Thoughtforms Life An Active Inference Model of Collective Intelligence by R. Kaufman, P. Gupta, and J. Taylor
Aug 7, 2024
Join Rafael Kaufmann, a mathematician at Google focusing on collective intelligence, and Pranav Gupta, a business researcher studying human-machine teams, as they explore fascinating concepts in active inference. They discuss how agent interactions influence collective behavior and the role of goal alignment in team success. Rafael shares insights on modeling dynamics between strong and weak agents, while Pranav highlights the importance of collective memory and attention. They even touch on how motivation can elevate performance in both human and AI collaborations.
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Model Born From Work At Google
- Rafael Kaufmann began this work while trying to make Alphabet behave more like a single intelligent entity.
- He combined his background in mathematics and organizational effectiveness with active inference modeling to explore collective behavior.
Linking Local Interactions To Collective Free Energy
- Formal models rarely connect local agent interactions to collective-level intelligent behavior.
- Active inference provides a lingua franca to measure that connection via free energy and basin mechanics.
Theory Of Mind And Endogenous Goals Matter
- Agents need extra cognitive features to model sophisticated collectives, especially theory of mind and goal alignment.
- Those features let agents infer others and coordinate endogenous goals, improving collective performance.