Appendix C. Bibliography

  1. Luftig JT, Ouellette SM, editors. Business performance excellence. Bloomsbury Academic; 2012.
  2. Luftig JT. TOTAL asset utilization. Measuring Business Excellence. 1999 Jan;3(1):20–25.
  3. Luftig JT. EMEN 5041 CU Boulder Fall 2011 class and post-class conversations. CU Boulder, Boulder, CO, USA; 2011 Fall.
  4. Drucker P. Managing for business effectiveness. Harvard Business Review. 1963 May:53–60.
  5. Strickland E. IBM Watson, heal thyself. IEEE Spectrum. 2019 Aug:24–31.
  6. Zumel N, Mount J. Practical data science with R. Shelter Island, NY: Manning Publications Co; 2014.
  7. Chollet F. Deep Learning with Python. Shelter Island, NY: Manning Publications; 2017.
  8. Chollet F, Allaire JJ. Deep learning with R. Shelter Island, NY: Manning Publications Co; 2018.
  9. Lee K-F. AI superpowers: China, Silicon Valley, and the new world order. Boston: Houghton Mifflin Harcourt; 2018.
  10. Peng T. Andrew Ng says enough papers, let’s build AI now! Synced. 2017 Nov 4 [cited 2019 Feb 15]. Available from: https://syncedreview.com/2017/11/04/andrew-ng-says-enough-papers-lets-build-ai-now/
  11. Amazon.com, Inc. Amazon Web Services. [Cited 2019 Jul 19.] Available from: https://aws.amazon.com
  12. Google, Inc. Build. Modernize. Scale. [Cited 2019 Jul 19.] Available from: https://cloud.google.com
  13. Microsoft Corporation. Microsoft Azure. [Cited 2019 Jul 19.] Available from: https://azure.microsoft.com/en-us/
  14. Apache Software Foundation. Apache SparkTM—Unified analytics engine for big data. [Cited 2018 Jul 4.] Available from: https://spark.apache.org/
  15. Apache Software Foundation. Welcome to ApacheTM Hadoop®! [Cited 2018 Jul 4.] Available from: http://hadoop.apache.org/
  16. Apache Software Foundation. Apache Flink®—Stateful computations over data streams. [Cited 2019 Jul 19.] Available from: https://flink.apache.org
  17. Wikimedia Foundation. Machine Learning. Wikipedia. [Cited 2019 Jul 12.] Available from: https://en.wikipedia.org/wiki/Machine_learning
  18. Techopedia. Artificial intelligence (AI). Technopedia. [Cited 2019 Jun 2.] Available from: https://www.techopedia.com/definition/190/artificial-intelligence-ai
  19. Domingos P. A few useful things to know about machine learning. Communications of the ACM. 2012; 55(10):78–87.
  20. Apollo 17 crew. The blue marble. 1972. Available from: https://en.wikipedia.org/w/index.php?title=The_Blue_Marble&oldid=846541979
  21. ASQ. Six Sigma belts, executives and champions—What does it all mean? [Cited 2018 Jul 5.] Available from: http://asq.org/learn-about-quality/six-sigma/overview/belts-executives-champions.html
  22. ASQ. Six Sigma definition—What is lean Six Sigma? [Cited 2018 Jul 5.] Available from: http://asq.org/learn-about-quality/six-sigma/overview/overview.html
  23. Whitehorn M. The parable of the beer and diapers. 2006 Aug 15 [cited 2018 Jul 5]. Available from: https://www.theregister.co.uk/2006/08/15/beer_diapers/
  24. ASQ. What is design of experiments (DOE)? [Cited 2018 Jul 7.] Available from: http://asq.org/learn-about-quality/data-collection-analysis-tools/overview/design-of-experiments.html
  25. Pearl J, Mackenzie D. The book of why: The new science of cause and effect. New York: Basic Books; 2018.
  26. Kleinberg S. Why: A guide to finding and using causes. Beijing; Boston: O’Reilly Media; 2015.
  27. Pearl J. Causality: Models, reasoning and inference. 2nd ed. Cambridge, UK; New York: Cambridge University Press; 2009.
