Glossary of artificial intelligence

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This glossary of artificial intelligence terms is about artificial intelligence, its sub-disciplines, and related fields.

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  • Darkforest – is a computer go program developed by Facebook, based on deep learning techniques using a convolutional neural network. Its updated version Darkfores2 combines the techniques of its predecessor with Monte Carlo tree search.[114][115] The MCTS effectively takes tree search methods commonly seen in computer chess programs and randomizes them.[116] With the update, the system is known as Darkfmcts3.[117]
  • Dartmouth workshop – The Dartmouth Summer Research Project on Artificial Intelligence was the name of a 1956 summer workshop now considered by many[118][119] (though not all[120]) to be the seminal event for artificial intelligence as a field.
  • Data fusion – is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source.[121]
  • Data integration – involves combining data residing in different sources and providing users with a unified view of them.[122] This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume (that is, big data[123]) and the need to share existing data explodes.[124] It has become the focus of extensive theoretical work, and numerous open problems remain unsolved.
  • Data mining – is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
  • Data science – is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured,[125][126] similar to data mining. Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data.[127] It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.
  • Data set – (or dataset) is a collection of data. Most commonly a data set corresponds to the contents of a single database table, or a single statistical data matrix, where every column of the table represents a particular variable, and each row corresponds to a given member of the data set in question. The data set lists values for each of the variables, such as height and weight of an object, for each member of the data set. Each value is known as a datum. The data set may comprise data for one or more members, corresponding to the number of rows.
  • Data warehouse – (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis.[128] DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place[129]
  • Datalog – is a declarative logic programming language that syntactically is a subset of Prolog. It is often used as a query language for deductive databases. In recent years, Datalog has found new application in data integration, information extraction, networking, program analysis, security, and cloud computing.[130]
  • Decision boundary – In the case of backpropagation based artificial neural networks or perceptrons, the type of decision boundary that the network can learn is determined by the number of hidden layers the network has. If it has no hidden layers, then it can only learn linear problems. If it has one hidden layer, then it can learn any continuous function on compact subsets of Rn as shown by the Universal approximation theorem, thus it can have an arbitrary decision boundary.
  • Decision support system – (DSS), is an information system that supports business or organizational decision-making activities. DSSs serve the management, operations and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance—i.e. unstructured and semi-structured decision problems. Decision support systems can be either fully computerized or human-powered, or a combination of both.
  • Decision theory – (or the theory of choice) is the study of the reasoning underlying an agent's choices.[131] Decision theory can be broken into two branches: normative decision theory, which gives advice on how to make the best decisions given a set of uncertain beliefs and a set of values, and descriptive decision theory which analyzes how existing, possibly irrational agents actually make decisions.
  • Decision tree learning – uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining and machine learning.
  • Declarative programming – is a programming paradigm—a style of building the structure and elements of computer programs—that expresses the logic of a computation without describing its control flow.[132]
  • Deductive classifier – is a type of artificial intelligence inference engine. It takes as input a set of declarations in a frame language about a domain such as medical research or molecular biology. For example, the names of classes, sub-classes, properties, and restrictions on allowable values. Compared to rule-based inference engines, which can only apply triggers like OFF or IF-THEN when a condition is not met, these classifiers seek to mimic human deductive logic.[133]
  • Deep Blue – was a chess-playing computer developed by IBM. It is known for being the first computer chess-playing system to win both a chess game and a chess match against a reigning world champion under regular time controls.
  • Deep learning – (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.[134][135][136]
  • DeepMindDeepMind Technologies is a British artificial intelligence company founded in September 2010, currently owned by Alphabet Inc. The company is based in London, with research centres in Canada,[137] France,[138] and the United States. Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[139] as well as a Neural Turing machine,[140] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[141][142] The company made headlines in 2016 after its AlphaGo program beat a human professional Go player Lee Sedol, the world champion, in a five-game match, which was the subject of a documentary film.[143] A more general program, AlphaZero, beat the most powerful programs playing go, chess and shogi (Japanese chess) after a few days of play against itself using reinforcement learning.[144]
  • Default logic – is a non-monotonic logic proposed by Raymond Reiter to formalize reasoning with default assumptions.
  • Description logicDescription logics (DL) are a family of formal knowledge representation languages. Many DLs are more expressive than propositional logic but less expressive than first-order logic. In contrast to the latter, the core reasoning problems for DLs are (usually) decidable, and efficient decision procedures have been designed and implemented for these problems. There are general, spatial, temporal, spatiotemporal, and fuzzy descriptions logics, and each description logic features a different balance between DL expressivity and reasoning complexity by supporting different sets of mathematical constructors.[145]
  • Developmental robotics – (DevRob), sometimes called epigenetic robotics, is a scientific field which aims at studying the developmental mechanisms, architectures and constraints that allow lifelong and open-ended learning of new skills and new knowledge in embodied machines.
