This is the third and final blog in a three-part series on China’s open source community’s historical advancements since January 2025’s “DeepSeek Moment.” The first blog on strategic changes and open artifact growth is available here, and the second blog on architectural and hardware shifts is available here.
In this third article, we examine paths and trajectories of prominent Chinese AI organizations, and posit future directions for open source.
For AI researchers and developers contributing to and relying on the open source ecosystem and for policymakers understanding the rapidly changing environment, due to intraorganizational and global community gains, open source is the dominant and popular approach for Chinese AI organizations for the near future. Openly sharing artifacts from models to papers to deployment infrastructure maps to a strategy with the goal of large-scale deployment and integration. — Read More
Daily Archives: February 4, 2026
Google Revealed “Attention Is All You Need” Part II
For years deep learning has followed one central idea. If we want smarter models, we stack more layers, run larger training, and scale everything upward. This simple formula has given us large language models that reason well and generate high-quality text. Yet they still share one huge weakness. They cannot learn on the fly. They cannot update themselves during use.
Any change needs heavy retraining, and this often destroys old knowledge.
Google Research recently published a paper called Nested Learning. It offers a very different way of thinking about how learning should work inside neural networks. The researchers claim that a model is not just a big stack of layers. It is a hierarchy of learners that operate at different timescales. If this view is correct, it could reshape how we build AI systems in the coming years. — Read More
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