Can Safeworld convince people that gen AI robots won’t hurt them?
Startup Safeworld has emerged from stealth with $12 million in funding to develop safety evaluation standards for generative AI-powered robots. The company uses simulations with realistic human models to assess the risks of robots operating in unstructured environments.
Why it matters
As generative AI is increasingly integrated into physical robotics, establishing safety protocols is critical to preventing accidents in human-centric spaces.
The big trend in robots is handing the keys over to a generative AI model, but that brings with it a problem: that architecture isn’t predictable the way traditional algorithms are. How can you be sure your brand new humanoid will be safe?
Dr. Ding Zhao, who directs the Safe AI lab at Carnegie Melon University, has been working on this problem for almost his entire career. Now, along with veteran start-up executive Kyle Wong and machine learning engineer Simo Rachidi, he’s founded a company, Safeworld, intended to solve it.
“The safety challenge that we’re talking about is a combination of, one, really advanced generative AI probabilistic evals — how do you underwrite the risk of a probabilistic system?” Zhao says. “The second part that’s really hard is the trust part, and you need both to deploy a robot.”
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