BlueDot Narrated

Audio versions of blogs and papers from BlueDot courses. Right now I’m working on finding a good objective to optimize with ML, rather than trying to make sure our models are robustly optimizing that objective. (This is roughly “outer alignment.”) That’s pretty vague, and it’s not obvious whether “find a good objective” is a meaningful goal rather than being inherently confused or sweeping key distinctions under the rug. So I like to focus on a more precise special case of alignment: solve alignment when decisions are “low stakes.” I think this case effectively isolates the problem of “find a good objective” from the problem of ensuring robustness and is precise enough to focus on productively. In this post I’ll describe what I mean by the low-stakes setting, why I think it isolates this subproblem, why I want to isolate this subproblem, and why I think that it’s valuable to work on crisp subproblems. Source:https://www.alignmentforum.org/posts/TPan9sQFuPP6jgEJo/low-stakes-alignmentNarrated for AI Safety Fundamentals by TYPE III AUDIO.---A podcast by BlueDot Impact.

Holden Karnofsky, a visiting scholar at the Carnegie Endowment for International Peace, delves into his innovative 'If-Then' commitments for managing AI risks. He outlines how these structured responses can ensure proactive safety measures without stifling innovation. The discussion highlights the importance of timely interventions as AI technology evolves, ensuring developments stay safe and beneficial. Karnofsky also touches on the challenges of implementing these commitments and the necessity of regulatory compliance across sectors.

Audio versions of blogs and papers from BlueDot courses.This article by Eric Schmidt, former CEO of Google, explains existing use cases for AI in the scientific community and outlines ways that sufficiently advanced, narrow AI models might transform scientific discovery in the near future. As you read, pay close attention to the existing case studies he describes.Original text: https://www.technologyreview.com/2023/07/05/1075865/eric-schmidt-ai-will-transform-science/ Author(s): Eric SchmidtA podcast by BlueDot Impact.

The discussion tackles the double-edged sword of releasing powerful foundation models. While transparency can fuel innovation, the potential for misuse by malicious actors is alarming. Cyberattacks, the development of biological weapons, and disinformation loom large. The conversation also emphasizes the necessity for responsible AI governance, advocating for structured access and staged releases as safer alternatives to total openness. Legal liabilities and the implications of regulation are scrutinized, highlighting the critical need for thorough risk assessments.

Discover the dynamic world of policy entrepreneurs and their role in driving innovation. Uncover strategies like problem framing and coalition building that empower them to tackle global challenges, especially climate change. Learn how collaborative networks help share vital knowledge and foster legislative support. Historical examples illustrate the impact of these leaders in areas like California’s stem cell research. Dive into the ongoing fight against violence in conflict, showcasing the critical need for advocacy and transformative policies.

Discover the debate surrounding open foundation models and their potential to drive innovation and competition. The discussion highlights the ethical dilemmas of open versus closed models and the need for thoughtful policy design. Examining the risks, such as disinformation, the speakers argue that evidence for these dangers is limited. They advocate for policies that consider the unique characteristics of open models to prevent stifling development while promoting transparency and reducing monopolistic power in AI.

The podcast discusses the U.S. Senate's AI policy roadmap, including AI Ready Data, National AI Research Resource, AI safety, and election security. It explores funding and collaboration in AI applications for defense and national security, as well as addressing legal and regulatory gaps in AI systems. Policy recommendations cover export controls, security mechanisms, and collaboration with international partners.

Ben Buchanan, author of the AI Triad framework, discusses the inputs powering machine learning: algorithms, data, and compute. The podcast explores the impact of these components on national security strategy, the disparities between machine learning and traditional programming, and the application of machine learning in national security, robotics, and AI advancements.

Authors Jamie Bernardi and Gabriel discuss societal adaptation to advanced AI systems in a paper, emphasizing the need for adaptive strategies and resilience. Topics include managing AI risks, interventions, loss of control to AI decision-makers, adaptive strategies, and responses to AI threat models.

Exploring OECD AI Principles for responsible stewardship of trustworthy AI, national policies, and international cooperation. Updates made in 2024 reflected in document. Importance of public trust, stable policy environment, accountability, and global collaboration emphasized. Focus on enhancing transparency, addressing misinformation, and promoting responsible practices in AI industry.