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Apple is increasing the price of MacBooks and iPads worldwide due to rising memory and storage chip costs . The iPhone maker has hiked the prices of some laptops and tablets by almost 20%, saying the electronics industry is facing an unprecedented challenge due to an extraordinary surge in demand for chips to power AI data centres. We have never seen a component price increase this much, this quickly, the company said - adding it was working tirelessly to find solutions. While Apple has not included iPhones in its price increases for some devices, tech analyst Paolo Pescatore said it showed the AI boom was now affecting consumer electronics. Apple's price hikes follow a slew of firms increasing device prices to help them absorb rising hardware costs.

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IBM hails new'block of flats' design breakthrough for ultra tiny chips Image caption, IBM's new sub-1 nm chip crams almost 100 billion transistors onto a surface the size of a fingernail IBM has unveiled a new chip design which it says could enable manufacturers to cram 100 billion transistors on a silicon chip the size of a fingernail. The current industry-standard size for chips, measured in a the unit of nanometres - a billionth of a metre and the size of a few atoms - is around two nanometres (nm). But IBM claims its new chip tech is the equivalent of around 0.7nm, which may make it the world's first known chip technology below 1nm. However, it will be several years before the chip tech could be ready to go into production. The firm claims in tests, its prototype performed 50% better than its own 2nm chip and was 70% more energy efficient.

British Police Built a Sprawling Crime-Prediction Machine. Some Results Couldn't Be Trusted As UK police embrace the AI revolution, a WIRED investigation reveals the messy inside story of one region's experiment with predictive analytics. The Think Family Database holds records on close to half a million people who live in the city of Bristol, England. For many years, few of them knew anything about it. Launched in 2016 by the Bristol City Council and the regional Avon and Somerset Police, the database has stored all manner of sensitive information--police intelligence reports, housing status, mental health records, teenage pregnancies, enrollment in parenting courses, free school meals. On top of this sensitive data, officials built machine-learning models to assign scores to thousands of adults and children. They hoped to build what they called a "picture of threat, harm, and risk" in the region. At an event in early 2022 to help officials tackle child exploitation crimes, one police data scientist described part of the approach this way: "I essentially dump all that data in a big bucket and stir it with a data-science spatula, and we come out with a lovely risk score for everybody." This risk scoring inside the Think Family Database was just one part of Avon and Somerset Police's sprawling predictive analytics program.

Lost books by ancient philosophers recovered from'unreadable' scrolls Long-lost works of ancient philosophy have been recovered from papyrus scrolls that were scorched by the AD 79 eruption of Mount Vesuvius and thought to be impossible to read. For the first time, researchers have used AI to extract the entire surviving text from super-high-resolution 3D scans of a scroll without unrolling it. The scrolls come from the library of Herculaneum, which was buried along with Pompeii nearly 2000 years ago. Scholars have been trying to read the carbonised scrolls, which resemble lumps of charcoal, since the library was discovered in 1752. Physically unwrapping them risks their destruction and the ink they are written in is mostly indistinguishable from the charred papyri - at least to human eyes.

The 99 Absolute Best Prime Day Deals We'd Spend Our Own Money On We've gone from A to Z to find Amazon's best Prime Day deals on the gear worth owning. Amazon Prime Day is here once again. Amazon's annual Prime Day deals aims to entice us with an endless scroll of "discounts (some real, many fake), hammering away with red slashes, big percentages off, and coupons you can only see after adding to cart. While Prime Day deals aren't what they once were--its success has inspired a massive number of fake deals and attracted obscure brands--there are still some very significant discounts to be found. For the next four days, the WIRED Reviews team will be pooling our hundreds of years of collective expertise to find actual savings on products we have personally tested and approved. Let us absorb the neon signage and "buy now" buttons on your behalf and share the deals worth sharing. We'll keep this list updated frequently for the duration of the sale, which runs from June 23 to June 26. This is our very favorite MagSafe power bank . Wireless and MagSafe charging aren't always the fastest or most efficient, but despite its bulk, this large-capacity bank can top off modern phones once (or maybe a little more than that) without overheating or taking forever. One of the best budget wireless chargers is even more affordable thanks to Prime Day. You can buy fancier, faster wireless chargers, but if you just want a simple option that'll top off your phone, this is worth checking out. It can deliver up to 10 watts, though you'll need to supply your own wall adapter. Want something that can fast charge your phone, juice up your tablet, and even refill your laptop? This generous 25,000-mAh capacity can do it all, but stops shy of the carry-on air travel limit. The maximum output is 165 watts for two devices, but 100 watts for a single device. It has lovely rounded edges, a retractable, flat, 2.3-foot USB-C cable on the top, and a snazzy, durable, braided 1-foot USB-C cable that doubles as a carry loop. Unlike so many Windows laptops around $500, the OmniBook 3 has excellent performance and battery life. And while the touchpad isn't the best, the specs alone make it the very best cheap laptop you can buy. You have to be careful when buying Chromebooks these days.

