🤖 AI资讯日报

探讨务实商业模式如何比AI炒作周期更具持久性，强调可持续商业实践的重要性。

讨论当前AI算法在实际应用中的痛点与挑战，反映技术落地难题。

探讨AI技术进步是否呈指数级增长，涉及技术发展速度的行业讨论。

讨论NLP、AI、ML及机器人技术是短暂趋势还是长期变革，评估技术影响力。

讨论纽约市Local Law 144法规对AI行业的影响，涉及伦理与合规议题。

介绍MIT非AI许可证，涉及开源许可在AI领域的特殊应用与法律考量。

提出AI领域“伪科学指数”，旨在识别和批判不切实际的AI炒作与虚假宣传。

征集学习AI的推荐阅读材料，反映行业对知识获取与教育资源的关注。

介绍生物信息学职位，体现AI在生命科学等交叉领域的应用与人才需求。

谷歌招聘Common Lisp与机器学习实习生，展示传统编程语言在AI中的实践机会。

宣传Chris Clark为AI领域的潜在领军人物，带有炒作性质的个人推广内容。

展示初创公司通过书籍销售筹集资金，与AI技术关联较弱，侧重商业模式创新。

提出自适应集成解码框架AdaFuse，动态选择融合单元，提升大语言模型推理性能。

分析长链思维推理的分子式结构，提出Mole-Syn方法提升大语言模型长链推理能力。

定义3D指令歧义检测新任务，构建Ambi3D基准和AmbiVer框架，提升具身AI安全性。

提出首个大规模视觉自回归视频生成框架VideoAR，结合多尺度下一帧预测，提升效率与一致性。

提出邻域一致性信念评估方法，诊断大语言模型信念鲁棒性，并引入结构感知训练减少知识脆弱性。

提出预测执行范式，内部化执行先验以替代昂贵运行时检查，加速自主机器学习代理收敛。

展示大语言模型代理能轻易对访谈数据进行去匿名化攻击，强调数据发布中的隐私风险。

As machine learning models become increasingly embedded in societal infrastructure, auditing them for bias is of growing importance. However, in real-world deployments, auditing is complicated by the fact that model owners may adaptively update their models in response to changing environments, such as financial markets. These updates can alter the underlying model class while preserving certain properties of interest, raising fundamental questions about what can be reliably audited under such shifts. In this work, we study group fairness auditing under arbitrary updates. We consider general shifts that modify the pre-audit model class while maintaining invariance of the audited property. Our goals are two-fold: (i) to characterize the information complexity of allowable updates, by identifying which strategic changes preserve the property under audit; and (ii) to efficiently estimate auditing properties, such as group fairness, using a minimal number of labeled samples. We propose a generic framework for PAC auditing based on an Empirical Property Optimization (EPO) oracle. For statistical parity, we establish distribution-free auditing bounds characterized by the SP dimension, a novel combinatorial measure that captures the complexity of admissible strategic updates. Finally, we demonstrate that our framework naturally extends to other auditing objectives, including prediction error and robust risk.

评估基于Vision Transformer的胰腺肿瘤分割模型在公共超声内镜数据集上的性能，展示强分割能力但需进一步改进。

提出塔防游戏环境TowerMind，作为评估大语言模型代理规划与决策能力的轻量级多模态基准。

探讨AI中介能否改善民主审议，分析其增强参与度、政治平等和有意义审议的潜力与挑战。

介绍Python开源框架Cedalion，统一多模态fNIRS和DOT数据分析，支持可复现、可扩展的神经影像工作流。