  28. Ries E. The lean startup: How today’s entrepreneurs use continuous innovation to create radically successful businesses. New York: Currency; 2011.
  29. Dalio R. Principles. New York: Simon and Schuster; 2018.
  30. Prahalad CK, Hamel G. The core competence of the corporation. Harvard Business Review. 1990 May–Jun.
  31. Richard B. Closing the strategy gap. CFO. 1996 Oct.
  32. Magretta J. The most common strategy mistakes. HBS Working Knowledge. 2011 Dec 21 [cited 2019 Dec 11]. Available from: http://hbswk.hbs.edu/item/the-most-common-strategy-mistakes
  33. Lee RG, Dale BG. Policy deployment: An examination of the theory. International Journal of Quality & Reliability Management. 1998;15(5):520–540.
  34. Wikimedia Foundation. PID controller. Wikipedia. [Cited 2017 Mar 12.] Available from: https://en.wikipedia.org/wiki/PID_controller
  35. Wikimedia Foundation. History of artificial intelligence. Wikipedia. [Cited 2019 Jun 28.] Available from: https://en.wikipedia.org/wiki/History_of_artificial_intelligence
  36. Nest. Create a connected home. Nest. [Cited 2018 Jul 2.] Available from: https://www.nest.com/
  37. ecobee. ecobee3. [Cited 2018 Jul 2.] Available from: https://www.ecobee.com/ecobee3/
  38. Wikimedia Foundation. Autonomous car. Wikipedia. [Cited 2018 Jun 30.] Available from: https://en.wikipedia.org/w/index.php?title=Autonomous_car&oldid=848201994
  39. ASQ. What is the plan-do-check-act (PDCA) cycle? ASQ. [Cited 04-Jul-2018.] Available from: http://asq.org/learn-about-quality/project-planning-tools/overview/pdca-cycle.html
  40. Wikimedia Foundation. PDCA. Wikipedia. [Cited 2018 Jun 26.] Available from: https://en.wikipedia.org/w/index.php?title=PDCA
  41. Wikimedia Foundation. OODA loop. Wikipedia. [Cited 2019 Jun 10.] Available from: https://en.wikipedia.org/w/index.php?title=OODA_loop
  42. Ullman D. ‘OO-OO-OO!’ The sound of a broken OODA loop. 2007 Apr 1 [cited 2017 Jun 25]. Available from: https://www.researchgate.net/publication/268415631_OO-OO-OO_The_sound_of_a_broken_OODA_loop
  43. Wikimedia Foundation. Cross-industry standard process for data mining. Wikipedia. [Cited 2019 Jul 12]. Available from: https://en.wikipedia.org/w/index.php?title=Cross-industry_standard_process_for_data_mining
  44. Godfrey-Smith P. Other minds: The octopus, the sea, and the deep origins of consciousness. New York: Farrar, Straus and Giroux; 2016.
  45. Brockman J. Know this: Today’s most interesting and important scientific ideas, discoveries, and developments. New York, NY: Harper Perennial; 2017.
  46. Wikimedia Foundation. Internet of things. Wikipedia. [Cited 2018 Jul 2]. Available from: https://en.wikipedia.org/wiki/Internet_of_things
  47. Wikimedia Foundation. Nicolas-Joseph Cugnot. [Cited 2019 Jul 15]. Available from: https://en.wikipedia.org/wiki/Nicolas-Joseph_Cugnot
  48. Wikimedia Foundation. History of the automobile. [Cited 2019 Jul 15]. Available from: https://en.wikipedia.org/wiki/History_of_the_automobile
