Digital Twin + LLM = Large Reality Model?

Most are familiar with digital twins—those real-time digital replicas of physical entities. We're driving towards the creation of the Large Reality Model (LRM), a leap in how we interpret, represent, and predict our physical world.

By bridging the concept of the digital twin with the deep learning capabilities of Neural Radiance Fields (NeRF) and Large Language Models (LLMs), we evolve from mere replicas to a robust and dynamic digital model that mirrors and predicts the real-world intricacies in a way that allows us to interact with the physical environment. NeRF's ability to synthesize novel views of a 3D scene, understanding the interaction between light and matter, is amplified with high-resolution spectral and temporal data, collected methodically from our physical surroundings.

In order to get to the Large Reality Model vision, you need a massive amount of data. Kelluu's approach to data acquisition is as innovative as the model itself. We deploys hydrogen-powered airships, which, flying below the clouds, autonomously capture unparalleled high-resolution spectral and temporal data, ensuring optimal data collection while also affirming our commitment to sustainability and environmental responsibility.

Consider the implications of LRM across sectors—urban planning that anticipates behavioural patterns and environmental shifts, infrastructure development that's in harmony with its surroundings, and environmental conservation that's proactive rather than reactive.

Similarly to how Large Language Models revolutionized how AI interact with humans by understanding and synthesizing human language, the Large Reality Model has the potential to redefine our relationship with the physical world, bridging the gap between the tangible and the digital.

At Kelluu, we have the opportunity to collect more high-resolution environmental data than ever before. It feels like we're paving the way for a more interconnected, comprehensively understood, and sustainably managed future.

An glimpse how this all will look - take a look at this research:

https://www.lerf.io/

https://instruct-nerf2nerf.github.io/

https://sarafridov.github.io/K-Planes/