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Poole, Mackworth & Goebel 1998, p. 1. Russell & Norvig 2003, p. 55. Definition of AI as the study of intelligent agents: Poole, Mackworth & Goebel (1998), which provides the version that is used in this article. These authors use the term "computational intelligence" as a synonym for artificial intelligence.[1] Russell & Norvig (2003) (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field".[2] Nilsson 1998 Legg & Hutter 2007 Russell & Norvig 2009, p. 2. McCorduck 2004, p. 204 Maloof, Mark. "Artificial Intelligence: An Introduction, p. 37" (PDF). georgetown.edu. Archived (PDF) from the original on 25 August 2018. "How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech". Hackernoon. Archived from the original on 11 September 2019. Retrieved 14 February 2020. Schank, Roger C. (1991). "Where's the AI". AI magazine. Vol. 12 no. 4. p. 38. Russell & Norvig 2009. "AlphaGo – Google DeepMind". Archived from the original on 10 March 2016. Allen, Gregory (April 2020). "Department of Defense Joint AI Center - Understanding AI Technology" (PDF). AI.mil - The official site of the Department of Defense Joint Artificial Intelligence Center. Archived (PDF) from the original on 21 April 2020. Retrieved 25 April 2020. Optimism of early AI: * Herbert Simon quote: Simon 1965, p. 96 quoted in Crevier 1993, p. 109. * Marvin Minsky quote: Minsky 1967, p. 2 quoted in Crevier 1993, p. 109. Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI: * 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 First AI Winter, Mansfield Amendment, Lighthill report * Crevier 1993, pp. 115–117 * Russell & Norvig 2003, p. 22 * NRC 1999, pp. 212–213 * Howe 1994 * Newquist 1994, pp. 189–201 Second AI winter: * McCorduck 2004, pp. 430–435 * Crevier 1993, pp. 209–210 * NRC 1999, pp. 214–216 * Newquist 1994, pp. 301–318 AI becomes hugely successful in the early 21st century * Clark 2015 Pamela McCorduck (2004, p. 424) writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other." 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 * Nilsson 1998 Kolata 1982. Maker 2006. Biological intelligence vs. intelligence in general: Russell & Norvig 2003, pp. 2–3, who make the analogy with aeronautical engineering. McCorduck 2004, pp. 100–101, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones." Kolata 1982, a paper in Science, which describes McCarthy's indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real".[19] McCarthy recently reiterated his position at the AI@50 conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence".[20]. Neats vs. scruffies: * McCorduck 2004, pp. 421–424, 486–489 * Crevier 1993, p. 168 * Nilsson 1983, pp. 10–11 Symbolic vs. sub-symbolic AI: * Nilsson (1998, p. 7), who uses the term "sub-symbolic". General intelligence (strong AI) is discussed in popular introductions to AI: * Kurzweil 1999 and Kurzweil 2005 See the Dartmouth proposal, under Philosophy, below. McCorduck 2004, p. 34. McCorduck 2004, p. xviii. McCorduck 2004, p. 3. McCorduck 2004, pp. 340–400. This is a central idea of Pamela McCorduck's Machines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition."[26] "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized."[27] "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction."[28] She traces the desire back to its Hellenistic roots and calls it the urge to "forge the Gods."[29] "Stephen Hawking believes AI could be mankind's last accomplishment". BetaNews. 21 October 2016. Archived from the original on 28 August 2017. Lombardo P, Boehm I, Nairz K (2020). "RadioComics – Santa Claus and the future of radiology". Eur J Radiol. 122 (1): 108771. doi:10.1016/j.ejrad.2019.108771. PMID 31835078. Ford, Martin; Colvin, Geoff (6 September 2015). "Will robots create more jobs than they destroy?". The Guardian. Archived from the original on 16 June 2018. Retrieved 13 January 2018. AI applications widely used behind the scenes: * Russell & Norvig 2003, p. 28 * Kurzweil 2005, p. 265 * NRC 1999, pp. 216–222 * Newquist 1994, pp. 189–201 AI in myth: * McCorduck 2004, pp. 4–5 * Russell & Norvig 2003, p. 939 AI in early science fiction. * McCorduck 2004, pp. 17–25 Formal reasoning: * Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Turing, Alan (1948), "Machine Intelligence", in Copeland, B. Jack (ed.), The Essential Turing: The ideas that gave birth to the computer age, Oxford: Oxford University Press, p. 412, ISBN 978-0-19-825080-7 Russell & Norvig 2009, p. 16. Dartmouth conference: * McCorduck 2004, pp. 111–136 * Crevier 1993, pp. 47–49, who writes "the conference is generally recognized as the official birthdate of the new science." * Russell & Norvig 2003, p. 17, who call the conference "the birth of artificial intelligence." * NRC 1999, pp. 200–201 McCarthy, John (1988). "Review of The Question of Artificial Intelligence". Annals of the History of Computing. 10 (3): 224–229., collected in McCarthy, John (1996). "10. Review of The Question of Artificial Intelligence". Defending AI Research: A Collection of Essays and Reviews. CSLI., p. 73, "[O]ne of the reasons for inventing the term "artificial intelligence" was to escape association with "cybernetics". Its concentration on analog feedback seemed misguided, and I wished to avoid having either to accept Norbert (not Robert) Wiener as a guru or having to argue with him." Hegemony of the Dartmouth conference attendees: * Russell & Norvig 2003, p. 17, who write "for the next 20 years the field would be dominated by these people and their students." * McCorduck 2004, pp. 129–130 Russell & Norvig 2003, p. 18. Schaeffer J. (2009) Didn't Samuel Solve That Game?. In: One Jump Ahead. Springer, Boston, MA Samuel, A. L. (July 1959). "Some Studies in Machine Learning Using the Game of Checkers". IBM Journal of Research and Development. 3 (3): 210–229. CiteSeerX 10.1.1.368.2254. doi:10.1147/rd.33.0210. "Golden years" of AI (successful symbolic reasoning programs 1956–1973): * McCorduck 2004, pp. 243–252 * Crevier 1993, pp. 52–107 * Moravec 1988, p. 9 * Russell & Norvig 2003, pp. 18–21 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. DARPA pours money into undirected pure research into AI during the 1960s: * McCorduck 2004, p. 131 * Crevier 1993, pp. 51, 64–65 * NRC 1999, pp. 204–205 AI in England: * Howe 1994 Lighthill 1973. Expert systems: * ACM 1998, I.2.1 * 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 Mead, Carver A.; Ismail, Mohammed (8 May 1989). Analog VLSI Implementation of Neural Systems (PDF). The Kluwer International Series in Engineering and Computer Science. 80. Norwell, MA: Kluwer Academic Publishers. doi:10.1007/978-1-4613-1639-8. ISBN 978-1-4613-1639-8. Archived from the original (PDF) on 6 November 2019. Retrieved 24 January 2020. Formal methods are now preferred ("Victory of the neats"): * Russell & Norvig 2003, pp. 25–26 * McCorduck 2004, pp. 486–487 McCorduck 2004, pp. 480–483. Markoff 2011. 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Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. 