Stuck in regulatory limbo, the self-driving-vehicle developer is encouraging residents of Washington, DC, to message public officials to help get its robotaxis onto roads. Waymo needs some help, according to an email message the self-driving developer sent to residents of Washington, DC, on Thursday. For more than a year, Waymo has been pushing city officials to pass new regulations allowing its robotaxis to operate in the district. So far, self-driving cars can test in the city with humans behind the wheel, but cannot operate in driver-free mode. The Alphabet subsidiary--and its lobbyists--have asked local lawmakers, including Mayor Muriel Bower and members of the city council, to create new rules allowing the tech to go truly driverless on its public roads.

The company's robotaxi service is supposed to launch in the US capital this year. But while service rollouts have been relatively smooth in other cities, DC's rules have made things tricky. Waymo, the Alphabet subsidiary that develops self-driving vehicle tech, has picked up speed. The company now operates robotaxis in six cities and has announced plans to launch in a dozen others this year. It j ust raised $16 billion in a new round of funding and says it has served over 20 million rides since the company launched its service in 2020, 14 million of them in 2025 alone.

Three West African juntas have turned to Russia. The US has declared a stark policy shift towards three West African countries which are battling Islamist insurgents and whose military governments have broken defence ties with France and turned towards Russia. The state department announced that Nick Checker, head of its Bureau of African Affairs, would visit Mali's capital Bamako to convey the United States' respect for Mali's sovereignty and chart a new course in relations, moving past policy missteps. It adds that the US also looks forward to co-operating with Mali's allies, neighbouring Burkina Faso and Niger, on shared security and economic interests. Absent from the agenda is the longstanding American concern for democracy and human rights.

Two important things happened on January 20, 2025. In Washington, D.C., Donald Trump was inaugurated as President of the United States. In Hangzhou, China, a little-known Chinese firm called DeepSeek released R1, an AI model that industry watchers called a "Sputnik moment" for the country's AI industry. "Whether we like it or not, we're suddenly engaged in a fast-paced competition to build and define this groundbreaking technology that will determine so much about the future of civilization," said Trump later that year, as he announced his administration's AI action plan, which was titled "Winning the Race." There are many interpretations of what AI companies and their governments are racing towards, says AI policy researcher Lennart Heim: to deploy AI systems in the economy, to build robots, to create human-like artificial general intelligence.

Since 2021, China has reportedly poured $100bn into support for AI datacentres. Since 2021, China has reportedly poured $100bn into support for AI datacentres. Beijing's AI policy is focused on real-life applications but Chinese companies are beginning to articulate their own grand visions S tanding on stage in the eastern China tech hub of Hangzhou, Alibaba's normally media-shy CEO made an attention-grabbing announcement. "The world today is witnessing the dawn of an AI-driven intelligent revolution," Eddie Wu told a developer conference in September. " Artificial general intelligence (AGI) will not only amplify human intelligence but also unlock human potential, paving the way for the arrival of artificial superintelligence (ASI)."

US approves sale of Nvidia's advanced AI chips to China The US government has given chip giant Nvidia the green light to sell its advanced artificial intelligence (AI) processors in China, the Department of Commerce said on Tuesday. The H200, Nvidia's second-most-advanced semiconductor, had been restricted by Washington over concerns that it would give China's technology industry and military an edge over the US. The Commerce Department said the chips can be shipped to China granted that there is sufficient supply of the processors in the US. President Donald Trump said last month that he would allow the chip sales to approved customers in China and collect a 25% fee. Nvidia's spokesperson told the BBC that the company welcomed the move, saying it will benefit manufacturing and jobs in the US.

Andrew Jackson's White House once hosted a cheese feeding frenzy The seventh president's farewell party featured 1,400 pounds of cheddar. In 1835, a New York dairy farmer sent President Andrew Jackson a 1,400-pound cheddar cheese to celebrate the president's second inauguration. Two years later, it was finally eaten. Breakthroughs, discoveries, and DIY tips sent every weekday. It's February 1837, and the White House is about to bear witness to one of the greatest feeding frenzies in this nation's proud history of competitive consumption.

