Can we build a generalist agent? Dr. Minqi Jiang and Dr. Marc Rigter
Machine Learning Street Talk (MLST)
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Navigating the Landscape of Intelligent Agents
This chapter focuses on Rich Sutton's 'reward is enough' concept, emphasizing the design of appropriate reward functions for intelligent agents and the complexities of their implementations. The conversation explores the integration of agents within physical and social contexts and the significance of adaptive model training, particularly the shift from specialized to generalist models. Additionally, the chapter discusses the role of creativity, imagination, and prior knowledge in developing AI systems that can autonomously generate new advancements.
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