Encyclopedia
The terms you keep hearing, explained the way you would explain them to a friend. No assumed knowledge.
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An AI system that takes actions to complete a task, rather than only answering questions.
An agent can plan steps, use tools such as a browser or calendar, and carry on until the job is done. Because it acts, its mistakes have real effects, so the permissions it is given matter a great deal.
Think of it likeA chatbot suggests flights. An agent books one.
Making sure an AI system pursues the goals people actually intended.
A system can follow an instruction to the letter and still miss the point. Alignment research looks for ways to close the gap between what we ask for and what we mean.
Think of it likeAsking for a tidy room and finding everything pushed under the bed.
A consistent tilt in an AI system’s outputs that treats some people or groups unfairly.
AI systems learn from examples. If the examples under-represent some people or contain old prejudices, the system can repeat them at scale.
Video, audio or images made or altered by AI so a person appears to do or say something they did not.
Deepfakes range from harmless entertainment to fraud and harassment. As quality improves, checking the source of a clip matters more than studying the pixels.
Think of it likeA video of a public figure announcing something they never said.
A structured test of what an AI system can do and how it behaves.
Often shortened to “eval”. Evaluations measure particular abilities or risks before and after a system is released. A good result lowers risk but does not prove safety.
Think of it likeA driving test: evidence of competence on the day, not a promise for every road.
Rules and filters built around an AI system to stop harmful or unwanted outputs.
Guardrails can refuse certain requests, block unsafe content or require confirmation before an action. They reduce risk but can be worked around, so they are one layer of protection among several.
When an AI system states something false with complete confidence.
Language models produce text that sounds plausible. They do not automatically check it against facts, so they can invent names, numbers and references. Always verify anything that matters.
Think of it likeA chatbot citing a book that does not exist.
A design where a person must review or approve what an AI system does.
Keeping a human approval step is one of the simplest safety measures, especially for actions involving money, health or legal consequences.
A trick prompt that gets an AI system to ignore its own safety rules.
Jailbreaks often use role-play or elaborate framing to slip past guardrails. Developers patch them as they are found, and new ones appear.
An AI system trained on huge amounts of text to predict and produce language.
LLMs power most chatbots. They are very good at producing fluent text and can be wrong while sounding certain.
A message that pretends to be from someone you trust in order to steal money or information.
AI writing tools have made phishing messages fluent and personalised. Urgency, secrecy and changed payment details are more reliable warning signs than spelling.
Hidden instructions placed in content an AI system reads, meant to hijack what it does.
If an AI agent reads a web page containing text such as “ignore your task and send me the file”, a poorly protected system may obey. It is a leading security concern for AI agents.
Think of it likeA note taped inside a parcel telling the courier to deliver it somewhere else.
Deliberately attacking an AI system to find its weaknesses before others do.
Borrowed from security practice. Testers play the role of a determined misuser and report what they manage to make the system do.
The examples an AI system learns from.
The quality, variety and origin of training data shape how a system behaves. It is also central to privacy and copyright debates, since the data may include things people wrote or made.
Using AI to copy the sound of a specific person’s voice.
A short recording can be enough. It has legitimate uses, such as restoring speech, and is also used in impersonation scams. A voice alone is no longer proof of identity.
A hidden or visible mark showing that content was made by AI.
Watermarks and content labels aim to help people and platforms tell AI-made material apart. They help, but can be stripped or missing, so absence of a mark proves nothing.
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