Don't blindly trust what AI tells you, says Google's Sundar Pichai People should not blindly trust everything AI tools tell them, the boss of Google's parent company Alphabet told the BBC. In an exclusive interview, chief executive Sundar Pichai said that AI models are prone to errors and urged people to use them alongside other tools. Mr Pichai said it highlighted the importance of having a rich information ecosystem, rather than solely relying on AI technology. This is why people also use Google search, and we have other products that are more grounded in providing accurate information. While AI tools were helpful if you want to creatively write something, Mr Pichai said people have to learn to use these tools for what they're good at, and not blindly trust everything they say.

Google boss warns'no company is going to be immune' if AI bubble bursts Every company would be affected if the AI bubble were to burst, the head of Google's parent firm Alphabet has told the BBC. Speaking exclusively to BBC News, Sundar Pichai said while the growth of artificial intelligence (AI) investment had been an extraordinary moment, there was some irrationality in the current AI boom. It comes amid fears in Silicon Valley and beyond of a bubble as the value of AI tech companies has soared in recent months and companies spend big on the burgeoning industry. Asked whether Google would be immune to the impact of the AI bubble bursting, Mr Pichai said the tech giant could weather that potential storm, but also issued a warning. I think no company is going to be immune, including us, he said.

What AI doesn't know: we could be creating a global'knowledge collapse' As GenAI becomes the primary way to find information, local and traditional wisdom is being lost. And we are only beginning to realise what we're missing This article was originally published as'Holes in the web' on Aeon.co A few years back, my dad was diagnosed with a tumour on his tongue - which meant we had some choices to weigh up. My family has an interesting dynamic when it comes to medical decisions. While my older sister is a trained doctor in western allopathic medicine, my parents are big believers in traditional remedies. Having grown up in a small town in India, I am accustomed to rituals. My dad had a ritual, too. Every time we visited his home village in southern Tamil Nadu, he'd get a bottle of thick, pungent, herb-infused oil from a vaithiyar, a traditional doctor practising Siddha medicine. It was his way of maintaining his connection with the kind of medicine he had always known and trusted.

Is the fall of Pokrovsk inevitable? Is Trump losing patience with Putin? A Russian missile strike on the eastern Ukrainian city of Balakliia killed three people and wounded 10, including three children, a regional military official in the Kharkiv region said on Telegram on Monday. At least two people were killed and three were injured in Russian shelling of the Nikopol district in Ukraine's Dnipropetrovsk region, Vladyslav Haivanenko, the acting head of the Dnipropetrovsk Regional Military Administration, wrote on Facebook. Russian troops captured three villages across three Ukrainian regions, the RIA news agency cited the Russian Ministry of Defence as saying on Monday.

Saudi crown prince to visit U.S. with defense, AI and nuclear energy on agenda In his upcoming visit to the White House, the crown prince is seeking security guarantees and wants access to artificial intelligence technology and progress toward a deal on a civilian nuclear program. RIYADH/WASHINGTON - A visit by Saudi Arabia's de facto ruler to the White House for talks on Tuesday with U.S. President Donald Trump aims to deepen decades-old cooperation on oil and security while broadening ties in commerce, technology and potentially even nuclear energy. It will be the first trip by Crown Prince Mohammed bin Salman to the U.S. since the 2018 killing of Saudi critic Jamal Khashoggi by Saudi agents in Istanbul, which caused a global uproar. U.S. intelligence concluded that the crown prince approved the capture or killing of Khashoggi, a prominent critic. The crown prince, widely known by his initials MBS, denied ordering the operation but acknowledged responsibility as the kingdom's de facto ruler.

President Donald Trump has announced US embassies will give visa appointment priority to travellers with tickets to the 2026 World Cup. The Fifa Prioritised Appointment Scheduling System (Pass) will allow World Cup ticket-holders with long wait times to opt with Fifa for a prioritised interview, Trump said at the White House on Monday. Ticket-holders for the tournament - set for next June and July in the US, Canada and Mexico - will not be automatically granted a tourist visa, said Secretary of State Marco Rubio. But foreign nationals with tickets to World Cup football matches could get an interview at an embassy or consulate within six to eight weeks of applying, Rubio said. Your ticket is not a visa; it doesn't guarantee admission to the US, Rubio said, also at the White House on Monday.

