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Starts 3 July 2025 19:39

Ends 3 July 2025

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Superintelligent Agents Pose Catastrophic Risks - Safety-Guaranteed LLMs

Join us in examining the significant risks associated with superintelligent AI agents. With insights from Yoshua Bengio, this discussion highlights a revolutionary approach to AI development: the creation of the 'Scientist AI.' This non-agentic model is designed to prioritize explanation over action, thereby accelerating scientific research wh.
Simons Institute via YouTube

Simons Institute

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Overview

Join us in examining the significant risks associated with superintelligent AI agents. With insights from Yoshua Bengio, this discussion highlights a revolutionary approach to AI development:

the creation of the 'Scientist AI.' This non-agentic model is designed to prioritize explanation over action, thereby accelerating scientific research while mitigating potential dangers linked to agency in AI systems.

This event offers valuable insights for anyone interested in artificial intelligence and its implications for both scientific progress and human safety.

Engage with the content on YouTube and explore how this new paradigm in AI can help secure a safer technological future.

Syllabus

  • Introduction to Superintelligent AI
  • Definition and Characteristics of Superintelligent Agents
    Historical Context and Developments
    Current and Potential Capabilities
  • Catastrophic Risks of Superintelligent Agents
  • Alignment Problem and Control Challenges
    Scenarios of Unaligned AI
    Case Studies and Theoretical Models
  • Overview of AI Safety Research
  • Key Concepts in AI Safety
    Existing Approaches and Their Limitations
    Ethical and Societal Implications
  • Yoshua Bengio's "Scientist AI" Concept
  • Overview of Non-Agentic AI Models
    Comparison with Agentic AIs
    Potential Benefits for Scientific Advancements
  • Designing Safety-Guaranteed LLMs
  • Principles for Safe LLM Design
    Ensuring Explainability and Transparency
    Mechanisms to Avoid Unwanted Agency
  • Practical Implementations and Case Studies
  • Successful Use Cases of Non-Agentic AIs
    Lessons Learned from Past Implementations
    Pathways to Wider Adoption
  • Critiques and Limitations
  • Potential Drawbacks of the "Scientist AI"
    Addressing Critics and Continuing Debate
  • Future Directions and Research Opportunities
  • Emerging Trends in AI Safety
    Potential Collaborations and Community Efforts
    Developing Guidelines for Safe AI Research and Deployment
  • Conclusion
  • Summary of Key Insights
    Reflecting on the Path Forward for Safe AI Innovators

Subjects

Computer Science