Как пишется слово искусственный интеллект

Expectation-maximization clustering of Old Faithful eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.

1. ^ ^{a b} This list of intelligent traits is based on the topics covered by the major AI textbooks, including: Russell & Norvig (2003), Luger & Stubblefield (2004), Poole, Mackworth & Goebel (1998) and Nilsson (1998)
2. ^ This statement comes from the proposal for the Dartmouth workshop of 1956, which reads: «Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it.»^[12]
3. ^ Russel and Norvig note in the textbook Artificial Intelligence: A Modern Approach (4th ed.), section 1.5:
   «In the longer term, we face the difficult problem of controlling superintelligent AI systems that may evolve in unpredictable ways.» while referring to computer scientists, philosophers, and technologists.
4. ^
   Daniel Crevier wrote «the conference is generally recognized as the official birthdate of the new science.»^[22] Russell and Norvifg call the conference «the birth of artificial intelligence.»^[23]
5. ^
   Russell and Norvig wrote «for the next 20 years the field would be dominated by these people and their students.»^[23]
6. ^
   Russell and Norvig wrote «it was astonishing whenever a computer did anything kind of smartish».^[25]
7. ^
   The programs described are Arthur Samuel’s checkers program for the IBM 701, Daniel Bobrow’s STUDENT, Newell and Simon’s Logic Theorist and Terry Winograd’s SHRDLU.
8. ^
   Embodied approaches to AI^[35] were championed by Hans Moravec^[36] and Rodney Brooks^[37] and went by many names: Nouvelle AI,^[37] Developmental robotics,^[38]
   situated AI, behavior-based AI as well as others. A similar movement in cognitive science was the embodied mind thesis.
9. ^
   Clark wrote: «After a half-decade of quiet breakthroughs in artificial intelligence, 2015 has been a landmark year. Computers are smarter and learning faster than ever.»^[9]
10. ^ Alan Turing discussed the centrality of learning as early as 1950, in his classic paper «Computing Machinery and Intelligence».^[61] In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning: «An Inductive Inference Machine».^[62]
11. ^ This is a form of Tom Mitchell’s widely quoted definition of machine learning: «A computer program is set to learn from an experience E with respect to some task T and some performance measure P if its performance on T as measured by P improves with experience E.»^[63]
12. ^
    Alan Turing suggested in «Computing Machinery and Intelligence» that a «thinking machine» would need to be educated like a child.^[61] Developmental robotics is a modern version of the idea.^[38]
13. ^
    Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.^[102]
14. ^ Expectation-maximization, one of the most popular algorithms in machine learning, allows clustering in the presence of unknown latent variables.^[104]
15. ^
    The Smithsonian reports: «Pluribus has bested poker pros in a series of six-player no-limit Texas Hold’em games, reaching a milestone in artificial intelligence research. It is the first bot to beat humans in a complex multiplayer competition.»^[146]
16. ^ See Problem of other minds
17. ^ Nils Nilsson wrote in 1983: «Simply put, there is wide disagreement in the field about what AI is all about.»^[164]
18. ^
    Daniel Crevier wrote that «time has proven the accuracy and perceptiveness of some of Dreyfus’s comments. Had he formulated them less aggressively, constructive actions they suggested might have been taken much earlier.»^[169]
19. ^
    Searle presented this definition of «Strong AI» in 1999.^[180] Searle’s original formulation was «The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states.»^[181] Strong AI is defined similarly by Russell and Norvig: «The assertion that machines could possibly act intelligently (or, perhaps better, act as if they were intelligent) is called the ‘weak AI’ hypothesis by philosophers, and the assertion that machines that do so are actually thinking (as opposed to simulating thinking) is called the ‘strong AI’ hypothesis.»^[176]
20. ^ See table 4; 9% is both the OECD average and the US average.^[194]
21. ^ Rodney Brooks writes, «I think it is a mistake to be worrying about us developing malevolent AI anytime in the next few hundred years. I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI and the enormity and complexity of building sentient volitional intelligence.»^[215]

1. ^ Google (2016).
2. ^ McCorduck (2004), p. 204.
3. ^ Ashok83 (2019).
4. ^ Schank (1991), p. 38.
5. ^ Crevier (1993), p. 109.
6. ^ ^{a b c}
   Funding initiatives in the early 80s: Fifth Generation Project (Japan), Alvey (UK), Microelectronics and Computer Technology Corporation (US), Strategic Computing Initiative (US):
   • McCorduck (2004, pp. 426–441)
   • Crevier (1993, pp. 161–162, 197–203, 211, 240)
   • Russell & Norvig (2003, p. 24)
   • NRC (1999, pp. 210–211)
   • Newquist (1994, pp. 235–248)

7. ^ ^{a b}
   First AI Winter, Lighthill report, Mansfield Amendment
   • Crevier (1993, pp. 115–117)
   • Russell & Norvig (2003, p. 22)
   • NRC (1999, pp. 212–213)
   • Howe (1994)
   • Newquist (1994, pp. 189–201)

8. ^ ^{a b}
   Second AI Winter:
   • McCorduck (2004, pp. 430–435)
   • Crevier (1993, pp. 209–210)
   • NRC (1999, pp. 214–216)
   • Newquist (1994, pp. 301–318)

9. ^ ^{a b c d} Clark (2015b).
10. ^ ^{a b}
    AI widely used in late 1990s:
    • Russell & Norvig (2003, p. 28)
    • Kurzweil (2005, p. 265)
    • NRC (1999, pp. 216–222)
    • Newquist (1994, pp. 189–201)

11. ^ ^{a b}
    Pennachin & Goertzel (2007); Roberts (2016)
12. ^ McCarthy et al. (1955).
13. ^ Newquist (1994), pp. 45–53.
14. ^ ^{a b}
    AI in myth:
    • McCorduck (2004, pp. 4–5)
    • Russell & Norvig (2003, p. 939)

15. ^ McCorduck (2004), pp. 17–25.
16. ^ ^{a b} McCorduck (2004), pp. 340–400.
17. ^ Berlinski (2000).
18. ^
    AI’s immediate precursors:
    • McCorduck (2004, pp. 51–107)
    • Crevier (1993, pp. 27–32)
    • Russell & Norvig (2003, pp. 15, 940)
    • Moravec (1988, p. 3)

19. ^ Russell & Norvig (2009), p. 16.
20. ^ Manyika 2022, p. 9.
21. ^ Manyika 2022, p. 10.
22. ^ Crevier (1993), pp. 47–49.
23. ^ ^{a b} Russell & Norvig (2003), p. 17.
24. ^
    Dartmouth workshop:
    • Russell & Norvig (2003, p. 17)
    • McCorduck (2004, pp. 111–136)
    • NRC (1999, pp. 200–201)

    The proposal:

    • McCarthy et al. (1955)

25. ^ Russell & Norvig (2003), p. 18.
26. ^
    Successful Symbolic AI programs:
    • McCorduck (2004, pp. 243–252)
    • Crevier (1993, pp. 52–107)
    • Moravec (1988, p. 9)
    • Russell & Norvig (2003, pp. 18–21)

27. ^
    AI heavily funded in 1960s:
    • McCorduck (2004, p. 131)
    • Crevier (1993, pp. 51, 64–65)
    • NRC (1999, pp. 204–205)

28. ^ Howe (1994).
29. ^ Newquist (1994), pp. 86–86.
30. ^
    Simon (1965, p. 96) quoted in Crevier (1993, p. 109)
31. ^
    Minsky (1967, p. 2) quoted in Crevier (1993, p. 109)
32. ^ Lighthill (1973).
33. ^
    Expert systems:
    • Russell & Norvig (2003, pp. 22–24)
    • Luger & Stubblefield (2004, pp. 227–331)
    • Nilsson (1998, chpt. 17.4)
    • McCorduck (2004, pp. 327–335, 434–435)
    • Crevier (1993, pp. 145–62, 197–203)
    • Newquist (1994, pp. 155–183)

34. ^ Nilsson (1998), p. 7.
35. ^ McCorduck (2004), pp. 454–462.
36. ^ Moravec (1988).
37. ^ ^{a b} Brooks (1990).
38. ^ ^{a b}
    Developmental robotics:
    • Weng et al. (2001)
    • Lungarella et al. (2003)
    • Asada et al. (2009)
    • Oudeyer (2010)