  • Diagnosis – is concerned with the development of algorithms and techniques that are able to determine whether the behaviour of a system is correct. If the system is not functioning correctly, the algorithm should be able to determine, as accurately as possible, which part of the system is failing, and which kind of fault it is facing. The computation is based on observations, which provide information on the current behaviour.
  • Dialogue system – or conversational agent (CA), is a computer system intended to converse with a human with a coherent structure. Dialogue systems have employed text, speech, graphics, haptics, gestures, and other modes for communication on both the input and output channel.
  • Dimensionality reduction – or dimension reduction, is the process of reducing the number of random variables under consideration[146] by obtaining a set of principal variables. It can be divided into feature selection and feature extraction.[147]
  • Discrete system – is a system with a countable number of states. Discrete systems may be contrasted with continuous systems, which may also be called analog systems. A final discrete system is often modeled with a directed graph and is analyzed for correctness and complexity according to computational theory. Because discrete systems have a countable number of states, they may be described in precise mathematical models. A computer is a finite state machine that may be viewed as a discrete system. Because computers are often used to model not only other discrete systems but continuous systems as well, methods have been developed to represent real-world continuous systems as discrete systems. One such method involves sampling a continuous signal at discrete time intervals.
  • Distributed artificial intelligence(DAI), also called Decentralized Artificial Intelligence,[148] is a subfield of artificial intelligence research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of Multi-Agent Systems.
  • Dynamic epistemic logic(DEL), is a logical framework dealing with knowledge and information change. Typically, DEL focuses on situations involving multiple agents and studies how their knowledge changes when events occur.

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  • Heuristic – is a technique designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution. This is achieved by trading optimality, completeness, accuracy, or precision for speed. In a way, it can be considered a shortcut. A heuristic function, also called simply a heuristic, is a function that ranks alternatives in search algorithms at each branching step based on available information to decide which branch to follow. For example, it may approximate the exact solution.[177]
  • Hidden layer – an internal layer of neurons in an artificial neural network, not dedicated to input or output
  • Hidden unit – an neuron in a hidden layer in an artificial neural network
  • Hyper-heuristic – is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics (or components of such heuristics) to efficiently solve computational search problems. One of the motivations for studying hyper-heuristics is to build systems which can handle classes of problems rather than solving just one problem.[178][179][180]

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See also

References and notes

  1. ^ a b For example: Josephson, John R.; Josephson, Susan G., eds. (1994). Abductive Inference: Computation, Philosophy, Technology. Cambridge, UK; New York: Cambridge University Press. doi:10.1017/CBO9780511530128. ISBN 978-0521434614. OCLC 28149683.
  2. ^ "Retroduction | Dictionary | Commens". Commens – Digital Companion to C. S. Peirce. Mats Bergman, Sami Paavola & João Queiroz. Retrieved 2014-08-24.
  3. ^ Colburn, Timothy; Shute, Gary (2007-06-05). "Abstraction in Computer Science". Minds and Machines. 17 (2): 169–184. doi:10.1007/s11023-007-9061-7. ISSN 0924-6495.
  4. ^ Kramer, Jeff (2007-04-01). "Is abstraction the key to computing?". Communications of the ACM. 50 (4): 36–42. CiteSeerX 10.1.1.120.6776. doi:10.1145/1232743.1232745. ISSN 0001-0782.
  5. ^ Michael Gelfond, Vladimir Lifschitz (1998) "Action Languages", Linköping Electronic Articles in Computer and Information Science, vol 3, nr 16.
  6. ^ "What is an Activation Function?". deepai.org.
  7. ^ Jang, Jyh-Shing R (1991). Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm (PDF). Proceedings of the 9th National Conference on Artificial Intelligence, Anaheim, CA, USA, July 14–19. 2. pp. 762–767.
  8. ^ Jang, J.-S.R. (1993). "ANFIS: adaptive-network-based fuzzy inference system". IEEE Transactions on Systems, Man and Cybernetics. 23 (3): 665–685. doi:10.1109/21.256541.
  9. ^ Abraham, A. (2005), "Adaptation of Fuzzy Inference System Using Neural Learning", in Nedjah, Nadia; de Macedo Mourelle, Luiza, Fuzzy Systems Engineering: Theory and Practice, Studies in Fuzziness and Soft Computing, 181, Germany: Springer Verlag, pp. 53–83, CiteSeerX 10.1.1.161.6135, doi:10.1007/11339366_3, ISBN 978-3-540-25322-8
  10. ^ Jang, Sun, Mizutani (1997) – Neuro-Fuzzy and Soft Computing – Prentice Hall, pp 335–368, ISBN 0-13-261066-3
  11. ^ Tahmasebi, P. (2012). "A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation" (PDF). Computers & Geosciences. 42: 18–27. doi:10.1016/j.cageo.2012.02.004. PMC 4268588. PMID 25540468.
