The goal of mechanistic interpretability is to reverse engineer neural networks. Having direct, programmable access to the internal neurons of models unlocks new ways for developers and users to interact with AI — from more precise steering to guardrails to novel user interfaces. While interpretability has long been an interesting research topic, it is now finding real-world use cases, making it an important tool for AI engineers. About Mark Bissell Mark Bissell is an applied researcher at Goodfire AI worki ...

技术突破：从理论根基到系统能力的全栈创新 全球人工智能领域再传DataCanvas强音!九章云极DataCanvas公司科研团队的两项原创成果《A Solvable Attention for Neural Scaling Laws》与《DyCAST: Learning Dynamic Causal Structure from Time Series》被人工智能三大顶级会议之一 ICLR(International Conference on Learning Representations)正式收录。这两项成果分别从神经网络基础理解与动态因果 系统建模两大方向取得进展，标志着九章云极DataCanvas团队在AI底层技术创新与国际学术影响力上实现跨越式提 升。 顶会严选：印证DataCanvas AI科研实力 ICLR与NeurIPS、ICML是人工智能领域公认的全球三大顶级学术会议之一，由深度学习先驱Yoshua Bengio、Yann LeCun等人于2013年发起成立。ICLR凭借其对深度学习核心问题的持续深耕、严苛的学术标准与开放协作的社区文 化，已成为全球AI学者发布里程碑成果的首选平台，在谷歌 ...