39. ^
    Revival of connectionism:
    • Crevier (1993, pp. 214–215)
    • Russell & Norvig (2003, p. 25)

40. ^
    Formal and narrow methods adopted in the 1990s:
    • Russell & Norvig (2003, pp. 25–26)
    • McCorduck (2004, pp. 486–487)

41. ^ McKinsey (2018).
42. ^ MIT Sloan Management Review (2018); Lorica (2017)
43. ^ ^{a b c d} UNESCO (2021).
44. ^
    Problem solving, puzzle solving, game playing and deduction:
    • Russell & Norvig (2003, chpt. 3–9)
    • Poole, Mackworth & Goebel (1998, chpt. 2,3,7,9)
    • Luger & Stubblefield (2004, chpt. 3,4,6,8)
    • Nilsson (1998, chpt. 7–12)

45. ^
    Uncertain reasoning:
    • Russell & Norvig (2003, pp. 452–644)
    • Poole, Mackworth & Goebel (1998, pp. 345–395)
    • Luger & Stubblefield (2004, pp. 333–381)
    • Nilsson (1998, chpt. 19)

46. ^ ^{a b c}
    Intractability and efficiency and the combinatorial explosion:
    • Russell & Norvig (2003, pp. 9, 21–22)

47. ^ ^{a b c}
    Psychological evidence of the prevalence sub-symbolic reasoning and knowledge:
    • Kahneman (2011)
    • Wason & Shapiro (1966)
    • Kahneman, Slovic & Tversky (1982)
    • Dreyfus & Dreyfus (1986)

48. ^
    Knowledge representation and knowledge engineering:
    • Russell & Norvig (2003, pp. 260–266, 320–363)
    • Poole, Mackworth & Goebel (1998, pp. 23–46, 69–81, 169–233, 235–277, 281–298, 319–345)
    • Luger & Stubblefield (2004, pp. 227–243),
    • Nilsson (1998, chpt. 17.1–17.4, 18)

49. ^ Russell & Norvig (2003), pp. 320–328.
50. ^ ^{a b c}
    Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts):
    • Russell & Norvig (2003, pp. 349–354),
    • Poole, Mackworth & Goebel (1998, pp. 174–177),
    • Luger & Stubblefield (2004, pp. 248–258),
    • Nilsson (1998, chpt. 18.3)

51. ^ ^{a b} Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem):
    • Russell & Norvig (2003, pp. 328–341),
    • Poole, Mackworth & Goebel (1998, pp. 281–298),
    • Nilsson (1998, chpt. 18.2)

52. ^ ^{a b}
    Causal calculus:
    • Poole, Mackworth & Goebel (1998, pp. 335–337)

53. ^ ^{a b}
    Representing knowledge about knowledge: Belief calculus, modal logics:
    • Russell & Norvig (2003, pp. 341–344),
    • Poole, Mackworth & Goebel (1998, pp. 275–277)

54. ^ ^{a b}
    Default reasoning, Frame problem, default logic, non-monotonic logics, circumscription, closed world assumption, abduction:
    • Russell & Norvig (2003, pp. 354–360)
    • Poole, Mackworth & Goebel (1998, pp. 248–256, 323–335)
    • Luger & Stubblefield (2004, pp. 335–363)
    • Nilsson (1998, ~18.3.3)

    (Poole et al. places abduction under «default reasoning». Luger et al. places this under «uncertain reasoning»).

55. ^ ^{a b}
    Breadth of commonsense knowledge:
    • Russell & Norvig (2003, p. 21),
    • Crevier (1993, pp. 113–114),
    • Moravec (1988, p. 13),
    • Lenat & Guha (1989, Introduction)

56. ^ Smoliar & Zhang (1994).
57. ^ Neumann & Möller (2008).
58. ^ Kuperman, Reichley & Bailey (2006).
59. ^ McGarry (2005).
60. ^ Bertini, Del Bimbo & Torniai (2006).
61. ^ ^{a b} Turing (1950).
62. ^ Solomonoff (1956).
63. ^ Russell & Norvig (2003), pp. 649–788.
64. ^
    Learning:
    • Russell & Norvig (2003, pp. 649–788)
    • Poole, Mackworth & Goebel (1998, pp. 397–438)
    • Luger & Stubblefield (2004, pp. 385–542)
    • Nilsson (1998, chpt. 3.3, 10.3, 17.5, 20)

65. ^
    Reinforcement learning:
    • Russell & Norvig (2003, pp. 763–788)
    • Luger & Stubblefield (2004, pp. 442–449)

66. ^ The Economist (2016).
67. ^ Jordan & Mitchell (2015).
68. ^
    Natural language processing (NLP):
    • Russell & Norvig (2003, pp. 790–831)
    • Poole, Mackworth & Goebel (1998, pp. 91–104)
    • Luger & Stubblefield (2004, pp. 591–632)

69. ^
    Applications of NLP:
    • Russell & Norvig (2003, pp. 840–857)
    • Luger & Stubblefield (2004, pp. 623–630)

70. ^ Modern statistical approaches to NLP:
    • Cambria & White (2014)

71. ^ Vincent (2019).
72. ^
    Machine perception:
    • Russell & Norvig (2003, pp. 537–581, 863–898)
    • Nilsson (1998, ~chpt. 6)

73. ^
    Speech recognition:
    • Russell & Norvig (2003, pp. 568–578)

74. ^
    Object recognition:
    • Russell & Norvig (2003, pp. 885–892)

75. ^
    Computer vision:
    • Russell & Norvig (2003, pp. 863–898)
    • Nilsson (1998, chpt. 6)

76. ^ MIT AIL (2014).
77. ^
    Affective computing:
    • Thro (1993)
    • Edelson (1991)
    • Tao & Tan (2005)
    • Scassellati (2002)

78. ^ Waddell (2018).
79. ^ Poria et al. (2017).
80. ^
    The Society of Mind:
    • Minsky (1986)

    Moravec’s «golden spike»:

    • Moravec (1988, p. 20)

    Multi-agent systems, hybrid intelligent systems, agent architectures, cognitive architecture:

    • Russell & Norvig (2003, pp. 27, 932, 970–972)
    • Nilsson (1998, chpt. 25)

81. ^ Domingos (2015), Chpt. 9.
82. ^
    Artificial brain as an approach to AGI:
    • Russell & Norvig (2003, p. 957)
    • Crevier (1993, pp. 271 & 279)
    • Goertzel et al. (2010)

    A few of the people who make some form of the argument:

    • Moravec (1988, p. 20)
    • Kurzweil (2005, p. 262)
    • Hawkins & Blakeslee (2005)

83. ^
    Search algorithms:
    • Russell & Norvig (2003, pp. 59–189)
    • Poole, Mackworth & Goebel (1998, pp. 113–163)
    • Luger & Stubblefield (2004, pp. 79–164, 193–219)
    • Nilsson (1998, chpt. 7–12)

84. ^ Forward chaining, backward chaining, Horn clauses, and logical deduction as search:
    • Russell & Norvig (2003, pp. 217–225, 280–294)
    • Poole, Mackworth & Goebel (1998, pp. ~46–52)
    • Luger & Stubblefield (2004, pp. 62–73)
    • Nilsson (1998, chpt. 4.2, 7.2)

85. ^
    State space search and planning:
    • Russell & Norvig (2003, pp. 382–387)
    • Poole, Mackworth & Goebel (1998, pp. 298–305)
    • Nilsson (1998, chpt. 10.1–2)

86. ^ Moving and configuration space:
    • Russell & Norvig (2003, pp. 916–932)

87. ^ Uninformed searches (breadth first search, depth-first search and general state space search):
    • Russell & Norvig (2003, pp. 59–93)
    • Poole, Mackworth & Goebel (1998, pp. 113–132)
    • Luger & Stubblefield (2004, pp. 79–121)
    • Nilsson (1998, chpt.

