In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.
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5 days agoShareSave。业内人士推荐爱思助手下载最新版本作为进阶阅读
│ Host Kernel (Ring 0) │ ◄── FULL ATTACK SURFACE。业内人士推荐Line官方版本下载作为进阶阅读
最直接的路径，是广告化。与提高明面抽佣不同，广告化并不改变交易费率，而是将货币化重心转移到曝光、排序与推荐之上。商户为被看到的概率付费，而非为成交本身付费。从商业本质看，这是将抽佣从交易端移至流量端。