The rapid advancement of artificial intelligence has introduced a host of complex and often confusing terms into tech conversations. A recent TechCrunch article aims to demystify some of these concepts by providing clear, plain-English definitions of key AI terminology. It covers foundational ideas like artificial general intelligence (AGI), which refers to AI systems with capabilities surpassing the average human across many tasks, and large language models (LLMs) that power popular AI assistants such as ChatGPT and Google’s Gemini. The glossary also touches on the emerging notion of “opaque recurrence,” a reasoning technique employed by OpenAI’s Astra model that has raised safety concerns due to its less transparent internal processing.

Among the essential terms explained is “AI agent,” which describes autonomous systems capable of completing multi-step tasks independently, such as booking reservations or writing code. The article highlights how specialized variations like coding agents automate complex software development activities by drafting, testing, and debugging code with minimal human input. It also explores technical concepts like “chain of thought” reasoning, whereby models break problems into sequential steps for higher accuracy, and “reinforcement learning,” a training method where AI improves by trial and error guided by rewards.

The glossary addresses challenges that affect AI quality, such as “hallucinations,” instances where models generate false or misleading information because of gaps in training data. To counteract these drawbacks, developers often rely on “fine-tuning” and “transfer learning” to adapt AI models with domain-specific knowledge, improving performance for targeted applications. Moreover, it sheds light on technical infrastructure terms like “compute,” referring to the hardware power needed for training AI, and “token throughput,” a measure of how efficiently language models process input and output data—critical for handling large numbers of simultaneous users.

Importantly, the article underscores ongoing safety and transparency debates within AI development. The opaque recurrence method, while compute-efficient, complicates the ability to monitor AI decision-making since it lacks a clear, human-readable reasoning trail. This poses risks in areas where oversight is key to preventing misbehavior. The glossary is designed as a living resource that will evolve with the technology, helping developers, investors, and users keep pace with the rapidly shifting AI landscape.