88. ^
    Heuristic or informed searches (e.g., greedy best first and A*):
    • Russell & Norvig (2003, pp. 94–109)
    • Poole, Mackworth & Goebel (1998, pp. pp. 132–147)
    • Poole & Mackworth (2017, Section 3.6)
    • Luger & Stubblefield (2004, pp. 133–150)

89. ^ Tecuci (2012).
90. ^ Optimization searches:
    • Russell & Norvig (2003, pp. 110–116, 120–129)
    • Poole, Mackworth & Goebel (1998, pp. 56–163)
    • Luger & Stubblefield (2004, pp. 127–133)

91. ^
    Genetic programming and genetic algorithms:
    • Luger & Stubblefield (2004, pp. 509–530)
    • Nilsson (1998, chpt. 4.2)

92. ^
    Artificial life and society based learning:
    • Luger & Stubblefield (2004, pp. 530–541)
    • Merkle & Middendorf (2013)

93. ^
    Logic:
    • Russell & Norvig (2003, pp. 194–310),
    • Luger & Stubblefield (2004, pp. 35–77),
    • Nilsson (1998, chpt. 13–16)

94. ^
    Satplan:
    • Russell & Norvig (2003, pp. 402–407),
    • Poole, Mackworth & Goebel (1998, pp. 300–301),
    • Nilsson (1998, chpt. 21)

95. ^
    Explanation based learning, relevance based learning, inductive logic programming, case based reasoning:
    • Russell & Norvig (2003, pp. 678–710),
    • Poole, Mackworth & Goebel (1998, pp. 414–416),
    • Luger & Stubblefield (2004, pp. ~422–442),
    • Nilsson (1998, chpt. 10.3, 17.5)

96. ^
    Propositional logic:
    • Russell & Norvig (2003, pp. 204–233),
    • Luger & Stubblefield (2004, pp. 45–50)
    • Nilsson (1998, chpt. 13)

97. ^ First-order logic and features such as equality:
    • Russell & Norvig (2003, pp. 240–310),
    • Poole, Mackworth & Goebel (1998, pp. 268–275),
    • Luger & Stubblefield (2004, pp. 50–62),
    • Nilsson (1998, chpt. 15)

98. ^
    Fuzzy logic:
    • Russell & Norvig (2003, pp. 526–527)
    • Scientific American (1999)

99. ^ Abe, Jair Minoro; Nakamatsu, Kazumi (2009). «Multi-agent Systems and Paraconsistent Knowledge». Knowledge Processing and Decision Making in Agent-Based Systems. Studies in Computational Intelligence. Vol. 170. Springer Berlin Heidelberg. pp. 101–121. doi:10.1007/978-3-540-88049-3_5. eISSN 1860-9503. ISBN 978-3-540-88048-6. ISSN 1860-949X. Retrieved 2 August 2022.
100. ^
     Stochastic methods for uncertain reasoning:
     • Russell & Norvig (2003, pp. 462–644),
     • Poole, Mackworth & Goebel (1998, pp. 345–395),
     • Luger & Stubblefield (2004, pp. 165–191, 333–381),
     • Nilsson (1998, chpt. 19)

101. ^
     Bayesian networks:
     • Russell & Norvig (2003, pp. 492–523),
     • Poole, Mackworth & Goebel (1998, pp. 361–381),
     • Luger & Stubblefield (2004, pp. ~182–190, ≈363–379),
     • Nilsson (1998, chpt. 19.3–4)

102. ^ Domingos (2015), chapter 6.
103. ^
     Bayesian inference algorithm:
     • Russell & Norvig (2003, pp. 504–519),
     • Poole, Mackworth & Goebel (1998, pp. 361–381),
     • Luger & Stubblefield (2004, pp. ~363–379),
     • Nilsson (1998, chpt. 19.4 & 7)

104. ^ Domingos (2015), p. 210.
105. ^
     Bayesian learning and the expectation-maximization algorithm:
     • Russell & Norvig (2003, pp. 712–724),
     • Poole, Mackworth & Goebel (1998, pp. 424–433),
     • Nilsson (1998, chpt. 20)
     • Domingos (2015, p. 210)

106. ^ Bayesian decision theory and Bayesian decision networks:
     • Russell & Norvig (2003, pp. 597–600)

107. ^ ^{a b c} Stochastic temporal models:
     • Russell & Norvig (2003, pp. 537–581)

     Dynamic Bayesian networks:

     • Russell & Norvig (2003, pp. 551–557)

     Hidden Markov model:

     • (Russell & Norvig 2003, pp. 549–551)

     Kalman filters:

     • Russell & Norvig (2003, pp. 551–557)

108. ^
     decision theory and decision analysis:
     • Russell & Norvig (2003, pp. 584–597),
     • Poole, Mackworth & Goebel (1998, pp. 381–394)

109. ^
     Information value theory:
     • Russell & Norvig (2003, pp. 600–604)

110. ^ Markov decision processes and dynamic decision networks:
     • Russell & Norvig (2003, pp. 613–631)

111. ^ Game theory and mechanism design:
     • Russell & Norvig (2003, pp. 631–643)

112. ^
     Statistical learning methods and classifiers:
     • Russell & Norvig (2003, pp. 712–754),
     • Luger & Stubblefield (2004, pp. 453–541)

113. ^
     Decision tree:
     • Domingos (2015, p. 88)
     • Russell & Norvig (2003, pp. 653–664),
     • Poole, Mackworth & Goebel (1998, pp. 403–408),
     • Luger & Stubblefield (2004, pp. 408–417)

114. ^
     K-nearest neighbor algorithm:
     • Domingos (2015, p. 187)
     • Russell & Norvig (2003, pp. 733–736)

115. ^
     kernel methods such as the support vector machine:
     • Domingos (2015, p. 88)
     • Russell & Norvig (2003, pp. 749–752)

     Gaussian mixture model:

     • Russell & Norvig (2003, pp. 725–727)

116. ^ Domingos (2015), p. 152.
117. ^
     Naive Bayes classifier:
     • Domingos (2015, p. 152)
     • Russell & Norvig (2003, p. 718)

118. ^ ^{a b}
     Neural networks:
     • Russell & Norvig (2003, pp. 736–748),
     • Poole, Mackworth & Goebel (1998, pp. 408–414),
     • Luger & Stubblefield (2004, pp. 453–505),
     • Nilsson (1998, chpt. 3)
     • Domingos (2015, Chapter 4)

119. ^
     Classifier performance:
     • van der Walt & Bernard (2006)
     • Russell & Norvig (2009, 18.12: Learning from Examples: Summary)

120. ^
     Backpropagation:
     • Russell & Norvig (2003, pp. 744–748),
     • Luger & Stubblefield (2004, pp. 467–474),
     • Nilsson (1998, chpt. 3.3)

     Paul Werbos’ introduction of backpropagation to AI:

     • Werbos (1974); Werbos (1982)

     Automatic differentiation, an essential precursor:

     • Linnainmaa (1970); Griewank (2012)

121. ^
     Competitive learning, Hebbian coincidence learning, Hopfield networks and attractor networks:
     • Luger & Stubblefield (2004, pp. 474–505)

122. ^
     Feedforward neural networks, perceptrons and radial basis networks:
     • Russell & Norvig (2003, pp. 739–748, 758)
     • Luger & Stubblefield (2004, pp. 458–467)

123. ^ Schulz & Behnke (2012).
124. ^
     Deep learning:
     • Goodfellow, Bengio & Courville (2016)
     • Hinton et al. (2016)
     • Schmidhuber (2015)

125. ^ Deng & Yu (2014), pp. 199–200.
126. ^ Ciresan, Meier & Schmidhuber (2012).
127. ^ Habibi (2017).
128. ^ Fukushima (2007).
129. ^
     Recurrent neural networks, Hopfield nets:
     • Russell & Norvig (2003, p. 758)
     • Luger & Stubblefield (2004, pp. 474–505)
     • Schmidhuber (2015)

130. ^ Schmidhuber (2015).
131. ^
     Werbos (1988);
     Robinson & Fallside (1987);
     Williams & Zipser (1994)
132. ^
     Goodfellow, Bengio & Courville (2016);
     Hochreiter (1991)
133. ^ Hochreiter & Schmidhuber (1997); Gers, Schraudolph & Schraudolph (2002)
134. ^ Russell & Norvig (2009), p. 1.
135. ^ European Commission (2020), p. 1.
136. ^ CNN (2006).
137. ^
     Targeted advertising:
     • Russell & Norvig (2009, p. 1)
     • Economist (2016)
     • Lohr (2016)