  12. ^ Tahmasebi, P. (2010). "Comparison of optimized neural network with fuzzy logic for ore grade estimation". Australian Journal of Basic and Applied Sciences. 4: 764–772.
  13. ^ Russell, S.J.; Norvig, P. (2002). Artificial Intelligence: A Modern Approach. Prentice Hall. ISBN 978-0-13-790395-5.
  14. ^ Rana el Kaliouby (Nov–Dec 2017). "We Need Computers with Empathy". Technology Review. 120 (6). p. 8.
  15. ^ Tao, Jianhua; Tieniu Tan (2005). "Affective Computing: A Review". Affective Computing and Intelligent Interaction. LNCS 3784. Springer. pp. 981–995. doi:10.1007/11573548.
  16. ^ Comparison of Agent Architectures Archived August 27, 2008, at the Wayback Machine
  17. ^ "Intel unveils Movidius Compute Stick USB AI Accelerator". 2017-07-21.
  18. ^ "Inspurs unveils GX4 AI Accelerator". 2017-06-21.
  19. ^ Shapiro, Stuart C. (1992). Artificial Intelligence In Stuart C. Shapiro (Ed.), Encyclopedia of Artificial Intelligence (Second Edition, pp. 54–57). New York: John Wiley. (Section 4 is on "AI-Complete Tasks".)
  20. ^ Solomonoff, R., "A Preliminary Report on a General Theory of Inductive Inference", Report V-131, Zator Co., Cambridge, Ma. (Nov. 1960 revision of the Feb. 4, 1960 report).
  21. ^ "Artificial intelligence: Google's AlphaGo beats Go master Lee Se-dol". BBC News. 2016-03-12. Retrieved 17 March 2016.
  22. ^ "AlphaGo | DeepMind". DeepMind.
  23. ^ "Research Blog: AlphaGo: Mastering the ancient game of Go with Machine Learning". Google Research Blog. 27 January 2016.
  24. ^ "Google achieves AI 'breakthrough' by beating Go champion". BBC News. 27 January 2016.
  25. ^ See Dung (1995)
  26. ^ See Besnard and Hunter (2001)
  27. ^ see Bench-Capon (2002)
  28. ^ Definition of AI as the study of intelligent agents:
  29. ^ Russell & Norvig 2009, p. 2.
  30. ^ "Artificial Neural Networks as Models of Neural Information Processing | Frontiers Research Topic". Retrieved 2018-02-20.
  31. ^ "Build with AI | DeepAI". DeepAI. Retrieved 2018-10-06.
  32. ^ "AAAI Corporate Bylaws".
  33. ^ "The Lengthy History of Augmented Reality". Huffington Post. May 15, 2016.
  34. ^ Schueffel, Patrick (2017). The Concise Fintech Compendium. Fribourg: School of Management Fribourg/Switzerland.
  35. ^ Ghallab, Malik; Nau, Dana S.; Traverso, Paolo (2004), Automated Planning: Theory and Practice, Morgan Kaufmann, ISBN 978-1-55860-856-6
  36. ^ Kephart, J.O.; Chess, D.M. (2003), "The vision of autonomic computing", Computer, 36: 41–52, CiteSeerX 10.1.1.70.613, doi:10.1109/MC.2003.1160055
  37. ^ [1]
  38. ^ Thrun, Sebastian (2010). "Toward Robotic Cars". Communications of the ACM. 53 (4): 99–106. doi:10.1145/1721654.1721679.
  39. ^ Gehrig, Stefan K.; Stein, Fridtjof J. (1999). Dead reckoning and cartography using stereo vision for an automated car. IEEE/RSJ International Conference on Intelligent Robots and Systems. 3. Kyongju. pp. 1507–1512. doi:10.1109/IROS.1999.811692. ISBN 0-7803-5184-3.
  40. ^ "Information Engineering Main/Home Page". www.robots.ox.ac.uk. Retrieved 2018-10-03.
  41. ^ Goodfellow, Ian; Bengio, Yoshua; Courville, Aaaron (2016) Deep Learning. MIT Press. p. 196. ISBN 9780262035613
  42. ^ "What is Backpropagation?". deepai.org.
  43. ^ Nielsen, Michael A. (2015). "Chapter 6". Neural Networks and Deep Learning.
  44. ^ "Deep Networks: Overview - Ufldl". ufldl.stanford.edu. Retrieved 2017-08-04.
  45. ^ Mozer, M. C. (1995). "A Focused Backpropagation Algorithm for Temporal Pattern Recognition". In Chauvin, Y.; Rumelhart, D. Backpropagation: Theory, architectures, and applications. ResearchGate. Hillsdale, NJ: Lawrence Erlbaum Associates. pp. 137–169. Retrieved 2017-08-21.
  46. ^ Robinson, A. J. & Fallside, F. (1987). The utility driven dynamic error propagation network (Technical report). Cambridge University, Engineering Department. CUED/F-INFENG/TR.1.