  49. Gulshan V, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016 Dec;316(22):2402.
  50. Apple, Inc. Siri. Apple. [Cited 2019 Jul 15]. Available from: https://www.apple.com/siri/
  51. Ackerman E, Guizzo E. iRobot brings visual mapping and navigation to the Roomba 980. IEEE Spectrum. 2015 Sep 16 [cited 2019 Jul 15]. Available from: https://spectrum.ieee.org/automaton/robotics/home-robots/irobot-brings-visual-mapping-and-navigation-to-the-roomba-980
  52. Amazon.com, Inc. Amazon Echo & Alexa Devices. [Cited 2019 Jul 22.] Available from: https://www.amazon.com/Amazon-Echo-And-Alexa-Devices/b?node=9818047011
  53. Google, Inc. Google Home. [Cited 2019 Jul 22.] Available from: https://store.google.com/product/google_home
  54. Google, Inc. Google Assistant is ready and built-in to specific speakers. Assistant. [Cited 2019 Sep 19.] Available from: https://assistant.google.com/platforms/speakers/
  55. Apple, Inc. The new sound of home. [Cited 2019 Jul 22.] Available from: https://www.apple.com/homepod/
  56. SAS Institute. Analytics, artificial intelligence and data management. [Cited 2019 Sep 19.] Available from: https://www.sas.com/en_us/home.html
  57. International Business Machines Corporation. SPSS Software. [Cited 2019 Sep 19.] Available from: https://www.ibm.com/analytics/spss-statistics-software
  58. Schmarzo B. Big data: Understanding how data powers big business. Indianapolis, IN: Wiley; 2013.
  59. Schmarzo B. Big data MBA: Driving business strategies with data science. Indianapolis, IN: Wiley; 2015.
  60. Wikimedia Foundation. Gradient boosting. Wikipedia. [Cited 2020 Jan 13.] Available from: https://en.wikipedia.org/wiki/Gradient_boosting.
  61. Gorman B. Kaggle master explains gradient boosting. Kaggle.com. 2017 Jan 23 [cited 2017 Jun 30]. Available from: http://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/
  62. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. arXiv. 2015 Dec;arXiv:1512.03385 [cs.CV].
  63. Szegedy C, et al. Going deeper with convolutions. arXiv. 2014 Sep;arXiv:1409.4842 [cs.CV].
  64. Suzuki K. Overview of deep learning in medical imaging. Radiological Physics and Technology. 2017 Sep;10(3):257–273.
  65. Liu Y, et al. A deep learning system for differential diagnosis of skin diseases. arXiv. 2019 Sep;arXiv:1909.05382 [eess.IV].
  66. Harris HD, Murphy SP, Vaisman M. Analyzing the analyzers: An introspective survey of data scientists and their work. Beijing: O’Reilly; 2013.
  67. Wikimedia Foundation. No free lunch theorem. Wikipedia. [Cited 2016 Apr 2.] Available from: https://en.wikipedia.org/wiki/No_free_lunch_theorem
  68. Cloudera, Inc. Hortonworks data platform for HDInsight: Component versions. [Cited 2019 Nov 24.] Available from: https://docs.cloudera.com/HDPDocuments/HDPforCloud/HDPforCloud-2.6.5/hdp-release-notes/content/hdp_comp_versions.html
  69. Wikimedia Foundation. Gap analysis. Wikipedia. [Cited 2019 Jul 10.] Available from: https://en.wikipedia.org/wiki/Gap_analysis
  70. Tolstoy L; Pevear R, Volokhonsky L, translators. Anna Karenina. New York: Penguin Books; 2004.
  71. Wikimedia Foundation. General Data Protection Regulation. Wikipedia. [Cited 2019 Jul 21.] Available from: https://en.wikipedia.org/wiki/General_Data_Protection_Regulation
  72. Wikimedia Foundation. Health Insurance Portability and Accountability Act. Wikipedia. [Cited 2019 Jul 21.] Available from: https://en.wikipedia.org/wiki/Health_Insurance_Portability_and_Accountability_Act
  73. U.S. Department of Health & Human Services. Summary of the HIPAA Security Rule. HHS.gov. [Cited 2019 Jul 21.] Available from: https://www.hhs.gov/hipaa/for-professionals/security/laws-regulations/index.html
  74. June Life, Inc. The do-it-all oven. [Cited 2019 Jul 15.] Available from: https://juneoven.com/
  75. Hubbard DW. How to measure anything: Finding the value of intangibles in business. 2nd ed. Hoboken, NJ: Wiley; 2010.
  76. Wikimedia Foundation. Artificial general intelligence. Wikipedia. [Cited 2018-Jun 13.] Available from: https://en.wikipedia.org/w/index.php?title=Artificial_general_intelligence
  77. Shani G, Gunawardana A. Evaluating recommendation systems. In: Ricci F, Rokach L, Shapira B, Kantor PB, editors. Recommender systems handbook. New York: Springer; 2011. p. 257–297.