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 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 Psychological evidence of sub-symbolic reasoning: * Wason & Shapiro (1966) showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive social intelligence, performance dramatically improves. (See Wason selection task) * Kahneman, Slovic & Tversky (1982) have shown that people are terrible at elementary problems that involve uncertain reasoning. (See list of cognitive biases for several examples). * Lakoff & Núñez (2000) have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See Where Mathematics Comes From) Knowledge representation: * ACM 1998, I.2.4, * Russell & Norvig 2003, pp. 320–363, * Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345, * Luger & Stubblefield 2004, pp. 227–243, * Nilsson 1998, chpt. 18 Knowledge engineering: * Russell & Norvig 2003, pp. 260–266, * Poole, Mackworth & Goebel 1998, pp. 199–233, * Nilsson 1998, chpt. ≈17.1–17.4 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 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 Causal calculus: * Poole, Mackworth & Goebel 1998, pp. 335–337 Representing knowledge about knowledge: Belief calculus, modal logics: * Russell & Norvig 2003, pp. 341–344, * Poole, Mackworth & Goebel 1998, pp. 275–277 Sikos, Leslie F. (June 2017). Description Logics in Multimedia Reasoning. Cham: Springer. doi:10.1007/978-3-319-54066-5. ISBN 978-3-319-54066-5. S2CID 3180114. Archived from the original on 29 August 2017. Ontology: * Russell & Norvig 2003, pp. 320–328 Smoliar, Stephen W.; Zhang, HongJiang (1994). "Content based video indexing and retrieval". IEEE Multimedia. 1 (2): 62–72. doi:10.1109/93.311653. S2CID 32710913. Neumann, Bernd; Möller, Ralf (January 2008). "On scene interpretation with description logics". Image and Vision Computing. 26 (1): 82–101. doi:10.1016/j.imavis.2007.08.013. Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). "Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations". Journal of the American Medical Informatics Association. 13 (4): 369–371. doi:10.1197/jamia.M2055. PMC 1513681. PMID 16622160. MCGARRY, KEN (1 December 2005). "A survey of interestingness measures for knowledge discovery". The Knowledge Engineering Review. 20 (1): 39–61. doi:10.1017/S0269888905000408. S2CID 14987656. Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM '06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682. Qualification problem: * McCarthy & Hayes 1969 * Russell & Norvig 2003[page needed] While McCarthy was primarily concerned with issues in the logical representation of actions, Russell & Norvig 2003 apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge. Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption, abduction (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning"): * 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 Breadth of commonsense knowledge: * Russell & Norvig 2003, p. 21, * Crevier 1993, pp. 113–114, * Moravec 1988, p. 13, * Lenat & Guha 1989 (Introduction) Dreyfus & Dreyfus 1986. Gladwell 2005. Expert knowledge as embodied intuition: * Dreyfus & Dreyfus 1986 (Hubert Dreyfus is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See Dreyfus' critique of AI) * Gladwell 2005 (Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge.) * Hawkins & Blakeslee 2005 (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.) Planning: * ACM 1998, ~I.2.8, * Russell & Norvig 2003, pp. 375–459, * Poole, Mackworth & Goebel 1998, pp. 281–316, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Information value theory: * Russell & Norvig 2003, pp. 600–604 Classical planning: * Russell & Norvig 2003, pp. 375–430, * Poole, Mackworth & Goebel 1998, pp. 281–315, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning: * Russell & Norvig 2003, pp. 430–449 Multi-agent planning and emergent behavior: * Russell & Norvig 2003, pp. 449–455 Turing 1950. Solomonoff 1956. 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Archived from the original on 11 June 2020. Retrieved 11 June 2020. Machine perception: * Russell & Norvig 2003, pp. 537–581, 863–898 * Nilsson 1998, ~chpt. 6 Speech recognition: * ACM 1998, ~I.2.7 * Russell & Norvig 2003, pp. 568–578 Object recognition: * Russell & Norvig 2003, pp. 885–892 Computer vision: * ACM 1998, I.2.10 * Russell & Norvig 2003, pp. 863–898 * Nilsson 1998, chpt. 6 Robotics: * ACM 1998, I.2.9, * Russell & Norvig 2003, pp. 901–942, * Poole, Mackworth & Goebel 1998, pp. 443–460 Moving and configuration space: * Russell & Norvig 2003, pp. 916–932 Tecuci 2012. Robotic mapping (localization, etc): * Russell & Norvig 2003, pp. 908–915 Cadena, Cesar; Carlone, Luca; Carrillo, Henry; Latif, Yasir; Scaramuzza, Davide; Neira, Jose; Reid, Ian; Leonard, John J. (December 2016). "Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age". IEEE Transactions on Robotics. 32 (6): 1309–1332. arXiv:1606.05830. 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Retrieved 26 April 2018. Domingos 2015. Artificial brain arguments: AI requires a simulation of the operation of the human brain * Russell & Norvig 2003, p. 957 * Crevier 1993, pp. 271 and 279 A few of the people who make some form of the argument: * Moravec 1988 * Kurzweil 2005, p. 262 * Hawkins & Blakeslee 2005 The most extreme form of this argument (the brain replacement scenario) was put forward by Clark Glymour in the mid-1970s and was touched on by Zenon Pylyshyn and John Searle in 1980. Goertzel, Ben; Lian, Ruiting; Arel, Itamar; de Garis, Hugo; Chen, Shuo (December 2010). "A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures". Neurocomputing. 74 (1–3): 30–49. doi:10.1016/j.neucom.2010.08.012. Nilsson 1983, p. 10. Nils Nilsson writes: "Simply put, there is wide disagreement in the field about what AI is all about."[163] AI's immediate precursors: * McCorduck 2004, pp. 51–107 * Crevier 1993, pp. 27–32 * Russell & Norvig 2003, pp. 15, 940 * Moravec 1988, p. 3 Haugeland 1985, pp. 112–117 The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt. Cognitive simulation, Newell and Simon, AI at CMU (then called Carnegie Tech): * McCorduck 2004, pp. 139–179, 245–250, 322–323 (EPAM) * Crevier 1993, pp. 145–149 Soar (history): * McCorduck 2004, pp. 450–451 * Crevier 1993, pp. 258–263 McCarthy and AI research at SAIL and SRI International: * McCorduck 2004, pp. 251–259 * Crevier 1993 AI research at Edinburgh and in France, birth of Prolog: * Crevier 1993, pp. 193–196 * Howe 1994 AI at MIT under Marvin Minsky in the 1960s : * McCorduck 2004, pp. 259–305 * Crevier 1993, pp. 83–102, 163–176 * Russell & Norvig 2003, p. 19 Cyc: * McCorduck 2004, p. 489, who calls it "a determinedly scruffy enterprise" * Crevier 1993, pp. 239–243 * Russell & Norvig 2003, p. 363−365 * Lenat & Guha 1989 Knowledge revolution: * McCorduck 2004, pp. 266–276, 298–300, 314, 421 * Russell & Norvig 2003, pp. 22–23 Frederick, Hayes-Roth; William, Murray; Leonard, Adelman. "Expert systems". AccessScience. doi:10.1036/1097-8542.248550. Embodied approaches to AI: * McCorduck 2004, pp. 454–462 * Brooks 1990 * Moravec 1988 Weng et al. 2001. Lungarella et al. 2003. Asada et al. 2009. Oudeyer 2010. Revival of connectionism: * Crevier 1993, pp. 214–215 * Russell & Norvig 2003, p. 25 Computational intelligence * IEEE Computational Intelligence Society Archived 9 May 2008 at the Wayback Machine Hutson, Matthew (16 February 2018). "Artificial intelligence faces reproducibility crisis". Science. pp. 725–726. Bibcode:2018Sci...359..725H. doi:10.1126/science.359.6377.725. Archived from the original on 29 April 2018. Retrieved 28 April 2018. Norvig 2012. Langley 2011. Katz 2012. The intelligent agent paradigm: * Russell & Norvig 2003, pp. 27, 32–58, 968–972 * Poole, Mackworth & Goebel 1998, pp. 7–21 * Luger & Stubblefield 2004, pp. 235–240 * Hutter 2005, pp. 125–126 The definition used in this article, in terms of goals, actions, perception and environment, is due to Russell & Norvig (2003). Other definitions also include knowledge and learning as additional criteria. Agent architectures, hybrid intelligent systems: * Russell & Norvig (2003, pp. 27, 932, 970–972) * Nilsson (1998, chpt. 25) Hierarchical control system: * Albus 2002 Lieto, Antonio; Lebiere, Christian; Oltramari, Alessandro (May 2018). "The knowledge level in cognitive architectures: Current limitations and possibile developments". Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Lieto, Antonio; Bhatt, Mehul; Oltramari, Alessandro; Vernon, David (May 2018). "The role of cognitive architectures in general artificial intelligence". Cognitive Systems Research. 48: 1–3. doi:10.1016/j.cogsys.2017.08.003. hdl:2318/1665249. S2CID 36189683. Russell & Norvig 2009, p. 1. White Paper: On Artificial Intelligence - A European approach to excellence and trust (PDF). Brussels: European Commission. 2020. p. 1. Archived (PDF) from the original on 20 February 2020. Retrieved 20 February 2020. CNN 2006. Using AI to predict flight delays Archived 20 November 2018 at the Wayback Machine, Ishti.org. N. Aletras; D. Tsarapatsanis; D. Preotiuc-Pietro; V. Lampos (2016). "Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective". PeerJ Computer Science. 2: e93. doi:10.7717/peerj-cs.93. "The Economist Explains: Why firms are piling into artificial intelligence". The Economist. 31 March 2016. Archived from the original on 8 May 2016. Retrieved 19 May 2016. Lohr, Steve (28 February 2016). "The Promise of Artificial Intelligence Unfolds in Small Steps". The New York Times. Archived from the original on 29 February 2016. Retrieved 29 February 2016. Frangoul, Anmar (14 June 2019). "A Californian business is using A.I. to change the way we think about energy storage". CNBC. Archived from the original on 25 July 2020. Retrieved 5 November 2019. Wakefield, Jane (15 June 2016). "Social media 'outstrips TV' as news source for young people". BBC News. Archived from the original on 24 June 2016. Smith, Mark (22 July 2016). "So you think you chose to read this article?". BBC News. Archived from the original on 25 July 2016. Brown, Eileen. "Half of Americans do not believe deepfake news could target them online". ZDNet. Archived from the original on 6 November 2019. Retrieved 3 December 2019. The Turing test: Turing's original publication: * Turing 1950 Historical influence and philosophical implications: * Haugeland 1985, pp. 6–9 * Crevier 1993, p. 24 * McCorduck 2004, pp. 70–71 * Russell & Norvig 2003, pp. 2–3 and 948 Dartmouth proposal: * McCarthy et al. 1955 (the original proposal) * Crevier 1993, p. 49 (historical significance) The physical symbol systems hypothesis: * Newell & Simon 1976, p. 116 * McCorduck 2004, p. 153 * Russell & Norvig 2003, p. 18 Dreyfus 1992, p. 156. Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules."[206] Dreyfus' critique of artificial intelligence: * Dreyfus 1972, Dreyfus & Dreyfus 1986 * Crevier 1993, pp. 120–132 * McCorduck 2004, pp. 211–239 * Russell & Norvig 2003, pp. 950–952, Gödel 1951: in this lecture, Kurt Gödel uses the incompleteness theorem to arrive at the following disjunction: (a) the human mind is not a consistent finite machine, or (b) there exist Diophantine equations for which it cannot decide whether solutions exist. Gödel finds (b) implausible, and thus seems to have believed the human mind was not equivalent to a finite machine, i.e., its power exceeded that of any finite machine. He recognized that this was only a conjecture, since one could never disprove (b). Yet he considered the disjunctive conclusion to be a "certain fact". The Mathematical Objection: * Russell & Norvig 2003, p. 949 * McCorduck 2004, pp. 448–449 Making the Mathematical Objection: * Lucas 1961 * Penrose 1989 Refuting Mathematical Objection: * Turing 1950 under "(2) The Mathematical Objection" * Hofstadter 1979 Background: * Gödel 1931, Church 1936, Kleene 1935, Turing 1937 Graham Oppy (20 January 2015). "Gödel's Incompleteness Theorems". Stanford Encyclopedia of Philosophy. Archived from the original on 22 April 2016. Retrieved 27 April 2016. These Gödelian anti-mechanist arguments are, however, problematic, and there is wide consensus that they fail. Stuart J. Russell; Peter Norvig (2010). "26.1.2: Philosophical Foundations/Weak AI: Can Machines Act Intelligently?/The mathematical objection". Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 978-0-13-604259-4. even if we grant that computers have limitations on what they can prove, there is no evidence that humans are immune from those limitations. Mark Colyvan. An introduction to the philosophy of mathematics. Cambridge University Press, 2012. From 2.2.2, 'Philosophical significance of Gödel's incompleteness results': "The accepted wisdom (with which I concur) is that the Lucas-Penrose arguments fail." Iphofen, Ron; Kritikos, Mihalis (3 January 2019). "Regulating artificial intelligence and robotics: ethics by design in a digital society". Contemporary Social Science: 1–15. doi:10.1080/21582041.2018.1563803. ISSN 2158-2041. "Ethical AI Learns Human Rights Framework". Voice of America. Archived from the original on 11 November 2019. Retrieved 10 November 2019. Crevier 1993, pp. 132–144. In the early 1970s, Kenneth Colby presented a version of Weizenbaum's ELIZA known as DOCTOR which he promoted as a serious therapeutic tool.[216] Joseph Weizenbaum's critique of AI: * Weizenbaum 1976 * Crevier 1993, pp. 132–144 * McCorduck 2004, pp. 356–373 * Russell & Norvig 2003, p. 961 Weizenbaum (the AI researcher who developed the first chatterbot program, ELIZA) argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life. Wendell Wallach (2010). Moral Machines, Oxford University Press. Wallach, pp 37–54. Wallach, pp 55–73. Wallach, Introduction chapter. Michael Anderson and Susan Leigh Anderson (2011), Machine Ethics, Cambridge University Press. "Machine Ethics". aaai.org. Archived from the original on 29 November 2014. Rubin, Charles (Spring 2003). "Artificial Intelligence and Human Nature". The New Atlantis. 1: 88–100. Archived from the original on 11 June 2012. Brooks, Rodney (10 November 2014). "artificial intelligence is a tool, not a threat". Archived from the original on 12 November 2014. "Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence". Observer. 19 August 2015. Archived from the original on 30 October 2015. Retrieved 30 October 2015. Chalmers, David (1995). "Facing up to the problem of consciousness". Journal of Consciousness Studies. 2 (3): 200–219. Archived from the original on 8 March 2005. Retrieved 11 October 2018. See also this link Archived 8 April 2011 at the Wayback Machine Horst, Steven, (2005) "The Computational Theory of Mind" Archived 11 September 2018 at the Wayback Machine in The Stanford Encyclopedia of Philosophy Searle 1980, p. 1. This version is from Searle (1999), and is also quoted in Dennett 1991, p. 435. 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." [230] Strong AI is defined similarly by Russell & Norvig (2003, p. 947): "The assertion that machines could possibly act intelligently