  47. ^ Werbos, Paul J. (1988). "Generalization of backpropagation with application to a recurrent gas market model". Neural Networks. 1 (4): 339–356. doi:10.1016/0893-6080(88)90007-x.
  48. ^ Feigenbaum, Edward (1988). The Rise of the Expert Company. Times Books. p. 317. ISBN 978-0-8129-1731-4.
  49. ^ Sivic, Josef (April 2009). "Efficient visual search of videos cast as text retrieval" (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. 31 (4): 591–605.
  50. ^ McTear et al 2016, p. 167.
  51. ^ "Understanding the backward pass through Batch Normalization Layer". kratzert.github.io. Retrieved 24 April 2018.
  52. ^ Ioffe, Sergey; Szegedy, Christian (2015). "Batch Normalization: Accelerating Deep Network Training b y Reducing Internal Covariate Shift". arXiv:1502.03167.
  53. ^ "Glossary of Deep Learning: Batch Normalisation". medium.com. 2017-06-27. Retrieved 24 April 2018.
  54. ^ "Batch normalization in Neural Networks". towardsdatascience.com. 2017-10-20. Retrieved 24 April 2018.
  55. ^ "Bayesian versus Frequentist Probability". deepai.org.
  56. ^ Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.
  57. ^ Pham, D.T., Castellani, M. (2009), The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems. Proc. ImechE, Part C, 223(12), 2919-2938.
  58. ^ Pham, D. T.; Castellani, M. (2014). "Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms". Soft Computing. 18 (5): 871–903. doi:10.1007/s00500-013-1104-9.
  59. ^ Pham, Duc Truong; Castellani, Marco (2015). "A comparative study of the Bees Algorithm as a tool for function optimisation". Cogent Engineering. 2. doi:10.1080/23311916.2015.1091540.
  60. ^ Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm, Computers 2017, 6(1), 5; (doi: 10.3390/computers6010005)
  61. ^ Cao, Longbing (2010). "In-depth Behavior Understanding and Use: the Behavior Informatics Approach". Information Science. 180 (17): 3067–3085. doi:10.1016/j.ins.2010.03.025.
  62. ^ Colledanchise Michele, and Ögren Petter 2016. How Behavior Trees Modularize Hybrid Control Systems and Generalize Sequential Behavior Compositions, the Subsumption Architecture, and Decision Trees. In IEEE Transactions on Robotics vol.PP, no.99, pp.1-18 (2016)
  63. ^ Colledanchise Michele, and Ögren Petter 2017. Behavior Trees in Robotics and AI: An Introduction.
  64. ^ Breur, Tom (July 2016). "Statistical Power Analysis and the contemporary "crisis" in social sciences". Journal of Marketing Analytics. 4 (2–3): 61–65. doi:10.1057/s41270-016-0001-3. ISSN 2050-3318.
  65. ^ Bachmann, Paul (1894). Analytische Zahlentheorie [Analytic Number Theory] (in German). 2. Leipzig: Teubner.
  66. ^ Landau, Edmund (1909). Handbuch der Lehre von der Verteilung der Primzahlen [Handbook on the theory of the distribution of the primes] (in German). Leipzig: B. G. Teubner. p. 883.
  67. ^ Rowan Garnier; John Taylor (2009). Discrete Mathematics: Proofs, Structures and Applications, Third Edition. CRC Press. p. 620. ISBN 978-1-4398-1280-8.
  68. ^ Steven S Skiena (2009). The Algorithm Design Manual. Springer Science & Business Media. p. 77. ISBN 978-1-84800-070-4.
  69. ^ Erman, L. D.; Hayes-Roth, F.; Lesser, V. R.; Reddy, D. R. (1980). "The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty". ACM Computing Surveys. 12 (2): 213. doi:10.1145/356810.356816.
  70. ^ Corkill, Daniel D. (September 1991). "Blackboard Systems" (PDF). AI Expert. 6 (9): 40–47.
  71. ^ * Nii, H. Yenny (1986). Blackboard Systems (PDF) (Technical report). Department of Computer Science, Stanford University. STAN-CS-86-1123. Retrieved 2013-04-12.
  72. ^ Hayes-Roth, B. (1985). "A blackboard architecture for control". Artificial Intelligence. 26 (3): 251–321. doi:10.1016/0004-3702(85)90063-3.
  73. ^ NZZ- Die Zangengeburt eines möglichen Stammvaters. Website Neue Zürcher Zeitung. Seen 16. August 2013.
  74. ^ Official Homepage Roboy Archived 2013-08-03 at the Wayback Machine. Website Roboy. Seen 16. August 2013.
  75. ^ Official Homepage Starmind. Website Starmind. Seen 16. August 2013.
  76. ^ Sabour, Sara; Frosst, Nicholas; Hinton, Geoffrey E. (2017-10-26). "Dynamic Routing Between Capsules". arXiv:1710.09829 [cs.CV].
  77. ^ "What is a chatbot?". techtarget.com. Retrieved 30 January 2017.
