AI 101 - SiliconNoon (Page 5)

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AI 101 — What is a learned kernel?

A learned kernel is the low-level math software that runs on a GPU and was designed — or tuned by trial and error — by an AI program, rather than hand-written by a human engineer. When a large language model does its math, it doesn't just "think" in a clean way: every layer boils down to enormous matrix multiplications that must be executed millions of times per second. On Nvidia GPUs those operations are performed by special routines called kernels. Today almost all of them come from hand-tune

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AI 101 — What is an AI hallucination?

An AI hallucination is a statement a chatbot or model produces with total confidence that simply isn't true. The model isn't lying, glitching, or behaving maliciously — it is doing exactly what it was trained to do, which is to generate the next most plausible string of words. Sometimes the most plausible thing is wrong, and the result is a confident falsehood you can't always spot without checking. Familiarity should not breed contempt for the term: hallucinations are still the single most imp

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AI 101 — What is model quantization?

A large language model is a giant bag of numbers. Quantization is the trick of shrinking those numbers so the model takes up less memory and runs faster — usually with only a small, barely-noticeable drop in quality. If you have ever tried to run a powerful open-weight model on your own laptop and watched it choke, quantization is the reason some people can and some can't. It is also why a phone can now hold an assistant that, a couple of years ago, needed a rack of servers. This explainer is a

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AI 101 — What are reasoning tokens?

Reasoning tokens are the hidden text a model generates to think through a problem before giving you its answer — invisible, billed to your account, and the reason modern AI can suddenly solve hard questions. When you ask a regular chatbot a question, it reads your words and starts writing a reply immediately. When you ask a reasoning model — OpenAI's o3, DeepSeek's R1, or Google's Gemini with "thinking" enabled — something different happens first: the model spends time generating an internal mo

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AI 101 — What is synthetic data?

Synthetic data is information generated by a computer — not collected from the real world — that mimics the patterns, structure, and statistical properties of real data well enough to train an AI model. It is one of the most important tools in modern AI development, and one of the least understood. Every major lab is using it, many products depend on it, and the debate over whether it is good enough to replace real-world data is shaping where the field goes next. Why it matters right now Tra

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AI 101 — What is an AI agent?

An AI agent is a system that uses a language model to do a job for you — not just answer a question, but pick the next step, use tools, and keep going until the work is finished. A chatbot waits for you. An agent takes the goal and acts. That is the whole difference, and it is why the word is on every product page this year. Why it matters right now Software used to automate work by following a script a human wrote in advance. Agents flip that: the model writes the next step as it goes. Open

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AI 101 — What is fine-tuning?

Fine-tuning is the step where an AI model that was already trained on a huge, general dataset gets trained a little more on a smaller, specific dataset — so it becomes an expert at one particular job instead of a jack-of-all-trades. It is the most common word in AI product development, and it is the difference between a model that knows everything and a model that knows your business. Every serious AI product you have used this year has probably been fine-tuned at least once. Why it matters r

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AI 101 — What is test-time training?

Test-time training (TTT) is a technique where an AI model keeps learning — updating its own weights — while it is actually working on a task, instead of arriving fully formed from its one-time training phase. It is the difference between a worker who shows up with a fixed instruction manual and one who reads the manual, learns on the job, and is subtly smarter by the end of the shift. Today's models are the first kind; test-time training aims to build the second. Why it matters right now In

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AI 101 — What is MCP?

MCP — the Model Context Protocol — is an open standard that lets AI assistants plug into external tools and data sources the way a USB-C cable plugs into any device. It gives a chatbot or agent a universal socket for databases, calendars, code repos, calculators, and thousands of other systems, instead of a bespoke adapter for each one. If you've read about agents that "check your calendar," "look up the docs," or "run code," MCP is the plumbing underneath. Why it's in the news MCP is rarely

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AI 101 — What is RAG?

RAG — retrieval-augmented generation — is a technique that lets an AI model look things up in a document store before it answers, by pulling the most relevant passages into the conversation. Instead of relying only on what it memorized during training, the model gets the right pages placed in front of it and writes its answer with those pages open. It is the standard way companies connect chatbots and agents to their own data. Why it's in the news RAG rarely makes headlines on its own — it's

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AI 101 — What are open-weight models?

An open-weight model is an AI model whose trained parameters — the billions of numbers that encode everything it has learned — are released for anyone to download, run, modify, and build on. The weights are the model: once they are public, the technology itself is out in the world, not locked behind a company's servers. That single distinction divides AI into two camps — the closed, API-only frontier and the open ecosystem — and it is the most consequential licensing question in the field right

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AI 101 — What is a context window?

A context window is the amount of text a language model can look at at one time — its working memory. It is measured in tokens (roughly three-quarters of an English word each), and it caps how much of your prompt, your conversation, or your document the model can actually see when it writes a reply. Everything you want the model to consider must fit inside that window, or it gets cut off, summarized, or ignored. Why it's in the news Context size has quietly become one of the biggest selling p

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