The American Revolution created a lasting democracy while the French Revolution became blood-soaked tyranny. But today's armchair revolutionaries echo similar calls.

Texas A&M eliminates Women's and Gender Studies certificate program after reviewing 5,400 course syllabi, canceling six courses representing 0.11% of total offerings.

CES 2026 showed that humanoid and embodied AI systems still have a long way to go before delivering real-world value, particularly in homes. At the same time, there is a growing sense that the path to deployment is becoming clearer. A consensus has emerged across platforms: multi-camera perception, often wrist-mounted, paired with VLA models, is sufficient for most tasks. Increasingly, tactile hands and VTLA software are added. There was a clear split between industrial and home-care humanoids.

Using LLMs to talk with your friends is efficient. My friend recently attended a funeral, and midway through the eulogy, he became convinced that it had been written by AI. There was the telltale proliferation of abstract nouns, a surfeit of assertions that the deceased was "not just --he was " coupled with a lack of concrete anecdotes, and more appearances of the word than you would expect from a rec-league hockey teammate. It was both too good, in terms of being grammatically correct, and not good enough, in terms of being particular. My friend had no definitive proof that he was listening to AI, but his position--and I agree with him--is that when you know, you know. His sense was that he had just heard a computer save a man from thinking about his dead friend.

Students originally from overseas attend entrance exam preparation classes for high school advancement at YSC Global School in the city of Fussa, Tokyo, on Jan. 22. As policies related to foreign nationals are expected to be a major issue in Sunday's Lower House election in Japan, some are calling for improvements to Japanese language education for the children of foreign residents. In 2010, Youth Support Center, a nonprofit organization in the city of Fussa, Tokyo, established YSC Global School to provide Japanese language education and support for high school entry for children and young people with foreign roots, tailored to their proficiency levels. The school offers a total of 14 face-to-face and online courses and annually admits about 250 to 300 children from countries such as China, the Philippines and Nepal. Limited classrooms and instructors, however, hinder its ability to accommodate more students. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Barnsley has struggled with unemployment and deprivation since the coal pits closed. Barnsley has struggled with unemployment and deprivation since the coal pits closed. Barnsley rebranded UK's first'tech town' as US giants join AI push In 2002 Barnsley toyed with a redesign as a Tuscan hill village as it sought out a brighter post-industrial future. In 2021 it adopted the airily vague slogan "the place of possibilities". Now it is trying a different image: Britain's first "tech town".

Multimodal learning combines information from multiple data modalities to improve predictive performance. However, modalities often contribute unequally and in a data dependent way, making it unclear which data modalities are genuinely informative and to what extent their contributions can be trusted. Quantifying modality level importance together with uncertainty is therefore central to interpretable and reliable multimodal learning. We introduce conformal Shapley intervals, a framework that combines Shapley values with conformal inference to construct uncertainty-aware importance intervals for each modality. Building on these intervals, we propose a modality selection procedure with a provable op-timality guarantee: conditional on the observed features, the selected subset of modalities achieves performance close to that of the optimal subset. We demonstrate the effectiveness of our approach on multiple datasets, showing that it provides meaningful uncertainty quantification and strong predictive performance while relying on only a small number of informative modalities.

Ultrafast online learning is essential for high-frequency systems, such as controls for quantum computing and nuclear fusion, where adaptation must occur on sub-microsecond timescales. Meeting these requirements demands low-latency, fixed-precision computation under strict memory constraints, a regime in which conventional Multi-Layer Perceptrons (MLPs) are both inefficient and numerically unstable. We identify key properties of Kolmogorov-Arnold Networks (KANs) that align with these constraints. Specifically, we show that: (i) KAN updates exploiting B-spline locality are sparse, enabling superior on-chip resource scaling, and (ii) KANs are inherently robust to fixed-point quantization. By implementing fixed-point online training on Field-Programmable Gate Arrays (FPGAs), a representative platform for on-chip computation, we demonstrate that KAN-based online learners are significantly more efficient and expressive than MLPs across a range of low-latency and resource-constrained tasks. To our knowledge, this work is the first to demonstrate model-free online learning at sub-microsecond latencies.

We study the effect of high-order statistics of data on the learning dynamics of neural networks (NNs) by using a moment-controllable non-Gaussian data model. Considering the expressivity of two-layer neural networks, we first construct the data model as a generative two-layer NN where the activation function is expanded by using Hermite polynomials. This allows us to achieve interpretable control over high-order cumulants such as skewness and kurtosis through the Hermite coefficients while keeping the data model realistic. Using samples generated from the data model, we perform controlled online learning experiments with a two-layer NN. Our results reveal a moment-wise progression in training: networks first capture low-order statistics such as mean and covariance, and progressively learn high-order cumulants. Finally, we pretrain the generative model on the Fashion-MNIST dataset and leverage the generated samples for further experiments. The results of these additional experiments confirm our conclusions and show the utility of the data model in a real-world scenario. Overall, our proposed approach bridges simplified data assumptions and practical data complexity, which offers a principled framework for investigating distributional effects in machine learning and signal processing.

Most existing offline RL methods presume the availability of action labels within the dataset, but in many practical scenarios, actions may be missing due to privacy, storage, or sensor limitations. We formalise the setting of action-free offline-to-online RL, where agents must learn from datasets consisting solely of $(s,r,s')$ tuples and later leverage this knowledge during online interaction. To address this challenge, we propose learning state policies that recommend desirable next-state transitions rather than actions. Our contributions are twofold. First, we introduce a simple yet novel state discretisation transformation and propose Offline State-Only DecQN (\algo), a value-based algorithm designed to pre-train state policies from action-free data. \algo{} integrates the transformation to scale efficiently to high-dimensional problems while avoiding instability and overfitting associated with continuous state prediction. Second, we propose a novel mechanism for guided online learning that leverages these pre-trained state policies to accelerate the learning of online agents. Together, these components establish a scalable and practical framework for leveraging action-free datasets to accelerate online RL. Empirical results across diverse benchmarks demonstrate that our approach improves convergence speed and asymptotic performance, while analyses reveal that discretisation and regularisation are critical to its effectiveness.