Progress in computer use agents (CUAs) has been constrained by the absence of large and high-quality datasets that capture how humans interact with a computer. While LLMs have thrived on abundant textual data, no comparable corpus exists for CUA trajectories. To address these gaps, we introduce FaraGen, a novel synthetic data generation system for multi-step web tasks. FaraGen can propose diverse tasks from frequently used websites, generate multiple solution attempts, and filter successful trajectories using multiple verifiers. It achieves high throughput, yield, and diversity for multi-step web tasks, producing verified trajectories at approximately $1 each. We use this data to train Fara-7B, a native CUA model that perceives the computer using only screenshots, executes actions via predicted coordinates, and is small enough to run on-device. We find that Fara-7B outperforms other CUA models of comparable size on benchmarks like WebVoyager, Online-Mind2Web, and WebTailBench -- our novel benchmark that better captures under-represented web tasks in pre-existing benchmarks. Furthermore, Fara-7B is competitive with much larger frontier models, illustrating key benefits of scalable data generation systems in advancing small efficient agentic models. We are making Fara-7B open-weight on Microsoft Foundry and HuggingFace, and we are releasing WebTailBench.

Tracking the behaviour of livestock enables early detection and thus prevention of contagious diseases in modern animal farms. Apart from economic gains, this would reduce the amount of antibiotics used in livestock farming which otherwise enters the human diet exasperating the epidemic of antibiotic resistance - a leading cause of death. We could use standard video cameras, available in most modern farms, to monitor livestock. However, most computer vision algorithms perform poorly on this task, primarily because, (i) animals bred in farms look identical, lacking any obvious spatial signature, (ii) none of the existing trackers are robust for long duration, and (iii) real-world conditions such as changing illumination, frequent occlusion, varying camera angles, and sizes of the animals make it hard for models to generalize. Given these challenges, we develop an end-to-end behaviour monitoring system for group-housed pigs to perform simultaneous instance level segmentation, tracking, action recognition and re-identification (STAR) tasks. We present starformer, the first end-to-end multiple-object livestock monitoring framework that learns instance-level embeddings for grouped pigs through the use of transformer architecture. For benchmarking, we present Pigtrace, a carefully curated dataset comprising video sequences with instance level bounding box, segmentation, tracking and activity classification of pigs in real indoor farming environment. Using simultaneous optimization on STAR tasks we show that starformer outperforms popular baseline models trained for individual tasks.

"It's a fight for talent…It's like'Game of Thrones' out there," Erik Caldwell, chief operating officer for supply chain in the Americas and Asia Pacific at XPO Logistics Inc., XPO 2.83% said at an industry conference earlier this year, discussing the company's use of robots to fulfill online orders. The use of robotics and other automation technology in industrial operations is growing, although the vast majority of warehouse work remains largely manual. About 16.5% of organizations across several industries including warehousing are now using commercial service robots, and 21.5% have them in pilot programs, according to a 2018 survey of 600 respondents by research firm IDC. The holiday shopping season highlights a warehouse-worker squeeze that is driving more logistics operators to embrace automation, as the growth of online commerce pushes more retail sales from storefronts to distribution centers. Online fulfillment centers--where companies like Amazon.com Inc. AMZN -0.94% pick, pack and ship consumer orders--require two to three times as many workers as traditional warehouses.

In 2015, Congress made a big change in the way healthcare providers are reimbursed. Instead of the previous fee-for-service model that paid providers for each service performed, reimbursement would now be provided based on the quality of care provided -- a concept known as value-based care. AI in healthcare goes beyond IBM Watson. In this e-guide, discover 4 uses for AI in healthcare – particularly how it can help improve patient engagement – and whether we can overcome security and interoperability concerns surrounding the technology. You forgot to provide an Email Address.