  78. ^ "Cloud Robotics and Automation A special issue of the IEEE Transactions on Automation Science and Engineering". IEEE. Retrieved 7 December 2014.
  79. ^ "RoboEarth".
  80. ^ Goldberg, Ken. "Cloud Robotics and Automation".
  81. ^ Li, R. "Cloud Robotics-Enable cloud computing for robots". Retrieved 7 December 2014.
  82. ^ Fisher, Douglas (1987). "Knowledge acquisition via incremental conceptual clustering" (PDF). Machine Learning. 2 (2): 139–172. doi:10.1007/BF00114265.
  83. ^ Fisher, Douglas H. (July 1987). "Improving inference through conceptual clustering". Proceedings of the 1987 AAAI Conferences. AAAI Conference. Seattle Washington. pp. 461–465.
  84. ^ William Iba and Pat Langley (2011-01-27). "Cobweb models of categorization and probabilistic concept formation". In Emmanuel M. Pothos and Andy J. Wills. Formal approaches in categorization. Cambridge: Cambridge University Press. pp. 253–273. ISBN 9780521190480.
  85. ^ Refer to the ICT website: http://cogarch.ict.usc.edu/
  86. ^ "Hewlett Packard Labs".
  87. ^ Terdiman, Daniel (2014) .IBM's TrueNorth processor mimics the human brain.http://www.cnet.com/news/ibms-truenorth-processor-mimics-the-human-brain/
  88. ^ Knight, Shawn (2011). IBM unveils cognitive computing chips that mimic human brain TechSpot: August 18, 2011, 12:00 PM
  89. ^ Hamill, Jasper (2013). Cognitive computing: IBM unveils software for its brain-like SyNAPSE chips The Register: August 8, 2013
  90. ^ Denning. P.J. (2014). "Surfing Toward the Future". Communications of the ACM. 57 (3): 26–29. doi:10.1145/2566967.
  91. ^ Dr. Lars Ludwig (2013). "Extended Artificial Memory. Toward an integral cognitive theory of memory and technology" (pdf). Technical University of Kaiserslautern. Retrieved 2017-02-07.
  92. ^ "Research at HP Labs".
  93. ^ "Automate Complex Workflows Using Tactical Cognitive Computing: Coseer". thesiliconreview.com. Retrieved 2017-07-31.
  94. ^ Cognitive science is an interdisciplinary field of researchers from Linguistics, psychology, neuroscience, philosophy, computer science, and anthropology that seek to understand the mind. How We Learn: Ask the Cognitive Scientist
  95. ^ Schrijver, Alexander (February 1, 2006). A Course in Combinatorial Optimization (PDF), page 1.
  96. ^ HAYKIN, S. Neural Networks - A Comprehensive Foundation. Second edition. Pearson Prentice Hall: 1999.
  97. ^ "PROGRAMS WITH COMMON SENSE". www-formal.stanford.edu. Retrieved 2018-04-11.
  98. ^ Ernest Davis; Gary Marcus (2015). "Commonsense reasoning". Communications of the ACM. Vol. 58 no. 9. pp. 92–103. doi:10.1145/2701413.
  99. ^ Hulstijn, J, and Nijholt, A. (eds.). Proceedings of the International Workshop on Computational Humor. Number 12 in Twente Workshops on Language Technology, Enschede, Netherlands. University of Twente, 1996.
  100. ^ "ACL - Association for Computational Learning".
  101. ^ Trappenberg, Thomas P. (2002). Fundamentals of Computational Neuroscience. United States: Oxford University Press Inc. p. 1. ISBN 978-0-19-851582-1.
  102. ^ What is computational neuroscience? Patricia S. Churchland, Christof Koch, Terrence J. Sejnowski. in Computational Neuroscience pp.46-55. Edited by Eric L. Schwartz. 1993. MIT Press "Archived copy". Archived from the original on 2011-06-04. Retrieved 2009-06-11.CS1 maint: Archived copy as title (link)
  103. ^ Press, The MIT. "Theoretical Neuroscience". The MIT Press. Retrieved 2018-05-24.
  104. ^ Gerstner, W.; Kistler, W.; Naud, R.; Paninski, L. (2014). Neuronal Dynamics. Cambridge, UK: Cambridge University Press. ISBN 9781107447615.
  105. ^ "WordNet Search—3.1". Wordnetweb.princeton.edu. Retrieved 14 May 2012.
  106. ^ Dana H. Ballard; Christopher M. Brown (1982). Computer Vision. Prentice Hall. ISBN 0-13-165316-4.
  107. ^ Huang, T. (1996-11-19). Vandoni, Carlo, E, ed. Computer Vision : Evolution And Promise (PDF). 19th CERN School of Computing. Geneva: CERN. pp. 21–25. doi:10.5170/CERN-1996-008.21. ISBN 978-9290830955.
