Jaguar Land Rover's decision to shed 4,000 jobs comes after the carmaker has travelled down a very rough road. The company has seen sales fall in all of its major markets and it has been dealing with the consequences of a devastating cyber-attack that paralysed production last year. At the same time, it has been investing billions in an effort to reinvent itself for an electric future, in which it is likely to face intense competition from aggressively expanding Chinese brands. Executives have now decided a major overhaul is needed. One of the main concerns for JLR is China.

Plus: OpenAI agents hijacked a German website before the Hugging Face hack. How much hydrogen awaits us underground? A flurry of exploration efforts is searching for underground stores of hydrogen gas, which could provide a valuable source of zero-carbon fuel. The hunt has spread all over the world and engaged dozens of startups, including the Bill Gates-backed Koloma, which has been poking around the US Midwest to reach ancient oceanic rocks associated with hydrogen production. But the search so far has come up short. No one has yet reported finding a commercially viable reservoir of the gas, and public data on what has been found remains in short supply.

What is Iran's Pickaxe Mountain? Vance says Trump'sending a message' to Iran with AI-generated video Share Vance says Trump'sending a message' to Iran with AI-generated video on social media An AI-generated video depicting missiles raining down on a key Iranian island to destroy what appears to be oil-processing infrastructure is meant as a warning to Iran, United States Vice President JD Vance says. President Donald Trump posted the video on his Truth Social account on Sunday night and on Monday followed up with a post saying Iran was "dead" and a "failed nation". "We know that they continue to shoot at commercial shipping. We continue to have a lot of tools at our disposal to prevent them from doing so or at least cut down on them from doing so, and that's what the president is saying," Vance told reporters at an Air Force base in Maryland.

The universe may never tell you if your choices mattered. A main theme of the nine-book sci-fi series is that sometimes you may never know the consequences of a choice you made. The universe, utterly indifferent black box that it is, "never tells us if we did right or wrong," as the character Naomi Nagata puts it in TV show. That's the line the developer of the upcoming Expanse video game has adopted as its own slogan. It's being developed by Owlcat Games, the Cyprus-based developer known for dense role-playing games like and .

Is the war entering a new phase? Wildberries under fire: Why is Ukraine targeting a Russian retail giant? Many have compared Wildberries, Russia's largest online retailer, to Amazon, and some claim that, as in the case of the US company, shopping on its website or app feels addictive. "I know many people that are really addicted to it," Aleksandra, an accountant and mother in the Urals Mountains city of Yekaterinburg, Russia's fourth-largest, told Al Jazeera, withholding her last name for security purposes. "They add things to their basket, wait for discounts, make up their minds about what to buy, then decide to buy it again."

'There's this deep mystery of what, actually, is this thing?': the philosopher inside Google DeepMind AI Since 2017, Iason Gabriel has worked at the tech giant, trying to anticipate - and think through - the impact of AI. But as commercial and geopolitical pressures escalate, can ethicists make any difference? In 2017, a 33-year-old political philosopher named Iason Gabriel was told by a friend that he ought to apply for a job at DeepMind, the London-based subsidiary of Google where much of its AI research was concentrated. The suggestion was not an obvious one. Gabriel was a cheerful but intense junior academic with a passion for Vipassana meditation and what his brother calls "enthusiastic" rock climbing. At the University of Oxford, where he was a fellow at St John's College, Gabriel taught courses on political theory and wrote papers on the moral contortions of "yuppie ethics" and the ethical blind spots of effective altruism. When he wasn't there, he did crisis work for the United Nations Development Programme in Sudan and Lebanon. DeepMind, meanwhile, was the world's leading AI research lab. In part, this was because it had the financial and computational backing of Google, which had bought the company in 2014 for $650m. In part, it was because DeepMind had recently shown it could put those resources to stunning use. In Seoul, in 2016, a DeepMind system called AlphaGo defeated Lee Sedol, a South Korean Go champion, in a five-game match. The victory was significant not least because of Go's legendary complexity; the game has more possible configurations than there are atoms in the universe. Thanks to the fuss around AlphaGo, Gabriel was aware of DeepMind.

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We design a new unconstrained coordinate system where a $p\times p$ symmetric positive definite (SPD) matrix $Θ$ is represented by a reverse telescoping map $Θ(x)=\rm{RT}(x)$, with $x=(v,d,r)\in\mathbb{R}\times\mathbb{R}^{(p-1)}\times\mathbb{R}^{p(p-1)/2}$, representing respectively the log volume or log determinant; and the shape, as encoded by log relative diagonal scales and partial covariances among the nodes. This construction results in important properties not available in other charts, e.g., matrix logarithm, such as Jacobian depending on only the log-determinant. A useful feature of our construction is $x$ contains a lossless symbolic representation of both the matrix and its inverse. Many important computations involving a matrix and its inverse can be performed in $O(p^2)$ in the transformed domain, while it is the rendering of results in matrix forms (on demand) that must incur an $O(p^3)$ cost. Moreover, two unit-determinant matrices in the transformed domain can be joined by a straight line with pathwise unit determinant. For generative modeling, this allows designing a split volume-shape flow model trained by conditional flow matching for transporting the shape over the unit-determinant path, with a separate one-dimensional flow for transporting the volume or the determinant. The forbidding SPD constraint, tamed thus into a powerful guiding force, leads to the surprising insight that it is in some sense easier to design a volume-normalized shape flow for SPD compared to the unconstrained $\mathbb{R}^{p\times p}$, with no intrinsic notion of volume to aid normalization, unlike the determinant of SPD matrices. We apply our construction for up to $p=200$ in generative modeling of SPD matrices on a difficult synthetic bimodal target, and in generating brain connectivity networks by models trained on fMRI data; as well as in intrinsic diffusion on the SPD manifold.

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Singular learning theory and information geometry have studied the same parameter spaces in mostly separate vocabularies: the former computes Bayesian invariants in resolved coordinates, the latter works in original coordinates under a non-degeneracy assumption that overparameterised models routinely violate. We bridge them through one primitive, the dead direction: a unit vector along which the Fisher metric degenerates, equivalently a tangent to the analytic singular set with a definite KL order, set by how fast the KL divergence vanishes. The two readings name the same vector; our central move shows its KL order is recoverable as the decay rate of the directional Fisher curvature approaching the singularity, in original parameter coordinates and without a Hironaka resolution. A selection rule on smooth fibres translates this rate into Watanabe's single-direction contribution to the real log canonical threshold, and we extend the recovery to multi-component crossings, multiplicity $m$, the singular fluctuation $ν$ (universal in the KL order for 1D directions), prior-RLCT shifts, and tempered posteriors. We then lift this rate to a deep network: a multi-layer K-FAC factorisation writes each Fisher block as a product of activation- and gradient-side rates with a duality between them, instantiated at modern-network primitives (residual streams, layer normalisation, attention). A quotient theorem carries the rate to the gauge quotient $Θ/G$ under gradient flow on a $G$-invariant metric; SGD qualifies, standard Adam does not, and we construct a $G$-equivariant Adam-family preconditioner (DDCAdam) that does. The bridge yields a parameter-coordinate handle on singular geometry, closed-form per-architecture predictions, and a trajectory-rate readout of Watanabe's triple $(λ, m, ν)$ from one checkpoint's forward and backward passes, without posterior sampling.