⚙️Human Resources Management System Microservices for employees, job seekers that captures all the essential functionalities required for any enterprise.

The goal of this project is to provide object detection and information on environment model on traffic activity which helps autonomous vehicles or surveillance systems. Computer vision is an essential component for autonomous scars. Accurate detection of vehicles, street buildings, pedestrians, and road signs could assist self-driving cars the drive as safely as humans. However, object detection has been a challenging task for years since images of objects in the real-world environment are affected by illumination, rotation, scale, and occlusion. A unified object detection model, You Only Look Once (YOLO), is used which could directly regress from the input image to object class scores and positions. In this project, we applied YOLO to two different datasets to test its general applicability. We fully analyzed its performance from various aspects on KITTI data set which is specialized for autonomous driving. We proposed a novel technique called memory map, which considers inter-frame information, to strengthen YOLO's detection ability in the driving scene. We broadened the model's applicability scope by applying it to a new orientation estimation task. For this project objective is to provide a information of quality and environmental model on traffic activity and to signal potentially anomalous situation and also apply various machine learning models for object detection such as SVM and CNN and compare and contrast the results with YOLO .

A memory manager essential for evolving AI to be more human-like, enabling dynamic, context-aware responses through structured memory handling. It intelligently routes data to appropriate memory types, supporting personalized and adaptive AI interactions.

As a series of tutorials on the most popular deep learning algorithms for new-entry deep learning research engineers, MTCNN has been widely adopted in industry for human face detection task which is an essential step for subsquential face recognition and facial expression analysis. This tutorial is designed to explain how to implement the algorithm. In this tutorial, developers will learn how to build MTCNN model for face detection task.

Mobile Robotics has been evolving as one of the most promising domains in the field of Robotics. The ability of these robots to explore and maneuver in complex environments without human intervention attracts the attention of researchers across the globe. The mobile robots are classified into three different areas viz. wheeled robots, tracked robots, and legged robots. Robot locomotion system is an essential characteristic of mobile design, which depends not only on working space but also on technical measures like maneuverability, controllability, terrain condition, efficiency, and stability. Applications involving locomotion over rough terrains or disaster management where the robot is needed to access the remote areas within the debris demand the use of legged robots. Legged robots are further classified depending on the number of legs the robot has. Hence the types of legged robots are pogo-stick robots or one-legged robots, bi-pedal or two-legged robots, quadrupedal or four-legged robots, six-legged or hexapod robots, and eight-legged robots. Each of the types has unique applications and special locomotion mechanisms. The GAIT behavior of the quadrupedal robots is inspired by the quadruped animals like horses, dogs, etc. This project is focused on the Simulation & Control of a Quadrupedal Robot, using trajectory generation for the locomotion and describing three types of GAIT behaviors for the robot, viz. Walking, Trotting and Galloping. These are based on the speed and leg-movement patterns of the robots. These behaviors can be transitioned depending on the application and the terrain pattern. All the mechanisms are designed and simulated in MATLAB, and Simulink.

Experimented with the three essential Reinforcement Learning with Human Feedback (RLHF) process stages. It starts by revisiting the Supervised Fine-Tuning (SFT) process, then proceeds with the training of a reward model, and finally concludes with the reinforcement learning phase. We explored and applied methods such as 4-bit quantization and LoRA

Essential Motor Cortex Signal Processing MATLAB Toolbox which implements various methods for three major aspects of investigating human motor cortex from Neuroscience view point: (1) ERP estimation and quantification, (2) Cortical Functional Connectivity analysis and (3) EMG quantification

Blockchain and AI are on just about every chief information officers watchlist of game-changing technologies that stand to reshape industries. Both technologies come with immense benefits, but both also bring their own challenges for adoption. It is also fair to say that the hype surrounding these technologies individually may be unprecedented, so the thought of bringing these two ingredients together may be viewed by some as brewing a modern-day version of IT pixie dust. At the same time, there is a logical way to think about this mash-up that is both sensible and pragmatic. Today, AI is for all intents and purposes a centralized process. An end user must have extreme faith in the central authority to produce a trusted business outcome. By decentralizing the three key elements of AI — that is, data, models, and analytics — blockchain can deliver the trust and confidence often needed for end users to fully adopt and rely on AI-based business processes. Let’s explore how blockchain is poised to enrich AI by bringing trust to data, models and analytics. Your data is your data Many of the world’s most notable AI technology services are centralized — including Amazon, Apple, Facebook, Google, as well as Chinese companies Alibaba, Baidu and Tencent. Yet all have encountered challenges in establishing trust among their eager, but somewhat cautious users. How can a business provide assurance to its users that its AI has not overstepped its bounds? Imagine if these AI services could produce a “forensic report,” verified by a third party, to prove to you, beyond a reasonable doubt, how and when businesses are using your data once those are ingested. Imagine further that your data could be used only if you gave permission to do so. A blockchain ledger can be used as a digital rights management system, allowing your data to be “licensed” to the AI provider under your terms, conditions and duration. The ledger would act as an access management system storing the proofs and permission by which a business can access and use the user’s data. Trusted AI models Consider the example of using blockchain technology as a means of providing trusted data and provenance of training models for machine learning. In this case, we’ve created a fictitious system to answer the question of whether a fruit is an apple or orange. This question-answering system that we build is called a model, and this model is created via a process called training. The goal of training is to create an accurate model that answers our questions correctly most of the time. Of course, to train a model, we need to collect data to train on — for this example, that could be the color of the fruit (as a wavelength of light) and the sugar content (as a percentage). With blockchain, you can track the provenance of the training data as well as see an audit trail of the evidence that led to the prediction of why a particular fruit is considered an apple versus an orange. A business can also prove that it is not “juicing up” its books by tagging fruit more often as apples, if that is the more expensive of the two fruits. Explaining AI decisions The European Union has adopted a law requiring that any decision made by a machine be readily explainable, on penalty of fines that could cost companies billions of dollars. The EU General Data Protection Regulation (GDPR), which came into force in 2018, includes a right to obtain an explanation of decisions made by algorithms and a right to opt out of some algorithmic decisions altogether. Massive amounts of data are being produced every second — more data than humans have the ability to assess and use as the basis for drawing conclusions. However, AI applications are capable of assessing large data sets and many variables, while learning about or connecting those variables relevant to its tasks and objectives. For this very reason, AI continues to be adopted in various industries and applications, and we are relying more and more on their outcomes. It is essential, however, that any decisions made by AI are still verified for accuracy by humans. Blockchain can help clarify the provenance, transparency, understanding, and explanations of those outcomes and decisions. If decisions and associated data points are recorded via transactions on a blockchain, the inherent attributes of blockchain will make auditing them much simpler. Blockchain is a key technology that brings trust to transactions in a network; therefore, infusing blockchain into AI decision-making processes could be the element needed to achieve the transparency necessary to fully trust the decisions and outcomes derived from AI. Blockchain and the Internet of Things More than a billion intelligent, connected devices are already part of today’s IoT. The expected proliferation of hundreds of billions more places us at the threshold of a transformation sweeping across the electronics industry and many other areas. With the advancement in IoT, industries are now enabled to capture data, gain insight from the data, and make decisions based on the data. Therefore, there is a lot of “trust” in the information obtained. But the real truth of the matter is, do we really know where these data came from and should we be making decisions and transacting based on data we cannot validate? For example, did weather data really originate from a censor in the Atlantic Ocean or did the shipping container really not exceed the agreed temperature limit? The IoT use cases are massive, but they all share the same issue with trust. IoT with blockchain can bring real trust to captured data. The underlying idea is to give devices, at the time of their creation, an identity that can be validated and verified throughout their lifecycle with blockchain. There is great potential for IoT systems in blockchain technology capabilities that rely on device identity protocols and reputation systems. With a device identity protocol, each device can have its own blockchain public key and send encrypted challenge and response messages to other devices, thereby ensuring a device remains in control of its identity. In addition, a device with an identity can develop a reputation or history that is tracked by a blockchain. Smart contracts represent the business logic of a blockchain network. When a transaction is proposed, these smart contracts are autonomously executed within the guidelines set by the network. In IoT networks, smart contracts can play a pivotal role by providing automated coordination and authorization for transactions and interactions. The original idea behind IoT was to surface data and gain actionable insight at the right time. For example, smart homes are a thing of the present and most everything can be connected. In fact, with IoT, when something goes wrong, these IoT devices can even take action — for example, ordering a new part. We need a way to govern the actions taken by these devices, and smart contracts are a great way to do so. In an ongoing experiment I have followed in Brooklyn, New York, a community is using a blockchain to record the production of solar energy and enable the purchase of excess renewable energy credits. The device itself has an identity and builds a reputation through its history of records and exchange. Through the blockchain, people can aggregate their purchasing power more easily, share the burden of maintenance, and trust that devices are recording actual solar production. As IoT continues to evolve and its adoption continues to grow, the ability to autonomously manage devices and actions taken by devices will be essential. Blockchain and smart contracts are positioned well to integrate those capabilities into IoT.

