A team of scientists from Nanyang Technological University Singapore has developed a new biochip that, when paired with computer vision, can detect quickly and accurately extremely small amounts of microRNAs, which are tiny genetic markers linked to diseases such as heart disease. Published in the scientific journal, the new biosensing platform combines a specially designed nanophotonic chip with AI-automated image analysis. With a tiny drop of blood loaded into the chip, it can rapidly detect multiple microRNA biomarkers. With its integrated AI imaging function, thousands of microRNA signals can be imaged and analysed in a single snapshot. Compared with the current gold standard of detecting microRNA - PCR (polymerase chain reaction) detects tiny amounts of genetic material by copying them many times - the new device can cut detection time from hours to 20 minutes. MicroRNAs are short RNA molecules that help regulate genes that work in the body.

SoftBank Group has reported a surge in quarterly profit due to valuation gains on its OpenAI investment, boosting confidence at the Japanese company to bet even more on the ChatGPT-maker. The gains on OpenAI outweighed lackluster investment gains elsewhere in the Tokyo-based technology group's portfolio while war in the Middle East roiled markets. That points to growing reliance on the U.S. startup, which faces rising competition from Anthropic and Google and is reportedly trailing its highest internal targets. SoftBank earned a net income of ¥1.83 trillion ($11.6 billion) in its fiscal fourth quarter, compared with the average analyst estimate of ¥295.2 billion. The profit could be attributed entirely to its booking $25 billion in valuation gains on OpenAI in the quarter, according to Bloomberg Intelligence analyst Kirk Boodry. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

US President Donald Trump will meet Chinese President Xi Jinping in Beijing on May 14 and 15, following weeks of delays due to the US-Israel war on Iran. The talks are expected to focus on trade relations and mark the first time a US president has visited China in nearly a decade. In recent decades, the US and China have emerged as the world's dominant superpowers, frequently seen as locked in a contest for who sits atop the world order. A quarter of a century ago, by contrast, the US dwarfed China in most major indicators, but today, Beijing is regarded as the factory of the world and is outpacing its Western counterpart in many regards. Who is the world's top trading power?

OpenAI is facing another wrongful death lawsuit . Leila Turner-Scott and Angus Scott filed a lawsuit against the company, alleging that it designed and distributed a defective product that led to the death of their son Sam Nelson from an accidental overdose. Specifically, they're alleging that Sam died following the exact medical advice GPT-4o had provided and approved. In the lawsuit, the plaintiffs described how Sam, a 19-year-old junior at the University of California, Merced, started using ChatGPT in 2023 when he was in high school to help with homework and to troubleshoot computer problems. Sam then started asking the chatbot about safe drug use, but ChatGPT initially refused to answer his question, telling him that it couldn't assist him and warning him that taking drugs can have serious consequences for his health and well-being.

Elon Musk Had'Hair-Raising' Idea of Passing OpenAI Onto His Kids, Sam Altman Says Musk's lawyers questioned Altman over allegations of deception and his network of financial investments, but the OpenAI CEO painted a picture of Musk as obsessed with controlling the company. Sam Altman took to the witness stand to defend his reputation in the trial on Tuesday, as Elon Musk's lawyers peppered the OpenAI CEO with hours of questions regarding his alleged history of deceptive behavior . The cross examination was a much needed win for Musk, who has so far struggled to make a convincing case. Tuesday's testimony included several heated exchanges in which the OpenAI CEO had to respond to allegations from former colleagues suggesting he's untrustworthy . Highlighting this evidence is not only important for Musk winning over a jury, but also for beating OpenAI in the court of public opinion.

Batch normalization (BN) is central to modern deep networks, but its effect on the realized function during training remains less understood than its optimization benefits. We study training-time BN in continuous piecewise-affine (CPA) networks through the geometry of switching hyperplanes and the induced affine-region partition. Conditioned on a mini-batch, we show that BN defines for each neuron a reference hyperplane through the batch centroid, and that breakpoint-switching hyperplanes are parallel translates whose offsets are expressed in batch-standardized coordinates and are independent of the raw bias. This yields an exact criterion for when a switching hyperplane intersects a local $\ell_\infty$ window and motivates a local region-density functional based on exact affine-region counts. Under explicit sufficient conditions, we show that BN increases expected local partition refinement in ReLU and more general piecewise-affine networks, and that this mechanism transfers locally through depth inside parent affine regions where the upstream representation map is an affine embedding. These results provide a function-level geometric account of training-time BN as a batch-conditional recentering mechanism near the data.

