来自加利福尼亚大学圣迭戈分校（UC San Diego）Biwei Huang 实验室的研究团队提出了一种自主因果分析智能体 ...

Understanding mental illness requires understanding psychiatric disorder causality. New brain research explores this in ...

近日，美国加利福尼亚大学圣迭戈分校王新跃博士和所在团队开发出一款名为 Causal-Copilot ...

Causal-Copilot的核心理念是将大模型的能力与领域专家的知识进行有效整合，成为连接两者的智能桥梁。这一智能体能够理解自然语言意图、生成执行代码，并整合专家知识推理，使得因果分析从“专家独享”变为“普惠可及”。

Causal-Copilot的核心理念是将大模型与自然语言处理相结合，能够理解用户的意图并生成执行代码。研究团队希望通过这一智能助手，将因果分析的技术从“专家独享”转变为“普惠可及”，为各领域的研究者提供便捷的工具。这一系统自在arXiv上公开后，便收获了来自AI社区的积极反馈。试用者表示，Causal-Copilot不仅降低了因果分析的入门门槛，还实现了无需专业知识的端到端自动化分析。

We generated exome sequencing data for 246 stillborn cases and followed established guidelines to identify causal variants in disease-associated genes. These genes included those that have been ...

A startup called causaLens has developed causal inference technology — presented as a no-code tool that doesn’t require a data scientist to use to introduce more nuance, reasoning and cause ...

Better causal inferences will help programs do more with fewer resources and waste less time doing it. And by integrating causal AI with human expertise, programs can avoid the mistakes that arise ...