  78. Konstan JA, McNee SM, Ziegler , Torres R, Kapoor N, Riedl JT. Lessons on applying automated recommender systems to information-seeking tasks. Proceedings of the Twenty-First National Conference on Artificial Intelligence; 2006.
  79. Wikimedia Foundation. Expected value of perfect information. Wikipedia. [Cited 2019 Aug 9.] Available from: https://en.wikipedia.org/wiki/Expected_value_of_perfect_information
  80. ACM. SIGKDD—KDD Cup. [Cited 2018 Jul 2.] Available from: http://www.kdd.org/kdd-cup
  81. Provost F, Fawcett T. Data science for business: What you need to know about data mining and data-analytic thinking. 1st ed., 2nd release. Beijing: O’Reilly; 2013.
  82. Osherove R. Elastic leadership: growing self-organizing teams. Shelter Island, NY: Manning; 2017.
  83. Bostrom N. Superintelligence: Paths, dangers, strategies. Oxford: Oxford University Press; 2014.
  84. Bird S, Klein E, Loper E. Natural language processing with Python: Analyzing text with the natural language toolkit. Beijing; Cambridge MA: O’Reilly Media; 2009.
  85. Kruchten PB. The 4+1 view model of architecture. IEEE Software. 1995 Nov; 12(6):42–50.
  86. Wikimedia Foundation. 4+1 architectural view model. Wikipedia. [Cited 2017 Mar 25.] Available from: https://en.wikipedia.org/w/index.php?title=4%2B1_architectural_view_model&oldid=772138375
  87. Sculley D, et al. Machine learning: The high interest credit card of technical debt. Google AI. 2014 [cited 02-Jul-2018]. Available from: https://ai.google/research/pubs/pub43146
  88. Conway M. Conway’s law. Datamation. 1968 Apr.
  89. Wikimedia Foundation. Conway’s law. Wikipedia. [Cited 2018 May 6.] Available from: https://en.wikipedia.org/w/index.php?title=Conway%27s_law&oldid=839894590
  90. Dahl G. Starting simple and machine learning in meds. [Cited 2018 Jul 2.] Available from: https://soundcloud.com/talkingmachines/episode-nine-starting-simple-and-machine-learning-in-meds
  91. TensorFlow. An end-to-end open source machine learning platform. TensorFlow. [Cited 2019 Jul 24.] Available from: https://www.tensorflow.org/
  92. image-net.org. ImageNet. ImageNet. [Cited 2019 Jul 24.] Available from: http://www.image-net.org/
  93. Apple, Inc. iOS 12. Apple. [Cited 2019 Jul 25.] Available from: https://www.apple.com/ios/ios-12/
  94. Google, Inc. Android: The world’s most popular mobile platform. Android. [Cited 2019 Jul 25.] Available from: https://www.android.com/
  95. Fowler M. Who needs an architect? IEEE Spectrum. 2003 Oct;20(5).
  96. Bass L, Clements P, Kazman R Software architecture in practice. Reading, MA: Addison-Wesley; 1998.
  97. Wikimedia Foundation. Architecture tradeoff analysis method. Wikipedia. [Cited 2019 Aug 12.] Available from: https://en.wikipedia.org/w/index.php?title=Architecture_tradeoff_analysis_method&oldid=909460419
  98. Poppendieck M, Poppendieck T. Lean software development: An Agile toolkit. Boston: Addison-Wesley Professional; 2003.
