The US firm behind the Roomba smart vacuum cleaner, iRobot, has filed for bankruptcy protection after facing competition from Chinese rivals and being hit by tariffs. Under the so-called pre-packaged Chapter 11 process, the main manufacturer of its devices, Shenzhen-based Picea Robotics, will take ownership of the firm. The tough commercial landscape had forced iRobot to cut its prices and make major investments in new technology, according to documents filed on Sunday. US import duties of 46% on goods from Vietnam, where most of iRobot's devices for the American market are made, increased its costs by $23m (£17.2m) this year, the firm said. The loss-making company was valued at $3.56bn in 2021 after the pandemic helped to drive strong demand for its products.

Background. Subdural hematoma (SDH) is a common neurosurgical emergency, with increasing incidence in aging populations. Rapid and accurate identification is essential to guide timely intervention, yet existing automated tools focus primarily on detection and provide limited interpretability or spatial localization. There remains a need for transparent, high-performing systems that integrate multimodal clinical and imaging information to support real-time decision-making. Methods. We developed a multimodal deep-learning framework that integrates structured clinical variables, a 3D convolutional neural network trained on CT volumes, and a transformer-enhanced 2D segmentation model for SDH detection and localization. Using 25,315 head CT studies from Hartford HealthCare (2015--2024), of which 3,774 (14.9\%) contained clinician-confirmed SDH, tabular models were trained on demographics, comorbidities, medications, and laboratory results. Imaging models were trained to detect SDH and generate voxel-level probability maps. A greedy ensemble strategy combined complementary predictors. Findings. Clinical variables alone provided modest discriminatory power (AUC 0.75). Convolutional models trained on CT volumes and segmentation-derived maps achieved substantially higher accuracy (AUCs 0.922 and 0.926). The multimodal ensemble integrating all components achieved the best overall performance (AUC 0.9407; 95\% CI, 0.930--0.951) and produced anatomically meaningful localization maps consistent with known SDH patterns. Interpretation. This multimodal, interpretable framework provides rapid and accurate SDH detection and localization, achieving high detection performance and offering transparent, anatomically grounded outputs. Integration into radiology workflows could streamline triage, reduce time to intervention, and improve consistency in SDH management.

Because of shifting storms and sweltering summers, Iran's capital faces a future "Day Zero" when the taps run dry. During the summer of 2025, Iran experienced an exceptional heat wave, with daytime temperatures across several regions, including Tehran, approaching 50 degrees Celsius (122 degrees Fahrenheit) and forcing the temporary closure of public offices and banks. During this period, major reservoirs supplying the Tehran region reached record-low levels, and water supply systems came under acute strain . By early November, the reservoir behind Amir Kabir Dam, a main source of drinking water for Tehran, had dropped to about 8 percent of its capacity . The present crisis reflects not only this summer's extreme heat but also several consecutive years of reduced precipitation and ongoing drought conditions across Iran.

The amplification process in scA T AC-seq experiments often introduces noise due to dropout events, which results in extreme sparsity that hinders accurate analysis.

Processing visual data often involves small adjustments or sequences of changes, e.g., image filtering, surface smoothing, and animation. While established graphics techniques like normal mapping and video compression exploit redundancy to encode such small changes efficiently, the problem of encoding small changes to neural fields -- neural network parameterizations of visual or physical functions -- has received less attention. We propose a parameter-efficient strategy for updating neural fields using low-rank adaptations (LoRA). LoRA, a method from the parameter-efficient fine-tuning LLM community, encodes small updates to pre-trained models with minimal computational overhead. We adapt LoRA for instance-specific neural fields, avoiding the need for large pre-trained models and yielding lightweight updates. We validate our approach with experiments in image filtering, geometry editing, video compression, and energy-based editing, demonstrating its effectiveness and versatility for representing neural field updates.

Training these models is an extremely computationally expensive task; frontier Artificial Intelligence (AI) research companies are investing billions of dollars into supercomputing infrastructure to train progressively larger models on increasingly massive datasets. Unfortunately, very little information about the scaling performance and training considerations of these large training pipelines is released publicly. Working with very large datasets and models can be complex and practical recommendations are scarce in the public literature for tuning training performance when scaling up large language models. In this paper, we aim to demystify the large language model pretraining pipeline somewhat - in particular with respect to distributed training, managing large datasets across hundreds of nodes, and scaling up data parallelism with an emphasis on fully leveraging available GPU compute capacity. Index T erms--large language models, distributed training, data parallelism.