Knowledge Distillation (KD) has emerged as a promising technique for model compression but faces critical limitations: (1) sensitivity to hyperparameters requiring extensive manual tuning, (2) capacity gap when distilling from very large teachers to small students, (3) suboptimal coordination in multi-teacher scenarios, and (4) inefficient use of computational resources. We present \textbf{HPM-KD}, a framework that integrates six synergistic components: (i) Adaptive Configuration Manager via meta-learning that eliminates manual hyperparameter tuning, (ii) Progressive Distillation Chain with automatically determined intermediate models, (iii) Attention-Weighted Multi-Teacher Ensemble that learns dynamic per-sample weights, (iv) Meta-Learned Temperature Scheduler that adapts temperature throughout training, (v) Parallel Processing Pipeline with intelligent load balancing, and (vi) Shared Optimization Memory for cross-experiment reuse. Experiments on CIFAR-10, CIFAR-100, and tabular datasets demonstrate that HPM-KD: achieves 10x-15x compression while maintaining 85% accuracy retention, eliminates the need for manual tuning, and reduces training time by 30-40% via parallelization. Ablation studies confirm independent contribution of each component (0.10-0.98 pp). HPM-KD is available as part of the open-source DeepBridge library.

Semi-supervised anomaly detection (AD) has shown great promise by effectively leveraging limited labeled data. However, existing methods are typically structured around scoring individual points or simple pairs. Such {point- or pair-centric} view not only overlooks the contextual nature of anomalies, which are defined by their deviation from a collective group, but also fails to exploit the rich supervisory signals that can be generated from the combinatorial composition of sets. Consequently, such models struggle to exploit the high-order interactions within the data, which are critical for learning discriminative representations. To address these limitations, we propose SetAD, a novel framework that reframes semi-supervised AD as a Set-level Anomaly Detection task. SetAD employs an attention-based set encoder trained via a graded learning objective, where the model learns to quantify the degree of anomalousness within an entire set. This approach directly models the complex group-level interactions that define anomalies. Furthermore, to enhance robustness and score calibration, we propose a context-calibrated anomaly scoring mechanism, which assesses a point's anomaly score by aggregating its normalized deviations from peer behavior across multiple, diverse contextual sets. Extensive experiments on 10 real-world datasets demonstrate that SetAD significantly outperforms state-of-the-art models. Notably, we show that our model's performance consistently improves with increasing set size, providing strong empirical support for the set-based formulation of anomaly detection.

Register-Transfer Level (RTL) verification is a primary bottleneck, consuming 60-70% of development time. While Large Language Models (LLMs) show promise for RTL automation, their performance and research focus have overwhelmingly centered on RTL generation rather than verification. Current methods for RTL verification rely on large scale proprietary models (e.g., GPT-4o) to generate Python-based functional references, incurring a high cost and raising data-privacy risks. To date, an end-to-end open-source solution for autonomous verification remains absent. We introduce PRO-V-R1, the first trainable open-source agentic framework for autonomous RTL verification. Our contributions are threefold: (1) we design PRO-V sys, a modular agentic system that couples LLM-based reasoning with programmatic tool use for RTL verification; (2) we establish a data construction pipeline that leverages existing RTL datasets to build simulation-validated, expert-level trajectories tailored for supervised fine-tuning (SFT) RTL verification agents; and (3) we implement an efficient reinforcement learning (RL) algorithm that uses verification-specific rewards derived from program-tool feedback to optimize the end-to-end verification workflow. Our empirical evaluation demonstrates PRO-V-R1 achieves a 57.7% functional correctness rate and 34.0% in robust fault detection, significantly outperforming the base model's 25.7% and 21.8% (respectively) from the state-of-the-art (SOTA) automatic verification system. This configuration also outperforms large-scale proprietary LLMs in functional correctness and shows comparable robustness for fault detection.