  99. Kuhn M. The caret package. [Cited 2018 Jul 2.] Available from: http://topepo.github.io/caret/index.html
  100. Meng X, Bradley J, Sparks E, Venkataraman S. ML pipelines: A new high-level API for MLlib. Databricks. 2015 Jan 7 [cited 2019 Jul 26]. Available from: https://databricks.com/blog/2015/01/07/ml-pipelines-a-new-high-level-api-for-mllib.html
  101. Google, Inc. TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. TensorFlow. [Cited 2019 Jul 26.] Available from: https://www.tensorflow.org/tfx
  102. LeCun Y, Cortes C, Burges C. MNIST handwritten digit database. [Cited 2019 Jul 24.] Available from: http://yann.lecun.com/exdb/mnist/
  103. Krunic V. What should your analytics organization focus on? In: Gorelik A. The enterprise big data lake: Delivering the promise of big data and data science. Sebastopol, CA: O’Reilly Media; 2019. p. 56–59.
  104. Benenson R. What is the class of this image? Discover the current state of the art in objects classification. 2016 Feb 22 [cited 2017 Apr 21]. Available from: https://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results
  105. Domingos P. The master algorithm: How the quest for the ultimate learning machine will remake our world. New York: Basic Books; 2015.
  106. Goodfellow I, Yoshua B, and Aaron C. Deep learning. Cambridge, MA: MIT Press; 2017.
  107. Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995 Sep;20(3): 273–297.
  108. Wikimedia Foundation. Support-vector machine. Wikipedia. [Cited 2019 Jul 26.] Available from: https://en.wikipedia.org/w/index.php?title=Support-vector_machine&oldid=906858102
  109. Wikimedia Foundation. Autoregressive integrated moving average. Wikipedia. [Cited 2019 Aug 9.] Available from: https://en.wikipedia.org/w/index.php?title=Autoregressive_integrated_moving_average&oldid=908993535
  110. Wikimedia Foundation. Long short-term memory. Wikipedia. [Cited 2019 Aug 9.] Available from: https://en.wikipedia.org/w/index.php?title=Long_short-term_memory&oldid=909220363
  111. Bischl, B. Machine learning in R. [Cited 2019 Nov 16.] Available from: https://mlr-org.com/
  112. Kuhn M, Johnson, K. Applied predictive modeling. New York: Springer; 2013.
  113. Keras documentation. [Cited 2018 Jul 2.] Available from: https://keras.io/
  114. Wikimedia Foundation. Minimax. Wikipedia. [Cited 2019 July 29.] Available from: https://en.wikipedia.org/wiki/Minimax
  115. Wikimedia Foundation. Sensitivity analysis. Wikipedia. [Cited 2019 Jun 20.] Available from: https://en.wikipedia.org/w/index.php?title=Sensitivity_analysis&oldid=846760482
  116. Loucks DP, van Beek E. Water resource systems planning and management: An introduction to methods, models, and applications. New York: Springer; 2017.
  117. Saltelli A, et al. Global sensitivity analysis: The primer. Chichester, UK: John Wiley & Sons, Ltd.; 2007.
  118. Agile Alliance. What is Agile software development? Agile Alliance. [Cited 2015 Jun 29.] Available from: https://www.agilealliance.org/agile101/
  119. Wikimedia Foundation. Agile software development. Wikipedia. [Cited 2017 Jul 3.] Available from: https://en.wikipedia.org/w/index.php?title=Agile_software_development
  120. Tucker FG, Zivan SM, Camp RC. How to measure yourself against the best. Harvard Business Review. 1987 Jan 1 [cited 2018 Jul 7]. Available from: https://hbr.org/1987/01/how-to-measure-yourself-against-the-best
  121. Hu B, Chen Y, Keogh E. Time series classification under more realistic assumptions. Proceedings of the 2013 SIAM International Conference on Data Mining. 2013:578–586.
  122. Wikimedia Foundation. Uncanny valley. Wikipedia. [Cited 2019 Dec 9.] Available from: https://en.wikipedia.org/wiki/Uncanny_valley
  123. St. George D. Automation dependency: ‘Children of the magenta’. Aviation Ideas and Discussion! [Cited 2019 Dec 9.] Available from: https://safeblog.org/2016/01/14/automation-dependency-children-of-the-magenta/
  124. Derczynski L. Complementarity, F-score, and NLP evaluation. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), Portorož, Slovenia. 2016:261–266.
  125. Taleb NN, Douady R. Mathematical definition, mapping, and detection of (anti)fragility. arXiv. 2012 Aug;arXiv:1208.1189 [q-fin.RM].
