新的AI：物理学的通用、合理和相关
The New AI: General & Sound & Relevant for Physics
摘要：
过去50年来，大多数传统的人工智能（AI）系统要么非常有限，要么基于人工智能，要么两者兼而有之。然而，新千年在理论上最优且实际可行的算法领域取得了实质性进展，这些算法用于预测、搜索、基于奥卡姆剃刀的归纳推理、问题解决、决策制定和非常一般类型环境中的强化学习。由于归纳推理是所有归纳科学的核心，其中一些结果不仅与人工智能和计算机科学有关，而且与物理学有关，引发了基于Zuse计算机生成宇宙的非传统预测。
英文摘要：
Most traditional artificial intelligence (AI) systems of the past 50 years are either very limited, or based on heuristics, or both. The new millennium, however, has brought substantial progress in the field of theoretically optimal and practically feasible algorithms for prediction, search, inductive inference based on Occam's razor, problem solving, decision making, and reinforcement learning in environments of a very general type. Since inductive inference is at the heart of all inductive sciences, some of the results are relevant not only for AI and computer science but also for physics, provoking nontraditional predictions based on Zuse's thesis of the computer-generated universe.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
