Normal Computing is an AI technology company focused on advancing reasoning and reliability for artificial intelligence in the physical world. Normal Computing develops probabilistic software infrastructure, algorithms, and hardware solutions that enable AI systems to understand their own limitations—particularly for complex, real-world challenges in semiconductors and industrial applications.
Their core approach spans the full technology stack, integrating innovations from machine learning, physics, and hardware engineering. By combining probabilistic computing techniques with physics-based modeling, Normal Computing aims to help AI systems become more robust, interpretable, and effective in safety-critical or high-complexity environments.
How was Normal Computing started?
Normal Computing was founded in 2022 in New York, NY, by a team of engineers and scientists with extensive backgrounds at Google Brain, Google X, and Google Quantum. The founding team—Faris Sbahi, Antonio Martinez, and Yui-Hong "Matthias" Tan—brings expertise in probabilistic machine learning and quantum/physics-based computing. Their prior work includes pioneering industry-leading Physics + ML tools for next-generation AI.
What problems does Normal Computing solve?
Normal Computing addresses the challenge of making AI reliable and explainable for applications involving the physical world, such as semiconductor manufacturing and industrial automation. Their solutions are designed to help AI reason about uncertainty and operational limits, enabling safer and more predictable deployment in critical industries.
Who uses Normal Computing?
Specific customer names have not been publicly disclosed. However, their primary market focus is on companies and organizations operating in semiconductors, industrials, and other sectors where advanced AI reasoning and reliability are essential.
What makes Normal Computing different?
Normal Computing distinguishes itself through its interdisciplinary approach, bringing together probabilistic software infrastructure, custom algorithms, and hardware/physics integration. Their team’s deep experience at the intersection of machine learning and physical sciences positions them to solve some of the most challenging problems encountered by AI in real-world, high-stakes environments.
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