7.6A AI - Introduction
Class Outline
AI History
Machine Learning and AI
Autonomous Systems
Super Intelligence
Applications
Class Description (AI for Everybody by Andrew Ng; short course(6 hours) from Coursera)
What is AI?
Building AI Projects
Building AI in Your Company
AI and Society
Class Description (Helsinki University, Elements of AI)
What is AI?
AI Problem Solving
Real World AI
Machine Learning
Neural Networks
Implications
Class Description (Kilnam Chon, AI: Past and Present; ppt)
AI Introduction: History
AI Economy and Industry
Super Intelligence
Complex Systems and Narrow AI
AI Governance
Future of AI
References
Class Video (UC Berkeley CS 188; First Class as AI Introduction)
Lecture Candidates: Jaeseung Jeong/KAIST, Joanna Bryson/Bath, Yudho Giri Sucahyu/Indonesia, Kilnam Chon/KAIST
7.6B AI Governance
Class Outline
AI History
AI Principles
AI Policies
Governance
Algorithm
Accountability and Explainability
Ethics
Safety and Security
Impact: Society and Economy
General AI and Its Implication
Class Description (Danit Gal)
This lecture on AI governance will be divided into three sections. The first will provide an overview of the ethical, inclusive, and societal aspects of AI. We will examine key documents such as the IEEE’s Ethically Aligned Design v.2, the Asilomar Principles, and the AI Now report. The lecturer will also offer first-hand insights from relevant conferences on these topics. The second will discuss the economic and regulatory aspects of AI. We will examine government regulations, policies, and standards from around the world and key documents such as the AI Index, reports from global firms, and other relevant documents. The third will examine the technical aspects of AI governance, including safety, robustness, and security. We will examine outputs and insights from leading institutions and thinkers in the field, and review existing best practices in the field. Each section will provide a brief overview of existing materials to inform participants of ongoing discourses in these fields, and offer questions for discussion. The following discussion will be themed around the relevance and application of these global discourses to Asia, with an emphasis on East and Southeast Asia.
Class Description (Sunyoung Yang)
Applicability of Existing Global Governance Models for the Formation of Global AI Governance
Recent innovations in Artificial Intelligence (AI) have been accelerated by computing technologies and big data. AI can be defined as “activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment” (Nilsson 2010). AI is both creating new frontiers for social experience, public infrastructure, and industrial innovation, but it also threatens to accelerate automation, social alienation, and global inequities. The impact of AI has worldwide implications, which calls for globally collaborative efforts to develop a joint governance of AI, as well as the need to allow diverse groups to contribute to the process. New regulations for self-driving cars and aircrafts, imposition of taxes for robot labor, and banning of autonomous weapons are examples where AI governance is needed. This lecture intends to articulate the components required to develop a global governance of AI based on the model of multi-stakeholder processes (Hemmati 2002). This lecture will determine what factors will be pertinent to AI governance from existing examples of global governance, including Internet Governance, the Intergovernmental Panel on Climate Change, more broadly the UN Environment, and global governance of nuclear technology. This lecture will then synthesize the existing examples of global governance and analyze the applicability of those examples for AI governance, examining Internet governance and AI governance within the broader context of digital governance.
Class Description (Thematic Pillars by Partnership of AI)
Safety-critical AI
Fair, Transparent and Accountable AI
Collaboration between People and AI
AI, Labor and Economy
Social and Societal Influences of AI
AI and Social Good
Class Description (Kilnam Chon)
Internet Governance and Digital Governance
Is Internet governance scheme applicable to AI governance?
AI History
Narrow AI and General AI
Areas of AI Governance
Principles, Policy, Social and Economic Impacts
Algorithm, Accountability and Explainability, Ethics, Security, Safety, Data
General AI, Existential Risk
Lecture Pool: Danit Gal
Lecture Candidates: Woodrow Herzog, Kilnam Chon, Sunyoung Yang, Ema Arisa, Alan Dafoe
please contact APSIG-sec@googlegroups.com if you want to subscribe to APSIG-AI mailint list.
check AI Governance - APSIG.asia webpage for further information.
AI Conferences
AAAI Annual Conference, since 1980s
ACM/AAAI AI, Ethics and Society, since 2018 (and AAAI Workshop in 2016 and 2017)(
FLI Beneficial AI, 2015, 2017, 2019
IJCAI International Joint Conference of AI (2019-08-10~16, Macao)
ITU AI for Good, since 2017
KAIST Taming AI, 2018
KIAS AI, Ethics, Governance, 2018
NeurIPS Montreal, 2018.12.2-8
OECD AI: Intelligent Machines and Smart Policies, 2017
SGAI International Conference on AI, since 1980s
SNU AI: Governance and Accountability, 2018, 2017
Tokyo University AI and Society, 2017
World Gov Sum Global Governance of AI and World Government Summit, Dubai, February 2019. [Summary Report]
References
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AAAI, Spring Symposium Series, Palo Alot, 2018.3.26-28.
Acceptable Intelligence with Responsibility (AIR)
ACM, Special Section on China Region, CACM, November 2018.
Philippe Aghion, Artificial Intelligence and Economic Growth, 2017.10.
The Algorithm, Inside China's AI Strategy, MIT Technology Review, 2019.2.8. [us report] [china report] [tsinghua report]
The Algorithm, 2019.3.8. Darktrace | Antigena White Paper, 2019.3.9.
Artificial Intelligence Channel, YouTube.
AI4ALL
AAAI
AI and Society, Tokyo, 2017.10.
