Active Inference is a novel framework for agentic AI based on world models, emerging from over 30 years of research in computational neuroscience.
From this paradigm, we offer an AI built for power and computational efficiency, designed to live on-device and on the edge.
Integrating with traditional computer vision stacks our intelligent decision-making systems provide an explainable output that allows organizations to build accountability into their AI tools and products.
A New Approach to Artificial Intelligence
Using Active Inference to build intelligent machines for the real world
Replacing training data with curiosity
Interrogating our agents’ beliefs for truly explainable AI
Building world model representations
Our Key Advancements
Energy efficiency
Computationally cheap
On-device
Explainable AI
Our Work
Stanhope AI teaches robots and machines how to make decisions in the real world, with a particular focus on allowing them to tackle situations in which they have received no training.
We are taking Active Inference from neuroscience into AI as the foundation for software that will allow robots and embodied platforms to make autonomous decisions like the human brain.
Current generative AIs require vast training data. In contrast, we build our models to enable learning and inference on devices that are low power and low cost. Deployed on the edge, we are teaching autonomous systems to develop lightweight, lean world models.
“We aim to bring known unknowns to the AI landscape.
Using generative models with interrogatable state spaces, we produce interpretable models that humans can rely on.”
Our Research
Forging new innovations in Artificial Intelligence