'I have to do it': Why one of the world's most brilliant AI scientists left the US for China In 2020, after spending half his life in the US, Song-Chun Zhu took a one-way ticket to China. By the time Song-Chun Zhu was six years old, he had encountered death more times than he could count. This was the early 1970s, the waning years of the Cultural Revolution, and his father ran a village supply store in rural China . There was little to do beyond till the fields and study Mao Zedong at home, and so the shop became a refuge where people could rest, recharge and share tales. Zhu grew up in that shop, absorbing a lifetime's worth of tragedies: a family friend lost in a car crash, a relative from an untreated illness, stories of suicide or starvation. "That was really tough," Zhu recalled recently. The young Zhu became obsessed with what people left behind after they died. One day, he came across a book that contained his family genealogy. When he asked the bookkeeper why it included his ancestors' dates of birth and death but nothing about their lives, the man told him matter of factly that they were peasants, so there was nothing worth recording. He resolved that his fate would be different. Today, at 56, Zhu is one of the world's leading authorities in artificial intelligence. In 1992, he left China for the US to pursue a PhD in computer science at Harvard. Later, at University of California, Los Angeles (UCLA), he led one of the most prolific AI research centres in the world, won numerous major awards, and attracted prestigious research grants from the Pentagon and the National Science Foundation. He was celebrated for his pioneering research into how machines can spot patterns in data, which helped lay the groundwork for modern AI systems such as ChatGPT and DeepSeek. He and his wife, and their two US-born daughters, lived in a hilltop home on Los Angeles's Mulholland Drive. He thought he would never leave. But in August 2020, after 28 years in the US, Zhu astonished his colleagues and friends by suddenly moving back to China, where he took up professorships at two top Beijing universities and a directorship in a state-sponsored AI institute.

'Let's talk about something fun!' Grem the AI chatbot toy. 'Let's talk about something fun!' Grem the AI chatbot toy. 'I love you too!' My family's creepy, unsettling week with an AI toy The cuddly chatbot Grem is designed to'learn' your child's personality, while every conversation they have is recorded, then transcribed by a third party. It wasn't long before I wanted this experiment to be over ... 'I'm going to throw that thing into a river!" my wife says as she comes down the stairs looking frazzled after putting our four-year-old daughter to bed. To be clear, "that thing" is not our daughter, Emma*. It's Grem, an AI-powered stuffed alien toy that the musician Claire Boucher, better known as Grimes, helped develop with toy company Curio. Designed for kids aged three and over and built with OpenAI's technology, the toy is supposed to "learn" your child's personality and have fun, educational conversations with them. It's advertised as a healthier alternative to screen time and is ...

The world's fourth biggest company, Google-owner Alphabet, has announced a new £5bn ($6.8bn) investment in UK artificial intelligence (AI). The money will be used for infrastructure and scientific research over the next two years - the first of several massive US investments being unveiled ahead of US President Donald Trump's state visit. Google's President and Chief Investment Officer Ruth Porat told BBC News in an exclusive interview that there were profound opportunities in the UK for its pioneering work in advanced science. The company will officially open a vast $1bn (£735m) data centre in Waltham Cross, Hertfordshire, with Chancellor Rachel Reeves on Tuesday. The investment will expand this site and also include funding for London-based DeepMind, run by British Nobel Prize winner Sir Demis Hassabis, which deploys AI to revolutionise advanced scientific research.

Artificial intelligence avatar Una will appear at the U.N. pavilion at Osaka Expo later this month as part of initiatives to combat climate change. An artificial intelligence avatar will appear at the U.N. Pavilion of the 2025 World Expo in Osaka in late September, sharing stories from Pacific island nations under threat from rising sea levels caused by climate change. The anime-inspired female character, Una, developed as part of climate initiatives supported by the Japanese government, will be showcased with the use of 3D hologram technology from Sept. 29 to Oct. 4. Launched online in May, Una can automatically respond to questions in English, Japanese and other languages. She will be like a strong voice to raise awareness on environment and climate, what is happening in the Pacific, Kanni Wignaraja, U.N. assistant secretary-general and regional director for Asia and the Pacific at the U.N. Development Programme (UNDP), said in an interview in Tokyo. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Is Chicago the violent crime capital of the US? How did India-US relations decline so fast? A Ukrainian drone attack killed two women in the village of Golovchino in Russia's Belgorod region, Russia's state TASS news agency reports. A man who was seriously injured in a Ukrainian drone attack in Russia's Belgorod region in April has died in hospital, TASS reports. TASS also reported that Russian forces shot down 82 Ukrainian drones in a 24-hour period.

Recursive decision trees have emerged as a leading methodology for heterogeneous causal treatment effect estimation and inference in experimental and observational settings. These procedures are fitted using the celebrated CART (Classification And Regression Tree) algorithm [Breiman et al., 1984], or custom variants thereof, and hence are believed to be "adaptive" to high-dimensional data, sparsity, or other specific features of the underlying data generating process. Athey and Imbens [2016] proposed several "honest" causal decision tree estimators, which have become the standard in both academia and industry. We study their estimators, and variants thereof, and establish lower bounds on their estimation error. We demonstrate that these popular heterogeneous treatment effect estimators cannot achieve a polynomial-in-$n$ convergence rate under basic conditions, where $n$ denotes the sample size. Contrary to common belief, honesty does not resolve these limitations and at best delivers negligible logarithmic improvements in sample size or dimension. As a result, these commonly used estimators can exhibit poor performance in practice, and even be inconsistent in some settings. Our theoretical insights are empirically validated through simulations.

