Deep image models have dazzled with accuracy, yet the most consequential story sat just out of view: not single neurons lighting up for neat human concepts, but webs of interconnected units assembling meaning layer by layer into circuits that actually drive what the model predicts and why it
Dustin Trainor sits down with Laurent Giraid, a technologist steeped in AI systems, machine learning, and the ethics that keep them safe and useful at scale. With MCP crossing its first year and surging to nearly two thousand servers, the conversation spans the hard edges of taking agentic systems
Hospitalsfaceastarkrealityinmedicalimagingwherelabeleddataarescarceanddomainsdivergewildlyacrosscenters. Across scanners, protocols, and patient cohorts, the visual look of the same anatomy can shift just enough to trip up segmentation systems trained under tidy lab assumptions. A new training
A sharper way to ask the hard question What if the leap in robot reliability came not from ever-larger models but from a smarter split between thinking and doing that keeps language plans on a short leash and loops real-world feedback back into every choice the machine makes? The premise is blunt:
I’m thrilled to sit down with Laurent Giraid, a trailblazing technologist whose expertise in artificial intelligence, machine learning, and natural language processing is reshaping the world of sports. With a keen focus on ethical AI applications, Laurent has been at the forefront of integrating
What if the key to transforming enterprise artificial intelligence lies not in colossal cloud servers, but in the unassuming devices already sitting on desks and in pockets? This provocative idea is at the heart of a groundbreaking shift led by Liquid AI, an MIT spin-off that has captured the
