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关于如何很好地使用人工智能的原创文章——如何判断工具、阅读基准、为模型提供更好的背景以及建立真正的素养。简单的英语，没有炒作，没有行话。
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常青的写作意味着在本周的模型发布被遗忘后很长一段时间内仍然有用。
人工智能素养
The new frontier of AI literacy: Beyond the prompt
As AI moves from a novelty to a foundational layer of education, finance, and corporate strategy, literacy is no longer about how to talk to a chatbot. It is about understanding the systemic risks and structural changes that follow.
人工智能素养
The Governance Gap: Why Your AI Policies Are Failing
As AI shifts from passive chat to autonomous action, written policies are no longer enough. Here is how to move from passive guidelines to active, identity-bound governance.
人工智能素养
The end of the 'Allow' button: Navigating the shift to autonomous execution
As AI systems move from passive chat to autonomous action, the 'human-in-the-loop' is being replaced by internal AI supervisors. Here is how to evaluate the risks of this transition and maintain control over your digital environment.
人工智能经济学
Can an AI product last? A practical guide to durability
AI products can attract millions of users and still face financial, infrastructure, or regulatory limits. Here is how to judge whether an AI service is durable enough to build around.
人工智能素养
The accountability gap: Why autonomous agents require new oversight
As AI shifts from passive chat to autonomous action, our legal and security frameworks are struggling to keep pace. Here is how to evaluate the risks of agentic systems and build meaningful safeguards.
人工智能素养
The Governance Gap: Why Your AI Policies Are Failing
Recent security leaks prove that written AI policies are no match for autonomous agents. Here is how to move from passive guidelines to active, identity-bound governance.
人工智能素养
The new security perimeter: Protecting your system from autonomous agents
As AI moves from passive chat to active task execution, your computer's security model is changing. Here is how to evaluate the risks of agentic tools and protect your local environment.
工具策略
Securing autonomous AI agents: a framework for enterprises
Enterprise AI agents promise productivity gains, but they also create new attack surfaces. This article offers a durable framework for evaluating, monitoring, and governing autonomous agents, drawing on recent product launches and high‑profile security incidents.
人工智能素养
The shift to agentic AI: Moving from chat to action
As AI systems transition from passive chatbots to autonomous agents capable of executing real-world tasks, the risks shift from misinformation to loss of control. Here is how to evaluate the reliability and safety of these new systems.
人工智能素养
The shift from static models to autonomous agents: A framework for control
As AI moves from answering questions to executing tasks, the risks shift from misinformation to loss of control. Here is how to evaluate the safety and reliability of agentic systems.
人工智能素养
Beyond the Doomsday Narrative: A Practical Framework for AI Governance
Public discourse is currently dominated by existential risk narratives and corporate posturing. Here is how to look past the headlines to evaluate the actual, mundane, and immediate risks of AI systems in your organization.
人工智能素养
How to tell whether an AI safety promise is real
AI companies increasingly promise monitoring, safeguards, and responsible deployment. Here is a practical framework for judging whether those promises create evidence, accountability, and meaningful limits.
人工智能素养
When AI becomes public infrastructure, what should we ask?
AI is moving from chat windows into highways, government offices, military records, and workplaces. A practical framework for judging these systems by their evidence, limits, permissions, and effects on people.
人工智能素养
What should schools teach when AI access rules keep changing?
School bans can limit immediate risks, but they cannot replace judgment. A practical framework for teaching students when to use AI, when to question it, and when to leave it out.
人工智能素养
When should an AI system stop, ask, or hand off?
Reliable AI is not just about producing good answers. It is about recognizing uncertainty, checking evidence, and knowing when a response should not become an action.
基准测试
Before AI takes a task, test the whole chain
A model can answer questions well and still fail at real work. Here is a practical framework for evaluating AI systems across evidence, tools, state, timing, and failure recovery.
人工智能素养
Does local AI make your data safer?
Running an AI model on your own device can reduce exposure to outside services, but it does not automatically make the system private, reliable, or safe. Here is a practical framework for judging the tradeoffs.
人工智能素养
When should you trust an AI agent with real work?
An AI agent is not dependable just because it succeeds once. Here is a practical framework for judging its reliability, permissions, security, and readiness for real-world tasks.
工具策略如何在付款前评估人工智能工具
演示是为了给人留下深刻印象，而不是为了提供信息。一个实用的、供应商中立的流程，用于在订阅开始之前根据您的实际工作测试人工智能工具。
提示提示被高估了——上下文才是最重要的
神奇的短语和秘密的提示公式没有抓住要点。人工智能答案的质量主要取决于你提供的信息，而不是你所包含的咒语。
人工智能素养2026 年人工智能素养意味着什么（以及实现这一目标的 30 天计划）
人工智能素养不是编码，也不是快速技巧——而是判断力。由四部分组成的定义和基于免费资源的现实 30 天计划。
人工智能经济学“免费”人工智能产品的真实成本
免费的人工智能工具确实有用，但实际上并不是免费的。您实际支付的数据、依赖和转换成本以及如何明智地使用免费套餐。
基准测试如何阅读人工智能基准而不被愚弄
每款车型的发布都附有一张新车型获胜的图表。基准分数实际衡量的是什么，需要注意的经典技巧，以及唯一重要的基准。
New essays are published regularly — check back soon, or start with the guides below.