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Category: robotics/AI
Claude AI finds cryptography weaknesses human experts missed
AI company Anthropic has announced that its unreleased model, Claude Mythos Preview, has discovered previously unknown mathematical weaknesses in cryptographic algorithms that human researchers had missed for years.
Cryptography uses sophisticated algorithms to protect web traffic, email, software updates and sensitive information like banking details. It does this by rendering the data completely unreadable.
The Anthropic team describes its work in two papers.
China wants to solve the hardest problem in robotics — making hands
Human hands – nimble, nerve-filled appendages that are the most flexible part of the human skeleton – are exceptionally complex. Many tasks that most people can do largely without thinking, from tying a pair of shoelaces to buttoning up a shirt, in fact require a complex set of neurological instructions and precise choreography. In thousands of years of human history, no machine has been able to truly replicate human’s greatest tool.
But now, as artificial intelligence (AI) races forwards, some companies think they are close to surpassing this final but most difficult hurdle in robotics. Most of them are in China.
A new suite of Chinese start-ups are leveraging China’s advantages in manufacturing and enthusiasm for what the government calls “embodied AI” to build the fully dextrous robotic hands that are needed to transform humanoid robots from dancing gimmicks into useful products.
China’s Moonshot pauses Kimi subscriptions amid hot demand, IPO push
SHANGHAI/HONG KONG, July 20 (Reuters) — Chinese startup Moonshot AI has temporarily paused new subscriptions after demand for its newly launched Kimi K3 model strained capacity, a bottleneck that comes as the company seeks fresh funding and prepares for a potential Hong Kong listing.
Moonshot is in the process of unwinding its current offshore structure ahead of a Hong Kong initial public offering, two sources with knowledge of the matter said.
New driving AI ranks possible routes for safer, clearer decisions
A research team led by Jun Won Choi, a professor in the Department of Electrical and Computer Engineering at Seoul National University College of Engineering, has developed SafeDrive, an end-to-end (E2E) autonomous driving AI model aligned with recent global trends in autonomous driving technology. The work was selected as a highlight paper at the Conference on Computer Vision and Pattern Recognition (CVPR) 2026.
Highlight papers at CVPR represent approximately 3% of all submissions and about 10% of accepted papers, recognizing a small group of highly impactful studies. The achievement by Choi’s team is regarded as a significant milestone demonstrating that Korean researchers can independently develop world-class autonomous driving AI technologies.
Recent advances in autonomous driving have increasingly shifted toward Physical AI-based approaches to improve safety and handle edge cases. In particular, end-to-end learning methods—where large-scale driving data is collected, refined and used to emulate human driving decisions—have emerged as a core technology for building autonomous driving foundation models.
Stephen Wolfram: To Understand the Future, Explore the Computational Universe
In January 2011, I spent an hour on the phone with Stephen Wolfram.
Put that in context. Deep learning had not yet won ImageNet. Transformers were six years away. Wolfram|Alpha was barely a year old, and most people filed it under “search engine that does your homework.”
His answer to my questions kept coming back to the same place: if you want to understand the future, go explore the computational universe. Run the simple programs. Watch what they actually do. Stop assuming that complicated behavior requires complicated causes.
I had spent three days preparing for that conversation and still came away thinking I had just talked to one of the smartest people alive.
Fifteen years later, the industry is quietly rediscovering his argument. You cannot know what a system will do without running it. Wolfram had a name for that long before anyone needed it to explain why #AI alignment is so hard.
What I find strangest on re-listen is what he said about the singularity itself, and how carefully he refused to say what everyone else in 2011 was saying.