138. ^ Lohr (2016).
139. ^ Smith (2016).
140. ^ Rowinski (2013).
141. ^ Frangoul (2019).
142. ^ Brown (2019).
143. ^ McCorduck (2004), pp. 480–483.
144. ^ Markoff (2011).
145. ^
     Google (2016); BBC (2016)
146. ^ Solly (2019).
147. ^ Bowling et al. (2015).
148. ^ Sample (2017).
149. ^ Anadiotis (2020).
150. ^ Heath (2020).
151. ^ Aletras et al. (2016).
152. ^ «Going Nowhere Fast? Smart Traffic Lights Can Help Ease Gridlock». 18 May 2022.
153. ^ «Intellectual Property and Frontier Technologies». WIPO.
154. ^ ^{a b} «WIPO Technology Trends 2019 – Artificial Intelligence» (PDF). WIPO. 2019. Archived (PDF) from the original on 9 October 2022.
155. ^ ^{a b} Turing (1950), p. 1.
156. ^
     Turing’s original publication of the Turing test in «Computing machinery and intelligence»:
     • Turing (1950)

     Historical influence and philosophical implications:

     • Haugeland (1985, pp. 6–9)
     • Crevier (1993, p. 24)
     • McCorduck (2004, pp. 70–71)
     • Russell & Norvig (2021, pp. 2 and 984)

157. ^ Turing (1950), Under «The Argument from Consciousness».
158. ^ Russell & Norvig (2021), chpt. 2.
159. ^ Russell & Norvig (2021), p. 3.
160. ^ Maker (2006).
161. ^ McCarthy 1999.
162. ^ Minsky (1986).
163. ^ «Artificial intelligence — Google Search». www.google.com. Retrieved 5 November 2022.
164. ^ Nilsson (1983), p. 10.
165. ^ Haugeland (1985), pp. 112–117.
166. ^
     Physical symbol system hypothesis:
     • Newell & Simon (1976, p. 116)

     Historical significance:

     • McCorduck (2004, p. 153)
     • Russell & Norvig (2003, p. 18)

167. ^
     Moravec’s paradox:
     • Moravec (1988, pp. 15–16)
     • Minsky (1986, p. 29)
     • Pinker (2007, pp. 190–91)

168. ^
     Dreyfus’ critique of AI:
     • Dreyfus (1972)
     • Dreyfus & Dreyfus (1986)

     Historical significance and philosophical implications:

     • Crevier (1993, pp. 120–132)
     • McCorduck (2004, pp. 211–239)
     • Russell & Norvig (2003, pp. 950–952)
     • Fearn (2007, Chpt. 3)

169. ^ Crevier (1993), p. 125.
170. ^ Langley (2011).
171. ^ Katz (2012).
172. ^
     Neats vs. scruffies, the historic debate:
     • McCorduck (2004, pp. 421–424, 486–489)
     • Crevier (1993, p. 168)
     • Nilsson (1983, pp. 10–11)

     A classic example of the «scruffy» approach to intelligence:

     • Minsky (1986)

     A modern example of neat AI and its aspirations:

     • Domingos (2015)

173. ^ Russell & Norvig (2003), pp. 25–26.
174. ^ Pennachin & Goertzel (2007).
175. ^ ^{a b} Roberts (2016).
176. ^ ^{a b} Russell & Norvig (2003), p. 947.
177. ^ Chalmers (1995).
178. ^ Dennett (1991).
179. ^ Horst (2005).
180. ^ Searle (1999).
181. ^ Searle (1980), p. 1.
182. ^
     Searle’s Chinese room argument:
     • Searle (1980). Searle’s original presentation of the thought experiment.
     • Searle (1999).

     Discussion:

     • Russell & Norvig (2003, pp. 958–960)
     • McCorduck (2004, pp. 443–445)
     • Crevier (1993, pp. 269–271)

183. ^
     Robot rights:
     • Russell & Norvig (2003, p. 964)
     • BBC (2006)
     • Maschafilm (2010) (the film Plug & Pray)

184. ^ Evans (2015).
185. ^ McCorduck (2004), pp. 19–25.
186. ^ Henderson (2007).
187. ^ Omohundro (2008).
188. ^ Vinge (1993).
189. ^ Russell & Norvig (2003), p. 963.
190. ^
     Transhumanism:
     • Moravec (1988)
     • Kurzweil (2005)
     • Russell & Norvig (2003, p. 963)

191. ^
     AI as evolution:
     • Edward Fredkin is quoted in McCorduck (2004, p. 401)
     • Butler (1863)
     • Dyson (1998)

192. ^
     Ford & Colvin (2015);
     McGaughey (2018)
193. ^ IGM Chicago (2017).
194. ^ Arntz, Gregory & Zierahn (2016), p. 33.
195. ^
     Lohr (2017);
     Frey & Osborne (2017);
     Arntz, Gregory & Zierahn (2016, p. 33)
196. ^ Morgenstern (2015).
197. ^ Mahdawi (2017); Thompson (2014)
198. ^ Harari (2018).
199. ^
     Weaponized AI:
     • Robitzski (2018)
     • Sainato (2015)

200. ^ Urbina, Fabio; Lentzos, Filippa; Invernizzi, Cédric; Ekins, Sean (7 March 2022). «Dual use of artificial-intelligence-powered drug discovery». Nature Machine Intelligence. 4 (3): 189–191. doi:10.1038/s42256-022-00465-9. PMC 9544280. PMID 36211133. S2CID 247302391. Retrieved 15 March 2022.
201. ^ CNA (2019).
202. ^ Goffrey (2008), p. 17.
203. ^ Lipartito (2011, p. 36); Goodman & Flaxman (2017, p. 6)
204. ^ Larson & Angwin (2016).
205. ^ Berdahl, Carl Thomas; Baker, Lawrence; Mann, Sean; Osoba, Osonde; Girosi, Federico (7 February 2023). «Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review». JMIR AI. 2: e42936. doi:10.2196/42936. ISSN 2817-1705. S2CID 256681439.
206. ^ Dockrill, Peter, Robots With Flawed AI Make Sexist And Racist Decisions, Experiment Shows, Science Alert, 27 June 2022
207. ^ Cellan-Jones (2014).
208. ^ Bostrom (2014); Müller & Bostrom (2014); Bostrom (2015)
209. ^ Rubin (2003).
210. ^ Müller & Bostrom (2014).
211. ^
     Leaders’ concerns about the existential risks of AI:
     • Rawlinson (2015)
     • Holley (2015)
     • Gibbs (2014)
     • Churm (2019)
     • Sainato (2015)

212. ^
     Funding to mitigate risks of AI:
     • Post (2015)
     • Del Prado (2015)
     • Clark (2015a)
     • FastCompany (2015)

213. ^
     Leaders who argue the benefits of AI outweigh the risks:
     • Thibodeau (2019)
     • Bhardwaj (2018)

214. ^
     Arguments that AI is not an imminent risk:
     • Brooks (2014)
     • Geist (2015)
     • Madrigal (2015)
     • Lee (2014)

215. ^ Brooks (2014).
216. ^ «Artificial intelligence and copyright». www.wipo.int. Retrieved 27 May 2022.
217. ^ Yudkowsky (2008).
218. ^ ^{a b} Anderson & Anderson (2011).
219. ^ AAAI (2014).
220. ^ Wallach (2010).
221. ^ Russell (2019), p. 173.
222. ^
     Regulation of AI to mitigate risks:
     • Berryhill et al. (2019)
     • Barfield & Pagallo (2018)
     • Iphofen & Kritikos (2019)
     • Wirtz, Weyerer & Geyer (2018)
     • Buiten (2019)

223. ^ Law Library of Congress (U.S.). Global Legal Research Directorate (2019).
224. ^ Kissinger, Henry (1 November 2021). «The Challenge of Being Human in the Age of AI». The Wall Street Journal.
225. ^ Buttazzo (2001).
226. ^ Anderson (2008).
227. ^ McCauley (2007).
228. ^ Galvan (1997).

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Later editions.

• Russell, Stuart J.; Norvig, Peter (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, New Jersey: Prentice Hall. ISBN 978-0-13-604259-4..
• Poole, David; Mackworth, Alan (2017). Artificial Intelligence: Foundations of Computational Agents (2nd ed.). Cambridge University Press. ISBN 978-1-107-19539-4.

