1601:
1482:
123:
1095:
Artificial Intelligence, Evolutionary Computing and Metaheuristics
477:
Artificial Intelligence, Evolutionary Computing and Metaheuristics
1914:
1772:
1752:
1626:
1370:
1285:
563:
The 1988 Annual Meeting of the International Studies Association.
86:
58:
1280:
1275:
496:
Luis von Ahn, Manuel Blum, Nicholas Hopper, and John Langford.
925:
Orynycz, Petro (2022), Degen, Helmut; Ntoa, Stavroula (eds.),
1970:
1606:
1159:, Cham: Springer International Publishing, pp. 117–130,
794:, Cham: Springer International Publishing, pp. 117–130,
507:. In Proceedings of Eurocrypt, Vol. 2656 (2003), pp. 294–311.
166:
828:
Strat, Thomas M.; Chellappa, Rama; Patel, Vishal M. (2020).
755:"If AI Can Fix Peer Review in Science, AI Can Do Anything"
49:
In the past, problems supposed to be AI-complete included
1767:
868:
639:(Definition of "AI-complete" first added to jargon file.)
284:
It is in the set of AI problems (Human Oracle-solvable).
142:
AI-complete problems have been hypothesized to include:
1195:
Bridging Human Intelligence and Artificial Intelligence
470:"Turing Test as a Defining Feature of AI-Completeness"
730:"Unveiling the Power of Large Language Models (LLMs)"
698:"Welcome to the Era of the AI Coworker | Backchannel"
361:
341:
317:
293:
262:
375:. This also gives as a consequence the existence of
241:
deals with the relative computational difficulty of
827:
367:
347:
323:
299:
268:
1983:
1453:
389:Groppe and Jain classify problems which require
1250:
1151:Šekrst, Kristina (2020), Skansi, Sandro (ed.),
786:Šekrst, Kristina (2020), Skansi, Sandro (ed.),
335:if and only if there is an AI-Complete problem
1236:
416:List of unsolved problems in computer science
34:(AI), tasks that are hypothesized to require
1192:
498:CAPTCHA: Using Hard AI Problems for Security
394:intelligence, while emphasizing the lack of
355:that is polynomial time Turing-reducible to
1243:
1229:
1124:
1092:
1065:
966:
586:: CS1 maint: location missing publisher (
467:
386:is a defining feature of AI-completeness.
382:Yampolskiy has also hypothesized that the
877:. Madrid Spain: ACM. pp. 3474–3477.
845:
648:
522:
516:
437:
27:Term describing difficult problems in AI
924:
559:
456:Encyclopedia of Artificial Intelligence
137:
14:
1984:
1150:
1039:
1021:from the original on December 15, 2022
785:
654:Building Large Knowledge-Based Systems
1224:
287:Any AI problem can be converted into
1702:Simple Knowledge Organization System
999:
752:
628:Raymond, Eric S. (1991, March 22).
24:
858:– via ABI/INFORM Collection.
468:Yampolskiy, Roman (January 2013).
307:by some polynomial time algorithm.
25:
2008:
1717:Thesaurus (information retrieval)
38:to solve are informally known as
989:from the original on 2022-10-09.
695:
601:Mueller, Erik T. (1987, March).
233:
1186:
1144:
1118:
1086:
1059:
1033:
993:
960:
918:
907:from the original on 2023-04-15
862:
821:
779:
746:
722:
689:
678:from the original on 2022-08-02
570:from the original on 2008-02-29
391:artificial general intelligence
239:Computational complexity theory
36:artificial general intelligence
1298:Natural language understanding
931:Artificial Intelligence in HCI
660:
656:, Addison-Wesley, pp. 1–5
642:
622:
595:
553:
510:
490:
461:
191:Natural language understanding
152:natural language understanding
55:natural language understanding
13:
1:
1822:Optical character recognition
1043:Guide to Deep Learning Basics
1015:Chris_Anderson_(entrepreneur)
967:Ide, N.; Veronis, J. (1998).
606:(Technical Report CSD-870017)
431:
311:On the other hand, a problem
1515:Multi-document summarization
1203:10.1007/978-3-030-84729-6_18
1165:10.1007/978-3-030-37591-1_11
1013:(Interview). Interviewed by
939:10.1007/978-3-031-05643-7_37
800:10.1007/978-3-030-37591-1_11
454:In Stuart C. Shapiro (Ed.),
120:when facing new situations.
7:
1845:Latent Dirichlet allocation
1817:Natural language generation
1682:Machine-readable dictionary
1677:Linguistic Linked Open Data
1252:Natural language processing
1103:10.1007/978-3-642-29694-9_1
604:Daydreaming and Computation
443:Shapiro, Stuart C. (1992).
