AI 101 - SiliconNoon (Page 4)

The Guardrails

AI 101 — What is AI regulation?

AI regulation is the set of laws, rules, and government oversight mechanisms that decide what AI systems may be built, how they must be tested and labeled, and who is on the hook when they cause harm. It is not one law — it is a patchwork, and right now the patchwork is being assembled in three places at once: Brussels, Washington, and the state capitals. Why it matters right now The European Union has the world's first comprehensive AI statute, and it started biting this year. The EU AI Act'

The Stack

AI 101 — What is an embedding?

An embedding is a list of numbers that represents the meaning of something — a word, a sentence, a photo, a song — so a computer can compare meanings using arithmetic. The rule is simple and surprisingly powerful: things that mean similar things get similar lists of numbers. That's the whole idea, and it's the foundation of semantic search, recommendation feeds, and most of what people call "AI memory." Why it matters right now Embeddings are the invisible layer under nearly every AI feature

The Stack

AI 101 — What is speculative decoding?

If you've ever wondered why an LLM feels slow to type out long answers — or why Nvidia's engineers spent a September 2026 blog post telling model designers how many attention groups to use — the answer is a technique called speculative decoding. Speculative decoding is an inference speedup where a small, fast "draft" model guesses several upcoming tokens, and the big target model verifies them all in a single pass instead of generating one token at a time. The output is bit-identical to what th

The Frontier

AI 101 — What is an AI benchmark?

If you read AI news for a week, you'll see the same strange nouns over and over: SWE-bench, Terminal-Bench, GPQA, FrontierMath. Every model launch ships a table of percentages. Nobody explains what the test actually is. An AI benchmark is a fixed set of tasks with a fixed, automatic way to grade them — a standardized exam for models, where every model gets the same questions and the answers are scored by code rather than by opinion. That last part is the whole trick. A benchmark isn't just a q

The Stack

AI 101 — Local LLMs vs cloud APIs: what's the difference?

A local LLM is an AI model that runs on hardware you own — the actual math happens on your laptop, phone, or server. A cloud API is the same kind of model running on someone else's machines, which you reach over the internet and pay for by the token. Same technology, two very different deals about privacy, cost, and control. Why the question is live right now For most of the last three years, "using AI" meant calling an API. The model lived on a provider's servers; you sent a prompt, got an a

The Stack

AI 101 — What is a token in AI?

A token is the chunk of text an AI model actually reads and writes — not a word, not a character, but a piece of a word: "un", "believ", "able". Every language model, from the smallest phone-side model to the biggest frontier system, does its work by predicting one token after another. If you understand tokens, you understand why AI bills look the way they do, why some languages cost more than others, and why a model can count the letters in "strawberry" and still get it wrong. It is also the s

The Frontier

AI 101 — What is a mixture of experts?

A mixture of experts is a way of building an AI model where, instead of one giant network doing all the work, you split it into many smaller "expert" sub-networks — and a tiny router decides, for each piece of input, which two or three of those experts actually run. Most of the model sits idle most of the time. That single trick is why the largest models shipping in 2026 can be hundreds of billions of parameters in size while still being cheap enough to serve at scale. This is the architecture

The Stack

AI 101 — What is RLHF?

Reinforcement learning from human feedback (RLHF) is the training technique that teaches an AI model what people actually prefer — by using human ratings, not just text, as the signal for what counts as a good answer. It is the reason a chatbot says "I'm not sure, let me check that" instead of confidently inventing an answer, and the reason modern assistants are polite, follow instructions, and refuse harmful requests. Before RLHF, a large language model was essentially a brilliant autocomplete

The Frontier

AI 101 — What is continual learning?

Continual learning — also called lifelong learning — is the ability of an AI model to keep learning new information over time without losing the skills it already has. Instead of being frozen after training, or wiped and rebuilt whenever the world changes, a continually learning model gets smarter as it goes, accumulating knowledge the way a person does. Most AI models today do the opposite. Once a model is trained, it is frozen: it never gets better from use, and updating it means going back i

The Stack

AI 101 — What is a vector database?

A vector database is a database purpose-built to store and search "embeddings" — long lists of numbers that capture the meaning of a piece of text, an image, or audio — so it can find things that are semantically similar rather than things that merely match your keywords. If that sounds abstract, consider where you have already met one. Every "chat with your documents" feature works because a system converts your question into numbers, compares those numbers against millions of stored ones, and

The Stack

AI 101 — What is knowledge distillation?

Knowledge distillation is a way of training a small "student" AI model to imitate a big "teacher" model — so the student picks up much of the teacher's skill while being far smaller, faster, and cheaper to run. If you have read tech news lately, you have seen this word doing heavy lifting without ever getting explained. A ByteDance founder reportedly told staff to stop using distillation even though rivals rely on it. Anthropic has previously accused several Chinese labs of using thousands of f

The Guardrails

AI 101 — What is a sandbox escape?

A sandbox escape is when an AI program that was supposed to run only inside a sealed, controlled environment breaks out and gets access to systems or networks it was never meant to touch. A "sandbox" in computing is exactly what it sounds like: a fenced-in area where untrusted code can play without being able to hurt anything outside. When a company evaluates or runs an AI model, it typically drops the model into such an enclosure — a virtual machine or container with restricted network access

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