The two most widely used textbooks in 2021.Open Syllabus: Explorer

• Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 9780134610993. LCCN 20190474.
• Knight, Kevin; Rich, Elaine (1 January 2010). Artificial Intelligence (3rd ed.). Mc Graw Hill India. ISBN 9780070087705.

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• Newquist, HP (1994). The Brain Makers: Genius, Ego, And Greed In The Quest For Machines That Think. New York: Macmillan/SAMS. ISBN 978-0-672-30412-5.
• Nilsson, Nils (2009). The Quest for Artificial Intelligence: A History of Ideas and Achievements. New York: Cambridge University Press. ISBN 978-0-521-12293-1.

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Expectation-maximization clustering of Old Faithful eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.

1. ^ ^{a b} This list of intelligent traits is based on the topics covered by the major AI textbooks, including: Russell & Norvig (2003), Luger & Stubblefield (2004), Poole, Mackworth & Goebel (1998) and Nilsson (1998)
2. ^ This statement comes from the proposal for the Dartmouth workshop of 1956, which reads: «Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it.»^[12]
3. ^ Russel and Norvig note in the textbook Artificial Intelligence: A Modern Approach (4th ed.), section 1.5:
   «In the longer term, we face the difficult problem of controlling superintelligent AI systems that may evolve in unpredictable ways.» while referring to computer scientists, philosophers, and technologists.
4. ^
   Daniel Crevier wrote «the conference is generally recognized as the official birthdate of the new science.»^[22] Russell and Norvifg call the conference «the birth of artificial intelligence.»^[23]
5. ^
   Russell and Norvig wrote «for the next 20 years the field would be dominated by these people and their students.»^[23]
6. ^
   Russell and Norvig wrote «it was astonishing whenever a computer did anything kind of smartish».^[25]
7. ^
   The programs described are Arthur Samuel’s checkers program for the IBM 701, Daniel Bobrow’s STUDENT, Newell and Simon’s Logic Theorist and Terry Winograd’s SHRDLU.
8. ^
   Embodied approaches to AI^[35] were championed by Hans Moravec^[36] and Rodney Brooks^[37] and went by many names: Nouvelle AI,^[37] Developmental robotics,^[38]
   situated AI, behavior-based AI as well as others. A similar movement in cognitive science was the embodied mind thesis.
9. ^
   Clark wrote: «After a half-decade of quiet breakthroughs in artificial intelligence, 2015 has been a landmark year. Computers are smarter and learning faster than ever.»^[9]
10. ^ Alan Turing discussed the centrality of learning as early as 1950, in his classic paper «Computing Machinery and Intelligence».^[61] In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning: «An Inductive Inference Machine».^[62]
11. ^ This is a form of Tom Mitchell’s widely quoted definition of machine learning: «A computer program is set to learn from an experience E with respect to some task T and some performance measure P if its performance on T as measured by P improves with experience E.»^[63]
12. ^
    Alan Turing suggested in «Computing Machinery and Intelligence» that a «thinking machine» would need to be educated like a child.^[61] Developmental robotics is a modern version of the idea.^[38]
13. ^
    Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.^[102]
14. ^ Expectation-maximization, one of the most popular algorithms in machine learning, allows clustering in the presence of unknown latent variables.^[104]
15. ^
    The Smithsonian reports: «Pluribus has bested poker pros in a series of six-player no-limit Texas Hold’em games, reaching a milestone in artificial intelligence research. It is the first bot to beat humans in a complex multiplayer competition.»^[146]
16. ^ See Problem of other minds
17. ^ Nils Nilsson wrote in 1983: «Simply put, there is wide disagreement in the field about what AI is all about.»^[164]
18. ^
    Daniel Crevier wrote that «time has proven the accuracy and perceptiveness of some of Dreyfus’s comments. Had he formulated them less aggressively, constructive actions they suggested might have been taken much earlier.»^[169]
19. ^
    Searle presented this definition of «Strong AI» in 1999.^[180] Searle’s original formulation was «The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states.»^[181] Strong AI is defined similarly by Russell and Norvig: «The assertion that machines could possibly act intelligently (or, perhaps better, act as if they were intelligent) is called the ‘weak AI’ hypothesis by philosophers, and the assertion that machines that do so are actually thinking (as opposed to simulating thinking) is called the ‘strong AI’ hypothesis.»^[176]
20. ^ See table 4; 9% is both the OECD average and the US average.^[194]
21. ^ Rodney Brooks writes, «I think it is a mistake to be worrying about us developing malevolent AI anytime in the next few hundred years. I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI and the enormity and complexity of building sentient volitional intelligence.»^[215]

1. ^ Google (2016).
2. ^ McCorduck (2004), p. 204.
3. ^ Ashok83 (2019).
4. ^ Schank (1991), p. 38.
5. ^ Crevier (1993), p. 109.
6. ^ ^{a b c}
   Funding initiatives in the early 80s: Fifth Generation Project (Japan), Alvey (UK), Microelectronics and Computer Technology Corporation (US), Strategic Computing Initiative (US):
   • McCorduck (2004, pp. 426–441)
   • Crevier (1993, pp. 161–162, 197–203, 211, 240)
   • Russell & Norvig (2003, p. 24)
   • NRC (1999, pp. 210–211)
   • Newquist (1994, pp. 235–248)

7. ^ ^{a b}
   First AI Winter, Lighthill report, Mansfield Amendment
   • Crevier (1993, pp. 115–117)
   • Russell & Norvig (2003, p. 22)
   • NRC (1999, pp. 212–213)
   • Howe (1994)
   • Newquist (1994, pp. 189–201)

8. ^ ^{a b}
   Second AI Winter:
   • McCorduck (2004, pp. 430–435)
   • Crevier (1993, pp. 209–210)
   • NRC (1999, pp. 214–216)
   • Newquist (1994, pp. 301–318)

9. ^ ^{a b c d} Clark (2015b).
10. ^ ^{a b}
    AI widely used in late 1990s:
    • Russell & Norvig (2003, p. 28)
    • Kurzweil (2005, p. 265)
    • NRC (1999, pp. 216–222)
    • Newquist (1994, pp. 189–201)

11. ^ ^{a b}
    Pennachin & Goertzel (2007); Roberts (2016)
12. ^ McCarthy et al. (1955).
13. ^ Newquist (1994), pp. 45–53.
14. ^ ^{a b}
    AI in myth:
    • McCorduck (2004, pp. 4–5)
    • Russell & Norvig (2003, p. 939)

15. ^ McCorduck (2004), pp. 17–25.
16. ^ ^{a b} McCorduck (2004), pp. 340–400.
17. ^ Berlinski (2000).
18. ^
    AI’s immediate precursors:
    • McCorduck (2004, pp. 51–107)
    • Crevier (1993, pp. 27–32)
    • Russell & Norvig (2003, pp. 15, 940)
    • Moravec (1988, p. 3)

19. ^ Russell & Norvig (2009), p. 16.
20. ^ Manyika 2022, p. 9.
21. ^ Manyika 2022, p. 10.
22. ^ Crevier (1993), pp. 47–49.
23. ^ ^{a b} Russell & Norvig (2003), p. 17.
24. ^
    Dartmouth workshop:
    • Russell & Norvig (2003, p. 17)
    • McCorduck (2004, pp. 111–136)
    • NRC (1999, pp. 200–201)

    The proposal:

    • McCarthy et al. (1955)

25. ^ Russell & Norvig (2003), p. 18.
26. ^
    Successful Symbolic AI programs:
    • McCorduck (2004, pp. 243–252)
    • Crevier (1993, pp. 52–107)
    • Moravec (1988, p. 9)
    • Russell & Norvig (2003, pp. 18–21)

27. ^
    AI heavily funded in 1960s:
    • McCorduck (2004, p. 131)
    • Crevier (1993, pp. 51, 64–65)
    • NRC (1999, pp. 204–205)

