Latent Space's first robotics episode features Dyna Robotics chief scientist Jason Ma on Dyna-2, physical agents designed to work autonomously for an hour. He discusses the company's bet on world action models and physical AI.
MIT researchers look at how machine-learning networks can learn without detailed instructions from a designer. Such networks may help explain how the brain reorganizes itself when it learns.
GitHub describes a context-aware classifier that improves how it detects leaked secrets such as credentials in code. The company reports an 11.5% rise in weighted acceptance and an 86% cut in median scan time.
Google DeepMind has published a post on a robotics version of its new Gemini 4 Argon model, extending the launch beyond chat and coding.
Meta describes the next stage of GEM, the recommendation foundation model it introduced last year for ads. The company wants to scale both compute and the transfer of the model's knowledge using next-generation hardware, and extend it across its whole advertising ecosystem.
Munich-based Spranic is building a spatial memory layer that records physical spaces and how they change over time. Linked to knowledge graphs, that data could help robots understand their surroundings across different tasks, its founder says.