UK Officials Said AI Would Add £400 Billion to the Economy. The AI minister now concedes the estimated figure is less than a third of that. Researchers blame the country's slow rate of adoption For the last two years, the UK government has been telling the British public that the country could receive an economic boost of £400 billion, thanks to AI, a figure intended to calm fears that the technology would lead to sweeping job losses and even an economic crash. But the UK AI minister admitted last week that figure was significantly higher than the government's actual projections after it was revealed that assessment came solely from a report commissioned by Google, and that its own estimation of AI's economic benefit was less than a third of that £400 billion figure. AI minister Kanishka Narayan, answering a parliamentary question on the issue, said that AI adoption and data investment could add £55 billion to £140 billion to the UK economy by 2030, bringing the central estimate to £120 billion.

An AI Breakthrough Has Made Decades of Computer Science Research'Irrelevant' Anthropic and OpenAI say they've disproved a key tenet of computer science. Researchers are struggling with the fall but also feel like "overexcited bunnies" as they look at new questions to explore. Linköping University professor Peter Jonsson is set to have his work presented in two weeks' time at one of the most prestigious conferences in theoretical computer science. As of Monday, much of that work is "dead," he says, after AI showed that a 27-year-old hypothesis underpinning it is wrong. Both OpenAI and Anthropic claimed this week to have disproved the exponential time hypothesis (ETH).

The Dell Laptop Buying Guide UK: Which Dell Laptop Should You Buy? When you purchase through links in our articles, we may earn a small commission. What does Amazon know about you? We all know companies profile us. But understanding that abstractly is one thing.

GPS spoofing, jamming, and electronic warfare pose growing threats to U.S. military technology and civilian infrastructure. A nationwide GPS loss could cost $1 billion per day.

The ability for players to smoke marijuana to detect an enemy has resulted in the game being banned in Australia. The ability for players to smoke marijuana to detect an enemy has resulted in the game being banned in Australia. Illicit deeds have just gotten a new Halloween video game banned in Australia - and it's not because of the violence Academics - and a former director of Australia's Classification Board - say Halloween: The Game's ban due to'incentivised drug use' highlights an incoherent set of standards H alloween's Michael Myers is one of film's most feared boogeymen, responsible for some of the most brutal on-screen deaths in horror cinema. US video game developer Illfonic looked to reproduce those terrors in its upcoming title Halloween: The Game, with gruesome killings a major focus of the gameplay: for instance, you can stomp another player's head through a toilet seat, or fry their brain by throwing them through a television screen. But Australians will not be able to play Halloween: The Game, because it has been refused classification by the Classification Board, which means it is banned from sale within the country.

In healthcare settings where patients use LLMs as a medical assistant, LLM performance differs between evaluation and deployment. Closing the gap requires making assumptions explicit, testing which assumptions hold, and updating evaluation protocols accordingly. Healthcare LLM benchmarks are one of the main paradigms by which LLMs are evaluated prior to clinical settings. Benchmarks provide a stable goalpost that allow researchers to iterate quickly and measure progress consistently. However, in high-stakes domains like healthcare, that same abstraction becomes a liability.

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Occam's razor has lost its edge. Can we sharpen our search for truth? Seeking out the simplest, most elegant explanations has served scientists well for centuries, but cognitive scientist Marina Dubova's experiments are revealing better ways to uncover reality Limited by the knowledge of his time, the ancient Greek astronomer Ptolemy imagined that the planets and sun of our solar system orbited Earth. Every new observation that pushed against this image required a slight tweak to that theory, until centuries later Nicolaus Copernicus's reimagining toppled it once and for all. A more elegant explanation proposed that all the planets orbited the sun, kicking off a scientific revolution that changed our understanding of the entire universe.

We establish approximation and learning guarantees for Fourier neural operators (FNOs) applied to time-$T$ solution operators of dissipative evolution equations. The analysis builds on the premise that FNOs can efficiently approximate and learn solution operators whenever these operators admit stable and accurate spectral discretizations. To formalize this idea, we introduce classes of evolution operators defined through spectral methods and derive FNO approximation bounds and polynomial sample complexity guarantees for these classes. For equations with polynomial nonlinearities, the learning rates depend primarily on the smoothness of the input space and the dimension of the physical domain. Our results hold uniformly over broad families of dissipative equations, rather than for a single fixed PDE, and apply in particular to the Navier--Stokes, Allen--Cahn, and Cahn--Hilliard equations. For equations with non-polynomial smooth nonlinearities, we prove that polynomial sample complexity still holds with rates that now additionally depend on the smoothness of the nonlinear terms and the dissipation strength. Overall, we connect classical spectral approximation theory with modern operator learning and explain when FNOs can learn nonlinear evolution operators efficiently.

We develop a framework for analyzing the learning dynamics of $\ell_2$-adversarial training of single-index models on Gaussian mixtures in the high-dimensional limit under streaming stochastic gradient descent (SGD). We derive deterministic equivalents for a broad class of statistics of the SGD iterates, including the adversarial risk and distance to adversarial optimality, in terms of the solution to a system of ODEs. We use them to study two idealized learning rate schedules: the Polyak stepsize and exact line search. In the case of $\ell_2$-adversarial least squares with a single class, we show that, unlike noiseless standard least squares, no constant learning rate guarantees monotone descent of SGD towards a minimizer of the adversarial risk. We identify anisotropic covariance and a mismatch in ridge parameters as the main sources of suboptimality of exact line search relative to the Polyak stepsize. We also introduce a stochastic differential equation (SDE), called adversarial homogenized SGD, that captures the evolution of statistics of the iterates of SGD. For $\ell_2$-adversarial least squares, using this SDE, we show the evolution of the risk is equivalent, up to dimension-free constants, to that of SGD on standard least squares with an adaptive learning rate and adaptive $\ell_2$-regularization. When the dynamics converge, the limiting adversarial risk and SGD iterate are determined by a fixed-point equation, with the limiting iterate being equivalent to the solution of a ridge regression problem whose regularization parameter is the limiting effective regularization of SGD.