28. ^ Howe (1994).
29. ^ Newquist (1994), pp. 86–86.
30. ^
    Simon (1965, p. 96) quoted in Crevier (1993, p. 109)
31. ^
    Minsky (1967, p. 2) quoted in Crevier (1993, p. 109)
32. ^ Lighthill (1973).
33. ^
    Expert systems:
    • Russell & Norvig (2003, pp. 22–24)
    • Luger & Stubblefield (2004, pp. 227–331)
    • Nilsson (1998, chpt. 17.4)
    • McCorduck (2004, pp. 327–335, 434–435)
    • Crevier (1993, pp. 145–62, 197–203)
    • Newquist (1994, pp. 155–183)

34. ^ Nilsson (1998), p. 7.
35. ^ McCorduck (2004), pp. 454–462.
36. ^ Moravec (1988).
37. ^ ^{a b} Brooks (1990).
38. ^ ^{a b}
    Developmental robotics:
    • Weng et al. (2001)
    • Lungarella et al. (2003)
    • Asada et al. (2009)
    • Oudeyer (2010)

39. ^
    Revival of connectionism:
    • Crevier (1993, pp. 214–215)
    • Russell & Norvig (2003, p. 25)

40. ^
    Formal and narrow methods adopted in the 1990s:
    • Russell & Norvig (2003, pp. 25–26)
    • McCorduck (2004, pp. 486–487)

41. ^ McKinsey (2018).
42. ^ MIT Sloan Management Review (2018); Lorica (2017)
43. ^ ^{a b c d} UNESCO (2021).
44. ^
    Problem solving, puzzle solving, game playing and deduction:
    • Russell & Norvig (2003, chpt. 3–9)
    • Poole, Mackworth & Goebel (1998, chpt. 2,3,7,9)
    • Luger & Stubblefield (2004, chpt. 3,4,6,8)
    • Nilsson (1998, chpt. 7–12)

45. ^
    Uncertain reasoning:
    • Russell & Norvig (2003, pp. 452–644)
    • Poole, Mackworth & Goebel (1998, pp. 345–395)
    • Luger & Stubblefield (2004, pp. 333–381)
    • Nilsson (1998, chpt. 19)

46. ^ ^{a b c}
    Intractability and efficiency and the combinatorial explosion:
    • Russell & Norvig (2003, pp. 9, 21–22)

47. ^ ^{a b c}
    Psychological evidence of the prevalence sub-symbolic reasoning and knowledge:
    • Kahneman (2011)
    • Wason & Shapiro (1966)
    • Kahneman, Slovic & Tversky (1982)
    • Dreyfus & Dreyfus (1986)

48. ^
    Knowledge representation and knowledge engineering:
    • Russell & Norvig (2003, pp. 260–266, 320–363)
    • Poole, Mackworth & Goebel (1998, pp. 23–46, 69–81, 169–233, 235–277, 281–298, 319–345)
    • Luger & Stubblefield (2004, pp. 227–243),
    • Nilsson (1998, chpt. 17.1–17.4, 18)

49. ^ Russell & Norvig (2003), pp. 320–328.
50. ^ ^{a b c}
    Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts):
    • Russell & Norvig (2003, pp. 349–354),
    • Poole, Mackworth & Goebel (1998, pp. 174–177),
    • Luger & Stubblefield (2004, pp. 248–258),
    • Nilsson (1998, chpt. 18.3)

51. ^ ^{a b} Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem):
    • Russell & Norvig (2003, pp. 328–341),
    • Poole, Mackworth & Goebel (1998, pp. 281–298),
    • Nilsson (1998, chpt. 18.2)

52. ^ ^{a b}
    Causal calculus:
    • Poole, Mackworth & Goebel (1998, pp. 335–337)

53. ^ ^{a b}
    Representing knowledge about knowledge: Belief calculus, modal logics:
    • Russell & Norvig (2003, pp. 341–344),
    • Poole, Mackworth & Goebel (1998, pp. 275–277)

54. ^ ^{a b}
    Default reasoning, Frame problem, default logic, non-monotonic logics, circumscription, closed world assumption, abduction:
    • Russell & Norvig (2003, pp. 354–360)
    • Poole, Mackworth & Goebel (1998, pp. 248–256, 323–335)
    • Luger & Stubblefield (2004, pp. 335–363)
    • Nilsson (1998, ~18.3.3)

    (Poole et al. places abduction under «default reasoning». Luger et al. places this under «uncertain reasoning»).

55. ^ ^{a b}
    Breadth of commonsense knowledge:
    • Russell & Norvig (2003, p. 21),
    • Crevier (1993, pp. 113–114),
    • Moravec (1988, p. 13),
    • Lenat & Guha (1989, Introduction)

56. ^ Smoliar & Zhang (1994).
57. ^ Neumann & Möller (2008).
58. ^ Kuperman, Reichley & Bailey (2006).
59. ^ McGarry (2005).
60. ^ Bertini, Del Bimbo & Torniai (2006).
61. ^ ^{a b} Turing (1950).
62. ^ Solomonoff (1956).
63. ^ Russell & Norvig (2003), pp. 649–788.
64. ^
    Learning:
    • Russell & Norvig (2003, pp. 649–788)
    • Poole, Mackworth & Goebel (1998, pp. 397–438)
    • Luger & Stubblefield (2004, pp. 385–542)
    • Nilsson (1998, chpt. 3.3, 10.3, 17.5, 20)

65. ^
    Reinforcement learning:
    • Russell & Norvig (2003, pp. 763–788)
    • Luger & Stubblefield (2004, pp. 442–449)

66. ^ The Economist (2016).
67. ^ Jordan & Mitchell (2015).
68. ^
    Natural language processing (NLP):
    • Russell & Norvig (2003, pp. 790–831)
    • Poole, Mackworth & Goebel (1998, pp. 91–104)
    • Luger & Stubblefield (2004, pp. 591–632)

69. ^
    Applications of NLP:
    • Russell & Norvig (2003, pp. 840–857)
    • Luger & Stubblefield (2004, pp. 623–630)

70. ^ Modern statistical approaches to NLP:
    • Cambria & White (2014)

71. ^ Vincent (2019).
72. ^
    Machine perception:
    • Russell & Norvig (2003, pp. 537–581, 863–898)
    • Nilsson (1998, ~chpt. 6)

73. ^
    Speech recognition:
    • Russell & Norvig (2003, pp. 568–578)

74. ^
    Object recognition:
    • Russell & Norvig (2003, pp. 885–892)

75. ^
    Computer vision:
    • Russell & Norvig (2003, pp. 863–898)
    • Nilsson (1998, chpt. 6)

76. ^ MIT AIL (2014).
77. ^
    Affective computing:
    • Thro (1993)
    • Edelson (1991)
    • Tao & Tan (2005)
    • Scassellati (2002)

78. ^ Waddell (2018).
79. ^ Poria et al. (2017).
80. ^
    The Society of Mind:
    • Minsky (1986)

    Moravec’s «golden spike»:

    • Moravec (1988, p. 20)

    Multi-agent systems, hybrid intelligent systems, agent architectures, cognitive architecture:

    • Russell & Norvig (2003, pp. 27, 932, 970–972)
    • Nilsson (1998, chpt. 25)

81. ^ Domingos (2015), Chpt. 9.
82. ^
    Artificial brain as an approach to AGI:
    • Russell & Norvig (2003, p. 957)
    • Crevier (1993, pp. 271 & 279)
    • Goertzel et al. (2010)

    A few of the people who make some form of the argument:

    • Moravec (1988, p. 20)
    • Kurzweil (2005, p. 262)
    • Hawkins & Blakeslee (2005)

83. ^
    Search algorithms:
    • Russell & Norvig (2003, pp. 59–189)
    • Poole, Mackworth & Goebel (1998, pp. 113–163)
    • Luger & Stubblefield (2004, pp. 79–164, 193–219)
    • Nilsson (1998, chpt. 7–12)

84. ^ Forward chaining, backward chaining, Horn clauses, and logical deduction as search:
    • Russell & Norvig (2003, pp. 217–225, 280–294)
    • Poole, Mackworth & Goebel (1998, pp. ~46–52)
    • Luger & Stubblefield (2004, pp. 62–73)
    • Nilsson (1998, chpt. 4.2, 7.2)

85. ^
    State space search and planning:
    • Russell & Norvig (2003, pp. 382–387)
    • Poole, Mackworth & Goebel (1998, pp. 298–305)
    • Nilsson (1998, chpt. 10.1–2)

86. ^ Moving and configuration space:
    • Russell & Norvig (2003, pp. 916–932)

87. ^ Uninformed searches (breadth first search, depth-first search and general state space search):
    • Russell & Norvig (2003, pp. 59–93)
    • Poole, Mackworth & Goebel (1998, pp. 113–132)
    • Luger & Stubblefield (2004, pp. 79–121)
    • Nilsson (1998, chpt.

