What is AI?

AI is a broad field covering many techniques. The most visible today is machine learning — where software learns patterns from huge amounts of data rather than following hand-written rules. ChatGPT, image generators, and recommendation algorithms are all machine learning systems.

Within machine learning, deep learning uses artificial neural networks loosely modeled on the human brain. These neural networks power most modern AI breakthroughs — language models, image recognition, and voice assistants all rely on them.

AI is already embedded in everyday tools: your phone's face unlock, spam filters, Netflix recommendations, Google Maps traffic prediction, and autocorrect. The recent wave of "generative AI" (tools that create text, images, and code) is a newer, more powerful branch built on these same foundations.

Large language models like GPT-4 work by predicting the statistically most likely next token in a sequence, based on patterns learned from hundreds of billions of words. This is why they sound authoritative but can be factually wrong — the model is optimizing for plausibility, not truth.

The newest wave is agentic AI: models that don't just respond to prompts but plan multi-step tasks, call external tools (web search, code execution, APIs), and self-correct based on results. The shift from "chatbot" to "agent" is where most of the commercial value in AI is being built right now.