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In this talk, Sheikh Abdur Raheem Ali:

  1. Discusses rare cases where deployed LLMs have been observed to engage in self-directed behavior which would have led to catastrophic outcomes if the agent escaping containment were equipped with stronger capabilities.
  2. Introduces arbitrium, a peptide-based communication method used by certain bacteriophages which release small molecules known as autoinducers to decide coordinated population-level behavior, and in particular lytic and lysogenic pathways, with a quorum-sensing mechanism.
  3. Explores landmark results from alignment science which inform our current understanding of LLM biology (<250 malicious documents required to poison training datasets vs <100 viral particles required to produce infection in humans).
  4. Analyzes early findings from experiments which attempt to investigate scalable improvements to defenses that monitor the internal activations of production transformer models (such as probes) and demonstrates how these enable targeted interventions on known distributions which are more difficult to achieve with input/output only methods (such as prompted classifiers) in certain cases (such as long context windows or latent reasoning models)

​​​Event Schedule
6:00 to 6:30 - Food and introductions
6:30 to 7:30 - Presentation and Q&A
7:30 to 9:00 - Open Discussions

​​​​​​If you can't make it in person, feel free to join the live stream starting at 6:30 pm, via this link.

Related topics

Events in Toronto, ON
AI and Society
Artificial Intelligence
Artificial Intelligence Applications
Artificial Intelligence Machine Learning Robotics
Critical Thinking

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