AI Democratization
AI, Ethics and Society, AAAI/ACM, New Orleans, 2018.2.2-3. [program]
AI, Ethics and Society, AAAI/ACM, Honolulu, 2019.1.27-28.
AI Organization List (from AI and Society)
AI Today, Seoul National University, Seoul, 2018.8.24.
Dario Amodei, et al., Concrete problems in AI Safety, 4th Machine Learning and AI Safety Workshop, MIRI, 2017.
Anand Murali, Big Eye, 2018.8.
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Gnneieve Bell, Beyond Trolly Problem: Ethics in AI, AIDC 2018, Intel, 2018.6.
Beneficial AI 2017 - Future of Life Institute, Asilomar, 2017.
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China Daily, Open access to datasets critical for nurturing AI talents, 2017.12.23.
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China Academy of Information and Communications Technology, artificial intelligence security white paper, 2018.
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Alan Dafoe, AI Strategy, Policy and Governance, Beneficial AI, 2019.
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DARPA, Explainable Artificial Intelligence (XAI) by David Gunning, 2018.
DARPA, Futurism, AI tech will arrive in three waves, 2018.1.
DARPA, DARPA Perspective on AI, 2018.
Dawn of Killer Robots, YouTube, 2015.
EFF, How militaries should plan for AI, 2018.8.
Arisa Ema, AI Ethics and Policy in Japan, Taming AI Seminar, Seoul, 2018.6.
European Parliament, Report with Recommendation to Commission on Civil Law Rules on Robotics, 2017.
Edward Felton, AI and Explainability: A computer science perspective, 2018.
Conference on Fairness, Accountability, and Transparency, NYU, 2018.2.23-24.
Forbes, This AI Can Recognize Anger, Awe, Desire, Fear, Hate, Grief, Love ... By How You Touch Your Phone, 2018.8.
Lex Fridman, Artificial General Intelligence, MIT 6.S099.
Future of Life Institute, Beneficial AI 2017.
FLI, Global AI Policy, 2018.12.
G7 (ICT) Ministry Meeting, 2016, 2017.
Glocom, AI and Japan 2017, Innovation Japan, 2018. [in Japanese; Full Report, Summary]
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Geoffrey Hinton, Andrew Ng's Interview of Geoggrey Hinton, 2018. [in youtube]
Helsinki University, Elements of AI, 2018.
HITE, "Human and AI - Co-development Symposium", 2018.3.14. [in Japanese]
Indian Government, Report of Task Force on Artificial Intelligence | Department of Industrial Policy & Promotion | MoCI | GoI, 2018.
ISO/IEC JTC1 SC42, Standardization on AI.
International Conference on Love & Sex with Robots, 2018.
Joi Itoh and Reid Hoffman, Future of Work, EmTech, 2017.
ITU, AI for Good, 2018.5.15-17.
JA Directives, 18 best AI courses, 2018.
Japanese Government (Soumusho), Conference Toward AI Network Society. (also AI R&D Principles), 2017.
Japanese Government (Cabinet Office), AI and Human Society, 2017.
David Gunning, Explainable AI (XAI), Darpa, Proceedings of AI Today, Seoul, 2018.8.24.
Japan Society on AI, The Open Discussion on , JSAI Ethics Committee, 2017.5.24. [Ethical Guideline] [Arisa Ema, AI Principles and Ethics in Japan and Abroad]
Jalak Jobanputra, Beyond finance: considering business and social impact of blockchain, Business of Blockchain, MIT, 2017.
Michael Jordan, AI: revolution has not happened yet, Medium, 2018.4.
Jingkwon Jun, Explainable AI: Key to sound governance?, KAIS, 2018.8.24.
Henry Kissinger, How the enlightenment ends, The Atlantic, 2018.6.
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A Lauterbach, AI: A strategic business and governance imperatives, NACD, 2016.9/10.
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Kai-Fu Lee, AI Superpowers: China, Silicon Valley and New World Order, 2018.8. [Fortune, November 2018], [TED Talk]
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List of AI Tools You can Use Today, 2018.5.
Yann LeCun, Deep Learning and Future of AI, 2018. Power and Limit of Deep Learning, 2017.
Bill McDermott (SAP), Machines can't dream, WEF, 2018.1.
McKinsey, AI: Time to act is now, 2018.1.
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McKinsey, Competing in AI economy with Interview of Andrew McAfee, 2018.3.
McKinsey, AI problems and promises, 2018.10.ssss
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Medium, 10 Predictions for Deep Learning in 2019 – Intuition Machine (by Carlos Perez), 2019.1.1.
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Medium, Advances in Generative Adversarial Networks – BeyondMinds, 2019.2.
Medium, An introduction to explainable AI, and why we need it, 2019.2.
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Medium, Welcome to Politics + AI – Politics + AI, 2019.2.21. (by Tim Dutton)
Microsoft, Future Computed: AI and its role in society, 2018.1.
Microsoft AI, 2018.2.
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MIRI (Machine Intelligence Research Institute), 4th Workshop on Machine Learning and AI Safety, Berkeley, 2017.
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MIT Media Lab, AI & Governance Symposium, 2016.
MIT Media Lab, AI Ethics and Governance Projects.
MIT Technology Review, Biased Algorithms Are Everywhere, and No One Seems to Care, 2017.
MIT Technology Review, AI software learns to make AI softwarae, 2017.1.
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MIT Technology Review, How the AI cloud produce the richest companies ever, 2018.3.
MIT Technology Review, To protect AI systems, show fake data, 2018.3.
MIT Technology Review, To protect AI systems, show fake data, 2018.3.
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MIT Technology Review, With this tool, AI could identify malware,...., 2018.4.
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