Can we build a generalist agent? Dr. Minqi Jiang and Dr. Marc Rigter
Machine Learning Street Talk (MLST)
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Navigating Intelligence: Abstraction and Curriculum Learning in AI
This chapter explores the intricacies of building intelligence in artificial agents through model abstraction and curriculum learning, drawing parallels to self-driving cars' selective focus on relevant information. It highlights the challenges of translating simulated training into real-world performance, the trade-offs in generalization versus detail modeling, and the significant role of creativity and agency in AI development. Through discussions on domain randomization and intrinsic motivation, the chapter emphasizes the need for advanced AI systems that can innovate and generate knowledge rather than merely replicating existing information.
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