Wellbeing has become such a priceless (or in many cases pricey) endeavour that we can't seem to get enough of it. Last year, we were mainlining magnesium, consuming creatine - a muscle boosting supplement that became mainstream, and we turned to AI chatbots for help with anything from a personalised training regime to a daily meal plan. What is the multi-trillion pound industry focussing on in 2026? Several experts give us their thoughts on what's on the wellbeing agenda. If 2025 was about smashing targets at the gym, tracking runs to the second and lifting heavier and heavier weights, then this year is all about recovery.

The quality of water is key for the quality of agrifood sector. Water is used in agriculture for fertigation, for animal husbandry, and in the agrifood processing industry. In the context of the progressive digitalization of this sector, the automatic assessment of the quality of water is thus becoming an important asset. In this work, we present the integration of Ultraviolet-Visible (UV-Vis) spectroscopy with Machine Learning in the context of water quality assessment aiming at ensuring water safety and the compliance of water regulation. Furthermore, we emphasize the importance of model inter-pretability by employing SHapley Additive exPlanations (SHAP) to understand the contribution of absorbance at different wavelengths to the predictions. Our approach demonstrates the potential for rapid, accurate, and interpretable assessment of key water quality parameters.

OpenAI's release of ChatGPT 3.5 set in motion an AI arms race that has changed the world. How that turns out for humanity is something we are still reckoning with and may be for quite some time. But a pair of recent books both attempt to get their arms around it. In Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI, Karen Hao tells the story of the company's rise to power and its far-reaching impact all over the world. Meanwhile, The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future, by the Wall Street Journal's Keach Hagey, homes in more on Altman's personal life, from his childhood through the present day, in order to tell the story of OpenAI.

The issue of hallucinations in large language models (LLMs) remains a critical barrier to the adoption of AI in enterprise and other high-stakes applications. Despite advancements in retrieval-augmented generation (RAG) systems, current state-of-the-art methods fail to achieve more than 80% accuracy in generating faithful and factually correct outputs, even when provided with relevant and accurate context. In this work, we introduce Acurai, a novel systematic approach that achieves 100% hallucination-free responses in LLMs by reformatting queries and context data prior to input. Leveraging a deep understanding of LLM internal representations, the importance of noun-phrase dominance, and the role of discrete functional units (DFUs), Acurai ensures alignment between input context and generated output. We validate this method using the RAGTruth corpus, demonstrating its ability to eliminate 100% hallucinations for both GPT-4 and GPT-3.5 Turbo. Acurai sets a new standard for achieving consistent, accurate, and faithful AI responses, marking a significant step forward in the development of trustworthy AI systems.

Predicting price spikes in critical metals such as Cobalt, Copper, Magnesium, and Nickel is crucial for mitigating economic risks associated with global trends like the energy transition and reshoring of manufacturing. While traditional models have focused on regression-based approaches, our work introduces a neurosymbolic ensemble framework that integrates multiple neural models with symbolic error detection and correction rules. This framework is designed to enhance predictive accuracy by correcting individual model errors and offering interpretability through rule-based explanations. We show that our method provides up to 6.42% improvement in precision, 29.41% increase in recall at 13.24% increase in F1 over the best performing neural models. Further, our method, as it is based on logical rules, has the benefit of affording an explanation as to which combination of neural models directly contribute to a given prediction.

Tiny self-propelled capsules shed their outer shells and deliver drugs directly to tumour cells. These microrobots, demonstrated in mice intestines, could one day be targeted treatments for cancers in hard to reach places in the body. "When the capsule reaches the tumour, we can activate it, break the capsule, release the micromotors and they will move around the tumour area. That motion is very important for drug delivery," says Wei Gao at the California Institute of Technology in Pasadena. He and his team created the micromotors in a series of layers.