Dallas | Worlds has announced the launch of WorldsNQ, an innovative platform that promises to redefine how companies use AI to measure and improve physical operations. WorldsNQ, which pioneers the Large World Model (LWM) concept, is specially engineered to work with real-world data captured by cameras and IoT sensors, offering a more dynamic and accurate understanding of the real-world in motion.
Today, whether it is a warehouse, manufacturer, or energy company, it takes a team of six people six months to effectively train, refine, and maintain AI models. With WorldsNQ, that process can all be done in a single day. With no human annotation required and completely automated training, each model becomes a closed-loop system that persistently learns over time as your world changes.
Real-world AI today has a giant, expensive flaw
The time and cost required to operationalize AI poses the greatest challenge for organizations aiming to measure and enhance their physical processes. For years, the field of AI has grappled with the challenges of model and data drift. Traditional methods involve laborious annotation processes, with AI trainers dedicating countless hours to manually labeling data. This approach consumes significant time and resources and results in static models that quickly become outdated while the world around them evolves. WorldsNQ emerges as a solution to this pervasive problem by offering a data-driven platform powered by your data that is constantly learning and never becoming obsolete.
The NQ Answer
WorldsNQ operationalizes real-world AI for the largest industrial enterprises globally. It’s a platform organizations can use to:
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Radically accelerate AI learning by 100x to 1000x compared to existing training platforms, eliminating the need for human annotation.
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Enable closed-loop systems that continually adapt and learn from their environment. This acceleration is not just faster but also qualitative, as the models stay up to date.
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Implement without any special hardware requirements. The platform is compatible with an organization’s existing cameras and sensors, dramatically lowering the cost and time required to implement the solution.
