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Why a Living AI Glossary Matters NowArtificial intelligence is reshaping every industry, but its rapid evolution has spawned a parallel explosion of terminology that can leave even seasoned technologists feeling insecure. TechCrunch’s new glossary aims to provide a single, regularly‑updated reference that translates the most common AI buzzwords into plain language.Key Definitions from AGI to RLHFThe article walks readers through a spectrum of concepts, including:Artificial General Intelligence (AGI) – AI that outperforms humans on most economically valuable tasks, as defined by OpenAI and Google DeepMind.AI Agent – An autonomous tool that can perform multi‑step tasks such as expense filing, ticket booking, or code maintenance.API Endpoints – “Buttons” that let software components interact, enabling agents to automate third‑party services.Chain‑of‑Thought Reasoning – A technique that breaks problems into intermediate steps to improve accuracy.Compute – The hardware (GPUs, CPUs, TPUs) that powers AI model training and inference.Deep Learning – Multi‑layered neural networks that learn features directly from data.Diffusion – The process behind many generative AI models that learns to reverse noise‑added data.Distillation – A teacher‑student method for creating smaller, faster models like GPT‑4 Turbo.Fine‑Tuning – Adding task‑specific data to a pre‑trained model to improve performance.GAN – Generative Adversarial Networks that pit a generator against a discriminator to produce realistic outputs.Hallucination – When models generate inaccurate or fabricated information.Inference – Running a trained model to make predictions, often accelerated by specialized hardware.LLM – Large Language Models that power assistants such as ChatGPT, Claude, Gemini, and Llama.Memory Cache (KV Caching) – An optimization that stores intermediate calculations to speed up inference.Open Source vs. Closed Source – The debate over publicly available model code (e.g., Meta’s Llama) versus proprietary systems (e.g., OpenAI’s GPT).Parallelization – Executing many calculations simultaneously, a cornerstone of modern AI hardware.RAMageddon – The current shortage of memory chips driven by AI data‑center demand.Recursive Self‑Improvement (RSI) – Models that can redesign themselves, a potential step toward singularity.Reinforcement Learning from Human Feedback (RLHF) – Training models with reward signals to improve helpfulness and safety.Tokens & Throughput – The basic units of text processing that determine cost and performance.Quantifying the AI Vocabulary ExplosionThe glossary covers more than 30 distinct terms, each accompanied by concise explanations and links to deeper resources. By cataloguing this breadth, the piece highlights how quickly the AI lexicon has expanded within just a few years of mainstream adoption.Implications for Developers, Investors, and the PublicUnderstanding this terminology is no longer optional. For developers, clear definitions accelerate product building and reduce miscommunication when integrating APIs or deploying agents. Investors gain a sharper lens for evaluating startup pitches that hinge on concepts like fine‑tuning or distillation. Meanwhile, the broader public can better assess claims about “AGI” or “hallucinations,” mitigating hype‑driven misinformation.Future of AI Terminology and Industry AdoptionTechCrunch positions the glossary as a “living document,” promising regular updates as new techniques (e.g., emerging diffusion variants or next‑gen RLHF methods) appear. As AI systems become more autonomous and specialized, the vocabulary will continue to evolve, making ongoing education essential for anyone interacting with the technology.

Record Quarter Highlights Amid Executive TransitionApple reported its best March quarter ever, posting $111.2 billion in revenue and double‑digit growth across every geographic segment. Tim Cook highlighted the iPhone 17 lineup as the primary driver of the sales surge, while also announcing his move to executive chairman and the upcoming promotion of John Ternus to CEO on September 1.Financial Snapshot and Memory‑Chip Cost SurgeRevenue: $111.2 billion (record for a March quarter)iPhone 17 sales: all‑time March‑quarter revenue recordMemory‑chip spend: higher in March than any prior quarterRAM price trend: costs have quadrupled in recent months, with expectations of "significantly higher memory costs" from June onwardSupply‑Chain Strain from the AI‑Driven "RAMageddon"The AI boom is driving unprecedented demand for DRAM, a phenomenon dubbed RAMageddon. Apple, as a hardware‑centric company, now faces tighter supply and higher component prices, which could erode profit margins despite strong top‑line growth.Potential Market Repercussions and Pricing PressureHigher memory costs may force Apple to adjust iPhone pricing or absorb margin pressure. John Ternus acknowledged the reduced flexibility in the supply chain, hinting at possible price increases for future iPhone models.Outlook Under New LeadershipWith John Ternus at the helm, Apple will need to balance its record sales momentum against the looming chip shortage. Strategies may include diversifying memory suppliers, leveraging stockpiled inventory, and potentially passing some costs to consumers. The next quarter will reveal how effectively the new CEO can mitigate the RAM supply shock while sustaining growth.