Today’s AI landscape is littered with acronyms and jargon that can confound anyone outside a narrow circle of researchers. To address that gap, a new online glossary has been published, laying out plain‑language definitions for more than a dozen core concepts that dominate current discussions.
The reference starts with the big picture, defining artificial general intelligence (AGI) as highly autonomous systems capable of outperforming humans on most economically valuable tasks. It cites OpenAI’s charter and the perspectives of its CEO Sam Altman, as well as Google DeepMind’s slightly different take that frames AGI as at least as capable as humans on most cognitive tasks.
Beyond AGI, the glossary breaks down what an “AI agent” actually does. Unlike a basic chatbot, an AI agent can string together multiple actions—booking a restaurant, filing expenses, or even writing code—by invoking API endpoints behind the scenes. The entry stresses that the term remains fluid, reflecting the evolving capabilities of autonomous systems that can orchestrate multistep workflows.
Another highlighted entry explains chain‑of‑thought reasoning for large language models. By breaking complex problems into intermediate steps, models improve answer accuracy, especially in logical or coding contexts. The entry notes that reinforcement‑learning techniques fine‑tune these reasoning models for better performance.
Developers will also find practical details about the infrastructure that powers AI. The glossary defines “compute” as the hardware—GPUs, CPUs, TPUs—that fuels model training and inference. It touches on memory‑caching techniques like KV caching that speed up inference by reusing calculations, a crucial efficiency boost as models grow larger.
Other sections cover deep learning, diffusion models, generative adversarial networks, and the phenomenon of AI “hallucinations,” where models generate incorrect information. Each definition is concise yet specific, avoiding vague language and focusing on real‑world implications.
Importantly, the resource is positioned as a living document. Its maintainers promise regular updates to reflect the fast‑moving nature of AI research, ensuring that the glossary stays relevant as new terms emerge and existing ones evolve.
By consolidating these definitions in one place, the glossary aims to reduce confusion and foster clearer communication across the AI ecosystem.
This article was written with the assistance of AI.
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