  126. Taleb NN, Canetti E, Kinda T, Loukoianova E, Schmieder C. A new heuristic measure of fragility and tail risks: Application to stress testing. IMF Working Papers. 2012 Aug;12.
  127. Taleb NN. The black swan: the impact of the highly improbable. 2nd ed. New York: Random House Trade Paperbacks; 2010.
  128. Johnson K. Nvidia trains world’s largest Transformer-based language model. VentureBeat. [Cited 2019 Aug 19.] Available from: https://venturebeat.com/2019/08/13/nvidia-trains-worlds-largest-transformer-based-language-model/
  129. Halevy A, Norvig P, Pereira F. The unreasonable effectiveness of data. IEEE Intelligent Systems. 2009 Mar;24(2):8–12.
  130. MMC Ventures. The state of AI: Divergence. 2019 [cited 2020 Jan 13]. Available from: https://www.stateofai2019.com/
  131. Wikimedia Foundation. Artificial intelligence: Definitions. [Cited 2019 May 22.] Available from: https://en.wikipedia.org/wiki/Artificial_intelligence#Definitions
  132. Poole DL, Mackworth AK, Goebel R. Computational intelligence: A logical approach. New York: Oxford University Press; 1998.
  133. Kaplan A, Haenlein M. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons. 2019 Jan;62(1):15–25.
  134. Wikimedia Foundation. AI effect. Wikipedia. [Cited 2019 Sep 10.] Available from: https://en.wikipedia.org/w/index.php?title=AI_effect&oldid=915081794
  135. Wikimedia Foundation. Deep Blue (chess computer). Wikipedia. [Cited 2019 Sep 10.] Available from: https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)
  136. Techopedia. Artificial intelligence. Techopedia.com. [Cited 2019 Sep 10.] Available from: https://www.techopedia.com/definition/190/artificial-intelligence-ai
  137. Mnih V, et al. Playing Atari with deep reinforcement learning. arXiv. 2013 Dec; arXiv:1312.5602 [cs.LG].
  138. Simonite T. When it comes to gorillas, Google Photos remains blind. WIRED. 2018 Jan 11 [cited 2018 Jul 2]. Available from: https://www.wired.com/story/when-it-comes-to-gorillas-google-photos-remains-blind/
  139. Gallagher S. UK, Australia, others also ground Boeing 737 MAX after crash [Updated]. Ars Technica. 2019 Mar 12 [cited 2020 Jan 8]. Available from: https://arstechnica.com/information-technology/2019/03/another-737-max-jet-crash-prompts-groundings-by-china-indonesia-ethiopia/
  140. Wikimedia Foundation. Maneuvering Characteristics Augmentation System. Wikipedia. [Cited 2019 Sep 10.] Available from: https://en.wikipedia.org/w/index.php?title=Maneuvering_Characteristics_Augmentation_System&oldid=914899059
  141. Leggett T. What went wrong inside Boeing’s cockpit? BBC News. [Cited 2020 Jan 8.] Available from: https://www.bbc.co.uk/news/resources/idt-sh/boeing_two_deadly_crashes
  142. Wikimedia Foundation. Boeing 737 MAX groundings. Wikipedia. [Cited 2020 Jan 8.] Available from: https://en.wikipedia.org/w/index.php?title=Boeing_737_MAX_groundings&oldid=934819447
  143. Wikimedia Foundation. Smart city. Wikipedia. [Cited 2019 Sep 10.] Available from: https://en.wikipedia.org/wiki/Smart_city
  144. Tesla Autopilot—Review including full self-driving for 2019. AutoPilot Review. 2019 Apr 23 [cited 2019 Sep 7]. Available from: https://www.autopilotreview.com/tesla-autopilot-features-review/
  145. Papernot N, McDaniel P, Goodfellow I, Jha S, Celik ZB, Swami A. Practical black-box attacks against machine learning. arXiv. 2016 Feb;arXiv:1602.02697 [cs.CR].