  108. ^ Milan Sonka; Vaclav Hlavac; Roger Boyle (2008). Image Processing, Analysis, and Machine Vision. Thomson. ISBN 0-495-08252-X.
  109. ^ Garson, James (27 November 2018). Zalta, Edward N., ed. The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University – via Stanford Encyclopedia of Philosophy.
  110. ^ "Ishtar for Belgium to Belgrade". European Broadcasting Union. Retrieved 19 May 2013.
  111. ^ LeCun, Yann. "LeNet-5, convolutional neural networks". Retrieved 16 November 2013.
  112. ^ Zhang, Wei (1988). "Shift-invariant pattern recognition neural network and its optical architecture". Proceedings of annual conference of the Japan Society of Applied Physics.
  113. ^ Zhang, Wei (1990). "Parallel distributed processing model with local space-invariant interconnections and its optical architecture". Applied Optics. 29 (32): 4790–7. Bibcode:1990ApOpt..29.4790Z. doi:10.1364/AO.29.004790. PMID 20577468.,
  114. ^ Tian, Yuandong; Zhu, Yan (2015). "Better Computer Go Player with Neural Network and Long-term Prediction". arXiv:1511.06410v1 [cs.LG].
  115. ^ "How Facebook's AI Researchers Built a Game-Changing Go Engine". MIT Technology Review. December 4, 2015. Retrieved 2016-02-03.
  116. ^ "Facebook AI Go Player Gets Smarter With Neural Network And Long-Term Prediction To Master World's Hardest Game". Tech Times. 2016-01-28. Retrieved 2016-04-24.
  117. ^ "Facebook's artificially intelligent Go player is getting smarter". VentureBeat. 2016-01-27. Retrieved 2016-04-24.
  118. ^ Solomonoff, R.J.The Time Scale of Artificial Intelligence; Reflections on Social Effects, Human Systems Management, Vol 5 1985, Pp 149-153
  119. ^ Moor, J., The Dartmouth College Artificial Intelligence Conference: The Next Fifty years, AI Magazine, Vol 27, No., 4, Pp. 87-9, 2006
  120. ^ Kline, Ronald R., Cybernetics, Automata Studies and the Dartmouth Conference on Artificial Intelligence, IEEE Annals of the History of Computing, October–December, 2011, IEEE Computer Society
  121. ^ a b Haghighat, Mohammad; Abdel-Mottaleb, Mohamed; Alhalabi, Wadee (2016). "Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition". IEEE Transactions on Information Forensics and Security. 11 (9): 1984–1996. doi:10.1109/TIFS.2016.2569061.
  122. ^ Maurizio Lenzerini (2002). "Data Integration: A Theoretical Perspective" (PDF). PODS 2002. pp. 233–246.
  123. ^ Big Data Integration
  124. ^ Frederick Lane (2006). "IDC: World Created 161 Billion Gigs of Data in 2006".
  125. ^ Dhar, V. (2013). "Data science and prediction". Communications of the ACM. 56 (12): 64–73. doi:10.1145/2500499.
  126. ^ Jeff Leek (2013-12-12). "The key word in "Data Science" is not Data, it is Science". Simply Statistics.
  127. ^ Hayashi, Chikio (1998-01-01). "What is Data Science? Fundamental Concepts and a Heuristic Example". In Hayashi, Chikio; Yajima, Keiji; Bock, Hans-Hermann; Ohsumi, Noboru; Tanaka, Yutaka; Baba, Yasumasa. Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer Japan. pp. 40–51. doi:10.1007/978-4-431-65950-1_3. ISBN 9784431702085.
  128. ^ Dedić, Nedim; Stanier, Clare (2016). Hammoudi, Slimane; Maciaszek, Leszek; Missikoff, Michele M. Missikoff; Camp, Olivier; Cordeiro, José, eds. An Evaluation of the Challenges of Multilingualism in Data Warehouse Development. International Conference on Enterprise Information Systems, 25–28 April 2016, Rome, Italy (PDF). Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016). 1. SciTePress. pp. 196–206. doi:10.5220/0005858401960206. ISBN 978-989-758-187-8.
  129. ^ "9 Reasons Data Warehouse Projects Fail". blog.rjmetrics.com. Retrieved 2017-04-30.
  130. ^ Huang, Green, and Loo, "Datalog and Emerging applications", SIGMOD 2011 (PDF), UC DavisCS1 maint: Multiple names: authors list (link).
  131. ^ Steele, Katie and Stefánsson, H. Orri, "Decision Theory", The Stanford Encyclopedia of Philosophy (Winter 2015 Edition), Edward N. Zalta (ed.), URL = [2]
  132. ^ Lloyd, J.W., Practical Advantages of Declarative Programming
  133. ^ "What is a Deductive Classifier?". deepai.org.
  134. ^ Bengio, Y.; Courville, A.; Vincent, P. (2013). "Representation Learning: A Review and New Perspectives". IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (8): 1798–1828. arXiv:1206.5538. doi:10.1109/tpami.2013.50.