88. ^
    Heuristic or informed searches (e.g., greedy best first and A*):
    • Russell & Norvig (2003, pp. 94–109)
    • Poole, Mackworth & Goebel (1998, pp. pp. 132–147)
    • Poole & Mackworth (2017, Section 3.6)
    • Luger & Stubblefield (2004, pp. 133–150)

89. ^ Tecuci (2012).
90. ^ Optimization searches:
    • Russell & Norvig (2003, pp. 110–116, 120–129)
    • Poole, Mackworth & Goebel (1998, pp. 56–163)
    • Luger & Stubblefield (2004, pp. 127–133)

91. ^
    Genetic programming and genetic algorithms:
    • Luger & Stubblefield (2004, pp. 509–530)
    • Nilsson (1998, chpt. 4.2)

92. ^
    Artificial life and society based learning:
    • Luger & Stubblefield (2004, pp. 530–541)
    • Merkle & Middendorf (2013)

93. ^
    Logic:
    • Russell & Norvig (2003, pp. 194–310),
    • Luger & Stubblefield (2004, pp. 35–77),
    • Nilsson (1998, chpt. 13–16)

94. ^
    Satplan:
    • Russell & Norvig (2003, pp. 402–407),
    • Poole, Mackworth & Goebel (1998, pp. 300–301),
    • Nilsson (1998, chpt. 21)

95. ^
    Explanation based learning, relevance based learning, inductive logic programming, case based reasoning:
    • Russell & Norvig (2003, pp. 678–710),
    • Poole, Mackworth & Goebel (1998, pp. 414–416),
    • Luger & Stubblefield (2004, pp. ~422–442),
    • Nilsson (1998, chpt. 10.3, 17.5)

96. ^
    Propositional logic:
    • Russell & Norvig (2003, pp. 204–233),
    • Luger & Stubblefield (2004, pp. 45–50)
    • Nilsson (1998, chpt. 13)

97. ^ First-order logic and features such as equality:
    • Russell & Norvig (2003, pp. 240–310),
    • Poole, Mackworth & Goebel (1998, pp. 268–275),
    • Luger & Stubblefield (2004, pp. 50–62),
    • Nilsson (1998, chpt. 15)

98. ^
    Fuzzy logic:
    • Russell & Norvig (2003, pp. 526–527)
    • Scientific American (1999)

99. ^ Abe, Jair Minoro; Nakamatsu, Kazumi (2009). «Multi-agent Systems and Paraconsistent Knowledge». Knowledge Processing and Decision Making in Agent-Based Systems. Studies in Computational Intelligence. Vol. 170. Springer Berlin Heidelberg. pp. 101–121. doi:10.1007/978-3-540-88049-3_5. eISSN 1860-9503. ISBN 978-3-540-88048-6. ISSN 1860-949X. Retrieved 2 August 2022.
100. ^
     Stochastic methods for uncertain reasoning:
     • Russell & Norvig (2003, pp. 462–644),
     • Poole, Mackworth & Goebel (1998, pp. 345–395),
     • Luger & Stubblefield (2004, pp. 165–191, 333–381),
     • Nilsson (1998, chpt. 19)

101. ^
     Bayesian networks:
     • Russell & Norvig (2003, pp. 492–523),
     • Poole, Mackworth & Goebel (1998, pp. 361–381),
     • Luger & Stubblefield (2004, pp. ~182–190, ≈363–379),
     • Nilsson (1998, chpt. 19.3–4)

102. ^ Domingos (2015), chapter 6.
103. ^
     Bayesian inference algorithm:
     • Russell & Norvig (2003, pp. 504–519),
     • Poole, Mackworth & Goebel (1998, pp. 361–381),
     • Luger & Stubblefield (2004, pp. ~363–379),
     • Nilsson (1998, chpt. 19.4 & 7)

104. ^ Domingos (2015), p. 210.
105. ^
     Bayesian learning and the expectation-maximization algorithm:
     • Russell & Norvig (2003, pp. 712–724),
     • Poole, Mackworth & Goebel (1998, pp. 424–433),
     • Nilsson (1998, chpt. 20)
     • Domingos (2015, p. 210)

106. ^ Bayesian decision theory and Bayesian decision networks:
     • Russell & Norvig (2003, pp. 597–600)

107. ^ ^{a b c} Stochastic temporal models:
     • Russell & Norvig (2003, pp. 537–581)

     Dynamic Bayesian networks:

     • Russell & Norvig (2003, pp. 551–557)

     Hidden Markov model:

     • (Russell & Norvig 2003, pp. 549–551)

     Kalman filters:

     • Russell & Norvig (2003, pp. 551–557)

108. ^
     decision theory and decision analysis:
     • Russell & Norvig (2003, pp. 584–597),
     • Poole, Mackworth & Goebel (1998, pp. 381–394)

109. ^
     Information value theory:
     • Russell & Norvig (2003, pp. 600–604)

110. ^ Markov decision processes and dynamic decision networks:
     • Russell & Norvig (2003, pp. 613–631)

111. ^ Game theory and mechanism design:
     • Russell & Norvig (2003, pp. 631–643)

112. ^
     Statistical learning methods and classifiers:
     • Russell & Norvig (2003, pp. 712–754),
     • Luger & Stubblefield (2004, pp. 453–541)

113. ^
     Decision tree:
     • Domingos (2015, p. 88)
     • Russell & Norvig (2003, pp. 653–664),
     • Poole, Mackworth & Goebel (1998, pp. 403–408),
     • Luger & Stubblefield (2004, pp. 408–417)

114. ^
     K-nearest neighbor algorithm:
     • Domingos (2015, p. 187)
     • Russell & Norvig (2003, pp. 733–736)

115. ^
     kernel methods such as the support vector machine:
     • Domingos (2015, p. 88)
     • Russell & Norvig (2003, pp. 749–752)

     Gaussian mixture model:

     • Russell & Norvig (2003, pp. 725–727)

116. ^ Domingos (2015), p. 152.
117. ^
     Naive Bayes classifier:
     • Domingos (2015, p. 152)
     • Russell & Norvig (2003, p. 718)

118. ^ ^{a b}
     Neural networks:
     • Russell & Norvig (2003, pp. 736–748),
     • Poole, Mackworth & Goebel (1998, pp. 408–414),
     • Luger & Stubblefield (2004, pp. 453–505),
     • Nilsson (1998, chpt. 3)
     • Domingos (2015, Chapter 4)

119. ^
     Classifier performance:
     • van der Walt & Bernard (2006)
     • Russell & Norvig (2009, 18.12: Learning from Examples: Summary)

120. ^
     Backpropagation:
     • Russell & Norvig (2003, pp. 744–748),
     • Luger & Stubblefield (2004, pp. 467–474),
     • Nilsson (1998, chpt. 3.3)

     Paul Werbos’ introduction of backpropagation to AI:

     • Werbos (1974); Werbos (1982)

     Automatic differentiation, an essential precursor:

     • Linnainmaa (1970); Griewank (2012)

121. ^
     Competitive learning, Hebbian coincidence learning, Hopfield networks and attractor networks:
     • Luger & Stubblefield (2004, pp. 474–505)

122. ^
     Feedforward neural networks, perceptrons and radial basis networks:
     • Russell & Norvig (2003, pp. 739–748, 758)
     • Luger & Stubblefield (2004, pp. 458–467)

123. ^ Schulz & Behnke (2012).
124. ^
     Deep learning:
     • Goodfellow, Bengio & Courville (2016)
     • Hinton et al. (2016)
     • Schmidhuber (2015)

125. ^ Deng & Yu (2014), pp. 199–200.
126. ^ Ciresan, Meier & Schmidhuber (2012).
127. ^ Habibi (2017).
128. ^ Fukushima (2007).
129. ^
     Recurrent neural networks, Hopfield nets:
     • Russell & Norvig (2003, p. 758)
     • Luger & Stubblefield (2004, pp. 474–505)
     • Schmidhuber (2015)

