The US economy is pumped up on tech-bro vanity. I f AI did not change your life in 2025, next year it will. That is one of few forecasts that can be made with confidence in unpredictable times. This is not an invitation to believe the hype about what the technology can do today, or may one day achieve. The hype doesn't need your credence.

The government at a Cabinet meeting Tuesday adopted its first basic plan on the development and utilization of artificial intelligence. The basic plan stipulates that Japan will create reliable AI while balancing technological innovation and risk management, with an aim to become a country that offers the best environment for AI development and utilization. Japan lags behind not only other advanced nations but also countries with smaller economies in terms of AI development, and the gap is becoming wider year by year, it warns. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Ting Cai, head of Rakuten Group's artificial intelligence team, has the task of creating AI systems that would augment the company's many businesses at a minimal cost. Rakuten Group is expanding its AI team under the stewardship of a Google veteran and building models with a focus on cost efficiency. Ting Cai, now three years into his tenure at the head of the e-commerce pioneer's artificial intelligence team, has the task of creating AI systems that would augment the company's many businesses and support the handling of commercial transactions at a minimal cost. He oversees a team that's grown to 1,000 this year and has a battery of "thousands" of Nvidia chips to work with. Tokyo-based Rakuten is wrestling with a struggling mobile business and constant competition in online shopping, both of which could get a significant boost from effective deployment of new AI tools.

It's too late to get a gift shipped and shopping in-store is a nightmare. We may earn revenue from the products available on this page and participate in affiliate programs. OK, so you waited too long to order a present online . You don't want to brave the crowds. And you do't want to disappoint everyone during the holidays.

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Things to Do in L.A. Tap to enable a layout that focuses on the article. Self-driving vehicles charge at the Waymo station in Santa Monica. This is read by an automated voice. Please report any issues or inconsistencies here . Waymo is taking the city of Santa Monica to court after the city ordered the company to cease charging its autonomous vehicles at two facilities overnight, claiming the lights and beeping at the lots were a nuisance to residents.

They work 24/7 at high speeds and get searingly hot - but data centre computer chips get plenty of pampering. Some of them basically live at the spa. We'll have fluid that comes up and [then] shower down, or trickle down, onto a component, says Jonathan Ballon, chief executive at liquid cooling firm Iceotope. Some things will get sprayed. In other cases, the industrious gizmos recline in circulating baths of fluid, which ferries away the heat they generate, enabling them to function at very high speeds, known as overclocking.

The search for primordial gravitational waves is a central goal of cosmic microwave background (CMB) surveys. Isolating the characteristic $B$-mode polarization signal sourced by primordial gravitational waves is challenging for several reasons: the amplitude of the signal is inherently small; astrophysical foregrounds produce $B$-mode polarization contaminating the signal; and secondary $B$-mode polarization fluctuations are produced via the conversion of $E$ modes. Current and future low-noise, multi-frequency observations enable sufficient precision to address the first two of these challenges such that secondary $B$ modes will become the bottleneck for improved constraints on the amplitude of primordial gravitational waves. The dominant source of secondary $B$-mode polarization is gravitational lensing by large scale structure. Various strategies have been developed to estimate the lensing deflection and to reverse its effects the CMB, thus reducing confusion from lensing $B$ modes in the search for primordial gravitational waves. However, a few complications remain. First, there may be additional sources of secondary $B$-mode polarization, for example from patchy reionization or from cosmic polarization rotation. Second, the statistics of delensed CMB maps can become complicated and non-Gaussian, especially when advanced lensing reconstruction techniques are applied. We previously demonstrated how a deep learning network, ResUNet-CMB, can provide nearly optimal simultaneous estimates of multiple sources of secondary $B$-mode polarization. In this paper, we show how deep learning can be applied to estimate and remove multiple sources of secondary $B$-mode polarization, and we further show how this technique can be used in a likelihood analysis to produce nearly optimal, unbiased estimates of the amplitude of primordial gravitational waves.

Conditional independence (CI) is central to causal inference, feature selection, and graphical modeling, yet it is untestable in many settings without additional assumptions. Existing CI tests often rely on restrictive structural conditions, limiting their validity on real-world data. Kernel methods using the partial covariance operator offer a more principled approach but suffer from limited adaptivity, slow convergence, and poor scalability. In this work, we explore whether representation learning can help address these limitations. Specifically, we focus on representations derived from the singular value decomposition of the partial covariance operator and use them to construct a simple test statistic, reminiscent of the Hilbert-Schmidt Independence Criterion (HSIC). We also introduce a practical bi-level contrastive algorithm to learn these representations. Our theory links representation learning error to test performance and establishes asymptotic validity and power guarantees. Preliminary experiments suggest that this approach offers a practical and statistically grounded path toward scalable CI testing, bridging kernel-based theory with modern representation learning.

Explainable machine learning aims to strike a balance between prediction accuracy and model transparency, particularly in settings where black-box predictive models, such as deep neural networks or kernel-based methods, achieve strong empirical performance but remain difficult to interpret. This work introduces a mixture of generalized additive models (GAMs) in which random Fourier feature (RFF) representations are leveraged to uncover locally adaptive structure in the data. In the proposed method, an RFF-based embedding is first learned and then compressed via principal component analysis. The resulting low-dimensional representations are used to perform soft clustering of the data through a Gaussian mixture model. These cluster assignments are then applied to construct a mixture-of-GAMs framework, where each local GAM captures nonlinear effects through interpretable univariate smooth functions. Numerical experiments on real-world regression benchmarks, including the California Housing, NASA Airfoil Self-Noise, and Bike Sharing datasets, demonstrate improved predictive performance relative to classical interpretable models. Overall, this construction provides a principled approach for integrating representation learning with transparent statistical modeling.