Abstract: Maybe AIs have alien contents and we need a fundamentally different kind ofmetasemantics for AI systems than we do for humans. The aim is to explore a cluster of closely related topics: (i) What would it be for a system to have alien (non-human) contents and howcould we recognize these as contents? (ii) What …
Date: November 22, 2024 (Friday) Speaker: Ms Sarah Jeong, Journalist Chair: Dr Frank Hong, The University of Hong Kong Abstract: Generative AI is now a ubiquitous feature of consumer electronics, incorporated even into smartphone photography. As photographs and other forms of media become trivially easy to doctor, the social consensus around what documentary evidence is …
Our Director, Professor Herman Cappelen, author of several books about AI, explains why he believes that AI can think, plan and have emotions, why some people do not want to listen to music created by AI, and why do the philosophers like the film The Matrix. Read the full article here.
Date: June 26, 2024 (Wednesday) Organizers: AIH Lab, Programme on Artificial Intelligence and the Law Keynote Speech Frank Pasquale, Professor of Law, Cornell Tech and Cornell Law School “The Regulation of Simulation in an Era of Generative AI” Chair: Haochen Sun, Professor & Director, Programme on AI and the Law, HKU Faculty of Law Panel 1 Chair: Anna Wu …
Regulatory Frameworks for AI: Developing ethical guidelines and regulations for the development and deployment of AI systems. International Cooperation and Standards: Fostering international collaboration on AI governance to address global challenges and ensure ethical consistency. Public Engagement and Participation: Involving the public in discussions and decision-making processes related to AI governance. Corporate Social Responsibility in AI: …
Defining AI Well-being: How can we conceptualize and define well-being for artificial entities? What might constitute a flourishing or thriving state for an AI? Subjective Experiences of AI: Can AI systems have subjective experiences, and if so, what are the ethical implications for their well-being? How can we understand and potentially measure their internal states? …
Developing standardized benchmarks and evaluation metrics for AI systems Investigating techniques for detecting and mitigating biases in AI models Exploring methods for assessing the robustness and generalizability of AI models Studying the limitations and potential failure modes of AI systems Examining the role of human judgment in evaluating and validating AI models
Developing techniques for ensuring AI alignment with human values and goals Investigating potential risks and unintended consequences of advanced AI systems Establishing best practices for AI development and deployment to minimize risks Exploring the concept of AI containment and control measures Studying the potential for AI to cause existential risks and developing mitigation strategies
Preventing Unintended Consequences: Researching methods to anticipate and mitigate unforeseen negative consequences of AI systems. Robustness and Reliability: Developing techniques to ensure AI systems are reliable and function as intended, even in unexpected situations. Value Alignment: Aligning AI systems with human values to ensure they operate ethically and in accordance with human goals. Security and …