Amazon, Microsoft and Google are operating datacentres that use vast amounts of water in some of the world's driest areas and are building many more, an investigation by SourceMaterial and the Guardian has found. With Donald Trump pledging to support them, the three technology giants are planning hundreds of datacentres in the US and across the globe, with a potentially huge impact on populations already living with water scarcity. "The question of water is going to become crucial," said Lorena Jaume-Palasí, founder of the Ethical Tech Society. "Resilience from a resource perspective is going to be very difficult for those communities." Efforts by Amazon, the world's largest online retailer, to mitigate its water use have sparked opposition from inside the company, SourceMaterial's investigation found, with one of its own sustainability experts warning that its plans are "not ethical".

Robustness is critical in zero-shot singing voice conversion (SVC). This paper introduces two novel methods to strengthen the robustness of the kNN-VC framework for SVC. First, kNN-VC's core representation, WavLM, lacks harmonic emphasis, resulting in dull sounds and ringing artifacts. To address this, we leverage the bijection between WavLM, pitch contours, and spectrograms to perform additive synthesis, integrating the resulting waveform into the model to mitigate these issues. Second, kNN-VC overlooks concatenative smoothness, a key perceptual factor in SVC. To enhance smoothness, we propose a new distance metric that filters out unsuitable kNN candidates and optimize the summing weights of the candidates during inference. Although our techniques are built on the kNN-VC framework for implementation convenience, they are broadly applicable to general concatenative neural synthesis models. Experimental results validate the effectiveness of these modifications in achieving robust SVC. Demo: http://knnsvc.com Code: https://github.com/SmoothKen/knn-svc

In the Quantum Supremacy regime, quantum computers may overcome classical machines on several tasks if we can estimate, mitigate, or correct unavoidable hardware noise. Estimating the error requires classical simulations, which become unfeasible in the Quantum Supremacy regime. We leverage Machine Learning data-driven approaches and Conformal Prediction, a Machine Learning uncertainty quantification tool known for its mild assumptions and finite-sample validity, to find theoretically valid upper bounds of the fidelity between noiseless and noisy outputs of quantum devices. Under reasonable extrapolation assumptions, the proposed scheme applies to any Quantum Computing hardware, does not require modeling the device's noise sources, and can be used when classical simulations are unavailable, e.g. in the Quantum Supremacy regime.

This paper introduces a novel reinforcement learning (RL) approach to scheduling mixed-criticality (MC) systems on processors with varying speeds. Building upon the foundation laid by [1], we extend their work to address the non-preemptive scheduling problem, which is known to be NP-hard. By modeling this scheduling challenge as a Markov Decision Process (MDP), we develop an RL agent capable of generating near-optimal schedules for real-time MC systems. Our RL-based scheduler prioritizes high-critical tasks while maintaining overall system performance. Through extensive experiments, we demonstrate the scalability and effectiveness of our approach. The RL scheduler significantly improves task completion rates, achieving around 80% overall and 85% for high-criticality tasks across 100,000 instances of synthetic data and real data under varying system conditions. Moreover, under stable conditions without degradation, the scheduler achieves 94% overall task completion and 93% for high-criticality tasks. These results highlight the potential of RL-based schedulers in real-time and safety-critical applications, offering substantial improvements in handling complex and dynamic scheduling scenarios.

Extinction is a part of nature. Of the five billion species that have existed on Earth, 99.9 per cent have vanished. The Triassic-Jurassic extinction, two hundred million years ago, finished off the crocodile-like phytosaur. Sixty-six million years ago, the end-Cretaceous extinction eliminated the Tyrannosaurus rex and the velociraptor; rapid climate change from an asteroid impact was the likely cause. The Neanderthals disappeared some forty thousand years ago. One day--whether from climate change, another asteroid, nuclear war, or something we can't yet imagine--humans will probably be wiped out, too.

Text-to-image diffusion models excel at generating diverse portraits, but lack intuitive shadow control. Existing editing approaches, as post-processing, struggle to offer effective manipulation across diverse styles. Additionally, these methods either rely on expensive real-world light-stage data collection or require extensive computational resources for training. To address these limitations, we introduce Shadow Director, a method that extracts and manipulates hidden shadow attributes within well-trained diffusion models. Our approach uses a small estimation network that requires only a few thousand synthetic images and hours of training-no costly real-world light-stage data needed. Shadow Director enables parametric and intuitive control over shadow shape, placement, and intensity during portrait generation while preserving artistic integrity and identity across diverse styles. Despite training only on synthetic data built on real-world identities, it generalizes effectively to generated portraits with diverse styles, making it a more accessible and resource-friendly solution.

We review the literature on algorithms for estimating the in dex space in a multi-index model. The primary focus is on computa tionally efficient (polynomial-time) algorithms in Gaussian space, the assumptions under which consistency is guaranteed by these methods, and their sample complexity. In many cases, a gap is observed between the sample c omplexity of the best known computationally efficient methods and the i nformation-theoretical minimum. We also review algorithms based on est imating the span of gradients using nonparametric methods, and algorit hms based on fitting neural networks using gradient descent.

Neural networks are complex functions of both their inputs and parameters. Much prior work in deep learning theory analyzes the distribution of network outputs at a fixed a set of inputs (e.g. a training dataset) over random initializations of the network parameters. The purpose of this article is to consider the opposite situation: we view a randomly initialized Multi-Layer Perceptron (MLP) as a Hamiltonian over its inputs. For typical realizations of the network parameters, we study the properties of the energy landscape induced by this Hamiltonian, focusing on the structure of near-global minimum in the limit of infinite width. Specifically, we use the replica trick to perform an exact analytic calculation giving the entropy (log volume of space) at a given energy. We further derive saddle point equations that describe the overlaps between inputs sampled iid from the Gibbs distribution induced by the random MLP. For linear activations we solve these saddle point equations exactly. But we also solve them numerically for a variety of depths and activation functions, including $\tanh, \sin, \text{ReLU}$, and shaped non-linearities. We find even at infinite width a rich range of behaviors. For some non-linearities, such as $\sin$, for instance, we find that the landscapes of random MLPs exhibit full replica symmetry breaking, while shallow $\tanh$ and ReLU networks or deep shaped MLPs are instead replica symmetric.

If you've noticed a new light blue circle appear in your WhatsApp chats recently, and wondered what it was, Meta has recently expanded its implementation of Meta AI into new markets--and now, it's in yours. While it began rolling out in the US and Canada in 2023, more recently it has started arriving on devices across countries in Europe, including the UK, as well as Australia, New Zealand, South Africa and India. In fact, the artificial intelligence-based chatbot is rolling out across the entire Meta ecosystem, including Messenger and Instagram, and can provide you with a few basic features like answering questions, generating text or creating content. However, its appearance has also raised privacy concerns with users, and questions as to whether it can be turned off. Here's what you need to know.

Yes, tariffs are a tool of power. You will protect our industries, our jobs, our economy,