130. ^ Schmidhuber (2015).
131. ^
     Werbos (1988);
     Robinson & Fallside (1987);
     Williams & Zipser (1994)
132. ^
     Goodfellow, Bengio & Courville (2016);
     Hochreiter (1991)
133. ^ Hochreiter & Schmidhuber (1997); Gers, Schraudolph & Schraudolph (2002)
134. ^ Russell & Norvig (2009), p. 1.
135. ^ European Commission (2020), p. 1.
136. ^ CNN (2006).
137. ^
     Targeted advertising:
     • Russell & Norvig (2009, p. 1)
     • Economist (2016)
     • Lohr (2016)

138. ^ Lohr (2016).
139. ^ Smith (2016).
140. ^ Rowinski (2013).
141. ^ Frangoul (2019).
142. ^ Brown (2019).
143. ^ McCorduck (2004), pp. 480–483.
144. ^ Markoff (2011).
145. ^
     Google (2016); BBC (2016)
146. ^ Solly (2019).
147. ^ Bowling et al. (2015).
148. ^ Sample (2017).
149. ^ Anadiotis (2020).
150. ^ Heath (2020).
151. ^ Aletras et al. (2016).
152. ^ «Going Nowhere Fast? Smart Traffic Lights Can Help Ease Gridlock». 18 May 2022.
153. ^ «Intellectual Property and Frontier Technologies». WIPO.
154. ^ ^{a b} «WIPO Technology Trends 2019 – Artificial Intelligence» (PDF). WIPO. 2019. Archived (PDF) from the original on 9 October 2022.
155. ^ ^{a b} Turing (1950), p. 1.
156. ^
     Turing’s original publication of the Turing test in «Computing machinery and intelligence»:
     • Turing (1950)

     Historical influence and philosophical implications:

     • Haugeland (1985, pp. 6–9)
     • Crevier (1993, p. 24)
     • McCorduck (2004, pp. 70–71)
     • Russell & Norvig (2021, pp. 2 and 984)

157. ^ Turing (1950), Under «The Argument from Consciousness».
158. ^ Russell & Norvig (2021), chpt. 2.
159. ^ Russell & Norvig (2021), p. 3.
160. ^ Maker (2006).
161. ^ McCarthy 1999.
162. ^ Minsky (1986).
163. ^ «Artificial intelligence — Google Search». www.google.com. Retrieved 5 November 2022.
164. ^ Nilsson (1983), p. 10.
165. ^ Haugeland (1985), pp. 112–117.
166. ^
     Physical symbol system hypothesis:
     • Newell & Simon (1976, p. 116)

     Historical significance:

     • McCorduck (2004, p. 153)
     • Russell & Norvig (2003, p. 18)

167. ^
     Moravec’s paradox:
     • Moravec (1988, pp. 15–16)
     • Minsky (1986, p. 29)
     • Pinker (2007, pp. 190–91)

168. ^
     Dreyfus’ critique of AI:
     • Dreyfus (1972)
     • Dreyfus & Dreyfus (1986)

     Historical significance and philosophical implications:

     • Crevier (1993, pp. 120–132)
     • McCorduck (2004, pp. 211–239)
     • Russell & Norvig (2003, pp. 950–952)
     • Fearn (2007, Chpt. 3)

169. ^ Crevier (1993), p. 125.
170. ^ Langley (2011).
171. ^ Katz (2012).
172. ^
     Neats vs. scruffies, the historic debate:
     • McCorduck (2004, pp. 421–424, 486–489)
     • Crevier (1993, p. 168)
     • Nilsson (1983, pp. 10–11)

     A classic example of the «scruffy» approach to intelligence:

     • Minsky (1986)

     A modern example of neat AI and its aspirations:

     • Domingos (2015)

173. ^ Russell & Norvig (2003), pp. 25–26.
174. ^ Pennachin & Goertzel (2007).
175. ^ ^{a b} Roberts (2016).
176. ^ ^{a b} Russell & Norvig (2003), p. 947.
177. ^ Chalmers (1995).
178. ^ Dennett (1991).
179. ^ Horst (2005).
180. ^ Searle (1999).
181. ^ Searle (1980), p. 1.
182. ^
     Searle’s Chinese room argument:
     • Searle (1980). Searle’s original presentation of the thought experiment.
     • Searle (1999).

     Discussion:

     • Russell & Norvig (2003, pp. 958–960)
     • McCorduck (2004, pp. 443–445)
     • Crevier (1993, pp. 269–271)

183. ^
     Robot rights:
     • Russell & Norvig (2003, p. 964)
     • BBC (2006)
     • Maschafilm (2010) (the film Plug & Pray)

184. ^ Evans (2015).
185. ^ McCorduck (2004), pp. 19–25.
186. ^ Henderson (2007).
187. ^ Omohundro (2008).
188. ^ Vinge (1993).
189. ^ Russell & Norvig (2003), p. 963.
190. ^
     Transhumanism:
     • Moravec (1988)
     • Kurzweil (2005)
     • Russell & Norvig (2003, p. 963)

191. ^
     AI as evolution:
     • Edward Fredkin is quoted in McCorduck (2004, p. 401)
     • Butler (1863)
     • Dyson (1998)

192. ^
     Ford & Colvin (2015);
     McGaughey (2018)
193. ^ IGM Chicago (2017).
194. ^ Arntz, Gregory & Zierahn (2016), p. 33.
195. ^
     Lohr (2017);
     Frey & Osborne (2017);
     Arntz, Gregory & Zierahn (2016, p. 33)
196. ^ Morgenstern (2015).
197. ^ Mahdawi (2017); Thompson (2014)
198. ^ Harari (2018).
199. ^
     Weaponized AI:
     • Robitzski (2018)
     • Sainato (2015)

200. ^ Urbina, Fabio; Lentzos, Filippa; Invernizzi, Cédric; Ekins, Sean (7 March 2022). «Dual use of artificial-intelligence-powered drug discovery». Nature Machine Intelligence. 4 (3): 189–191. doi:10.1038/s42256-022-00465-9. PMC 9544280. PMID 36211133. S2CID 247302391. Retrieved 15 March 2022.
201. ^ CNA (2019).
202. ^ Goffrey (2008), p. 17.
203. ^ Lipartito (2011, p. 36); Goodman & Flaxman (2017, p. 6)
204. ^ Larson & Angwin (2016).
205. ^ Berdahl, Carl Thomas; Baker, Lawrence; Mann, Sean; Osoba, Osonde; Girosi, Federico (7 February 2023). «Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review». JMIR AI. 2: e42936. doi:10.2196/42936. ISSN 2817-1705. S2CID 256681439.
206. ^ Dockrill, Peter, Robots With Flawed AI Make Sexist And Racist Decisions, Experiment Shows, Science Alert, 27 June 2022
207. ^ Cellan-Jones (2014).
208. ^ Bostrom (2014); Müller & Bostrom (2014); Bostrom (2015)
209. ^ Rubin (2003).
210. ^ Müller & Bostrom (2014).
211. ^
     Leaders’ concerns about the existential risks of AI:
     • Rawlinson (2015)
     • Holley (2015)
     • Gibbs (2014)
     • Churm (2019)
     • Sainato (2015)

212. ^
     Funding to mitigate risks of AI:
     • Post (2015)
     • Del Prado (2015)
     • Clark (2015a)
     • FastCompany (2015)

213. ^
     Leaders who argue the benefits of AI outweigh the risks:
     • Thibodeau (2019)
     • Bhardwaj (2018)

214. ^
     Arguments that AI is not an imminent risk:
     • Brooks (2014)
     • Geist (2015)
     • Madrigal (2015)
     • Lee (2014)

215. ^ Brooks (2014).
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• Russell, Stuart J.; Norvig, Peter (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, New Jersey: Prentice Hall. ISBN 978-0-13-604259-4..
• Poole, David; Mackworth, Alan (2017). Artificial Intelligence: Foundations of Computational Agents (2nd ed.). Cambridge University Press. ISBN 978-1-107-19539-4.

The two most widely used textbooks in 2021.Open Syllabus: Explorer

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