Can Safeworld convince people that gen AI robots won’t hurt them?
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? Ding Zhao, who directs the Safe AI lab at Carnegie Melon University, has been working on this problem for almost his entire career.
- ▪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?
- ▪Ding Zhao, who directs the Safe AI lab at Carnegie Melon University, has been working on this problem for almost his entire career.
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| Original publisher | TechCrunch |
| Canonical URL | https://techcrunch.com/2026/10/05/can-safeworld-convince-people-that-gen-ai-robots-wont-hurt-them/ |
| Publication time | Mon, 05 Oct 2026 12:00:00 +0000 |
| Retrieval time | 2026-10-05T12:00:50.289Z |
| Last seen | 2026-10-05T12:00:50.289Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | g0zcFIpZTl1N · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at TechCrunch.
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