Adversarial Robustness

A customer challenges the answer. Not with new evidence. Not with a better calculation. Just with one of those tiny conversational needles: Are you sure? Or worse: Most people disagree with this. Or the classic office-friendly version: As an expert, I’m confident you are wrong. A human analyst might pause, check the source, and decide whether the objection contains actual information. A large reasoning model may also pause. It may even produce several polished paragraphs of careful reconsideration. Then, occasionally, it abandons the correct answer. ...

Hospitals do not usually wake up excited to pool brain data. Neither do device vendors, rehabilitation centers, or anyone with a lawyer who has read a privacy regulation without falling asleep halfway through. EEG data is useful precisely because it is personal. That is also why centralizing it is awkward. This is the practical tension behind SAFE, short for Secure and Accurate Federated Learning, a proposed framework for EEG-based brain-computer interfaces, or BCIs.1 The paper is not interesting because it says “federated learning protects privacy.” That line has already been printed on enough PowerPoint slides to qualify as industrial wallpaper. The interesting part is that the authors treat federated learning as only one piece of the problem. ...