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We show that, compared with surgeon predictions and existing risk-prediction tools, our machine-learning model can enhance ...

NANOG has multifaceted roles in naïve hESCs, orchestrating transcriptional, metabolic, and epigenetic signatures in naïve hESCs, and governing acetyl-CoA synthesis, bivalent metabolism, and redox ...

DataCamp, the leading online learning platform for data and AI skill building, announced today that it has completed the acquisition of Dubai-based Optima, an AI-native learning platform for building ...

What are The Most Popular AI Models in 2025? We Looked at 2.3 Million Chats to Find Out At Overchat AI, we're building a platform that brings text, image, and video models together ? giving everyone ...

The research aim is to develop an intelligent agent for cybersecurity systems capable of detecting abnormal user behavior ...

据Gartner统计，超过70%的网络安全事件源于已知漏洞未及时修复，而攻击者平均只需数小时即可扫描全网并锁定存在漏洞的目标。在这样的背景下，漏洞评估（Vulnerability ...

More and more companies are using AI or planning to use it. However, 95% of projects fail. The reasons for this are ...

在数字化犯罪日益复杂的今天，一起普通的刑事案件可能涉及手机通话记录、社交媒体聊天数据、车载GPS轨迹、云存储文档，甚至智能家居设备的操作日志——证据不再局限于传统的计算机硬盘，而是分散在数十种不同设备与平台中。传统取证工具往往需要针对单一数据源（如仅支持手机或仅支持电脑）单独提取分析，不仅效率低下，更可能导致关键线索因"数据孤岛"而被遗漏。

在如今的 AI 领域，大多数大语言模型在很大程度上仍以「黑箱」方式工作，即使是专业的研究者也难以完全理解它们的内部计算过程。因此，提升模型的透明度有助于分析和解释大语言模型产生幻觉、行为不稳定或在关键场景做出不可靠判断的潜在原因。 就在今天，OpenAI 发布了一项新研究，使用新方法来训练内部机制更易于解释的小型稀疏模型，其神经元之间的连接更少、更简单，从而观察它们的计算过程是否更容易被人理解。

OpenAI的新论文介绍了他们在破解大模型「黑箱」上的一次重要突破，通过训练结构更简单、连接更稀疏的神经网络，为构建既强大又透明的模型提供全新方向。 破解大模型「黑箱」的钥匙找到了？ 刚刚，在理解大模型复杂行为的道路上，OpenAI又迈出了关键一步。 他们从自己训练出来的稀疏模型里，发现存在结构小而清晰、既可理解又能完成任务的电路（这里的电路，指神经网络内部一组协同工作的特征与连接模式，是AI可解 ...