Worlds Unveils WorldsNQ: A LWM Platform that Accelerates AI Training 1000x over Current Systems

Worlds has announced the launch of Worlds, an innovative platform that promises to redefine how companies use AI to measure and improve physical operations. Worlds, 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.

“After extensive development on Worlds^NQ, we’ve found a way to overcome these limitations using a new approach we call Large World Models. These models, built using Worlds^NQ automatically transform sensor data into a concise, efficient model of reality, mimicking human learning through observation.”

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 Worlds, 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.

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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 This approach consumes significant time and resources and results in static models that quickly become outdated while the world around them evolves. Worlds^NQ 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

Worlds^NQ operationalizes real-world AI for the largest industrial enterprises globally. It’s a platform organizations can use to:

• Radically accelerate AI learning by 100x to 1000x compared to existing training platforms, eliminating the need for human annotation.
• 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.
• 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.

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The launch event for Worlds^NQ was presented by Accenture, Microsoft, and Chevron and took place at Capital Factory’s Future of AI Salon on February 20 sponsored by JP Morgan Chase & Co.

Today’s news comes on the heels of two new business partnerships for Worlds with global professional services company Accenture and Chevron, the second-largest integrated energy company in the United States. These partnerships aim to jumpstart AI services that will unlock the full potential of generative AI in real-world automation.

“Our vision with Worlds^NQ is to enable our customers and partners to create Large World Models of their operating environments that are not static but dynamic entities,” said Dave Copps, CEO of Worlds. “AI Models that don’t just learn; they evolve continuously, adapting to new data without the need for constant human intervention. We truly see this as the future of AI in the physical world, and we have made it real today with Worlds^NQ.”

“One of the largest challenges in teaching AI to understand the physical world like humans do is giving it access to the same vast amount of data that humans gather through their senses,” added Ross Bates, CTO of Worlds. “After extensive development on Worlds^NQ, we’ve found a way to overcome these limitations using a new approach we call Large World Models. These models, built using Worlds^NQ automatically transform sensor data into a concise, efficient model of reality, mimicking human learning through observation.”

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