A batch size of 2048 is used during training with a learning rate of 1e-4. Both training and rendering were conducted using A WS. A.2 PixelNeRF We used a constant learning rate of 1e-4. To train PixelNeRF on Objaverse-XL we render the meshes in Blender. Each model is normalize to a bounding cube. We believe that models such as Zero123-XL, and those trained on Objaverse-XL, will enhance the ease of 3D content creation, enabling broader accessibility for individuals and businesses to participate.

This post contains a list of the AI-related seminars that are scheduled to take place between 4 February and 31 March 2026. All events detailed here are free and open for anyone to attend virtually. Carolina Osorio (Google Research and HEC Montreal) Association of European Operational Research Societies To receive the seminar link, sign up to the mailing list . Sashank Varma (Georgia Tech) University of Minnesota Zoom registration is here . Vicky Kalogeiton (École Polytechnique) AIDA Zoom link is here .

Trump Admin's Plans for $500 Million USIP Building May Violate Court Order, Say Former Workers The State Department is poised to take over a building DOGE seized from the US Institute of Peace, former staffers claim--possibly for Donald Trump's "Board of Peace." Last year, the Trump administration and members of the so-called Department of Government Efficiency (DOGE) forcibly took over the US Institute of Peace (USIP), an independent nonprofit. Since then, the organization's fired board and employees have been fighting to regain control of the USIP building in Washington, DC and for the reinstatement of their jobs in a drawn-out court battle. Now, in a letter sent to the Department of Justice (DOJ), representatives for the USIP's fired board and employees argue that the administration is violating a court-issued stay by making physical changes to the building and, to their understanding, moving ahead with new agreements. Specifically, the letter asks for information on whether the State Department has signed an agreement to use the building for the "Board of Peace," a new international organization under the personal lifetime control of President Donald Trump that seeks to oversee the reconstruction of Gaza.

The government has launched a series of free AI training courses designed to help people learn how to use the technology at work. The online lessons give advice on things such as how to prompt chatbots or use them to assist with admin tasks. Many of the courses are free, with others subsidised, and the government aims to reach 10 million workers by 2030 - calling it the most ambitious training scheme since the launch of the Open University in 1971. But the Institute for Public Policy Research (IPPR) has warned workers will need to know more than just how to prompt a chatbot as the workforce adapts to the growth of AI. Skills for the age of AI can't be reduced to short technical courses alone, said Roa Powell, senior research fellow at the IPPR.

As the Institute's first VP for energy and climate, Evelyn Wang '00 is marshaling MIT's expertise to meet the greatest challenge of our age. Professor Evelyn Wang '00 sits beside a compact, portable water-harvesting device that she developed in collaboration with Professor Rohit Karnik of MIT and Krista Walton, then a professor at Georgia Tech. It's designed for portable and emergency use. Water shortages in Southern California made an indelible impression on Evelyn Wang '00 when she was growing up in Los Angeles. "I was quite young, perhaps in first grade," she says. "But I remember we weren't allowed to turn our sprinklers on. And everyone in the neighborhood was given disinfectant tablets for the toilet and encouraged to keep flushing to a minimum. I didn't understand exactly what was happening. But I saw that everyone in the community was affected by the scarcity of this resource."

This post contains a list of the AI-related seminars that are scheduled to take place between 5 January and 28 February 2026. All events detailed here are free and open for anyone to attend virtually. Fiona Spuler (University of Reading) ECMWF Teams link is here . Iyad Rahwan (Max Planck Institute for Human Development) The Digital Humanism (DIGHUM) Initiative The talk will be livestreamed on YouTube here . Christopher O'Reilly (University of Reading) ECMWF Teams link is here .

Are these AI prompts damaging your thinking skills? What was the last thing you asked an AI chatbot to do for you? Maybe you asked it for an essay structure to help answer a tricky question, provide an insightful analysis of a chunky data set, or to check if your cover letter matches the job description. Some experts worry that outsourcing these kinds of tasks means your brain is working less - and could even be harming your critical thinking and problem-solving skills. Earlier this year, the Massachusetts Institute of Technology (MIT) published a study showing that people who used ChatGPT to write essays showed less activity in brain networks associated with cognitive processing while undertaking the exercise.

In a small room in San Diego last week, a man in a black leather jacket explained to me how to save the world from destruction by AI. Max Tegmark, a notable figure in the AI-safety movement, believes that "artificial general intelligence," or AGI, could precipitate the end of human life. I was in town for NeurIPS, one of the largest AI-research conferences, and Tegmark had invited me, along with five other journalists, to a briefing on an AI-safety index that he would release the next day. No company scored better than a C+. The threat of technological superintelligence is the stuff of science fiction, yet it has become a topic of serious discussion in the past few years.

This paper presents a mathematically rigorous framework for brain-inspired representation learning founded on the interplay between persistent topological structures and cohomological flows. Neural computation is reformulated as the evolution of cochain maps over dynamic simplicial complexes, enabling representations that capture invariants across temporal, spatial, and functional brain states. The proposed architecture integrates algebraic topology with differential geometry to construct cohomological operators that generalize gradient-based learning within a homological landscape. Synthetic data with controlled topological signatures and real neural datasets are jointly analyzed using persistent homology, sheaf cohomology, and spectral Laplacians to quantify stability, continuity, and structural preservation. Empirical results demonstrate that the model achieves superior manifold consistency and noise resilience compared to graph neural and manifold-based deep architectures, establishing a coherent mathematical foundation for topology-driven representation learning.