Observed Signal · Sep 7, 2026 · Policy Update · Source: techcrunch · Impact: 3/5 · Sentiment: Neutral
AI Glossary: Terms from Opaque Recurrence to RAMageddon
This TechCrunch article serves as a comprehensive glossary of common AI terms, defining concepts like AGI, AI agents, chain-of-thought reasoning, and large language models. It introduces emerging terminology such as 'opaque recurrence,' a reasoning technique in OpenAI's new Astra model that concerns safety researchers due to its lack of readable traces. The glossary covers foundational concepts (deep learning, neural networks, training) and industry trends (RAM shortage, token throughput). It provides plain-English explanations to help readers navigate the evolving AI vocabulary.
Provides a comprehensive glossary of AI terms including new safety concerns about 'opaque recurrence', relevant to AI infrastructure and model development in AdTech.
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Key Takeaways & Evidence Grounding
- OpenAI's Astra model uses 'opaque recurrence', a reasoning technique that loops queries internally, alarming AI safety researchers.
- Opaque recurrence is also known as 'recurrent depth' in engineering terms.
- Model Context Protocol (MCP), introduced by Anthropic in 2024, is now adopted by OpenAI, Google, and Microsoft.
- RAMageddon refers to an increasing shortage of RAM chips, caused by high demand from AI data centers, leading to price surges.
- OpenAI has said its Astra model keeps its chain of thought legible, pushing back on 'neuralese' concerns.
Connected Companies & Entities
7 Entities mapped“OpenAI’s new Astra model that’s got AI safety researchers rattled... OpenAI has said its Astra model keeps its chain of thought legible... (...”
“Google DeepMind’s understanding differs slightly from these two definitions; the lab views AGI as “AI that’s at least as capable as humans a...”
“Anthropic introduced MCP in 2024 and later handed it over to the Linux Foundation, and it’s since been adopted by OpenAI, Google, and Micros...”
“Anthropic introduced MCP in 2024 and later handed it over to the Linux Foundation, and it’s since been adopted by OpenAI, Google, and Micros...”
“Mistral AI’s Mixtral model is a well-known example; OpenAI’s newer GPT models are also widely believed to use some version of this approach....”
“Meta’s Llama family of models is a prominent example; Linux is the famous historical parallel in operating systems....”
“Anthropic introduced MCP in 2024 and later handed it over to the Linux Foundation......”
Ontology Mapping & Concepts
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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 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.
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