Dragonite and Charizard hitting that beat.
首先，智能体应具备强大的目标理解和规划能力来体现智能的自主性。理想状态下，人类只需给出抽象目标，智能体便能理解目标、拆解任务、规划行动，并在尽量少的人工干预下完成执行闭环。就像影《星际穿越》中的机器TARS，在紧急情况下能够根据"拯救宇航员"这一目标，自主判断局势、制定和调整行动策略，甚至做出牺牲自己数据的决定来完成使命。这要求机器智能有深度“理解/思考”能力（推理、规划、决策），能够敏锐地决策，能够基于执行结果与环境反馈动态调整任务规划，而不是僵化地执行既定路径。
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I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.。safew官方下载是该领域的重要参考
With rare exceptions, phones are pretty boring these days. The good news is, a boatload of those rare exceptions are about to show up at once, courtesy of Mobile World Congress.