  135. ^ Schmidhuber, J. (2015). "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85–117. arXiv:1404.7828. doi:10.1016/j.neunet.2014.09.003. PMID 25462637.
  136. ^ Bengio, Yoshua; LeCun, Yann; Hinton, Geoffrey (2015). "Deep Learning". Nature. 521 (7553): 436–444. Bibcode:2015Natur.521..436L. doi:10.1038/nature14539. PMID 26017442.
  137. ^ "About Us | DeepMind". DeepMind.
  138. ^ "A return to Paris | DeepMind". DeepMind.
  139. ^ "The Last AI Breakthrough DeepMind Made Before Google Bought It". The Physics arXiv Blog. 2014-01-29. Retrieved 12 October 2014.
  140. ^ Graves, Alex; Wayne, Greg; Danihelka, Ivo (2014). "Neural Turing Machines". arXiv:1410.5401 [cs.NE].
  141. ^ Best of 2014: Google's Secretive DeepMind Startup Unveils a "Neural Turing Machine", MIT Technology Review
  142. ^ Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Colmenarejo, Sergio Gómez; Grefenstette, Edward; Ramalho, Tiago (12 October 2016). "Hybrid computing using a neural network with dynamic external memory". Nature. 538 (7626): 471–476. Bibcode:2016Natur.538..471G. doi:10.1038/nature20101. ISSN 1476-4687. PMID 27732574.
  143. ^ Kohs, Greg (29 September 2017), AlphaGo, Ioannis Antonoglou, Lucas Baker, Nick Bostrom, retrieved 9 January 2018
  144. ^ Silver, David; Hubert, Thomas; Schrittwieser, Julian; Antonoglou, Ioannis; Lai, Matthew; Guez, Arthur; Lanctot, Marc; Sifre, Laurent; Kumaran, Dharshan; Graepel, Thore; Lillicrap, Timothy; Simonyan, Karen; Hassabis, Demis (5 December 2017). "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm". arXiv:1712.01815 [cs.AI].
  145. ^ Sikos, Leslie F. (2017). Description Logics in Multimedia Reasoning. Cham: Springer International Publishing. doi:10.1007/978-3-319-54066-5. ISBN 978-3-319-54066-5.
  146. ^ Roweis, S. T.; Saul, L. K. (2000). "Nonlinear Dimensionality Reduction by Locally Linear Embedding". Science. 290 (5500): 2323–2326. Bibcode:2000Sci...290.2323R. CiteSeerX 10.1.1.111.3313. doi:10.1126/science.290.5500.2323. PMID 11125150.
  147. ^ Pudil, P.; Novovičová, J. (1998). "Novel Methods for Feature Subset Selection with Respect to Problem Knowledge". In Liu, Huan; Motoda, Hiroshi. Feature Extraction, Construction and Selection. p. 101. doi:10.1007/978-1-4615-5725-8_7. ISBN 978-1-4613-7622-4.
  148. ^ Demazeau, Yves, and J-P. Müller, eds. Decentralized Ai. Vol. 2. Elsevier, 1990.
  149. ^ Hendrickx, Iris; Van den Bosch, Antal (October 2005). "Hybrid algorithms with Instance-Based Classification". Machine Learning: ECML2005. Springer. pp. 158–169.
  150. ^ a b Adam Ostrow (March 5, 2011). "Roger Ebert's Inspiring Digital Transformation". Mashable Entertainment. Retrieved 2011-09-12. With the help of his wife, two colleagues and the Alex-equipped MacBook that he uses to generate his computerized voice, famed film critic Roger Ebert delivered the final talk at the TED conference on Friday in Long Beach, California....
  151. ^ JENNIFER 8. LEE (March 7, 2011). "Roger Ebert Tests His Vocal Cords, and Comedic Delivery". The New York Times. Retrieved 2011-09-12. Now perhaps, there is the Ebert Test, a way to see if a synthesized voice can deliver humor with the timing to make an audience laugh.... He proposed the Ebert Test as a way to gauge the humanness of a synthesized voice.
  152. ^ "Roger Ebert's Inspiring Digital Transformation". Tech News. March 5, 2011. Retrieved 2011-09-12. Meanwhile, the technology that enables Ebert to “speak” continues to see improvements – for example, adding more realistic inflection for question marks and exclamation points. In a test of that, which Ebert called the “Ebert test” for computerized voices,
  153. ^ Alex_Pasternack (Apr 18, 2011). "A MacBook May Have Given Roger Ebert His Voice, But An iPod Saved His Life (Video)". Motherboard. Retrieved 2011-09-12. He calls it the “Ebert Test,” after Turing’s AI standard...
  154. ^ Herbert Jaeger and Harald Haas. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication. Science 2 April 2004: Vol. 304. no. 5667, pp. 78 – 80 doi:10.1126/science.1091277 PDF
  155. ^ Herbert Jaeger (2007) Echo State Network. Scholarpedia.
