Computer vision: teaching machines to see
From unlocking your phone with your face to spotting tumors on a scan, computer vision turns pixels into understanding.
Data science sits at the intersection of statistics, programming, and domain knowledge. Here's a realistic map of the field for anyone considering it.
From unlocking your phone with your face to spotting tumors on a scan, computer vision turns pixels into understanding.
No labeled dataset, no fixed answer key — just trial, error, and a reward signal. That's how machines learned to master games humans spent decades perfecting.
Two of the most basic categories in machine learning come down to one question: does the training data come with the right answers attached?
From the EU's AI Act to sector-specific rules elsewhere, governments are starting to draw lines around how AI can be built and used.
You don't need to work at a big lab to run a capable AI model anymore. Open weights are reshaping who gets to build with this technology.
A chatbot answers questions. An agent takes actions — booking, browsing, filing, coding — on your behalf, in multiple steps, with far less hand-holding.
AI models learn from data created by people, in a world full of historical inequities. That means bias isn't a bug that sneaks in — it's a default to actively guard against.
Tools like GitHub Copilot changed how many developers write code day to day. They're not replacing programmers — they're changing what the job looks like.
Typing a sentence and getting a finished image back feels like magic. Underneath, it's a process called diffusion, run in reverse.