AI 101 - SiliconNoon

The Guardrails

AI 101 — What is data poisoning?

Data poisoning is slipping malicious examples into the data an AI system learns from, so the model quietly picks up a lesson the attacker chose — while behaving completely normally on everything else. The finished model passes its tests; the bad habit is baked into its weights, waiting for the right trigger. Why it matters right now Poisoning used to sound like a theoretical worry aimed at giant pretraining runs. Three developments in the last few years moved it into everyday territory. F

The Everyday

AI 101 — What is voice cloning?

Voice cloning is AI that learns how a specific person sounds from a short sample of them talking — then reads any new sentence in that voice. Type a sentence you never spoke, and it comes back in your cadence, your accent, your pitch. The voice is the output; the sample was just the lesson. Why it matters right now Voice cloning stopped being a research demo and became a price line in September, when Google shipped Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite — text-to-speech models where

The Guardrails

AI 101 — What is a jailbreak?

A jailbreak is a prompt — or a carefully arranged stack of inputs — engineered to talk an AI system past its own safety rules, so that it produces content or takes actions it would normally refuse. Nothing is broken in the technical sense: the model, the servers, and the locks all keep working. What gets broken is the instruction to say no. Why it matters right now The word "jailbreak" shows up constantly in AI coverage — in stories about chatbots misbehaving, about guardrails, about agents

The Frontier

AI 101 — What is a foundation model?

A foundation model is one AI model trained on a huge, general pile of data and then adapted to many different jobs — a single base that chatbots, coding assistants, search features, and scientific tools are all built on top of. Instead of building a separate model for every task, companies build (or license) one foundation model and specialize it. Why it matters right now. The AI industry stopped shipping individual products and started shipping model families. This week alone, Anthropic relea

The Guardrails

AI 101 — What is an AI guardrail?

An AI guardrail is a check that sits around a language model — screening what goes in, reviewing what comes out, and controlling what the model is allowed to do — so the system stays inside the lines even when the model itself strays. The model proposes; the guardrail disposes. It is the difference between a system that usually behaves and one that has to. Why it matters right now. AI is moving from answering questions to taking actions: this week alone, Utah's new law lets AI systems issue pr

The Frontier

AI 101 — What are scaling laws?

Scaling laws are the empirical rules describing how an AI model's performance improves in a smooth, predictable curve as three things grow: the size of the model, the amount of data it trains on, and the computing power spent training it. They are not physics — nobody proved them in a lab the way gravity was proven — they are patterns that kept holding every time someone measured them, which is why the entire AI industry now plans around them. The pattern got its canonical statement in a Janua

The Stack

AI 101 — What is prompt engineering?

Prompt engineering is the practice of writing instructions for an AI model so it gives you what you actually wanted — from a one-line question in a chat box to the multi-paragraph specifications companies embed in their products. "Prompt" is just the text you send the model; "engineering" is the unglamorous work of making that text specific enough to work. Why it matters right now. Prompting is still how almost everyone touches AI, and the craft keeps getting documented rather than disappearin

The Everyday

AI 101 — What is vibe coding?

Vibe coding is writing software by describing what you want in plain language and letting an AI write the code — while you judge the result by whether it works, not by reading every line. Andrej Karpathy, co-founder of OpenAI, named it in a February 2025 post; by November, Collins Dictionary had named "vibe coding" its word of the year for 2025. Why it matters right now. Code is the one thing AI does that people actually depend on, and the numbers show it: Stack Overflow's 2025 Developer Surve

The Stack

AI 101 — What is AI inference?

Every answer an AI gives you is inference: running a trained model on new input to produce an output. Training is how a model learns; inference is how it works. If training is teaching, inference is doing. Why it matters right now. Training makes the headlines — a new model, a bigger cluster, a record run — but training happens once per model, while inference happens every time anyone asks a question. That asymmetry is why the money has shifted: Google Cloud describes inference as the phase "wh

The Frontier

AI 101 — What is AGI?

AGI — short for artificial general intelligence — is a hypothetical AI system that could do any intellectual job a person can do, instead of being good at one narrow task. Every AI you can actually use today is narrow: it writes, translates, spots patterns in scans, plays games — each system built for its lane. AGI names the destination where one system covers all the lanes. It is a goal nobody has reached, not a product anyone can buy. Why it matters right now "AGI" is one of the most-used w

The Frontier

AI 101 — What is a large language model?

A large language model (LLM) is a very big neural network trained on enormous amounts of text to do one job well: predict the next piece of a text, given everything that came before it — then tuned so that when you talk to it, it actually helps instead of rambling. ChatGPT, Claude, Gemini, Llama, DeepSeek: every chatbot in the news is an LLM, and most AI features in the software you already use have one quietly bolted inside. That prediction job sounds too simple to matter, and it is the whole

The Stack

AI 101 — What is a neural network?

A neural network is a computing system made of layers of simple math functions whose internal numbers are tuned by examples until the whole thing gets the right answers — a design loosely inspired by the brain, and the foundation of every AI system in the news. ChatGPT, Midjourney, AlphaFold: all neural networks, all variations on the same idea. The concept is older than computers. In 1943, Warren McCulloch and Walter Pitts described an artificial neuron — a thing that takes inputs and fires w

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