While everyone else has been talking about Nvidia's GPUs, Lisa Su has discreetly turned AMD into a chipmaking phenom. Su, the leader of AMD, moves fast these days, though I suspect that's always been the case. Her company's chips underpin the artificial intelligence that's changing the world at breakneck speeds. To hear Su and literally everyone else in semiconductors talk about it, the US is in an AI with China--and the rules keep changing . The Trump administration has once again shifted its stance on what kind of chips can and can't be shipped to China, with the latest decree being that the US will take a 15 percent cut of AMD and Nvidia chip sales to China. Meanwhile, on the home front, Su has claimed that AMD's newest AI chips can outperform Nvidia's--part of her strategy to keep eroding Nvidia's dominance in the market. So, yeah: Be ready to keep up. Under Lisa Su, the stalwart American semiconductor company has reasserted itself as a force in the age of AI. "Reasserted" doesn't do it justice: Su took a struggling AMD and executed a 10-year turnaround that has been, as one economist put it, nothing short of remarkable. Since 2014, when Su took over as CEO, AMD's market cap has risen from around $2 billion to nearly $300 billion. Aside from her well-known bona fides, Su herself--what drives her, what inspires her, what irritates her, where her politics lie--is less known. This is what I was hoping to learn when I visited AMD's offices and labs in the hills of Austin, Texas, on a day in late June when the wind seemed to do little more than push heat around. Our conversation kicked off with China, which accounts for nearly a quarter of AMD's business. Su now travels frequently to Washington, DC, to grease the wheels. "We've come to realize that export controls are a bit of a fact of life," she told me, "just given how critical the chips that we make are." In other words, it's precisely because AMD's chips are so darn important--to national security, to national economies--that they're now at the heart of modern statecraft.

Donald Trump has flagged allowing Nvidia to sell chips in China that are more advanced than currently allowed, in another "deal" that would loosen export restrictions despite deep-seated fears in Washington that Beijing could harness US tech to harm national security. At a briefing on Monday, Trump was questioned over recent revelations that he had struck an unprecedented deal with Nvidia and AMD to grant them export licenses to sell previously banned chips to China, in return for the companies giving the US government 15% of the sales revenue. The US president defended the deal, which analysts have likened to a "shakedown" payment, or unconstitutional export taxes, before adding that he was expecting further negotiations over another, more advanced Nvidia chip. Trump said Nvidia had a "super-duper advanced" new chip, the Blackwell, with which he would not make a deal, but it was possible he would make a deal with a "somewhat enhanced – in a negative way – Blackwell", suggesting it could be downgraded by 30-50%. "I think he's coming to see me again about that, but that will be an un-enhanced version of the big one," he added, in reference to Nvidia's chief executive, Jensen Huang, who has repeatedly met Trump about China export restrictions.

U.S. President Donald Trump on Monday suggested he might allow Nvidia to sell a scaled-down version of its next-generation advanced graphics processing unit chip in China, despite deep-seated fears in Washington that Beijing could harness U.S. artificial intelligence capabilities to supercharge its military. The move could open the door to China securing more advanced computing power from the U.S. even as the two countries battled for technology supremacy, critics said. "Jensen (Huang, Nvidia CEO) also has the new chip, the Blackwell. In other words, take 30% to 50% off of it," Trump told reporters in an apparent reference to slashing the chip's computing power.

Predictions and generations from large language models are increasingly being explored as an aid to computational social science and human subject research in limited data regimes. While previous technical work has explored the potential to use model-predicted labels for unlabeled data in a principled manner, there is increasing interest in using large language models to generate entirely new synthetic samples (also termed as synthetic simulations), such as in responses to surveys. However, it is not immediately clear by what means practitioners can combine such data with real data and yet produce statistically valid conclusions upon them. In this work, we introduce a new estimator based on generalized method of moments, providing a hyperparameter-free solution with strong theoretical guarantees to address the challenge at hand. Surprisingly, we find that interactions between the moment residuals of synthetic data and those of real data can improve estimates of the target parameter. We empirically validate the finite-sample performance of our estimator across different regression tasks in computational social science applications, demonstrating large empirical gains.

This paper considers Ecogame, an innovative art project of 1970, whose creators believed in a positive vision of a technological future; an understanding, posited on cybernetics, of a future that could be participatory via digital means, and therefore more democratised. Using simulation and early machine learning techniques over a live network, Ecogame combined the power of visual art with cybernetic concepts of adaptation, feedback, and control to propose that behaviour had implications for the total system. It provides an historical precedent for contemporary AI-driven art about using AI in a more human-centred way.

