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Laplace Intelligence applies AI agents to perform automated red-teaming on robotics and autonomous vehicles within interactive simulations, aiming to identify safety vulnerabilities before physical deployment. This approach directly addresses the unpredictability of AI systems transitioning from digital environments to the real world by using machine intelligence to stress-test other machine intelligence. The startup's goal is to establish a verifiable safety foundation that can be trusted by insurers and regulators prior to public rollout.
Laplace Intelligence red-teams robots and self-driving cars before they hit the real world
University of Michigan spinout Laplace Intelligence uses AI agents and interactive simulation to stress-test robots and self-driving vehicles before deployment. The profitable startup is competing in Startup Battlefield at TechCrunch Disrupt, October 13-15 in San Francisco.
If there’s one lesson the OpenAI-Hugging Face incident taught us, it’s that AI agents can be unpredictable. “Imagine if something like that happened in the physical world,” Howie Sun, CEO and co-founder of Laplace Intelligence, told TechCrunch. “AI agents are moving from computers into the real world, and we don’t really want physical AI to fail in the real world.” Ann Arbor-based Laplace is building automated red-teaming — essentially, stress-testing a system by deliberately trying to make it fail — for robotics and physical AI. The company is betting that the only way to make sure physical AI is safe is…
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