Enterprise AI is moving into a new phase as organizations shift attention from proving AI concepts to putting them into everyday business operations. The change is creating a different implementation challenge. AI prototypes can demonstrate value in weeks. But turning them into secure and dependable production systems requires deeper work across enterprise data, technology infrastructure

When you buy through links on our articles, Future and its syndication partners may earn a commission. Credit: Picture it: a suited exec walks onto a stage and announces a new game about influencing and controlling people, about good and evil. And the clincher: it'll all be driven by machine learning. In 2026, that game

Cornell University is expanding its expertise in artificial intelligence and data science thanks to the arrival of the following faculty members as part of a special collaborative program: danah boyd , Jelena Bradic , Maryam Fazel , Shoham Sabach, and  Jiang-Xun Wang .     “These intellectual giants will bring new strengths to Cornell, not

Google spent September 30, 2026 telling the world about Gemini 4 Argon, a frontier model with a 1-million-token output ceiling built for cybersecurity defenders and long-form reasoning. Three days later, the company quietly told everyone else they would have less to work with. According to a report from 9to5Google, an updated Google support document now

Google spent the last week of September showing off its best AI model. It spent the first week of October taking models away from everyone else. That split-screen rollout, Gemini 4 Argon going out to a small circle of trusted cyber defenders while the free Gemini app gets squeezed down to a single lightweight model

NVIDIA will offer the DGX Spark with 64GB of unified memory starting October 23rd through partners including Acer, ASUS, Dell, Gigabyte, HP and MSI. This new configuration allows developers to run up to 100-billion-parameter models fully on device, bypassing cloud reliance for local AI work. NVIDIA states that DGX Spark combines Grace Blackwell compute, unified

Artificial intelligence has transformed many corners of medicine, yet one stubborn problem continues to limit its reach in clinics: most diagnostic algorithms are built to consume a single, tidy stream of data. Real patients do not come that way. A heart patient arrives with an echocardiogram video, a twelve-lead electrocardiogram tracing, a panel of myocardial

Depression is one of the most widespread and disabling health conditions on the planet, yet its diagnosis still rests almost entirely on subjective clinical interviews and self-reported questionnaires. There is no blood test, no scan, no single objective marker that a physician can point to and say, with confidence, that a patient is depressed. That