We propose a joint learning framework for Byzantine-resilient spectrum sensing and secure intelligent reflecting surface (IRS)--assisted opportunistic access under channel state information (CSI) uncertainty. The sensing stage performs logit-domain Bayesian updates with trimmed aggregation and attention-weighted consensus, and the base station (BS) fuses network beliefs with a conservative minimum rule, preserving detection accuracy under a bounded number of Byzantine users. Conditioned on the sensing outcome, we pose downlink design as sum mean-squared error (MSE) minimization under transmit-power and signal-leakage constraints and jointly optimize the BS precoder, IRS phase shifts, and user equalizers. With partial (or known) CSI, we develop an augmented-Lagrangian alternating algorithm with projected updates and provide provable sublinear convergence, with accelerated rates under mild local curvature. With unknown CSI, we perform constrained Bayesian optimization (BO) in a geometry-aware low-dimensional latent space using Gaussian process (GP) surrogates; we prove regret bounds for a constrained upper confidence bound (UCB) variant of the BO module, and demonstrate strong empirical performance of the implemented procedure. Simulations across diverse network conditions show higher detection probability at fixed false-alarm rate under adversarial attacks, large reductions in sum MSE for honest users, strong suppression of eavesdropper signal power, and fast convergence. The framework offers a practical path to secure opportunistic communication that adapts to CSI availability while coherently coordinating sensing and transmission through joint learning.

Deep neural networks (DNNs) are being utilized in various aspects of our daily lives, including high-stakes decision-making applications that impact individuals. However, these systems reflect and amplify bias from the data used during training and testing, potentially resulting in biased behavior and inaccurate decisions. For instance, having different misclassification rates between white and black sub-populations. However, effectively and efficiently identifying and correcting biased behavior in DNNs is a challenge. This paper introduces FairFLRep, an automated fairness-aware fault localization and repair technique that identifies and corrects potentially bias-inducing neurons in DNN classifiers. FairFLRep focuses on adjusting neuron weights associated with sensitive attributes, such as race or gender, that contribute to unfair decisions. By analyzing the input-output relationships within the network, FairFLRep corrects neurons responsible for disparities in predictive quality parity. We evaluate FairFLRep on four image classification datasets using two DNN classifiers, and four tabular datasets with a DNN model. The results show that FairFLRep consistently outperforms existing methods in improving fairness while preserving accuracy. An ablation study confirms the importance of considering fairness during both fault localization and repair stages. Our findings also show that FairFLRep is more efficient than the baseline approaches in repairing the network.

Hyperspectral imaging (HSI) is an advanced sensing modality that simultaneously captures spatial and spectral information, enabling non-invasive, label-free analysis of material, chemical, and biological properties. This Primer presents a comprehensive overview of HSI, from the underlying physical principles and sensor architectures to key steps in data acquisition, calibration, and correction. We summarize common data structures and highlight classical and modern analysis methods, including dimensionality reduction, classification, spectral unmixing, and AI-driven techniques such as deep learning. Representative applications across Earth observation, precision agriculture, biomedicine, industrial inspection, cultural heritage, and security are also discussed, emphasizing HSI's ability to uncover sub-visual features for advanced monitoring, diagnostics, and decision-making. Persistent challenges, such as hardware trade-offs, acquisition variability, and the complexity of high-dimensional data, are examined alongside emerging solutions, including computational imaging, physics-informed modeling, cross-modal fusion, and self-supervised learning. Best practices for dataset sharing, reproducibility, and metadata documentation are further highlighted to support transparency and reuse. Looking ahead, we explore future directions toward scalable, real-time, and embedded HSI systems, driven by sensor miniaturization, self-supervised learning, and foundation models. As HSI evolves into a general-purpose, cross-disciplinary platform, it holds promise for transformative applications in science, technology, and society.

This paper proposes a strategy to encircle and intercept a non-cooperative aerial point-mass moving target by leveraging noisy range measurements for state estimation. In this approach, the guardians actively ensure the observability of the target by using an anti-synchronization (AS), 3D ``vibrating string" trajectory, which enables rapid position and velocity estimation based on the Kalman filter. Additionally, a novel anti-target controller is designed for the guardians to enable adaptive transitions from encircling a protected target to encircling, intercepting, and neutralizing a hostile target, taking into consideration the input constraints of the guardians. Based on the guaranteed uniform observability, the exponentially bounded stability of the state estimation error and the convergence of the encirclement error are rigorously analyzed. Simulation results and real-world UAV experiments are presented to further validate the effectiveness of the system design.

The introduction of 5G and the Open Radio Access Network (O-RAN) architecture has enabled more flexible and intelligent network deployments. However, the increased complexity and openness of these architectures also introduce novel security challenges, such as data manipulation attacks on the semi-standardised Shared Data Layer (SDL) within the O-RAN platform through malicious xApps. In particular, malicious xApps can exploit this vulnerability by introducing subtle Unicode-wise alterations (hypoglyphs) into the data that are being used by traditional machine learning (ML)-based anomaly detection methods. These Unicode-wise manipulations can potentially bypass detection and cause failures in anomaly detection systems based on traditional ML, such as AutoEncoders, which are unable to process hypoglyphed data without crashing. We investigate the use of Large Language Models (LLMs) for anomaly detection within the O-RAN architecture to address this challenge. We demonstrate that LLM-based xApps maintain robust operational performance and are capable of processing manipulated messages without crashing. While initial detection accuracy requires further improvements, our results highlight the robustness of LLMs to adversarial attacks such as hypoglyphs in input data. There is potential to use their adaptability through prompt engineering to further improve the accuracy, although this requires further research. Additionally, we show that LLMs achieve low detection latency (under 0.07 seconds), making them suitable for Near-Real-Time (Near-RT) RIC deployments.