We propose FACTORS, a framework that combines design of experiments with Shapley decomposition to address performance and stability issues that are sensitive to combinations of training factors. Our approach consistently estimates main effects and two-factor interactions, then integrates them into a risk-adjusted objective function that jointly accounts for uncertainty and cost, enabling reliable selection of configurations under a fixed budget. Effect estimation is implemented through two complementary paths: a plug-in path based on conditional means, and a least-squares path that reconstructs Shapley contributions from samples. These paths are designed to work complementarily even when design density and bias levels differ. By incorporating standardization of estimates, bias correction, and uncertainty quantification, our procedure ensures comparability across heterogeneous factor spaces and designs, while a lightweight search routine yields configurations within practical time even for large factor spaces. On the theoretical side, we provide error decompositions, sample complexity analysis, and upper bounds on optimality gaps. On the interpretive side, we summarize main effects and interactions in map form, highlighting adjustment priorities and safe improvement pathways. Across diverse datasets and design conditions, our approach improves rank preservation and optimal configuration identification, reduces decision-making risks, and offers a tuning foundation that delivers interpretable justification alongside stable performance gains even under budget constraints.

Given-data methods for variance-based sensitivity analysis have significantly advanced the feasibility of Sobol' index computation for computationally expensive models and models with many inputs. However, the limitations of existing methods still preclude their application to models with an extremely large number of inputs. In this work, we present practical extensions to the existing given-data Sobol' index method, which allow variance-based sensitivity analysis to be efficiently performed on large models such as neural networks, which have $>10^4$ parameterizable inputs. For models of this size, holding all input-output evaluations simultaneously in memory -- as required by existing methods -- can quickly become impractical. These extensions also support nonstandard input distributions with many repeated values, which are not amenable to equiprobable partitions employed by existing given-data methods. Our extensions include a general definition of the given-data Sobol' index estimator with arbitrary partition, a streaming algorithm to process input-output samples in batches, and a heuristic to filter out small indices that are indistinguishable from zero indices due to statistical noise. We show that the equiprobable partition employed in existing given-data methods can introduce significant bias into Sobol' index estimates even at large sample sizes and provide numerical analyses that demonstrate why this can occur. We also show that our streaming algorithm can achieve comparable accuracy and runtimes with lower memory requirements, relative to current methods which process all samples at once. We demonstrate our novel developments on two application problems in neural network modeling.

Role-playing Large language models (LLMs) are increasingly deployed in high-stakes domains such as healthcare, education, and governance, where failures can directly impact user trust and well-being. A cost effective paradigm for LLM role-playing is few-shot learning, but existing approaches often cause models to break character in unexpected and potentially harmful ways, especially when interacting with hostile users. Inspired by Retrieval-Augmented Generation (RAG), we reformulate LLM role-playing into a text retrieval problem and propose a new prompting framework called RAGs-to-Riches, which leverages curated reference demonstrations to condition LLM responses. We evaluate our framework with LLM-as-a-judge preference voting and introduce two novel token-level ROUGE metrics: Intersection over Output (IOO) to quantity how much an LLM improvises and Intersection over References (IOR) to measure few-shot demonstrations utilization rate during the evaluation tasks. When simulating interactions with a hostile user, our prompting strategy incorporates in its responses during inference an average of 35% more tokens from the reference demonstrations. As a result, across 453 role-playing interactions, our models are consistently judged as being more authentic, and remain in-character more often than zero-shot and in-context Learning (ICL) methods. Our method presents a scalable strategy for building robust, human-aligned LLM role-playing frameworks.

Branched broomrape (Phelipanche ramosa) is a chlorophyll-deficient parasitic weed that threatens tomato production by extracting nutrients from the host. We investigate early detection using leaf-level spectral reflectance (400-2500 nm) and ensemble machine learning. In a field experiment in Woodland, California, we tracked 300 tomato plants across growth stages defined by growing degree days (GDD). Leaf reflectance was acquired with a portable spectrometer and preprocessed (band denoising, 1 nm interpolation, Savitzky-Golay smoothing, correlation-based band reduction). Clear class differences were observed near 1500 nm and 2000 nm water absorption features, consistent with reduced leaf water content in infected plants at early stages. An ensemble combining Random Forest, XGBoost, SVM with RBF kernel, and Naive Bayes achieved 89% accuracy at 585 GDD, with recalls of 0.86 (infected) and 0.93 (noninfected). Accuracy declined at later stages (e.g., 69% at 1568 GDD), likely due to senescence and weed interference. Despite the small number of infected plants and environmental confounders, results show that proximal sensing with ensemble learning enables timely detection of broomrape before canopy symptoms are visible, supporting targeted interventions and reduced yield losses.