  146. Goodfellow I, et al. Generative adversarial networks. arXiv. 2014 Jun;arXiv: 1406.2661 [stat.ML].
  147. Eykholt K, et al. Robust physical-world attacks on deep learning models. arXiv. 2017 Jul;arXiv:1707.08945 [cs.CR].
  148. Lei Q, Wu L, Chen P-Y, Dimakis AG, Dhillon IS, and Witbrock M. Discrete adversarial attacks and submodular optimization with applications to text classification. arXiv. 2018 Dec;arXiv:1812.00151 [cs.LG].
  149. Shokri R, Stronati M, Song C, Shmatikov V. Membership inference attacks against machine learning models. arXiv. 2016 Oct;arXiv:1610.05820 [cs.CR].
  150. Tramèr F, Zhang F, Juels A, Reiter MK, Ristenpart T. Stealing machine learning models via prediction APIs. arXiv. 2016 Sep;arXiv:1609.02943 [cs.CR].
  151. Marcus G. Deep learning: A critical appraisal. arXiv. 2018 Jan;arXiv:1801.00631 [cs.AI].
  152. Anderson C. The end of theory: The data deluge makes the scientific method obsolete. WIRED. 2008 Jun 23 [cited 2018 Jul 2]. Available from: https://www.wired.com/2008/06/pb-theory/
  153. Wikimedia Foundation. AlphaGo versus Lee Sedol. Wikipedia. [Cited 2018 Jun 21.] Available from: https://en.wikipedia.org/w/index.php?title=AlphaGo_versus_Lee_Sedol&oldid=846917953
  154. DeepMind. AlphaGo. DeepMind. [Cited 2018 Jul 2.] Available from: https://deepmind.com/research/alphago/
  155. Wikimedia Foundation. AlphaGo. Wikipedia. [Cited 2019 Jul 10.] Available from: https://en.wikipedia.org/w/index.php?title=AlphaGo
  156. The AlphaStar Team. AlphaStar: Mastering the real-time strategy game StarCraft II. DeepMind. [Cited 2019 Sep 9.] Available from: https://deepmind.com/blog/article/alphastar-mastering-real-time-strategy-game-starcraft-ii
  157. Caruana R, Simard P, Weinberger K, LeCun Y. The great AI debate—NIPS2017. 2017.
  158. Xian Y, Schiele B, Akata Z. Zero-shot learning—The good, the bad and the ugly. arXiv. 2017 Mar;arXiv:1703.04394 [cs.CV].
  159. Wikimedia Foundation. Knowledge graph. Wikipedia. [Cited 2019 Sep 10.] Available from: https://en.wikipedia.org/wiki/Knowledge_Graph
  160. Zhou J, et al. Graph neural networks: A review of methods and applications. arXiv. 2018 Dec;arXiv:1812.08434 [cs.LG].
  161. Zhang Z, Cui P, Zhu W. Deep learning on graphs: A survey. arXiv. 2018 Dec; arXiv:1812.04202 [cs.LG].
  162. Sutton R. The bitter lesson. Incomplete Ideas. 2019 Mar 13 [cited 2019 Apr 8]. Available from: http://www.incompleteideas.net/IncIdeas/BitterLesson.html
  163. Wikimedia Foundation. Complex system. Wikipedia. [Cited 2018 Jul 1.] Available from: https://en.wikipedia.org/w/index.php?title=Complex_system&oldid=848412761
  164. Marcus GT, Davis E. Rebooting AI: Building artificial intelligence we can trust. New York: Pantheon Books; 2019.
  165. GPS.gov. GPS accuracy. NOAA. [Cited 2019 Sep 7] Available from: https://www.gps.gov/systems/gps/performance/accuracy/
  166. Wikimedia Foundation. Anomaly detection. Wikipedia. [Cited 2018 May 16.] Available from: https://en.wikipedia.org/w/index.php?title=Anomaly_detection&oldid=841569898
  167. Burt A. Is there a ‘right to explanation’ for machine learning in the GDPR? [Cited 2019 Sep 9.] Available from: https://iapp.org/news/a/is-there-a-right-to-explanation-for-machine-learning-in-the-gdpr/
  168. Hall P, Gill N. An introduction to machine learning interpretability. Sebastopol, CA: O’Reilly Media, Inc.; 2018.
  169. Ribeiro MT, Singh S, Guestrin C. ‘Why should I trust you?’: Explaining the predictions of any classifier. arXiv. 2016 Feb;arXiv:1602.04938 [cs.LG].