404:
248:
10:
2013:
1597:Explicit semantic analysis
1346:Deep linguistic processing
1139:10.1007/s00354-024-00251-8
1066:Yampolskiy, Roman (2012),
280:if it has two properties:
72:
1948:
1903:
1858:
1830:
1790:
1735:
1657:
1645:
1576:
1533:
1505:
1440:Word-sense disambiguation
1316:
1293:Computational linguistics
1258:
976:Computational Linguistics
630:Jargon File Version 2.8.1
203:word-sense disambiguation
193:(and subproblems such as
183:(and subproblems such as
160:automated theorem proving
1966:Natural Language Toolkit
1890:Pronunciation assessment
1792:Automatic identification
1622:Latent semantic analysis
1578:Distributional semantics
1463:Compound-term processing
1361:Named-entity recognition
1127:New Generation Computing
847:10.1609/aimag.v41i2.5299
396:computational complexity
256:suggests that a problem
1992:Artificial intelligence
1870:Automated essay scoring
1840:Document classification
1507:Automatic summarization
883:10.1145/3477495.3531702
445:Artificial Intelligence
77:The term was coined by
32:artificial intelligence
1997:Computational problems
1727:Universal Dependencies
1420:Terminology extraction
1403:Semantic decomposition
1398:Semantic role labeling
1388:Part-of-speech tagging
1356:Information extraction
1341:Coreference resolution
1331:Collocation extraction
652:; Guha, R. V. (1989),
540:Cite journal requires
421:Synthetic intelligence
369:
349:
325:
301:
270:
1488:Sentence segmentation
830:"Vision and robotics"
370:
350:
326:
302:
271:
132:large language models
113:commonsense knowledge
1940:Voice user interface
1651:datasets and corpora
1592:Document-term matrix
1445:Word-sense induction
668:"A Generalist Agent"
359:
339:
315:
291:
260:
243:computable functions
221:or even the kind of
138:AI-complete problems
1920:Interactive fiction
1850:Pachinko allocation
1807:Speech segmentation
1763:Google Ngram Viewer
1535:Machine translation
1525:Text simplification
1520:Sentence extraction
1408:Semantic similarity
199:machine translation
156:automated reasoning
67:brute-force attacks
1930:Question answering
1802:Speech recognition
1667:Corpus linguistics
1647:Language resources
1430:Textual entailment
1413:Sentiment analysis
1003:(April 14, 2022).
635:2011-06-04 at the
611:2020-10-30 at the
503:2016-03-04 at the
450:2016-02-01 at the
365:
345:
321:
297:
266:
209:Autonomous driving
185:object recognition
61:aim to do, and in
1979:
1978:
1935:Virtual assistant
1860:Computer-assisted
1786:
1785:
1543:Computer-assisted
1501:
1500:
1493:Word segmentation
1455:Text segmentation
1393:Semantic analysis
1381:Syntactic parsing
1366:Ontology learning
1212:978-3-030-84728-9
1174:978-3-030-37591-1
1112:978-3-642-29693-2
1053:978-3-030-37591-1
948:978-3-031-05642-0
892:978-1-4503-8732-3
809:978-3-030-37591-1
566:, St. Louis, MO,
368:{\displaystyle H}
348:{\displaystyle C}
324:{\displaystyle H}
300:{\displaystyle C}
269:{\displaystyle C}
91:complexity theory
63:computer security
16:(Redirected from
2004:
1956:Formal semantics
1905:Natural language
1812:Speech synthesis
1794:and data capture
1697:Semantic network
1672:Lexical resource
1655:
1654:
1473:Lexical analysis
1451:
1450:
1376:Semantic parsing
1245:
1238:
1231:
1222:
1221:
1216:
1215:
1190:
1184:
1183:
1182:
1181:
1148:
1142:
1141:
1122:
1116:
1115:
1090:
1084:
1083:
1082:
1081:
1072:
1063:
1057:
1056:
1037:
1031:
1030:
1028:
1026:
1010:TED (conference)
997:
991:
990:
988:
973:
964:
958:
957:
956:
955:
922:
916:
915:
913:
912:
866:
860:
859:
849:
825:
819:
818:
817:
816:
783:
777:
776:
774:
773:
753:Stockton, Nick.
