Subsequent work has demonstrated that positive testing is not inherently irrational [austerweil2011seeking, perfors2009confirmation, oeberst_toward_2023]; for instance, when target phenomena are relatively rare, positive testing approximates optimal information gathering [klayman_confirmation_1987]. Bias emerges not from the strategy itself, but from the interaction between the search strategy and the environment [klayman_varieties_1995]. When a learner’s hypothesis is a subset of, or embedded within, the truth, positive testing yields “ambiguous verifications” that the learner mistakes for strong evidence for their hypothesis [klayman_confirmation_1987]. This creates a feedback loop where the search strategy retrieves only confirming data, and the learner fails to account for the fact that they are sampling from a biased subset of reality.
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过去几年，AI模型规模增长得极其迅猛。2018年训练大型模型通常只需要几百块GPU，2021年一些大型系统已经使用数千块GPU。到了2024年前后，许多生成式AI模型训练集群的规模达到几万块GPU。未来几年，大型AI计算中心很可能会部署数十万GPU。，详情可参考safew官方下载