  170. Microsoft Corporation. FATE: Fairness, accountability, transparency, and ethics in AI. Microsoft Research. [Cited 2019 Sep 9.] Available from: https://www.microsoft.com/en-us/research/group/fate/
  171. Pichai S. AI at Google: Our principles. Google. 2018 Jun 7 [cited 2018 Jun 30]. Available from: https://www.blog.google/technology/ai/ai-principles/
  172. O’Neil C. Weapons of math destruction: How big data increases inequality and threatens democracy. New York: Crown; 2016.
  173. Corbett-Davies S, Goel S. The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv. 2018 Jul;arXiv:1808.00023 [cs.CY].
  174. Zöller M-A, Huber MF. Benchmark and survey of automated machine learning frameworks. arXiv. 2019 Apr;arXiv:1904.12054 [cs.LG].
  175. He X, Zhao K, Chu X. AutoML: A survey of the state-of-the-art. arXiv. 2019 Aug; arXiv:1908.00709 [cs.LG].
  176. Silver D, et al. Mastering the game of Go with deep neural networks and tree search. Nature. 2016 Jan;529(7587):484–489.
  177. Google, Inc. Cloud AutoML. Google Cloud. [Cited 2019 Sep 9.] Available from: https://cloud.google.com/automl/docs/
  178. Microsoft Corporation. What is automated machine learning? Microsoft Azure. [Cited 2019 Sep 9.] Available from: https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-automated-ml
  179. Amazon.com, Inc. H2O.ai H2O-3 Automl Algorithm. AWS Marketplace. [Cited 2019 Sep 9.] Available from: https://aws.amazon.com/marketplace/pp/H2Oai-H2Oai-H2O-3-Automl-Algorithm/prodview-vbm2cls5zcnky
  180. O’Reilly T. WTF: What’s the future and why it’s up to us. New York: Harper Business, an imprint of HarperCollins Publishers; 2017.
  181. Google, Inc. Classification: Accuracy. Machine Learning Crash Course. Google Developers. [Cited 2019 Sep 29.] Available from: https://developers.google.com/machine-learning/crash-course/classification/accuracy
  182. Wikimedia Foundation. Actuary. Wikipedia. [Cited 2019 Sep 12.] Available from: https://en.wikipedia.org/w/index.php?title=Actuary&oldid=914560578
  183. Wikimedia Foundation. Bias–variance tradeoff. Wikipedia. [Cited 2019 Jul 25.] Available from: https://en.wikipedia.org/w/index.php?title=Bias%E2%80%93variance_tradeoff&oldid=904412736
  184. Wikimedia Foundation. Cross-industry standard process for data mining. Wikipedia. [Cited 2019 Jul 12.] https://en.wikipedia.org/w/index.php?title=Cross-industry_standard_process_for_data_mining
  185. Needham J. Disruptive possibilities: How big data changes everything. Beijing: O’Reilly; 2013.
  186. Wikimedia Foundation. Opportunity cost. Wikipedia. [Cited 2019 Oct 7.] Available from: https://en.wikipedia.org/w/index.php?title=Opportunity_cost&oldid=916733399
  187. Kenton W. Quantitative Analysis (QA) Definition. Investopedia. [Cited 2019 Oct 10.] Available from: https://www.investopedia.com/terms/q/quantitativeanalysis.asp
  188. Wikimedia Foundation. Mean squared error. Wikipedia. [Cited 2019 Sep 29.] Available from: https://en.wikipedia.org/wiki/Mean_squared_error
  189. Object Management Group, Inc. What is UML. [Cited 2019 Sep 11]. Available from: http://uml.org/what-is-uml.htm
  190. Kotter International. 8-step process. [Cited 2019 Jul 15.] Available from: https://www.kotterinc.com/8-steps-process-for-leading-change/
  191. Kaggle, Inc. Kaggle: Your home for data science. Kaggle. [Cited 2018 Jul 2.] Available from: https://www.kaggle.com/
..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset
3.14.131.212