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Unpacking AGI: Techniques and Timelines
This chapter explores the resurgence of historical research ideas in reinforcement learning and deep learning, particularly through transformative projects like AlphaGo and DQN. The discussion highlights the significance of combining traditional techniques with modern advancements to propel artificial general intelligence (AGI) development, emphasizing the scaling hypothesis and the challenges of grounding AI. It culminates in reflecting on timelines for AGI and the implications of its realization, weighing both the benefits and potential risks associated with these powerful technologies.