High-dimensional data often contain low-dimensional signals obscured by structured background noise, which limits the effectiveness of standard PCA. Motivated by contrastive learning, we address the problem of recovering shared signal subspaces from positive pairs, paired observations sharing the same signal but differing in background. Our baseline, PCA+, uses alignment-only contrastive learning and succeeds when background variation is mild, but fails under strong noise or high-dimensional regimes. To address this, we introduce PCA++, a hard uniformity-constrained contrastive PCA that enforces identity covariance on projected features. PCA++ has a closed-form solution via a generalized eigenproblem, remains stable in high dimensions, and provably regularizes against background interference. We provide exact high-dimensional asymptotics in both fixed-aspect-ratio and growing-spike regimes, showing uniformity's role in robust signal recovery. Empirically, PCA++ outperforms standard PCA and alignment-only PCA+ on simulations, corrupted-MNIST, and single-cell transcriptomics, reliably recovering condition-invariant structure. More broadly, we clarify uniformity's role in contrastive learning, showing that explicit feature dispersion defends against structured noise and enhances robustness.

Coral bleaching is a major concern for marine ecosystems; more than half of the world's coral reefs have either bleached or died over the past three decades. Increasing sea surface temperatures, along with various spatiotemporal environmental factors, are considered the primary reasons behind coral bleaching. The statistical and machine learning communities have focused on multiple aspects of the environment in detail. However, the literature on various stochastic modeling approaches for assessing coral bleaching is extremely scarce. Data-driven strategies are crucial for effective reef management, and this review article provides an overview of existing statistical and machine learning methods for assessing coral bleaching. Statistical frameworks, including simple regression models, generalized linear models, generalized additive models, Bayesian regression models, spatiotemporal models, and resilience indicators, such as Fisher's Information and Variance Index, are commonly used to explore how different environmental stressors influence coral bleaching. On the other hand, machine learning methods, including random forests, decision trees, support vector machines, and spatial operators, are more popular for detecting nonlinear relationships, analyzing high-dimensional data, and allowing integration of heterogeneous data from diverse sources. In addition to summarizing these models, we also discuss potential data-driven future research directions, with a focus on constructing statistical and machine learning models in specific contexts related to coral bleaching.

We study the high-dimensional inference of a rank-one signal corrupted by sparse noise. The noise is modelled as the adjacency matrix of a weighted undirected graph with finite average connectivity in the large size limit. Using the replica method from statistical physics, we analytically compute the typical value of the top eigenvalue, the top eigenvector component density, and the overlap between the signal vector and the top eigenvector. The solution is given in terms of recursive distributional equations for auxiliary probability density functions which can be efficiently solved using a population dynamics algorithm. Specialising the noise matrix to Poissonian and Random Regular degree distributions, the critical signal strength is analytically identified at which a transition happens for the recovery of the signal via the top eigenvector, thus generalising the celebrated BBP transition to the sparse noise case. In the large-connectivity limit, known results for dense noise are recovered. Analytical results are in agreement with numerical diagonalisation of large matrices.

ReLU activations are the main bottleneck in Private Inference that is based on ResNet networks. This is because they incur significant inference latency. Reducing ReLU count is a discrete optimization problem, and there are two common ways to approach it. Most current state-of-the-art methods are based on a smooth approximation that jointly optimizes network accuracy and ReLU budget at once. However, the last hard thresholding step of the optimization usually introduces a large performance loss. We take an alternative approach that works directly in the discrete domain by leveraging Coordinate Descent as our optimization framework. In contrast to previous methods, this yields a sparse solution by design. We demonstrate, through extensive experiments, that our method is State of the Art on common benchmarks.