60 Leaders on AI - References

  1. A Data Driven Company, Richard Benjamins, ISBN: 1912555883

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  3. A Theory of Justice, Rawls, John: 1971, Belknap Press.

  4. Accelerating materials discovery using artificial intelligence, high performance computing and robotics, Edward O. Pyzer-Knapp, Jed W. Pitera, Peter W. J. Staar, Seiji Takeda, Teodoro Laino, Daniel P. Sanders, James Sexton, John R. Smith & Alessandro Curioni

  5. Advances in neural information processing systems, Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A. and Agarwal, S., 2020. Language models are few-shot learners, 33, pp.1877-1901

  6. An image is worth 16x16 words: Transformers for image recognition at scale, Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S. and Uszkoreit, J., 2020. arXiv preprint arXiv:2010.11929.

  7. Architects of Intelligence: The Truth About AI from the People Building It - Amazon.com

  8. Artificial Intelligence Act

  9. Artificial Intelligence and Management: The Automation-Augmentation Paradox, Raisch, S., & Krakowski, S. (2020). Academy of Management Review.

  10. Bert: Pre-training of deep bidirectional transformers for language understanding, Devlin, J., Chang, M.W., Lee, K. and Toutanova, K., 2018. arXiv preprint arXiv:1810.04805.

  11. Bias in AI: What it is, Types, Examples & 6 Ways to Fix it in 2022, Dilgemani, Cem: 2020

  12. Chalfen, Mike, ‘The Challenges Of Building AI Apps’. TechCrunch. Retrieved 27 November 2021

  13. Characteristics of publicly available skin cancer image datasets - The Lancet Digital Health

  14. Code: Perseus Books Group, New York, 2006, v2.0, pp. 200-232

  15. Coeckelbergh, Mark: 2020: AI Ethics. MIT Press

  16. DARPA's Explainable AI (XAI) Program: Applied AI Letters: Vol 2, No 4 (wiley.com)

  17. Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2. Front. Microbiol., 28 October 2021 

  18. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, He, K., Zhang, X., Ren, S. and Sun, J., 2016. (pp. 770-778).

  19. Differences between germline genomes of monozygotic twins, H. Jonsson et al.

  20. Discriminating algorithms: 5 times AI showed prejudice | New Scientist

  21. Exploring Bayesian networks for automated breast cancer detection, 2009, pp. 153-157

  22. Hip Implant Failure for Men and Women (researchgate.net)

  23. Human intelligence and brain networks, Roberto Colom 1, Sherif Karama, Rex E Jung, Richard J Haier

  24. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists - Annals of Oncology

  25. Millions of black people affected by racial bias in health-care algorithms (nature.com)

  26. Science & Technology Outlook 2021, IBM Research

  27. Sex Differences in Acute Complications of Cardiac Implantable Electronic Devices - ahajournals.org

  28. Surveillance giants. Amnesty International, Naidoo (2019).

  29. The AI first Company, Ash Fontana, ISBN : 0593423089

  30. The Cambridge handbook of artificial intelligence. Frankish, Keith., Ramsey, William M., 1960-. Cambridge

  31. The construction of work in AI. Science, Technology, & Values, Forsythe, D.E. (1993), 18(4), 460-480

  32. When decision support systems fail: Insights for strategic information systems from Formula 1, Aversa, P., Cabantous, L., & Haefliger, S. (2018). The Journal of Strategic Information Systems, 27(3), 221-236