In this paper, we proposed a sequential hybrid model based on a transformer to summarize Arabic articles. We used two approaches of summarization to make our model. The First is the extractive approach which depends on the most important sentences from the articles to be the summary, so we used Deep Learning techniques specifically transformers such as AraBert to make our summary, The second is abstractive, and this approach is similar to human summarization, which means that it can use some words which have the same meaning but different from the original text. We apply this kind of summary using MT5 Arabic pre-trained transformer model. We sequentially applied these two summarization approaches to building our A3SUT hybrid model. The output of the extractive module is fed into the abstractive module. We enhanced the summary’s quality to be closer to the human summary by applying this approach. We tested our model on the ESAC dataset and evaluated the extractive summary using the Rouge score technique; we got a precision of 0.5348 and a recall of 0.5515, and an f1 score of 0.4932 and the evaluation of the abstractive model is evaluated by user satisfaction. We add some features to our summary to make it more understandable by applying the metadata generation task” data about data” and classification. By applying metadata generation, we add facilities to our summary, identification, and summary organization. Metadata provides essential contextual details, as not all summaries are self-describing. Also, classify the original text to determine the summary topic before reading. We acquire 97.5% accuracy by using Support Vector Machine (SVM) and trained it using NADA corpus.

"QuollEyeTree" is a Cocoa File Management application for Mac OS X. It requires v10.6 Snow Leopard or later. QuollEyeTree is inspired by XTree (and its successor ZTreeWin for Windows), but rather than trying to duplicate either, aims to implement the essential functionality, while retaining the Mac look and feel, and honouring the Apple Human Interface Guidelines.

