AI Understanding 블로그
명확한 사고 AI에 대해.
AI를 효과적으로 사용하는 방법에 대한 독창적인 에세이 - 도구를 판단하고, 벤치마크를 읽고, 모델에 더 나은 맥락을 제공하고, 실제 읽고 쓰는 능력을 키우는 방법. 평범한 영어, 과대 광고, 전문 용어 없음.
- 24에세이
- 5주제
- 무료No paywall, ever
에세이
모든 게시물
이번 주 모델 출시가 잊혀진 후에도 오랫동안 유용하게 유지될 에버그린 글쓰기입니다.
AI 리터러시
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.
AI 리터러시
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.
AI 리터러시
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.
AI 경제학
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.
AI 리터러시
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.
AI 리터러시
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.
AI 리터러시
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.
도구 전략
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.
AI 리터러시
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.
AI 리터러시
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.
AI 리터러시
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.
AI 리터러시
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.
AI 리터러시
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.
AI 리터러시
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.
AI 리터러시
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.
벤치마크
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.
AI 리터러시
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.
AI 리터러시
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.
도구 전략지불하기 전에 AI 도구를 평가하는 방법
데모는 정보를 제공하기 위한 것이 아니라 감동을 주기 위해 만들어졌습니다. 구독이 시작되기 전에 실제 작업에 대해 AI 도구를 테스트하기 위한 실용적이고 공급업체 중립적인 프로세스입니다.
프롬프트프롬프트는 과대평가되었습니다. 맥락이 중요합니다
마법의 문구와 비밀 프롬프트 공식은 요점을 놓치고 있습니다. AI 답변의 품질은 대부분 답변을 포장하는 주문이 아니라 제공하는 정보에 따라 결정됩니다.
AI 리터러시2026년 AI 활용 능력이 의미하는 것(그리고 거기에 도달하기 위한 30일 계획)
AI 리터러시는 코딩이나 신속한 트릭이 아니라 판단입니다. 무료 리소스를 기반으로 구축된 4부분으로 구성된 정의와 현실적인 30일 계획입니다.
AI 경제학"무료" AI 제품의 실제 비용
무료 AI 도구는 정말 유용하지만 실제로는 무료가 아닙니다. 데이터, 의존성, 전환 비용으로 실제로 지불하는 비용과 무료 계층을 현명하게 사용하는 방법.
벤치마크속지 않고 AI 벤치마크를 읽는 방법
모든 모델 출시에는 새 모델이 승리하는 차트가 함께 제공됩니다. 벤치마크 점수가 실제로 측정하는 것, 주목해야 할 고전적인 트릭, 중요한 유일한 벤치마크입니다.
새로운 에세이는 정기적으로 게시됩니다. 나중에 다시 확인하거나 아래 가이드로 시작하세요.
기초
기본을 먼저 원하시나요?
블로그는 의견이자 전략입니다. 가이드는 기초입니다. AI가 무엇인지, 어떻게 학습하는지, 어디서 실패하는지 모든 사람에게 설명합니다.
무료로 학습을 시작해 보세요