New antibiotics capable of killing drug-resistant gonorrhoea have been developed by AI. Experts believe that Artificial Intelligence could signify a'second golden age' of antibiotic discovery, after creating two drugs that could be capable of killing superbugs such as gonorrhea and MRSA. Led by Professor James Collins at the Massachusetts Institute of Technology (MIT), a specialist research team used generative AI algorithms to interrogate 36million compounds. The experts then trained the AI to help it learn how bacteria was affected by different molecular structures built of atoms in order to design new antibiotics. In order to do this, they gave it the chemical structure of known compounds and data on their ability to hinder the growth of different bacteria species. Throughout the study, published in the journal Cell, anything too similar to the current antibiotics available, or with the potential to be toxic to human beings, was eradicated.

Reliable seizure detection is critical for diagnosing and managing epilepsy, yet clinical workflows remain dependent on time-consuming manual EEG interpretation. While machine learning has shown promise, existing approaches often rely on dataset-specific optimisations, limiting their real-world applicability and reproducibility. Here, we introduce an innovative, open-source machine-learning framework that enables robust and generalisable seizure detection across varied clinical datasets. We evaluate our approach on two publicly available EEG datasets that differ in patient populations and electrode configurations. To enhance robustness, the framework incorporates an automated pre-processing pipeline to standardise data and a majority voting mechanism, in which multiple models independently assess each second of EEG before reaching a final decision. We train, tune, and evaluate models within each dataset, assessing their cross-dataset transferability. Our models achieve high within-dataset performance (AUC 0.904+/-0.059 for CHB-MIT and 0.864+/-0.060 for TUSZ) and demonstrate strong generalisation across datasets despite differences in EEG setups and populations (AUC 0.615+/-0.039 for models trained on CHB-MIT and tested on TUSZ and 0.762+/-0.175 in the reverse case) without any post-processing. Furthermore, a mild post-processing improved the within-dataset results to 0.913+/-0.064 and 0.867+/-0.058 and cross-dataset results to 0.619+/-0.036 and 0.768+/-0.172. These results underscore the potential of, and essential considerations for, deploying our framework in diverse clinical settings. By making our methodology fully reproducible, we provide a foundation for advancing clinically viable, dataset-agnostic seizure detection systems. This approach has the potential for widespread adoption, complementing rather than replacing expert interpretation, and accelerating clinical integration.

Multiperspective Fusion (MPF) is a novel posttraining alignment framework for large language models (LLMs) developed in response to the growing need for easy bias mitigation. Built on top of the SAGED pipeline, an automated system for constructing bias benchmarks and extracting interpretable baseline distributions, MPF leverages multiperspective generations to expose and align biases in LLM outputs with nuanced, humanlike baselines. By decomposing baseline, such as sentiment distributions from HR professionals, into interpretable perspective components, MPF guides generation through sampling and balancing of responses, weighted by the probabilities obtained in the decomposition. Empirically, we demonstrate its ability to align LLM sentiment distributions with both counterfactual baselines (absolute equality) and the HR baseline (biased for Top Univeristy), resulting in small KL divergence, reduction of calibration error and generalization to unseen questions. This shows that MPF offers a scalable and interpretable method for alignment and bias mitigation, compatible with deployed LLMs and requiring no extensive prompt engineering or finetuning.

'The CyberGuy' Kurt Knutsson joins'Fox & Friends Weekend' to discuss the potential effects of artificial intelligence software like ChatGPT on the brain. Massachusetts Institute of Technology researchers are studying ChatGPT's effects on the brain. BRAIN DANGER: Using ChatGPT on a long-term basis could have negative effects on brain function. That's according to a study led by the Massachusetts Institute of Technology (MIT), which found that using a large language model (LLM) to write multiple essays over a four-month period could hamper cognitive abilities. 'ERRATIC': Videos taken this week by passengers showed Tesla robotaxis – which are Model Y vehicles with advanced software – braking suddenly, speeding, conducting improper drop-offs, entering the wrong lane and driving over a curb, according to Reuters.