1 Introduction As human beings we are usually first exposed to the gift economy of our families and tight knit communities: goods and services are provided without an explicit agreement for immediate or future rewards, but the exchange of love, kindness, and confidence. As the relationship circle is enlarged this mutual trust weakens, but the urge and willingness to cooperate does not diminish: this is when the need for an exchange economy arises. [Hayek Money: The Cryptocurrency Price Stability Solution] Cryptocurrency is a digital asset and becoming popular after the success of different crypto tokens in the market. The main technology behind the development and success of cryptocurrencies is blockchain technology. This modern technology work on the principle of decentralization. It provides better security, safety, and privacy. It reduces the risk of business and allows transparency with the key feature of decentralization. The memes Tokens gained fame in the 2021 bull market where currencies such as Dogecoin and Shiba inu hit market capitalization billions, turning thousands of small investors into millionaires in a matter of months.Turning into a real fever, accompanied by major marketing campaigns. 1.1 Blockchain Infrastructure Blockchain Technology can enhance the basic services that are essential in traditional finance and it has the potential to become the foundation for decentralized business models, empowering entrepreneurs and innovators with all the right tools. By means of a trustless and distributed infrastructure, blockchain technology is optimizing transactional costs and allows the rise of decentralized, innovative, inter-operable, borderless and transparent applications which facilitate open access and encourage permissionless innovations. [Bitcoin: A Peer-To-Peer Electronic Cash System (2009)]. Binance Smart Chain (BSC) is a blockchain that was developed as a means of utilizing solidity-based smart contracts with much greater speed and efficiency than other, competing chains. With decentralized exchanges on BSC offering lightning-fast swaps and extremely low fees, BSC has started to become one of the most widely used blockchains for Decentralized Finance (Defi). BSC uses a token protocol developed by the Binance Team called BEP-20. Binance Smart Chain is unique for several reasons: • It’s a sovereign blockchain, which will provide security and safety to all users and developers. • Its native dual chain interoperability will allow cross-chain communication and scaling of high-performance dApps that require a fast and smooth user experience • It’s EVM-compatible and will support all of the existing Ethereum toolings along with faster and cheaper transactions. • on-chain governance with Proof of Staked Authority consensus, built on 21 validators who validate the transactions, will provide decentralization and enable significant community involvement. 2 What is PEACOCK? ”When you come to the appointed place at the appointed time and meet some person, introduce yourself as Peacock. If they do the same, then you have met a special person.” - Peacock Token is a fan-driven deflationary meme token that came to dominate the meme game. appears as the father of Shiba Inu, the real Dogecoin Killer, is a deflationary BEP20 token that aims to propose an alternative to Dogecoin’s infinite supply and be an improved version of Shiba Inu, without the high fees of the ethereum network, protection against damage from diving and preventing dumping of whales. After each transaction there is a 10% burnout, this is to supply and increase demand, you can relax and watch your Peacock investment grow, the community aims to establish itself as Smart Chain’s biggest token meme our goal is to build a strong squad that will HODL their Peacock and never sell, with the aim of bringing the price of Peacock to a $1 dollar. Do not be greedy, we need all the support from the community so that you never sell all your Peacock at the same time, due to the demand there may not be liquidity available making its value fall, when you believe it is necessary, just remove the invested amount, let’s make this the first meme coin to reach $1. 2.1 Tokenomics Peacock is a token that has an inelastic supply, which means that each transaction made, its offer is reduced in order to add value to the asset. To ensure a fair distribuition we have decided to burn 50% of the total supply. 42% of the supply is currentyly serving a life sentence at PancakeSwap correctional facility and is never getting out an 8% for Marketing and Dev team. • Total Supply: 420,000,000,000,000,000,000,000,000 • 50% Tokens Burnt Wallet: 0x000000000000000000000000000000000000dead • 42% locked in Pancakeswap Pancakeswap Address: 0x52F778A1E1c12C5520Be9a31511a8A256F0b1065 • 8% Team wallet Dev wallet: 0xe4b8141Ef61f7eb8601b71399aF7855256d1c414 Marketing wallet: 0x1dC6d1c6d518B409CcdeDb256b2a0296Ac263e0d Exchange liquidity: 0x12B08D7FA6C0913809EAE575FdDeC61f84aCABaD • 10% of fees burned with each transaction. Figure 1: 3 Community Our main objective is to build a strong community, where people from all over the world can exchange experiences and debate issues of global interest, using Peacock as a utility token. If a community is well maintained and constantly expanded, this can result ina trusting relationship between the target group and the Peacock, which can lay the foundation for loyal and stable repeat users. 3.1 Dip Damage Protection We have designed a Dip Damage Protection Mechanism Wallet where people from the community can contribute anything above $10 in BNB (BEP20) to make the protection stronger, so no whales will be able to beat our protection, members will be able to see all the transactions on blockchain happening in the Dip Damage Protection wallet. 3.2 Warning PEACOCK tokens is purely entertainment, not an investment. Purely an experimental GAME. Before purchasing PEACOCK tokens, you must ensure that the nature, complexity, and risks inherent in the trading of cryptocurrency are suitable for your objectives in light of your circumstances and financial position. You should only purchase PEACOCK to have fun and to experience this experimental game with us. Many factors outside of the control of PEACOCK Token will affect the market price, including, but not limited to, national and international economic, financial, regulatory, political, terrorist, military, and other events, adverse or positive news events and publicity, and generally extreme, uncertain, and volatile market conditions. Extreme changes in price may occur at any time, resulting in a potential loss of value, complete or partial loss of purchasing power, and difficulty, or a complete inability to sell or exchange your digital currency. PEACOCK tokens shall be under no obligation to purchase or to broker the purchase back from you of your cryptocurrency in circumstances where there is no viable market for the purchase of the same. None of the content published in this paper constitutes a recommendation that any particular cryptocurrency, portfolio of cryptocurrencies, transaction, or investment strategy is suitable for any specific person. None of the information providers or their affiliates will advise you personally concerning the nature, potential, value, or suitability of any particular cryptocurrency, portfolio of cryptocurrencies, transaction, investment strategy, or other matter. The products and services presented may only be purchased in jurisdictions in which their marketing and distribution are authorized. Play at your own risk and may the odds be ever in your favor. References BITCOIN: A PEER-TO-PEER ELECTRONIC CASH SYSTEM. Internet: Satoshi Nakamoto, v. 1, 2009. Dispon´ıvel em: https://bitcoin.org/bitcoin.pdf. Acesso em: 30 maio 2021. WANG, Qin; LI, Rujia; WANG, Qi; CHEN, Shiping. Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges. Arxiv.Org, [s. l], p. 1-1, 2021. Cornel University. Dispon´ıvel em: 2105.07447. Acesso em: 30 maio 2021. Hayek Money: The Cryptocurrency Price Stability Solution

Domain Discovery Operations API formalizes the human domain discovery process by defining a set of operations that capture the essential tasks that lead to domain discovery on the Web as we have discovered in interacting with the Subject Matter Experts (SME)s.

A human-robot interaction system for robotic teleoperation based on the P5 Essential Reality robotic glove to control the UR10e collaborative robot.

🧠️🎞️ The core of the MediaEssentials repository for NUNIX, containing timeless, very important media that is essential to human knowledge/culture.

The earliest work in computerized knowledge representation was focused on general problem solvers such as the General Problem Solver (GPS) system developed by Allen Newell and Herbert A. Simon in 1959. These systems featured data structures for planning and decomposition. The system would begin with a goal. It would then decompose that goal into sub-goals and then set out to construct strategies that could accomplish each subgoal. In these early days of AI, general search algorithms such as A* were also developed. However, the amorphous problem definitions for systems such as GPS meant that they worked only for very constrained toy domains (e.g. the "blocks world"). In order to tackle non-toy problems, AI researchers such as Ed Feigenbaum and Frederick Hayes-Roth realized that it was necessary to focus systems on more constrained problems. These efforts led to the cognitive revolution in psychology and to the phase of AI focused on knowledge representation that resulted in expert systems in the 1970s and 80s, production systems, frame languages, etc. Rather than general problem solvers, AI changed its focus to expert systems that could match human competence on a specific task, such as medical diagnosis. Expert systems gave us the terminology still in use today where AI systems are divided into a Knowledge Base with facts about the world and rules and an inference engine that applies the rules to the knowledge base in order to answer questions and solve problems. In these early systems the knowledge base tended to be a fairly flat structure, essentially assertions about the values of variables used by the rules.[2] In addition to expert systems, other researchers developed the concept of frame-based languages in the mid-1980s. A frame is similar to an object class: It is an abstract description of a category describing things in the world, problems, and potential solutions. Frames were originally used on systems geared toward human interaction, e.g. understanding natural language and the social settings in which various default expectations such as ordering food in a restaurant narrow the search space and allow the system to choose appropriate responses to dynamic situations. It was not long before the frame communities and the rule-based researchers realized that there was synergy between their approaches. Frames were good for representing the real world, described as classes, subclasses, slots (data values) with various constraints on possible values. Rules were good for representing and utilizing complex logic such as the process to make a medical diagnosis. Integrated systems were developed that combined Frames and Rules. One of the most powerful and well known was the 1983 Knowledge Engineering Environment (KEE) from Intellicorp. KEE had a complete rule engine with forward and backward chaining. It also had a complete frame based knowledge base with triggers, slots (data values), inheritance, and message passing. Although message passing originated in the object-oriented community rather than AI it was quickly embraced by AI researchers as well in environments such as KEE and in the operating systems for Lisp machines from Symbolics, Xerox, and Texas Instruments.[3] The integration of Frames, rules, and object-oriented programming was significantly driven by commercial ventures such as KEE and Symbolics spun off from various research projects. At the same time as this was occurring, there was another strain of research that was less commercially focused and was driven by mathematical logic and automated theorem proving. One of the most influential languages in this research was the KL-ONE language of the mid-'80s. KL-ONE was a frame language that had a rigorous semantics, formal definitions for concepts such as an Is-A relation.[4] KL-ONE and languages that were influenced by it such as Loom had an automated reasoning engine that was based on formal logic rather than on IF-THEN rules. This reasoner is called the classifier. A classifier can analyze a set of declarations and infer new assertions, for example, redefine a class to be a subclass or superclass of some other class that wasn't formally specified. In this way the classifier can function as an inference engine, deducing new facts from an existing knowledge base. The classifier can also provide consistency checking on a knowledge base (which in the case of KL-ONE languages is also referred to as an Ontology).[5] Another area of knowledge representation research was the problem of common sense reasoning. One of the first realizations learned from trying to make software that can function with human natural language was that humans regularly draw on an extensive foundation of knowledge about the real world that we simply take for granted but that is not at all obvious to an artificial agent. Basic principles of common sense physics, causality, intentions, etc. An example is the frame problem, that in an event driven logic there need to be axioms that state things maintain position from one moment to the next unless they are moved by some external force. In order to make a true artificial intelligence agent that can converse with humans using natural language and can process basic statements and questions about the world, it is essential to represent this kind of knowledge. One of the most ambitious programs to tackle this problem was Doug Lenat's Cyc project. Cyc established its own Frame language and had large numbers of analysts document various areas of common sense reasoning in that language. The knowledge recorded in Cyc included common sense models of time, causality, physics, intentions, and many others.[6] The starting point for knowledge representation is the knowledge representation hypothesis first formalized by Brian C. Smith in 1985:[7] Any mechanically embodied intelligent process will be comprised of structural ingredients that a) we as external observers naturally take to represent a propositional account of the knowledge that the overall process exhibits, and b) independent of such external semantic attribution, play a formal but causal and essential role in engendering the behavior that manifests that knowledge. Currently one of the most active areas of knowledge representation research are projects associated with the Semantic Web. The Semantic Web seeks to add a layer of semantics (meaning) on top of the current Internet. Rather than indexing web sites and pages via keywords, the Semantic Web creates large ontologies of concepts. Searching for a concept will be more effective than traditional text only searches. Frame languages and automatic classification play a big part in the vision for the future Semantic Web. The automatic classification gives developers technology to provide order on a constantly evolving network of knowledge. Defining ontologies that are static and incapable of evolving on the fly would be very limiting for Internet-based systems. The classifier technology provides the ability to deal with the dynamic environment of the Internet. Recent projects funded primarily by the Defense Advanced Research Projects Agency (DARPA) have integrated frame languages and classifiers with markup languages based on XML. The Resource Description Framework (RDF) provides the basic capability to define classes, subclasses, and properties of objects. The Web Ontology Language (OWL) provides additional levels of semantics and enables integration with classification engines.[8][9]

