Inflation is heating up but don't expect another crisis The fallout from the war in Iran cranked up the heat on households last month as energy bills rose - how uncomfortable could inflation get from here? The first thing to remember is that a small amount of price rises is normal. It is the sign of a healthy, functioning economy. But with food alone costing a third more than it did four years ago, thanks to a spike in inflation early in the war in Ukraine, it can feel a battle to make ends meet. The good news is inflation since the war in Iran erupted has actually been more muted than economists initially feared.

OpenAI says it has slowed down training some of its most advanced AI models to improve security. In a blog post, external, the ChatGPT-maker said it was introducing new measures after its AI agents autonomously bypassed safeguards and hacked the tech start-up Hugging Face . It said training would be slowed for two weeks while it puts the upgrades in place. The capabilities of frontier models are rapidly accelerating, the company said. Our ability to understand...and secure them must stay ahead.

This is a month to look out for some powerful new books, with authors taking on challenges of all sorts and imagining whole new worlds. There are fresh ways to think about a cancer diagnosis, a book tackling the real inner world of hormones, in which we are all hormonal all the time, plus a major re-envisioning of the natural world where we abandon the shallows of competition for the depth and intricacies of connection and togetherness. It's quite hard going to get an up-to-date grip on human evolution, even for the best-briefed adult, so a book with sophisticated text and excellent illustrations and diagrams can only be a good thing. Especially if it is curated and edited by Alice Roberts, biological anthropologist, palaeopathologist, broadcaster - and professor of public engagement in science at the University of Birmingham, UK. She worked with a generous-sized international team of experts in many fields of human evolution, including archaeology, palaeontology, anthropology and cognitive science.

In binary classification applications, conservative decision-making that allows for abstention can be advantageous. To this end, we introduce a novel approach that determines the optimal cutoff interval for risk scores, which can be directly available or derived from fitted models. Within this interval, the algorithm refrains from making decisions, while outside the interval, classification accuracy is maximized. Our approach is inspired by support vector machines (SVM), but differs in that it minimizes the classification margin rather than maximizing it. We provide the theoretical optimal solution to this problem, which holds important practical implications. Our proposed method not only supports conservative decision-making but also inherently results in a risk-coverage curve. Together with the area under the curve (AUC), this curve can serve as a comprehensive performance metric for evaluating and comparing classifiers, akin to the receiver operating characteristic (ROC) curve. To investigate and illustrate our approach, we conduct both simulation studies and a real-world case study in the context of diagnosing prostate cancer.

Accurate and timely cancer diagnosis from histopathological slides is vital for effective clinical decision-making. This paper introduces DepViT-CAD, a deployable AI system for multi-class cancer diagnosis in histopathology. At its core is MAViT, a novel Multi-Attention Vision Transformer designed to capture fine-grained morphological patterns across diverse tumor types. MAViT was trained on expert-annotated patches from 1008 whole-slide images, covering 11 diagnostic categories, including 10 major cancers and non-tumor tissue. DepViT-CAD was validated on two independent cohorts: 275 WSIs from The Cancer Genome Atlas and 50 routine clinical cases from pathology labs, achieving diagnostic sensitivities of 94.11% and 92%, respectively. By combining state-of-the-art transformer architecture with large-scale real-world validation, DepViT-CAD offers a robust and scalable approach for AI-assisted cancer diagnostics. To support transparency and reproducibility, software and code will be made publicly available at GitHub.

The training and deployment of large language models (LLMs) create a feedback loop with human users: models learn human beliefs from data, reinforce these beliefs with generated content, reabsorb the reinforced beliefs, and feed them back to users again and again. This dynamic resembles an echo chamber. We hypothesize that this feedback loop entrenches the existing values and beliefs of users, leading to a loss of diversity and potentially the lock-in of false beliefs. We formalize this hypothesis and test it empirically with agent-based LLM simulations and real-world GPT usage data. Analysis reveals sudden but sustained drops in diversity after the release of new GPT iterations, consistent with the hypothesized human-AI feedback loop. Code and data available at https://thelockinhypothesis.com

The rising incidence of skin cancer, coupled with limited public awareness and a shortfall in clinical expertise, underscores an urgent need for advanced diagnostic aids. Artificial Intelligence (AI) has emerged as a promising tool in this domain, particularly for distinguishing malignant from benign skin lesions. Leveraging publicly available datasets of skin lesions, researchers have been developing AI-based diagnostic solutions. However, the integration of such computer systems in clinical settings is still nascent. This study aims to bridge this gap by employing a fusion of image processing techniques and machine learning algorithms, specifically neuro-fuzzy and colonial competition approaches. Applied to dermoscopic images from the ISIC database, our method achieved a notable accuracy of 94% on a dataset of 560 images. These results underscore the potential of our approach in aiding clinicians in the early detection of melanoma, thereby contributing significantly to skin cancer diagnostics.

Most people spend their lunch breaks grabbing a sandwich or going for a walk. But soon it could be possible to get a full-body MRI scan which detects the earliest stages of cancer during your lunch hour, thanks to AI. Health tech pioneer Ezra has launched its screening service in the UK, marking a major expansion beyond the US. Their AI-powered scans currently last an hour and cover 13 organs, with the added option of an extra lung CT scan and heart disease screening. As cancer rates are rising – especially among young people – the company say they are the best defence against the disease. With early detection, treatment can start earlier and prognosis improves dramatically.

The rapid advancement of deep learning (DL) has transformed healthcare, particularly in cancer detection and diagnosis. DL surpasses traditional machine learning and human accuracy, making it a critical tool for identifying diseases. Despite numerous reviews on DL in healthcare, a comprehensive analysis of its role in cancer detection remains limited. Existing studies focus on specific aspects, leaving gaps in understanding its broader impact. This paper addresses these gaps by reviewing advanced DL techniques, including transfer learning (TL), reinforcement learning (RL), federated learning (FL), Transformers, and large language models (LLMs). These approaches enhance accuracy, tackle data scarcity, and enable decentralized learning while maintaining data privacy. TL adapts pre-trained models to new datasets, improving performance with limited labeled data. RL optimizes diagnostic pathways and treatment strategies, while FL fosters collaborative model development without sharing sensitive data. Transformers and LLMs, traditionally used in natural language processing, are now applied to medical data for improved interpretability. Additionally, this review examines these techniques' efficiency in cancer diagnosis, addresses challenges like data imbalance, and proposes solutions. It serves as a resource for researchers and practitioners, providing insights into current trends and guiding future research in advanced DL for cancer detection.

Professionals increasingly use Artificial Intelligence (AI) to enhance their capabilities and assist with task execution. While prior research has examined these uses separately, their potential interaction remains underexplored. We propose that AI-driven training ("tutor" effect) and AI-assisted task completion ("tool" effect) can be complementary and test this hypothesis in the context of lung cancer diagnosis. In a field experiment with 336 medical students, we manipulated AI deployment in training, in practice, and in both. Our findings reveal that while AI-integrated training and AI assistance independently improved diagnostic performance, their combination yielded the highest accuracy. These results underscore AI's dual role in enhancing human performance through both learning and real-time support, offering insights into AI deployment in professional settings where human expertise remains essential.