Verses AI publishes research on foundation for next-gen AI

VERSES AI announces that a team, led by Chief Scientist, Dr. Karl Friston, has published a paper titled, “From pixels to planning: scale-free active inference,” which introduces an efficient alternative to deep learning, reinforcement learning and generative AI called Renormalizing Generative Models that address foundational problems in artificial intelligence, namely versatility, efficiency, explainability and accuracy, using a physics based approach. ‘Active inference’ is a framework with origins in neuroscience and physics that describes how biological systems, including the human brain, continuously generate and refine predictions based on sensory input with the objective of becoming increasingly accurate. While the science behind active inference has been well established and is considered to be a promising alternative to state of the art AI, it has not yet demonstrated a viable pathway to scalable commercial solutions until now. RGM’s accomplish this using a “scale-free” technique that adjusts to any scale of data. “RGMs are more than an evolution; they’re a fundamental shift in how we think about building intelligent systems from first principles that can model space and time dimensions like we do,” said Gabriel Rene, CEO of VERSES. “This could be the ‘one method to rule them all’; because it enables agents that can model physics and learn the causal structure of information we can design multimodal agents that can not only recognize objects, sounds and activities but can also plan and make complex decisions based on that real world understanding-all from the same underlying model. This promises to dramatically scale AI development, expanding its capabilities, while reducing its cost.”