🧠️🎞️📖️ The official documentation source repository for the NUNIX MediaEssentials project, containing timeless, very important media that is essential to human knowledge/culture.

🧠️🎞️📙️ The official core source repository for the literature chapter of the NUNIX MediaEssentials project, containing timeless, very important literature that is essential to human knowledge/culture.

🧠️🎞️📙️📖️ The official documentation source repository for the literature chapter of the NUNIX MediaEssentials project, containing timeless, very important literature that is essential to human knowledge/culture.

The long way from K.G.Jung to contemporary polygraphists. The progress is evident, but maybe something important and essential is lost? Let's try to be clear and sort things out with Arduino. "Aha, a looking glass into the unconscious!" - Carl Jung If you have interest, Arduino board, some cheap components, patience to understand several simple but fundamental regularities, then you have the opportunity to have at your disposal no less impressive equipment and method, as Jung had. THE STORY IS ABOUT SERIOUS LOOK TO ELECTRODERMAL ACTIVITY OF HUMAN SKIN – THE MOST POPULAR PSYCHOPHYSIOLOGICAL REACTION DURING MORE THAN 100 YEARS.

This is a Web Technology Project for university. The Group members are, 1.Piash, Tamal Akter 2.Mohammad, Nur 3. Oni, S. M. Yeamin . Until now in Bangladesh Prisons, there are only followed manual system to manage every details about a particular prison. Our goal is to create such a system by which every details can be input by assigned staffs semi manually but can be maintained by a automated system to make sure the details are secured as well as the prison. Prison management. Prisons, essential to the criminal justice system, have a formal organization and specific functions to perform. The main objective of the Prison Management System (PMS) is not just to confine inmates and prevent them from committing crimes, but to reform and rehabilitate. Impacts and objective of the system : It will help to improve existing manual system by introducing computerized system. It will reduce human dependability and lapses. It will be easy to storage information, update and retrieval. Court Report Can be prepared easily.

