Nvidia's Vera Rubin platform combines CPUs and GPUs into a single system, reflecting the company's growing ambition to power every layer of AI infrastructure. Nvidia is hyping up its new Vera Rubin chip system this week, revealing new performance benchmarks for the GPU and CPU combo ahead of rival AMD's annual product event in San Francisco on Thursday. During a lengthy technical workshop last week at the company's headquarters in Santa Clara, California, Nvidia executives boasted to a small group of journalists about the chip system's increased power and efficiency capabilities. The biggest takeaway: Nvidia, which has long specialized in making GPUs, is increasingly trying to position itself as a supplier of CPUs that can power AI agents. While GPUs are still the main hardware that companies use to train and run their AI models, the industry's shift toward more complex, agentic systems has increased demand for CPUs, which can orchestrate data flows, networking, and other software tasks.

Reliant on Nvidia hardware for AI training and inferencing since its outset, Turing now handles roughly 10% of its AI training needs with Advanced Micro Devices graphics processing units. Self-driving tech developer Turing has added AMD Ventures to its list of backers and begun adopting Advanced Micro Devices' AI accelerators in its systems. The five-year-old Japanese startup is adding to its capabilities as it builds toward a commercial launch. Reliant on Nvidia hardware for AI training and inferencing since its outset, Turing now handles roughly 10% of its AI training needs with AMD graphics processing units, company executives said in an interview. AMD, headquartered a stone's throw away from Nvidia in Santa Clara, California, presented a good chance to diversify supply and achieve lower costs, the executives said. "We've made notable progress with the technology.

The complaints, obtained by WIRED, described Bad Bunny's performance as being overly sexual and protested that the show was in Spanish. Bad Bunny performs during halftime of Super Bowl LX at Levi's Stadium in Santa Clara, California. Even before Bad Bunny took to the field, his Super Bowl halftime performance drew controversy, especially from MAGA influencers upset over the Puerto Rican star's comments against Immigration and Customs Enforcement and the fact that he sings in Spanish. Following the performance, which was watched by more than 128 million people, those complaints continued--but they were largely focused on perceived vulgarity in the artist's performance. Following a Freedom of Information Act (FOIA) request from WIRED, the Federal Communications Commission, which regulates communications including broadcast, released 2,155 complaints the agency received about the Super Bowl, most of which were about the halftime show.

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Machine learning has opened new frontiers in purely data-driven algorithms for data assimilation in, and for forecasting of, dynamical systems; the resulting methods are showing some promise. However, in contrast to model-driven algorithms, analysis of these data-driven methods is poorly developed. In this paper we address this issue, developing a theory to underpin data-driven methods to solve smoothing problems arising in data assimilation and forecasting problems. The theoretical framework relies on two key components: (i) establishing the existence of the mapping to be learned; (ii) the properties of the operator learning architecture used to approximate this mapping. By studying these two components in conjunction, we establish novel universal approximation theorems for purely data driven algorithms for both smoothing and forecasting of dynamical systems. We work in the continuous time setting, hence deploying neural operator architectures. The theoretical results are illustrated with experiments studying the Lorenz `63, Lorenz `96 and Kuramoto-Sivashinsky dynamical systems.

Furthermore, we propose an adapted embedding method to incorporate numerical awareness.

The real-time estimation of time-varying parameters from high-dimensional, heavy-tailed and corrupted data-streams is a common sub-routine in systems ranging from those for network monitoring and anomaly detection to those for traffic scheduling in data-centers.

Our experimental results demonstrate that this approach significantly improves the training efficiency as compared to maximum likelihood training.

In the policy evaluation steps, the critic is updated to capture the soft Q-function. In the policy improvement steps, the actor is adjusted in accordance with the updated soft Q-function.