In a building in central Berkeley, not far from the university campus, a group of modern-day Cassandras are looking into concerns around the latest AI models. In a building in central Berkeley, not far from the university campus, a group of modern-day Cassandras are looking into concerns around the latest AI models. The office block where AI'doomers' gather to predict the apocalypse On the other side of San Francisco bay from Silicon Valley, where the world's biggest technology companies tear towards superhuman artificial intelligence, looms a tower from which fearful warnings emerge. At 2150 Shattuck Avenue, in the heart of Berkeley, is the home of a group of modern-day Cassandras who rummage under the hood of cutting-edge AI models and predict what calamities may be unleashed on humanity - from AI dictatorships to robot coups. Here you can hear an AI expert express sympathy with an unnerving idea: San Francisco may be the new Wuhan, the Chinese city where Covid originated and wreaked havoc on the world.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by Refinitiv Lipper . Concerns remain over AI's impact on young people amid boom Tech expert praises New York's school cellphone ban as social media concerns rise Trump advisor details administration's push to boost AI hiring Kash Patel to close FBI's Hoover building in DC permanently Santa is'PACKING HEAT' during a traffic stop Trump has made AI a'key part' of his agenda, expert says Youth conservative movement has'never been in better hands,' podcast host says Most-used passwords of 2025 include '123456' and'password' Megan Garcia, a mother who lost her son to suicide after he allegedly became emotionally attached to an AI chatbot, discusses the dangers of the technology on'Fox News Sunday.'

Four new books grapple with a global mental-health crisis and the dawn of algorithmic therapy. A technician adjusts the wiring inside the Mark I Perceptron. This early AI system was designed not by a mathematician but by a psychologist. More than a billion people worldwide suffer from a mental-health condition, according to the World Health Organization. The prevalence of anxiety and depression is growing in many demographics, particularly young people, and suicide is claiming hundreds of thousands of lives globally each year. Given the clear demand for accessible and affordable mental-health services, it's no wonder that people have looked to artificial intelligence for possible relief.

Throughout the year we've reported on some of the larger stories, and some of the lesser-covered happenings, in our regular monthly digests. We look back through the archives and pick out one or two stories from each of our digests. This month, AI startup DeepSeek released DeepSeek R1, a reasoning model designed for good performance on logic, maths, and pattern-finding tasks. The company has also launched six smaller versions of R1 that are tiny enough to run locally on laptops. In Wired, Zeyi Yang reported on who is behind the startup, whilst Tongliang Liu (in The Conversation) looked at how DeepSeek achieved its results with a fraction of the cash and computing power of its competitors.

We introduce a training-efficient framework for time-series learning that combines random features with controlled differential equations (CDEs). In this approach, large randomly parameterized CDEs act as continuous-time reservoirs, mapping input paths to rich representations. Only a linear readout layer is trained, resulting in fast, scalable models with strong inductive bias. Building on this foundation, we propose two variants: (i) Random Fourier CDEs (RF-CDEs): these lift the input signal using random Fourier features prior to the dynamics, providing a kernel-free approximation of RBF-enhanced sequence models; (ii) Random Rough DEs (R-RDEs): these operate directly on rough-path inputs via a log-ODE discretization, using log-signatures to capture higher-order temporal interactions while remaining stable and efficient. We prove that in the infinite-width limit, these model induces the RBF-lifted signature kernel and the rough signature kernel, respectively, offering a unified perspective on random-feature reservoirs, continuous-time deep architectures, and path-signature theory. We evaluate both models across a range of time-series benchmarks, demonstrating competitive or state-of-the-art performance. These methods provide a practical alternative to explicit signature computations, retaining their inductive bias while benefiting from the efficiency of random features.

Score estimation, the task of learning the gradient of the log density, has become a crucial part of generative diffusion models [Song and Ermon, 2019, Song et al., 2021]. These models achieve state-of-the-art performance in a wide range of domains including images, audio and video synthesis [Dhariwal and Nichol, 2021, Kong et al., 2021, Ho et al., 2022]. To sample from the desired distribution, one needs to have an accurate score function estimator along the Ornstein-Uhlenbeck process. In the context of diffusion models the score estimation is done through the minimization of denoising score matching loss function over the class of neural networks [Song et al., 2021, Vincent, 2011, Oko et al., 2023]. Another recipe for score estimation is implicit score matching proposed by Hyv arinen [2005]. The proposed objective includes not only the score function, but also its Jacobian trace. A crucial research question is to determine the iteration complexity of the distribution estimation given inaccurate score function. The convergence theory of diffusion models has received much attention in the recent years. Some works [De Bortoli, 2022, Chen et al., 2023b, Benton et al., 2024, Li and Y an, 2024] study SDE-based samplers under the assumption that the score estimator is L

