As AI rapidly evolves, understanding its growing vocabulary—from LLMs to hallucinations—is becoming essential, and this glossary aims to make the technology more accessible without oversimplifying its complexity. (Source: Image by RR)

Guide Covers Key Terms Including LLMs, Tokens and Neural Networks

TechCrunch has published a comprehensive glossary aimed at demystifying the growing body of terminology surrounding artificial intelligence. As AI systems become more widespread, the industry’s reliance on specialized jargon—from “LLMs” to “hallucinations”—has made it increasingly difficult for non-experts to follow developments. The guide seeks to bridge that gap by defining key concepts in clear, accessible language while acknowledging that some terms remain fluid even among experts.

The glossary, as noted in techcrunch.com, outlines foundational concepts such as large language models, neural networks, and training, explaining how modern AI systems learn from vast datasets and generate outputs through probabilistic pattern matching. It also highlights critical infrastructure elements like compute and tokens, which underpin both the performance and cost structure of AI systems, as well as optimization techniques like distillation, fine-tuning, and transfer learning that allow companies to refine and scale models efficiently.

Beyond core mechanics, the guide addresses emerging paradigms such as AI agents and chain-of-thought reasoning, which reflect a shift toward more autonomous, multi-step systems capable of performing complex tasks. It also explores generative techniques like diffusion and GANs that power modern image, audio, and video creation tools, illustrating how different model architectures are suited to different applications.

Importantly, the glossary emphasizes current limitations and risks, particularly the issue of “hallucinations,” where AI systems generate incorrect or fabricated information. It notes that such challenges are driving interest in more specialized AI systems and raising broader questions about reliability, safety, and scalability. Overall, the guide reflects an industry still in rapid evolution, where both the technology and the language used to describe it are continuously being refined.

read more at techcrunch.com