750:
744:
743:
741:
740:
726:
720:
719:
717:
716:
693:
687:
686:
684:
683:
672:www.deepmind.com
664:
658:
657:
646:
640:
626:
620:
599:
593:
591:
585:
577:
576:
575:
557:
551:
549:
543:
538:
536:
528:
526:
514:
508:
494:
488:
487:
485:
479:. Archived from
474:
465:
459:
441:
374:
372:
371:
366:
354:
352:
351:
346:
330:
328:
327:
322:
306:
304:
303:
298:
275:
273:
272:
267:
254:Roman Yampolskiy
176:Bongard problems
81:by analogy with
30:In the field of
21:
2012:
2011:
2007:
2006:
2005:
2003:
2002:
2001:
1982:
1981:
1980:
1975:
1944:
1924:Syntax guessing
1906:
1899:
1885:Predictive text
1880:Grammar checker
1861:
1854:
1826:
1793:
1782:
1748:Bank of English
1731:
1659:
1650:
1641:
1572:
1529:
1497:
1449:
1351:Distant reading
1326:Argument mining
1312:
1308:Text processing
1254:
1249:
1219:
1213:
1191:
1187:
1179:
1177:
1175:
1149:
1145:
1123:
1119:
1113:
1091:
1087:
1079:
1077:
1070:
1064:
1060:
1054:
1038:
1034:
1024:
1022:
998:
994:
986:
971:
965:
961:
953:
951:
949:
923:
919:
910:
908:
893:
867:
863:
826:
822:
814:
812:
810:
784:
780:
771:
769:
751:
747:
738:
736:
728:
727:
723:
714:
712:
696:Katz, Miranda.
694:
690:
681:
679:
666:
665:
661:
647:
643:
637:Wayback Machine
627:
623:
613:Wayback Machine
600:
596:
579:
578:
573:
571:
558:
554:
541:
539:
530:
529:
515:
511:
505:Wayback Machine
495:
491:
483:
472:
466:
462:
452:Wayback Machine
442:
438:
434:
407:
360:
357:
356:
340:
337:
336:
316:
313:
312:
292:
289:
288:
261:
258:
257:
251:
236:
181:Computer vision
140:
75:
51:computer vision
28:
23:
22:
15:
12:
11:
5:
2010:
2000:
1999:
1994:
1977:
1976:
1974:
1973:
1968:
1963:
1958:
1952:
1950:
1946:
1945:
1943:
1942:
1937:
1932:
1927:
1917:
1911:
1909:
1907:user interface
1901:
1900:
1898:
1897:
1892:
1887:
1882:
1877:
1872:
1866:
1864:
1856:
1855:
1853:
1852:
1847:
1842:
1836:
1834:
1828:
1827:
1825:
1824:
1819:
1814:
1809:
1804:
1798:
1796:
1788:
1787:
1784:
1783:
1781:
1780:
1775:
1770:
1765:
1760:
1755:
1750:
1745:
1739:
1737:
1733:
1732:
1730:
1729:
1724:
1719:
1714:
1709:
1704:
1699:
1694:
1689:
1684:
1679:
1674:
1669:
1663:
1661:
1652:
1643:
1642:
1640:
1639:
1634:
1632:Word embedding
1629:
1624:
1619:
1612:Language model
1609:
1604:
1599:
1594:
1589:
1583:
1581:
1574:
1573:
1571:
1570:
1565:
1563:Transfer-based
1560:
1555:
1550:
1545:
1539:
1537:
1531:
1530:
1528:
1527:
1522:
1517:
1511:
1509:
1503:
1502:
1499:
1498:
1496:
1495:
1490:
1485:
1480:
1475:
1470:
1465:
1459:
1457:
1448:
1447:
1442:
1437:
1432:
1427:
1422:
1416:
1415:
1410:
1405:
1400:
1395:
1390:
1385:
1384:
1383:
1378:
1368:
1363:
1358:
1353:
1348:
1343:
1338:
1336:Concept mining
1333:
1328:
1322:
1320:
1314:
1313:
1311:
1310:
1305:
1300:
1295:
1290:
1289:
1288:
1283:
1273:
1268:
1262:
1260:
1256:
1255:
1248:
1247:
1240:
1233:
1225:
1218:
1217:
1211:
1185:
1173:
1143:
1117:
1111:
1085:
1058:
1052:
1032:
992:
959:
947:
917:
891:
861:
820:
808:
778:
745:
721:
688:
659:
650:Lenat, Douglas
641:
621:
594:
552:
550:(unpublished?)
542:|journal=
524:10.1.1.105.129
509:
489:
486:on 2013-05-22.