When two or more parameters or labels produce similar data, they are degenerate, or hard to distinguish. Degeneracies render both label prediction and inverse problems difficult, since both machine learning algorithms and probabilistic samplers rely on the distinguishability of data and its gradients with respect to parameters. However, identifying degeneracies in physical models or real-world datasets can be elucidating about the choice of model or the underlying process that produces the data. We present the degeneracy distillery, a method that (1) detects and (2) resolves degenerate parameter combinations (a) automatically and (b) symbolically, from parameter-data (or parameter-simulation) pairs alone, through estimation and flattening of the Fisher information matrix. By exploring the information geometry of the likelihood, we characterize degeneracies as an intrinsic property of the physical model, requiring no realised data observation. We demonstrate our approach on a range of synthetic and real-world problems, discovering symbolic coordinate transformations that identify the combinations of parameters of a model which yield independent effects on the data. The resulting coordinates flatten the Fisher information in expectation globally, in contrast to posterior-based methods that flatten only at a single point, and substantially reduce the simulation budget required for downstream neural posterior estimation. In test cases we require up to $10\times$ fewer simulations for posterior estimation at matched validation calibration whilst simultaneously gaining physical insight on the system.

Building on the large-sample analysis of infinitesimal gradient boosting (Dombry and Duchamps, 2024b), we study the fluctuations of the process around its deterministic limit and establish a functional central limit theorem: the rescaled deviations converge in distribution to a Gaussian process. The analysis is carried out in a reproducing kernel Hilbert space (RKHS) naturally associated with the softmax gradient tree base learner, in which the boosting process is characterized as the solution of an autonomous ordinary differential equation (ODE). The proof rests on a general stochastic perturbation analysis of ODEs in Banach spaces, which is of independent interest: whenever a sequence of vector fields converges and satisfies a central limit theorem, so does the associated ODE solution. We first illustrate this perturbation approach in the simpler setting of kernel gradient flow, where the Gaussian limit admits an explicit characterization, and then consider the more complicated tree-based gradient boosting setting.

We prove a single algebraic mixed coincidence identity that unifies a broad swath of information-theoretic variational results. For any family of priors $\{π_i\}$ and real exponents $\{ α_i \}$, the log of the mixed count $E_{x\simν}\!\left[\prod_{i=1}^W π_i^{α_i}(x)\right]$ is simultaneously a Boltzmann coincidence weight, an exponential-family normalizer, a maximum-entropy value, and a KL-barycenter optimum. The identity yields a unified derivation of classical cornerstones of information theory: concentration of empirical distributions (Sanov-type decompositions and Gibbs conditioning), hypothesis-testing error exponents (Chernoff information and its multi-way analogue), change-of-measure inequalities (Donsker-Varadhan and PAC-Bayes), and laws governing rare-pattern coincidences (Erdos-Renyi run-length, iterative guesswork, rate-distortion, and birthday thresholds). Each is recovered as a specialization of the same algebraic equality. It strictly generalizes the classical Renyi entropy and divergence variational formulas (one and two priors respectively) to a $W$-prior simplex, and holds for unnormalized and continuum-indexed priors. Among its consequences are an exact multi-prior PAC-Bayes penalty that subtracts an explicit "coincidence bonus" from the usual single-prior posterior penalty, and the asymptotic MAP error exponent for $W$-ary hypothesis testing as an edge-restricted simplex optimum. We demonstrate the calculus at scale on two large alphabets encoding richly modeled sequential languages: on language-model next-token predictives where we recover contrastive decoding, and on human genomic regulatory sequence where it separates correlated from diverse prior families along a sliding-window trace.