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Supply chains detect fast, act slow: How AI agents fix it

Supply chain disruption cost businesses about $184 billion in 2025, according to the J.S. Held Global Risk Report, and most of that bill still buys faster detection, not faster action. That figure is usually treated as weather (i.e. storms happen, costs follow.) Treated as a product specification instead, it highlights an operating model that can […]

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Motional and MIT AI explains self-driving car decisions

Motional and MIT researchers have built a system that lets self-driving cars explain their decisions in real-time, tackling the black-box problem in autonomous vehicle AI. The work, published in Nature, comes from a team at Motional that includes CEO Laura Major, working alongside researchers from MIT’s Computer Science and Artificial Intelligence Laboratory. Their proposed method,

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MIT AI forecasts extreme weather without historical data

MIT engineers have built an AI tool that forecasts extreme weather without training on historical disaster data. Kai Chang, a mechanical engineering graduate student, and Professor Themis Sapsis developed the tool. It produces maps of events that have not appeared in a region’s historical record but remain statistically-possible. Each map also carries estimates of the

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Samsung health AI models analyse wearable biosignal data

Samsung Research America’s Digital Health Team has presented two AI foundation models designed to learn from wearable biosignals. The work centres on data captured by smartwatches, including heart activity, sleep, and physical activity. The company discussed its Connected Care vision at the Health Forum during Galaxy Unpacked in July 2026. Samsung described a future of

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Okta targets AI agent token costs with MCP scoping

Okta says identity-scoped Model Context Protocol (MCP) tool lists can reduce AI agent token costs. Each model call made by an AI agent can include schemas, names, descriptions and parameters for every tool exposed by a MCP server. Okta calls the resulting prompt overhead the “tool tax”: tokens consumed as a model considers tools, including

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Google tests AMIE for clinical video consultations

Google’s research medical AI system, AMIE (Video), conducted synchronous video consultations with professional patient actors and received clinical evaluator ratings on par with primary care physicians across several core measures. Fifteen trained actors portrayed conditions across cardiopulmonary, abdominal, HEENT, neurological or psychiatric, and musculoskeletal presentations. Google says studies involving real patients and their own health

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PRISM2 model uses clinical dialogue to interpret pathology slides

Built by Paige and Microsoft, PRISM2 reads whole-slide images through a perceiver-based encoder trained jointly on tissue tiles and clinical dialogue drawn from pathology reports. The model aggregates thousands of tile embeddings per slide into one representation, then generates text that answers diagnostic questions rather than simply classifying pixels.  Training data spans 2.3 million whole-slide

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Why biological data matters more in AI drug discovery

GSK has entered into a research collaboration with British biotechnology company Relation Therapeutics worth up to $110 million, expanding the companies’ existing work in AI-assisted drug discovery. Under the agreement, Relation will generate large-scale datasets measuring how human cells respond to genetic changes and drug interventions. The data will be used to train AI models

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Google’s Gemini 3.6 Flash targets enterprise agent token costs

Google has released Gemini 3.6 Flash and 3.5 Flash-Lite as new workhorses designed to cut latency and token costs for enterprise AI agents. The economics of running autonomous software agents inside a production environment come down to a fixed equation few vendors advertise directly. A model needs to reason through a multi-step task competently, but

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