Real‑time reasoning for agents that adapt, explain, and endure in the real world.
We create reasoning-based AI and on‑robot autonomy that learns from experience and reasons on the fly. Our neuro‑symbolic stack integrates deep neural network perception, adaptive model‑predictive control, and empirical reasoning to handle uncertainty reliably.
NARTECH is our reasoning-first AI technology: a neuro-symbolic reasoning engine operating on top of state-of-the-art deep learning for perception.
Our breakthrough: an effective neuro-inspired inference control mechanism focuses compute on what matters, in real time.
NARTECH is our reasoning-first technology: a neuro-symbolic reasoning engine operating on top of state-of-the-art deep learning for perception.
For robotics, NARTECH integrates seamlessly with ROS 2 perception and control stacks, enabling adaptive autonomy in real environments.
Experience our technology in real-world environments: unknown terrain, uncertainty, and on‑the‑fly improvisation. Featuring robust transfer to unfamiliar environments, with decisions you can inspect and trust.
Reasoning capabilities showcased in complex domains with easy integration into other applications featuring high real-time perception throughput.
Testing autonomous reasoning systems in real urban environments with the Aarhus City Lab team.
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Interview on NARS-based reasoning-learning systems and real-time autonomy.
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Partners driving advanced reasoning tech into healthcare, manufacturing, and urban infrastructure.
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An AI assistant, Trusted and Explainable Artificial Intelligence for Saving Lives (TruePAL), to provide real-time warning of risks of potential crashes to the first responders.
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NASA’s TruePAL platform brings real-time situational awareness to improve road-side safety for officers.
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Temple University’s longstanding NARS (Non-Axiomatic Reasoning System) project—a real-time, experience-learning AGI system—continues its pioneering work, now partnering with Future AI.
Read full story →Real-time empirical reasoning on a mobile robot; integration with perception and control for robust autonomy.
Neural perception + symbolic reasoning for collision risk estimation with interpretable warnings.
Framework unifying experience-based reasoning with learning for sample-efficient, adaptive behavior.
We collaborate with academia and industry.
AI researcher and roboticist focused on neuro-symbolic learning and real-time reasoning systems.
Cognitive scientist and control expert pioneering adaptive systems that mimic natural intelligence.