glossary.

An AI system that can match humans in any intellectual task, rather than the current situation where AI is brilliant at some things and not yet able to match us humans at others. The definition is not agreed around the world and certainly not between labs, with some claiming we're there already and others disagreeing. Tests have been proposed such as Google DeepMind's 10 part cognitive tests for example, which would evaluate any supposed AGI against categories such as reasoning and social cognition. To reach the singularity, we need to have got to at least AGI first, followed by ASI.

An AI-powered word predictor. An LLM is a type of AI model trained on huge amounts of text to be able to become a prediction machine for more text based on what you tell and ask it in your prompt and context. LLMs can answer questions, summarise, write code, all based on language. But it is just a predictor and an LLM is not a search engine or database and it recreates patterns it's observed without having any understanding or consciousness of the world around it. In an LLM, words are chunked down into tokens, essentially giving them coordinates in space allowing it to then work out which word is most likely to come next. The model is assembled with statistics and a neural network (a mesh of possibilities) where words and numbers become tokens with weights showing probability of being next. Usually when we refer to "model" in AI, we are actually using shorthand to refer to something bigger than just an LLM. For example, ChatGPT can be powered by a GPT-5.6 Sol model and within the 5.6 Sol model there will be an LLM but also a load of other ingredients that help the whole thing work together as one cohesive system.