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A Peking University system reconstructs familiar physical laws from noisy mechanics data, testing a route toward automated scientific discovery.

I'm trying to grok the AI framework Hyperon. This series of articles chronicles my learning journey.

A new direction for making transformers AGI-friendly—via judiciously mixing continuous symbolic program learning into open-weights transformers.

Researchers develop a multi-agent AI framework combining language models, computer vision and structural sensor data to improve the monitoring of historic masonry buildings.

Hyperon is the artificial intelligence (AI) framework developed by Ben Goertzel and his team. This continues part 1.

Beyond bigger language models, Ben Goertzel proposes beneficial AGI built on self-improving agent hives, shared knowledge and decentralized governance. Could his roadmap reshape the race to superintelligence?

Can Our Current Proto-AGIs Help Build Their Successors?

Why we dropped the Claw from OmegaClaw (Mind over hands!!), and why this is the moment for a lot more people to start raising their own AI helpers...

Ben Goertzel’s distinction calculus proposes mathematics for AI agents that split, merge and rewrite themselves, raising a central AGI question: how can changing minds preserve what matters?