  156. ^ Vikhar, P. A. "Evolutionary algorithms: A critical review and its future prospects". Proceedings of the 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). Jalgaon, 2016, pp. 261-265. ISBN 978-1-5090-0467-6.
  157. ^ Russell, Stuart; Norvig, Peter (2009). "26.3: The Ethics and Risks of Developing Artificial Intelligence". Artificial Intelligence: A Modern Approach. Prentice Hall. ISBN 978-0-13-604259-4.
  158. ^ Bostrom, Nick (2002). "Existential risks". Journal of Evolution and Technology (9.1): 1–31.
  159. ^ "Your Artificial Intelligence Cheat Sheet". Slate. 1 April 2016. Retrieved 16 May 2016.
  160. ^ Jackson, Peter (1998), Introduction To Expert Systems (3 ed.), Addison Wesley, p. 2, ISBN 978-0-201-87686-4
  161. ^ "Conventional programming". Pcmag.com. Retrieved 2013-09-15.
  162. ^ Martignon, Laura; Vitouch, Oliver; Takezawa, Masanori; Forster, Malcolm. "Naive and Yet Enlightened: From Natural Frequencies to Fast and Frugal Decision Trees", published in Thinking : Psychological perspectives on reasoning, judgement and decision making (David Hardman and Laura Macchi; editors), Chichester: John Wiley & Sons, 2003.
  163. ^ "What is Feature Extraction?". deepai.org.
  164. ^ Y. Bengio; A. Courville; P. Vincent (2013). "Representation Learning: A Review and New Perspectives". IEEE Trans. PAMI, special issue Learning Deep Architectures. 35: 1798–1828. arXiv:1206.5538. doi:10.1109/tpami.2013.50.
  165. ^ Hodgson, Dr. J. P. E., "First Order Logic", Saint Joseph's University, Philadelphia, 1995.
  166. ^ Hughes, G. E., & Cresswell, M. J., A New Introduction to Modal Logic (London: Routledge, 1996), p.161.
  167. ^ Feigenbaum, Edward (1988). The Rise of the Expert Company. Times Books. p. 318. ISBN 978-0-8129-1731-4.
  168. ^ Hayes, Patrick. "The Frame Problem and Related Problems in Artificial Intelligence" (PDF). University of Edinburgh.
  169. ^ Sardar, Z. (2010) The Namesake: Futures; futures studies; futurology; futuristic; Foresight -- What’s in a name? Futures, 42 (3), pp. 177–184.
  170. ^ Pedrycz, Witold (1993). Fuzzy control and fuzzy systems (2 ed.). Research Studies Press Ltd.
  171. ^ Hájek, Petr (1998). Metamathematics of fuzzy logic (4 ed.). Springer Science & Business Media.
  172. ^ Myerson, Roger B. (1991). Game Theory: Analysis of Conflict, Harvard University Press, p. 1. Chapter-preview links, pp. vii–xi.
  173. ^ Mitchell 1996, p. 2.
  174. ^ Trudeau, Richard J. (1993). Introduction to Graph Theory (Corrected, enlarged republication. ed.). New York: Dover Pub. p. 19. ISBN 978-0-486-67870-2. Retrieved 8 August 2012. A graph is an object consisting of two sets called its vertex set and its edge set.
  175. ^ Nikolaos G. Bourbakis (1998). Artificial Intelligence and Automation. World Scientific. p. 381. ISBN 9789810226374. Retrieved 2018-04-20.
  176. ^ Yoon, Byoung-Ha; Kim, Seon-Kyu; Kim, Seon-Young (March 2017). "Use of Graph Database for the Integration of Heterogeneous Biological Data". Genomics & Informatics. 15 (1): 19–27. doi:10.5808/GI.2017.15.1.19. ISSN 1598-866X. PMC 5389944. PMID 28416946.
  177. ^ Pearl, Judea (1984). Heuristics: intelligent search strategies for computer problem solving. United States: Addison-Wesley Pub. Co., Inc., Reading, MA. p. 3. Retrieved June 13, 2017.
  178. ^ E. K. Burke, E. Hart, G. Kendall, J. Newall, P. Ross, and S. Schulenburg, Hyper-heuristics: An emerging direction in modern search technology, Handbook of Metaheuristics (F. Glover and G. Kochenberger, eds.), Kluwer, 2003, pp. 457–474.
  179. ^ P. Ross, Hyper-heuristics, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques (E. K. Burke and G. Kendall, eds.), Springer, 2005, pp. 529-556.
  180. ^ E. Ozcan, B. Bilgin, E. E. Korkmaz, A Comprehensive Analysis of Hyper-heuristics, Intelligent Data Analysis, 12:1, pp. 3-23, 2008.
  181. ^ IEEE CIS Scope
  182. ^ Paskin, Mark. "A Short Course on Graphical Models" (PDF). Standford.
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