  33. Why Bias in AI is a Problem & Why Business Leaders Should Care, Ebert, Alexandra: 2020

  34. Winning with analytics Accenture, 2015, D. Simchi-Levi, J. Gadewadikar, B. McCarthy and L. LaFiandra

  35. Yolov3: An incremental improvement, Redmon, J. and Farhadi, A., 2018. arXiv preprint arXiv:1804.02767

  1. A Drug Addiction Risk Algorithm and Its Grim Toll on Chronic Pain Sufferers | WIRED

  2. A Health Care Algorithm Offered Less Care to Black Patients | WIRED

  3. A Meta-Transfer Objective for Learning to Disentangle Causal (pdf)

  4. A Short Speech on Artificial Intelligence (thevideoink.com)

  5. AI-maturity-model-whitepaper.pdf (amdocs.com)

  6. Aligning Language Models to Follow Instructions (openai.com)

  7. AlphaFold - Wikipedia

  8. Amazon scraps secret AI recruiting tool that showed bias against women | Reuters

  9. Amazon scraps secret AI recruiting tool that showed bias against women | Reuters

  10. Amazon scraps secret AI recruiting tool that showed bias against women | Reuters

  11. Amazon’s sexist AI recruiting tool: how did it go so wrong? Becoming Human: AI Magazine

  12. Amazon's AI Was Biased Against Women (businessinsider.com)

  13. Apple Card algorithm sparks gender bias inquiry - The Washington Post

  14. Apple Card algorithm sparks gender bias inquiry - The Washington Post

  15. Artificial general intelligence - Wikiwand

  16. Artificial intelligence and the circular economy (ellenmacarthurfoundation.org)

  17. Artificial Intelligence Has an Enormous Carbon Footprint | by Emil Walleser | Towards Data Science

  18. Artificial Intelligence is it a threat? What are the concerns? | The Innovation Mode

  19. Auguries of Innocence - Wikipedia

  20. Automating Inequality | Guide books (acm.org)

  21. British Grading Debacle Shows Pitfalls of Automating Government - The New York Times

  22. Chinese room - Wikipedia

  23. Columbia Climate School –Artificial Intelligence—A Game Changer for Climate Change and the Environment

  24. Common Crawl - Wikipedia

  25. Common Voice - Wikipedia

  26. Dive into Deep Learning — Dive into Deep Learning 0.17.5 documentation (d2l.ai)

  27. Drug Target - an overview | ScienceDirect Topics

  28. Economist: Climate Issue 2019

  29. Elon Musk has a complex relationship with the A.I. community (cnbc.com)

  30. Energy demand management - Wikipedia

  31. Eric Schmidt Warns Of 'National Emergency' If China Overtakes U.S. In AI Tech (forbes.com)

  32. Facial Recognition Leads To False Arrest Of Black Man In Detroit : NPR

  33. Fairness-related harms in AI systems - Microsoft Research

  34. Food | United Nations

  35. Fourth Industrial Revolution - Wikiwand

  36. Fourth Industrial Revolution - Wikiwand

  37. Geoff Hinton: On Radiology - YouTube

  38. Gerhard Lenski - Wikiwand

  39. Global AI market predicted to reach nearly $1 trillion by 2028 (thenextweb.com)

  40. Google apologises for Photos app's racist blunder - BBC News

  41. Google effect - Wikipedia

  42. GPT-3 - Wikipedia

  43. GPT-3 Has No Idea What It Is Saying | by Steve Shwartz | Towards Data Science

  44. Gym (openai.com)

  45. Healthily's Explainability Statement - Healthily (livehealthily.com)

  46. HireVue’s AI Explainability Statement, An HR industry first | HireVue

  47. Home - DeepLearning.AI

  48. How AI can help tackle climate change

  49. How AI revolutionizes Retail | The Innovation Mode

  50. How the law got it wrong with Apple Card | TechCrunch

  51. How to stop Fake News and misinformation using digital technologies | The Innovation Mode

  52. Human Compatible: AI and the Problem of Control

  53. Human-in-the-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI

  54. Identify Skin Conditions with DermAssist - Google Health

  55. IKEA effect - Wikipedia

  56. Implicit bias in healthcare professionals: a systematic review - PMC (nih.gov)

  57. Jobs lost, jobs gained: Workforce Transitions in a time of automation (mckinsey.com)

  58. JournalismAI.com | Yoshua Bengio: From System 1 Deep Learning to System 2 Deep Learning

  59. Language Models are Few-Shot Learners (pdf)

  60. LAYING DOWN HARMONISED RULES ON ARTIFICIAL INTELLIGENCE (europa.eu)

  61. Luciano Floridi - Wikipedia

  62. Made in China 2025: Xi Jinping's plan to turn China into the AI world leader - ABC News

  63. Masakhane

  64. Microsoft Chat Bot Goes On Racist, Genocidal Twitter Rampage | HuffPost Impact

  65. MLOps - Wikiwand

  66. NIST Study Evaluates Effects of Race, Age, Sex on Face Recognition Software | NIST

  67. Our approach to responsible AI at Microsoft

  68. Physical Internet - Wikipedia

  69. Polarization and Fake News: ACM Transactions on the Web: Vol 13, No 2

  70. Q&A: How do climate models work? - Carbon Brief

  71. Racial Bias in Pulse Oximetry Measurement - PMC (nih.gov)

  72. Redlining was banned 50 years ago. It’s still hurting minorities today. - The Washington Post

  73. Responsible AI principles from Microsoft

  74. Science, technology, engineering, and mathematics - Wikiwand

  75. Seeing AI App from Microsoft

  76. Sizing the prize (pwc.com)

  77. Skynet (Terminator) - Wikipedia

  78. Speech by the President: World Leader for Peace & Security (europa.eu)

  79. Stateoftheart AI

  80. Stephen Hawking warns of dangerous AI - BBC News

  81. Superintelligence - Wikiwand

  82. Technological singularity - Wikiwand

  83. TensorFlow Quantum

  84. That TikTok notification about a settlement payment isn't a scam (today.com)

  85. The Global AI Index - Tortoise (tortoisemedia.com)

  86. The ins and outs of becoming a data-driven organization - Telefónica (telefonica.com)

  87. The Marvelous Mathematics of Computational Linguistics | MIT Technology Review

  88. This is how AI could feed the world’s hungry while sustaining the planet (weforum.org)

  89. Three Laws of Robotics - Wikipedia

  90. Turing-NLG: A 17-billion-parameter language model by Microsoft - Microsoft Research

  91. Universal basic income - Wikiwand

  92. Using satellites and AI to help fight poverty in Africa | Stanford News

  93. Weak AI - Wikiwand

  94. WEF_AI_and_Ageing_Workshop_Report_2021.pdf (weforum.org)

  95. What Happens When Police Use AI to Predict and Prevent Crime? - JSTOR Daily

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