460:
435:
433:
430:
429:
428:
423:
418:
413:
406:
403:
364:
344:
320:
309:
308:
296:
285:
265:
250:
247:
235:
232:
231:
230:
227:expert systems
211:
206:
188:
178:
173:
139:
136:
105:Expert systems
79:Fanya Montalvo
74:
71:
65:to circumvent
26:
9:
6:
4:
3:
2:
2009:
1998:
1995:
1993:
1990:
1989:
1987:
1972:
1969:
1967:
1964:
1962:
1961:Hallucination
1959:
1957:
1954:
1953:
1951:
1947:
1941:
1938:
1936:
1933:
1931:
1928:
1925:
1921:
1918:
1916:
1913:
1912:
1910:
1908:
1902:
1896:
1895:Spell checker
1893:
1891:
1888:
1886:
1883:
1881:
1878:
1876:
1873:
1871:
1868:
1867:
1865:
1863:
1857:
1851:
1848:
1846:
1843:
1841:
1838:
1837:
1835:
1833:
1829:
1823:
1820:
1818:
1815:
1813:
1810:
1808:
1805:
1803:
1800:
1799:
1797:
1795:
1789:
1779:
1776:
1774:
1771:
1769:
1766:
1764:
1761:
1759:
1756:
1754:
1751:
1749:
1746:
1744:
1741:
1740:
1738:
1734:
1728:
1725:
1723:
1720:
1718:
1715:
1713:
1710:
1708:
1707:Speech corpus
1705:
1703:
1700:
1698:
1695:
1693:
1690:
1688:
1687:Parallel text
1685:
1683:
1680:
1678:
1675:
1673:
1670:
1668:
1665:
1664:
1662:
1656:
1653:
1648:
1644:
1638:
1635:
1633:
1630:
1628:
1625:
1623:
1620:
1617:
1613:
1610:
1608:
1605:
1603:
1600:
1598:
1595:
1593:
1590:
1588:
1585:
1584:
1582:
1579:
1575:
1569:
1566:
1564:
1561:
1559:
1556:
1554:
1551:
1549:
1548:Example-based
1546:
1544:
1541:
1540:
1538:
1536:
1532:
1526:
1523:
1521:
1518:
1516:
1513:
1512:
1510:
1508:
1504:
1494:
1491:
1489:
1486:
1484:
1481:
1479:
1478:Text chunking
1476:
1474:
1471:
1469:
1468:Lemmatisation
1466:
1464:
1461:
1460:
1458:
1456:
1452:
1446:
1443:
1441:
1438:
1436:
1433:
1431:
1428:
1426:
1423:
1421:
1418:
1417:
1414:
1411:
1409:
1406:
1404:
1401:
1399:
1396:
1394:
1391:
1389:
1386:
1382:
1379:
1377:
1374:
1373:
1372:
1369:
1367:
1364:
1362:
1359:
1357:
1354:
1352:
1349:
1347:
1344:
1342:
1339:
1337:
1334:
1332:
1329:
1327:
1324:
1323:
1321:
1319:
1318:Text analysis
1315:
1309:
1306:
1304:
1301:
1299:
1296:
1294:
1291:
1287:
1284:
1282:
1279:
1278:
1277:
1274:
1272:
1269:
1267:
1264:
1263:
1261:
1259:General terms
1257:
1253:
1246:
1241:
1239:
1234:
1232:
1227:
1226:
1223:
1214:
1208:
1204:
1200:
1196:
1189:
1176:
1170:
1166:
1162:
1158:
1154:
1147:
1140:
1136:
1132:
1128:
1121:
1114:
1108:
1104:
1100:
1096:
1089:
1076:
1069:
1062:
1055:
1049:
1045:
1044:
1036:
1020:
1017:. Vancouver.
1016:
1012:
1011:
1006:
1002:
996:
985:
981:
977:
970:
963:
950:
944:
940:
936:
932:
928:
921:
906:
902:
898:
894:
888:
884:
880:
876:
872:
865:
857:
853:
848:
843:
839:
835:
831:
824:
811:
805:
801:
797:
793:
789:
782:
768:
764:
760:
756:
749:
735:
731:
725:
711:
707:
703:
699:
692:
677:
673:
669:
663:
655:
651:
645:
638:
634:
631:
625:
618:
614:
610:
607:
605:
598:
589:
583:
569:
565:
564:
556:
547:
534:
525:
520:
513:
506:
502:
499:
493:
482:
478:
471:
464:
457:
453:
449:
446:
440:
436:
427:
426:Practopoiesis
424:
422:
419:
417:
414:
412:
409:
408:
402:
399:
397:
392:
387:
385:
380:
378:
362:
342:
334:
318:
294:
286:
283:
282:
281:
279:
263:
255:
246:
244:
240:
234:Formalization
228:
224:
220:
216:
212:
210:
207:
204:
200:
196:
192:
189:
186:
182:
179:
177:
174:
171:
170:expert system
168:
165:
161:
157:
153:
149:
145:
144:
143:
135:
133:
129:
125:
121:
119:
114:
110:
106:
102:
100:
96:
92:
88:
84:
80:
70:
68:
64:
60:
56:
52:
47:
45:
41:
37:
33:
19:
1875:Concordancer
1271:Bag-of-words
1265:
1194:
1188:
1178:, retrieved
1156:
1146:
1130:
1126:
1120:
1094:
1088:
1078:, retrieved
1074:
1061:
1046:, Springer,
1042:
1035:
1025:December 15,
1023:. Retrieved
1008:
995:
979:
975:
962:
952:, retrieved
930:
920:
909:. Retrieved
874:
864:
840:(2): 49–65.
