Chain of News 12/07/2026

**Top Story** The pursuit of gradient-free methods in deep learning has been a long-standing goal in the field of artificial intelligence, and a recent breakthrough has been made with the use of the Monte Carlo method to train deep neural networks. This method has the potential to overcome the inherent troubles of backpropagation, such as vanishing and exploding gradients. The implications of this development are significant, as it could lead to more efficient and effective training of deep neural networks. This, in turn, could have a major impact on the development of AI systems, enabling them to learn and improve more quickly and accurately. As a result, developers should pay close attention to this development, as it could potentially revolutionize the field of deep learning. The ability to train deep neural networks without relying on gradients could also lead to the development of more complex and sophisticated AI systems. **AI Models & Research** The development of SkelGen4D, a weakly-supervised skeleton-based 4D generation method for text-driven mesh animation, is a significant advancement in the field of computer animation. This method has the potential to synthesize temporally coherent sequences of 3D geometry for animation and content creation, and could be used to generate realistic animations and special effects. The introduction of Otari LLM Control Plane by Mozilla.ai is also noteworthy, as it provides a control plane for large language models, enabling developers to build and deploy AI-powered applications more easily. Additionally, the development of a hybrid quantum-classical diffusion model for image generation is an interesting area of research, as it combines the benefits of quantum and classical computing to generate high-quality images. The use of physical activities to enable scalable foundation modeling for broad-spectrum health prediction is also a promising area of research, as it has the potential to improve the accuracy and effectiveness of health prediction models. **Developer Tools & Frameworks** The launch of Otari LLM Control Plane by Mozilla.ai is a significant development for developers, as it provides a control plane for large language models, enabling them to build and deploy AI-powered applications more easily. With this control plane, developers can now manage and optimize the performance of their AI models, and integrate them with other applications and services. The development of new tools and frameworks for evaluating general-purpose robot policies is also noteworthy, as it enables developers to test and deploy robot policies in real-world environments. Furthermore, the release of new libraries and frameworks for building and deploying AI-powered applications is an important development, as it provides developers with the tools and resources they need to build and deploy AI-powered applications quickly and efficiently. **Industry & Business** Google CEO Sundar Pichai has stated that the company is "losing" the AI race to Anthropic and OpenAI, highlighting the intense competition in the field of artificial intelligence. This statement is significant, as it underscores the importance of innovation and investment in AI research and development. The fact that AI code has 70% more bugs and trust has fallen to 33% is also a concern, as it highlights the need for more robust and reliable AI systems. The development of new partnerships and collaborations between companies and research institutions is also an important trend, as it enables the sharing of knowledge and resources, and accelerates the development of new AI technologies. **Worth Watching** The development of hierarchical acoustic-semantic modeling for full-duplex spoken language models is an interesting area of research, as it has the potential to improve the performance and accuracy of spoken language models. The use of clustering, classification, and large language models for structured data extraction from real estate documents is also a promising area of research, as it has the potential to improve the efficiency and effectiveness of data extraction and analysis. Additionally, the development of new methods and techniques for evaluating and improving the reliability and trustworthiness of AI systems is an important area of research, as it has the potential to improve the adoption and deployment of AI-powered applications in a wide range of industries and domains.