Observed Signal · Apr 12, 2026 · Publication · Source: techcrunch · Impact: 2/5 · Sentiment: Neutral
TechCrunch Guide: Common AI Terms Explained
TechCrunch published a regularly updated glossary that defines core artificial intelligence terms used across its coverage. The glossary explains concepts ranging from high-level goals like AGI and AI agents to technical building blocks such as neural networks, weights, tokens, distillation, fine-tuning, diffusion models, GANs, and inference. It cites differing AGI definitions from OpenAI (including a Sam Altman quote and OpenAI’s charter) and Google DeepMind, gives examples of popular LLMs and assistants (ChatGPT, Claude, Gemini, Meta’s Llama, Microsoft Copilot, Mistral’s Le Chat), and covers operational topics such as compute, memory caching (KV caching), and supply pressures dubbed “RAMageddon.” The article frames hallucinations as a major quality risk and notes model-development techniques (transfer learning, distillation, fine-tuning) used to specialize or compress models.
Informational glossary that clarifies AI concepts relevant to AdTech/MarTech practitioners; useful background but not a breaking product, policy, or platform announcement.
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Key Takeaways & Evidence Grounding
- TechCrunch published a glossary of common artificial intelligence terms and said it will update the glossary regularly.
- The glossary presents multiple definitions of AGI, citing OpenAI (including a quote from CEO Sam Altman and OpenAI’s charter) and Google DeepMind’s definition.
- It defines and explains technical concepts including LLMs, chain-of-thought, distillation, fine-tuning, diffusion models, GANs, tokens, inference, memory/KV caching, and weights.
- The article lists examples of large language models and assistants: ChatGPT, Claude, Google’s Gemini, Meta’s AI Llama, Microsoft Copilot, and Mistral’s Le Chat.
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TechCrunch AI Glossary of Common Terms
TechCrunch published a living glossary of common artificial intelligence terms on 2026-05-09. The article defines and explains a wide range of AI concepts — including AGI, AI agents, LLMs, chain-of-thought reasoning, RAG, RLHF, inference, training, fine-tuning, distillation, diffusion, GANs, hallucinations, KV caching, token throughput and a coined supply-term “RAMageddon.” It cites definitions and perspectives from sources such as OpenAI (including a quote from CEO Sam Altman) and Google DeepMind, provides practical context for developer-facing concepts (e.g., API endpoints, coding agents), and notes the glossary is updated regularly as the field evolves.
TechCrunch glossary explains common AI terms
TechCrunch published a living glossary of common artificial intelligence terms on May 29, 2026. The article provides plain-language definitions and context for dozens of AI concepts — from AGI, LLMs, and hallucinations to technical topics like inference, KV caching, token throughput, distillation, and recursive self-improvement. It cites example systems (ChatGPT, Claude, Google’s Gemini, Meta’s Llama, Microsoft Copilot, Mistral’s Le Chat), contrasts differing AGI definitions from OpenAI and Google DeepMind, and highlights infrastructure issues such as compute demands and RAM supply constraints (coined “RAMageddon”). The glossary is described as a living document that TechCrunch updates regularly as the field evolves.
TechCrunch publishes updated AI glossary
TechCrunch published an updated, regularly maintained AI glossary on 2026-07-03 that explains core AI and generative-AI concepts in plain language. The guide defines terms ranging from AGI, LLMs, chain-of-thought and reinforcement learning to infrastructure topics like compute, token throughput, KV caching and supply-chain concerns dubbed “RAMageddon.” It highlights interoperability developments such as the Model Context Protocol (MCP) — introduced by Anthropic in 2024, handed to the Linux Foundation, and adopted by OpenAI, Google and Microsoft — and discusses model architectures like Mixture of Experts (MoE). The piece is positioned as a living reference for builders, investors and readers trying to keep pace with AI terminology and technical trends.
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