Saxo Bank Review Info Update 2022 otoxs July 03, 2022 Saxo bank is considered one of the most relied on names inside the world of foreign exchange buying and selling. The dealer provides a huge collection of belongings for margin-based totally trading, low spreads, and lightning speedy execution of trades. The broker holds a couple of policies global. In this article, I review some functions that Saxo Bank has and why buyers and nontraders could make money with the broker’s provision.Overview Saxo Bank A/S is privately owned; Geely Financials Denmark A/S, a subsidiary of Zhejiang Geely Holding Group Co., Ltd, owns fifty two percentage of the stocks of its shares. The founder and CEO of Saxo Bank, Kim Fournals, owns 25.71% of the Bank shares. Shampoo Plc, a main Nordic economic offerings enterprise, additionally owns 19.9% of the financial institution shares. Minority shareholders, such as numerous modern and former personnel of the Bank, preserve the ultimate stocks. The Bank turned into the primary broking in Denmark to gain approval from the European Investment Directive in 1992. Saxo Group presently operates international, taking part carefully with supervisory government in each jurisdiction it exists in. The broking is regulated through the Australian Securities and Investments in Australia, the Securities and Futures Commission in Hong Kong, the Japanese Financial Services Agency in Japan, Financial Conduct Authority inside the UK, Bank of Netherlands within the Netherland. It is also regulated in Italy, Czech, Singapore, Switzerland, U.A.E., Denmark, and many others. Aside from having a extensive agency jurisdiction, it additionally has a completely extensive variety of markets reachable to traders. The markets furnished encompass foreign exchange, stocks, ETFs, Bonds, Mutual Funds, Futures, Listed Options, and more. It also gives controlled portfolios wherein professionals navigate and manipulate your investments without your input. The controlled portfolio additionally gives you the liberty to withdraw at any time. The Bank has provisions for retail traders and institutional buyers.ProsConsStrongly regulated Wide variety of markets Useful studies toolsHigh minimum deposit Only to be had for six days a week High bond, alternatives and destiny costs Who is Saxo Bank for? The broking offers a extensive variety of offerings, and it has one of a kind aspects, which makes it beneficial for one of a kind forms of humans. The SaxoSelect program affords a controlled portfolio that's beneficial for individuals who do not have any form of history in trading. Through the program, specialists carry out buying and selling and manipulate portfolio for your behalf. The SaxoSelect is not just beneficial for amateur investors; other buyers who do no longer have time to exchange or desire to construct a separate portfolio other than their regular trades also can make earnings through the program. Beginners and advanced buyers also can make money through trading the lots of gadgets which are to be had at an cheap rate. The broker additionally provides a wholesome ground for institutional buyers and people who're inquisitive about partnering with the corporation. Rating: four.five/5Is Saxo Bank Safe? Saxo Bank A/S is integrated in Denmark as a certified bank and is regulated along side Saxo Bank A/S Italy, Saxo Bank AS the Czech Republic, Saxo Bank Netherlands via the Danish Financial Supervisory Authority (F.S.A.). As a member of the European Union, the E.U. Banking and Investment Directives were incorporated through Denmark into the Danish Law. Saxo financial institution A/C and its branches are also regulated in one of a kind jurisdictions. It is regulated and licensed in the U.K. by using the Financial Conduct Authority; BG SAXO Societal did. Intermediazione Mobiliaire S.p.A. is certified by way of the Italian Market Authority. It is licensed in the Czech Republic with the aid of Czech National Bank. Saxo Bank A/S Netherlands is registered by means of the Bank of Netherlands. Saxo Bank Pte. Ltd. Singapore is supervised through the Monetary Authority of Singapore. It is a capital markets offerings license holder and an exempt financial marketing consultant. Other rules and licenses include the Central Bank of U.A.E. as a consultant workplace, Japanese Financial Services Agency, Securities and Futures Commission in Hong Kong, Australia Securities and Investments Commission in Australia.[2] So a long way, what I will say is that the bank is duly certified and controlled by way of the top and trusted agencies and businesses. For more statistics approximately the licenses and guidelines, please click right here. Rating: 4.5/5Year founded:1992Publicly traded: NoLicenses: ASIC, FCA, FMS (Swiss), SFC, JFSA (Japanese), MAS (Singapore), and moreBacklist: NoOffering of InvestmentsStock Saxo financial institution provides get admission to to more than 19,000 stocks across several core and rising markets on extra than forty exchanges worldwide. I find this thoughts-blowing because it is not commonplace to look the range of shares furnished with the aid of the broker on many other brokerage systems. These exchanges encompass NASDAQ, NYS, Singapore Exchange, Hong Kong Exchange, Australian Securities Exchange, Tokyo Stock Exchange.ETFs It also has more than 3000 ETFs from extra than 30 exchanges across the worldBonds Mutual Funds there are extra than 250 top-rated mutual finances to be had on the platform. The mutual price range are from the sector’s largest investors.Futures They are also to be had of their thousand Listed Products Traders and traders have get admission to to extra than 1,two hundred listed options from 23 exchanges global. These listed alternatives are throughout equities, hobby rates, energy, metals, indices, and extra. Others Asides from all that have been listed above, you could additionally alternate cryptocurrency CFD and ETPs. The ETPs help to take a long-time period function without leverage. Forex trading and lots of extra. The dealer provides eighty two FX spot pairs and 140 forwards throughout fundamental, minor and distinguished pairs. Metals are also available. Rating: 4.5/5Forex: Spot TradingYesCurrency Pairs (Total the Forex market pairs)182Social Trading / Copy-TradingYesCFDs instrumentsforex, stocks, crypto, ETFs, Bonds, Mutual fund, futures, indexed products, and so on.Commissions & Fees The dealer gives enterprise-leading costs. Traders and traders get competitive spreads and commissions throughout all asset training. The rates additionally grow to be better as change volume will increase. The dealer offers commissions from $1 on US stocks and the USA-indexed ETFs. It also costs commissions as low as $1.25 futures and indexed options; there aren't any expenses for making an investment in mutual funds. Many of the options, bonds, and futures expenses are a little high as compared to some other agents. For more facts on the expenses Rating: 4/5Minimum Initial DepositUSD 10,00, 500(GBP) for UK, USD3000 for Australia and SGD three hundred for Singapore Average Spread EUR/USD – Standard0.8All-in Cost EUR/USD – Active0.6Minimum withdrawal Platforms & Tools SaxoTraderGO It has improved trade tickets, fundamental and technical evaluation equipment, an in depth charting package, performance analysis, and some other essential capabilities. It helps a one-screen setup even though the chart can be dragged to a 2nd display screen. I located that the platform is obtainable through mobile and exclusive variations of windows.SaxoTraderPRO it's miles completely customizable and is a expert-grade platform. It provides functions like algorithmic orders, one-click buying and selling, and different charting applications. I found that it's miles customizable throughout six screens, with superior workspace and more functions thru my check. This platform isn't reachable on mobile gadgets. It is restrained to PC and MAC. The two platforms are well constructed and are consumer-friendly. Connectivity and APIs: this device allows you hook up with Saxo’s capital markets infrastructure through your selected interface. Third-celebration equipment These tools assist to execute trades from one in all Saxo’s companion’s structures. This enables you join your Saxo account to a 3rd-birthday celebration platform to access custom tools that healthy your unique buying and selling requirement. These equipment, although useful, is probably too technical for beginners to understand how to use them. Such novices could need to undergo a getting to know procedure to master them. The broking also gives a demo account in which traders can alternate a selected marketplace earlier than trading it on a stay account. Rating: 4/5Virtual Trading (Demo)YesProprietary PlatformYesDesktop Platform (Windows)YesWeb PlatformYesMetaTrader four (MT4)YesMetaTrader 5 (MT5)NoCharting – Indicators / Studies (Total)58Charting – Drawing Tools (Total)19Charting – Trade From ChartYesOrder Type – Trailing StopYesResearch The studies tools cover all of the assets furnished. Saxo Bank affords day by day commentary and in-intensity analysis across its markets. There are also articles written through monetary experts from special fields that can be beneficial for buyers to get statistics from. The studies data helps you understand how investors view the markets and notice what the experts are seeing.

As human being’s communication is very crucial part of our life, it is essential for sharing thoughts and ideas for our survival. We communicate through speech, body gestures, reading, writing etc., however speech being the most used among them. But unfortunately for the speaking and hearing impaired minority it is difficult or impossible to communicate through speech. Sign language is one of the oldest forms of language used for communication by speech impaired people. But most of the common people have no knowledge about sign language and its interpretations. This is one of the major problems faced by these kinds of people during their communications. Getting an interpreter is not easy every time. To solve this issue, a model is developed using neural networks for fingerspelling based on the various hand gestures. In this user independent model, two classification machine learning algorithms are trained using a set of image and hand skeleton dataset. The skeleton dataset is created using the hand image dataset and is done to improve the accuracy of the model. By using this model, a speech impaired person can easily communicate with a person who has no knowledge of it, the model translating the hand gestures to sentence in English. The main issue with the current system is that, there are some group of alphabets, whose sign language symbol look alike. This make the image base classification model bit difficult to correctly classify and the accuracy of the prediction can also be low. This issue can be solved using a hand skeleton classifier. As in the hand skeleton image the position and structure of each fingers can be identified more accurately than from an image. This make the neural network model to learn better and can classify with higher accuracy.

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XGEP for expression-based prediction of human essential genes and associated lncRNAs in cancer cells

The Voice Analyzer Using MATLAB is a user-friendly project designed to analyze and process audio recordings of human voices. It offers various essential features, such as pitch, intensity, formant analysis, and spectrogram generation, making it a valuable tool for speech pathology, linguistics, and vocal performance evaluation.

Human activities recognition is an important research focus area that has been the subject of a lot of research in the past two decades because it is so essential in many areas such as security, health, daily activity, robotics, and energy consumption. In many applications, simply recognizing a single gesture is not enough, we may need to detect human movements moment by moment, especially in monitoring systems. Since in a previous paper we built the model \textbf{SPD Siamese network} capable of recognizing hand gestures with high performance in segmented skeletal sequences, we decided to develop this model so that we could also identify human activities that depend on other parts of the body, not just the hands. Then, we build a system based on this model in order to detect different human activities in an unsegmented sequence.