Овечкин продлил безголевую серию в составе Вашингтона09:40
2014年，依托NLP技术，儿童机器人赛道单月出货量达到数百万台，一片繁荣。但到了2017年，阿里、小米、百度都纷纷下场做智能音箱，并通过低价策略迅速抢占市场，那时几乎人手一个天猫精灵，或者是小度、小爱同学。儿童机器人几乎全军覆没。
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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.
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