The text explores the limitations of current reinforcement learning AI, despite its successes in game-playing.
While AI has achieved superhuman performance in games like Go and
Atari, this success is largely confined to those specific domains; the AI systems struggle with transfer learning, meaning they cannot easily apply knowledge gained in one game to another, unlike humans. This lack of generalizability highlights a significant gap between current AI and human-level intelligence.
Furthermore, even within a single game, AI's performance is highly
sensitive to minor changes, indicating a superficial understanding
rather than genuine comprehension. The text concludes by discussing the
challenges of applying these AI techniques to complex real-world tasks,
emphasizing the need for significant advancements in transfer learning
and the ability to deal with the unpredictable nature of real-world
environments.