837:
833:
823:
813:, retrieved
791:
781:
770:. Retrieved
758:
748:
737:. Retrieved
734:www.unite.ai
733:
724:
713:. Retrieved
701:
691:
680:. Retrieved
671:
662:
653:
644:
624:
616:
603:
597:
572:, retrieved
562:
555:
533:cite journal
512:
492:
481:the original
476:
463:
455:
439:
411:ASR-complete
400:
388:
381:
376:
332:
310:
277:
252:
237:
141:
122:
103:
95:Eric Raymond
76:
48:
43:
39:
29:
1832:Topic model
1712:Text corpus
1558:Statistical
1425:Text mining
1266:AI-complete
982:(1): 2–40.
834:AI Magazine
617:AI-complete
384:Turing Test
278:AI-Complete
195:text mining
150:(composite
148:peer review
99:Jargon File
83:NP-complete
40:AI-complete
18:AI complete
1986:Categories
1553:Rule-based
1435:Truecasing
1303:Stop words
1180:2024-04-05
1080:2024-04-05
1001:Musk, Elon
954:2023-04-15
911:2023-04-15
815:2024-03-25
772:2024-04-27
739:2024-04-28
715:2024-04-28
682:2022-05-26
574:2007-04-27
432:References
215:navigation
164:formalized
1862:reviewing
1660:standards
1658:Types and
901:250340282
856:220687545
767:1059-1028
710:1059-1028
519:CiteSeerX
223:reasoning
1778:Wikidata
1758:FrameNet
1743:BabelNet
1722:Treebank
1692:PropBank
1637:Word2vec
1602:fastText
1483:Stemming
1019:Archived
984:Archived
905:Archived
676:Archived
633:Archived
609:Archived
582:citation
568:archived
501:Archived
448:Archived
405:See also
249:Research
225:done by
219:planning
124:DeepMind
111:without
97:'s 1991
59:CAPTCHAs
1949:Related
1915:Chatbot
1773:WordNet
1753:DBpedia
1627:Seq2seq
1371:Parsing
1286:Trigram
1133:: 1–5,
377:AI-Easy
333:AI-Hard
118:brittle
109:brittle
87:NP-hard
73:History
44:AI-hard
1922:(c.f.
1580:models
1568:Neural
1281:Bigram
1276:n-gram
1209:
1171:
1109:
1050:
945:
899:
889:
854:
806:
765:
708:
521:
201:, and
1971:spaCy
1616:large
1607:GloVe
1071:(PDF)
987:(PDF)
972:(PDF)
897:S2CID
852:S2CID
759:Wired
702:Wired
484:(PDF)
473:(PDF)
167:logic
1736:Data
1587:BERT
1207:ISBN
1169:ISBN
1107:ISBN
1048:ISBN
1027:2022
943:ISBN
887:ISBN
804:ISBN
763:ISSN
706:ISSN
588:link
546:help
128:Gato
85:and
1768:UBY
1199:doi
1161:doi
1135:doi
1099:doi
935:doi
879:doi
842:doi
796:doi
331:is
276:is
217:or
146:AI
89:in
42:or
1988::
1205:,
1167:,
1155:,
1131:42
1129:,
1105:,
1073:,
1007:.
980:24
978:.
974:.
941:,
929:,
903:.
895:.
885:.
873:.
850:.
838:42
836:.
832:.
802:,
790:,
761:.
757:.
732:.
704:.
700:.
674:.
670:.
584:}}
580:{{
537::
535:}}
531:{{
475:.
197:,
162:,
158:,
154:,
134:.
101:.
69:.
53:,
1926:)
1649:,
1618:)
1614:(
1244:e
1237:t
1230:v
1201::
1163::
1137::
1101::
1029:.
937::
914:.
881::
844::
798::
775:.
742:.
718:.
685:.
592:.
590:)
548:)
544:(
527:.
363:H
343:C
319:H
295:C
264:C
229:.
205:)
187:)
172:)
20:)
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.