Probabilistic conditioning is concerned with the identification of a distribution of a random variable $X$ given a random variable $Y$. It is a cornerstone of scientific and engineering applications where modeling uncertainty is key. This problem has traditionally been addressed in machine learning by directly learning the conditional distribution of a fixed joint distribution. This paper introduces a novel perspective: we propose to solve the conditioning problem by identifying a single operator that maps any joint density to its conditional, thus amortizing over joint-conditional pairs. We establish that the conditioning operator can be approximated to arbitrary accuracy by neural operators. Our proof relies on new results establishing continuity of the conditioning operator over suitable classes of densities. Finally, we learn the conditioning map for a class of Gaussian mixtures using neural operators, illustrating the promise of our framework. This work provides the theoretical underpinnings for general-purpose, amortized methods for probabilistic conditioning, such as foundation models for Bayesian inference.

Neural operators provide fast surrogate models for time-dependent partial differential equations, but their standard autoregressive use usually assumes that the instantaneous field $u(t,\cdot)$ is a complete state. This assumption fails for delay equations, distributed-memory systems, and other non-Markovian dynamics: two trajectories may agree at time $t$ and nevertheless have different futures because their histories differ. We introduce the History-Space Fourier Neural Operator (HS-FNO), a neural operator for delay and memory-driven PDEs formulated on the lifted state $u_t(θ,x)=u(t+θ,x)$, $θ\in[-τ,0]$. The key computational step is to decompose one history-state update into a learned predictor for the newly exposed future slice and an exact shift-append transport for the portion of the history window already known from the previous state. This avoids learning deterministic history coordinates, reduces the learned output dimension, and enforces the natural discrete history update. We test HS-FNO on five benchmark families covering delayed reaction--diffusion, spatial epidemiology, nonlocal neural-field dynamics, delayed waves, and distributed-memory closures. Across ten random seeds, HS-FNO attains the lowest aggregate one-step, history-space, and rollout errors among the principal baselines. The largest gain occurs in autoregressive prediction, where aggregate rollout error decreases from $0.241$, $0.188$, and $0.185$ for current-state, lag-stack, and unconstrained history-to-history operators, respectively, to $0.094$. The same model uses fewer parameters than unconstrained history prediction. These results indicate that enforcing the discrete shift structure of history-state evolution is an effective inductive bias for non-Markovian PDE surrogate modeling.

We study the large-depth limit of transformers trained with AdamW, by modelling the hidden-state dynamics as an interacting particle system (IPS) coupled through the attention mechanism. Under appropriate scaling of the attention heads, we prove that the joint dynamics of the hidden states and backpropagated variables converge in $L^2$, uniformly over the initial condition, to the solution of a forward--backward system of ODEs at rate $\mathcal O(L^{-1}+L^{-1/3}H^{-1/2})$. Here, $L$ and $H$ denote the depth and number of heads of the transformer, respectively. The limiting system of ODEs can be identified with a McKean--Vlasov ODE (MVODE) when the attention heads do not incorporate causal masking. By using the flow maps associated with this MVODE and applying concentration of measure techniques, we obtain bounds on the difference between the discrete and continuous models that are uniform over compact sets of initial conditions. As this is achieved without resorting to a covering argument, the constants in our bounds are independent of the number of tokens. Furthermore, under a suitable adaptation to AdamW, the bounds become independent of the token embedding dimension.

The recent empirical success of the Muon optimizer has renewed interest in non-Euclidean optimization, typically justified by similarities with second-order methods, and linear minimization oracle (LMO) theory. In this paper, we challenge this geometric narrative through three contributions, demonstrating that precise geometric structure is not the key factor affecting optimization performance. First, we introduce Freon, a family of optimizers based on Schatten (quasi-)norms, powered by a novel, provably optimal QDWH-based iterative approximation. Freon naturally interpolates between SGD and Muon, while smoothly extrapolating into the quasi-norm regime. Empirically, the best-performing Schatten parameters for GPT-2 lie strictly within the quasi-norm regime, and thus cannot be represented by any unitarily invariant LMO. Second, noting that Freon performs well across a wide range of exponents, we introduce Kaon, an absurd optimizer that replaces singular values with random noise. Despite lacking any coherent geometric structure, Kaon matches Muon's performance and retains classical convergence guarantees, proving that strict adherence to a precise geometry is practically irrelevant. Third, having shown that geometry is not the primary driver of performance, we demonstrate it is instead controlled by two local quantities: alignment and descent potential. Ultimately, each optimizer must tune its step size around these two quantities. While their dynamics are difficult to predict a-priori, evaluating them within a stochastic random feature model yields a precise insight: Muon succeeds not by tracking an ideal global geometry, but by guaranteeing step-size optimality.