We investigate machine learning models for stock return prediction in non-stationary environments, revealing a fundamental nonstationarity-complexity tradeoff: complex models reduce misspecification error but require longer training windows that introduce stronger non-stationarity. We resolve this tension with a novel model selection method that jointly optimizes model class and training window size using a tournament procedure that adaptively evaluates candidates on non-stationary validation data. Our theoretical analysis demonstrates that this approach balances misspecification error, estimation variance, and non-stationarity, performing close to the best model in hindsight. Applying our method to 17 industry portfolio returns, we consistently outperform standard rolling-window benchmarks, improving out-of-sample $R^2$ by 14-23% on average. During NBER-designated recessions, improvements are substantial: our method achieves positive $R^2$ during the Gulf War recession while benchmarks are negative, and improves $R^2$ in absolute terms by at least 80bps during the 2001 recession as well as superior performance during the 2008 Financial Crisis. Economically, a trading strategy based on our selected model generates 31% higher cumulative returns averaged across the industries.

Machine Learning (ML) has been a foundational topic in artificial intelligence (AI), providing both theoretical groundwork and practical tools for its exciting advancements. From ResNet for visual recognition to Transformer for vision-language alignment, the AI models have achieved superior capability to humans. Furthermore, the scaling law has enabled AI to initially develop general intelligence, as demonstrated by Large Language Models (LLMs). To this stage, AI has had an enormous influence on society and yet still keeps shaping the future for humanity. However, distribution shift remains a persistent ``Achilles' heel'', fundamentally limiting the reliability and general usefulness of ML systems. Moreover, generalization under distribution shift would also cause trust issues for AIs. Motivated by these challenges, my research focuses on \textit{Trustworthy Machine Learning under Distribution Shifts}, with the goal of expanding AI's robustness, versatility, as well as its responsibility and reliability. We carefully study the three common distribution shifts into: (1) Perturbation Shift, (2) Domain Shift, and (3) Modality Shift. For all scenarios, we also rigorously investigate trustworthiness via three aspects: (1) Robustness, (2) Explainability, and (3) Adaptability. Based on these dimensions, we propose effective solutions and fundamental insights, meanwhile aiming to enhance the critical ML problems, such as efficiency, adaptability, and safety.

Accurate spatial interpolation of the air quality index (AQI), computed from concentrations of multiple air pollutants, is essential for regulatory decision-making, yet AQI fields are inherently non-Gaussian and often exhibit complex nonlinear spatial structure. Classical spatial prediction methods such as kriging are linear and rely on Gaussian assumptions, which limits their ability to capture these features and to provide reliable predictive distributions. In this study, we propose \textit{deep classifier kriging} (DCK), a flexible, distribution-free deep learning framework for estimating full predictive distribution functions for univariate and bivariate spatial processes, together with a \textit{data fusion} mechanism that enables modeling of non-collocated bivariate processes and integration of heterogeneous air pollution data sources. Through extensive simulation experiments, we show that DCK consistently outperforms conventional approaches in predictive accuracy and uncertainty quantification. We further apply DCK to probabilistic spatial prediction of AQI by fusing sparse but high-quality station observations with spatially continuous yet biased auxiliary model outputs, yielding spatially resolved predictive distributions that support downstream tasks such as exceedance and extreme-event probability estimation for regulatory risk assessment and policy formulation.

This paper develops a general approach for deep learning for a setting that includes nonparametric regression and classification. We perform a framework from data that fulfills a generalized Bernstein-type inequality, including independent, $ϕ$-mixing, strongly mixing and $\mathcal{C}$-mixing observations. Two estimators are proposed: a non-penalized deep neural network estimator (NPDNN) and a sparse-penalized deep neural network estimator (SPDNN). For each of these estimators, bounds of the expected excess risk on the class of Hölder smooth functions and composition Hölder functions are established. Applications to independent data, as well as to $ϕ$-mixing, strongly mixing, $\mathcal{C}$-mixing processes are considered. For each of these examples, the upper bounds of the expected excess risk of the proposed NPDNN and SPDNN predictors are derived. It is shown that both the NPDNN and SPDNN estimators are minimax optimal (up to a logarithmic factor) in many classical settings.