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.

We show that a tiny Co$^4$ machine(Adeel,2025) with a single layer, two heads, and 8M parameters, operating at an approximate cost of $O(N)$ (where $N$ is the number of input tokens), outpaces the BabyLM Challenge baselines GPT-2 (124M, 12 layers, $O(N^2))$ and GPT-BERT (30M, 12 layers, $O(N^2))$ in just two epochs, while both are trained for ten. Co$^4$ achieves orders-of-magnitude greater training efficiency on 10M tokens, demonstrating highly sample efficient pretraining. Using the BabyLM challenge evaluation pipeline across complex benchmarks, Co$^4$ exhibits strong zero-shot and fine-tuning performance on SuperGLUE tasks. Specifically, Co$^4$ outperforms GPT-2 on 5 out of 7 zero-shot metrics and 6 out of 7 fine-tuning tasks, and GPT-BERT on 4 out of 7 metrics in both cases. These results suggest the need to rethink prevailing deep learning paradigms and associated scaling laws.

A composite estimator successively applies two (or more) estimators: on data decomposed into disjoint parts, it applies the first estimator on each part, then the second estimator on the outputs of the first estimator. And so on, if the composition is of more than two estimators. Informally, the breakdown point is the minimum fraction of data points which if significantly modified will also significantly modify the output of the estimator, so it is typically desirable to have a large breakdown point. Our main result shows that, under mild conditions on the individual estimators, the breakdown point of the composite estimator is the product of the breakdown points of the individual estimators. We also demonstrate several scenarios, ranging from regression to statistical testing, where this analysis is easy to apply, useful in understanding worst case robustness, and sheds powerful insights onto the associated data analysis.

Attending to what is relevant is fundamental to both the mammalian brain and modern machine learning models such as Transformers. Yet, determining relevance remains a core challenge, traditionally offloaded to learning algorithms like backpropagation. Inspired by recent cellular neurobiological evidence linking neocortical pyramidal cells to distinct mental states, this work shows how models (e.g., Transformers) can emulate high-level perceptual processing and awake thought (imagination) states to pre-select relevant information before applying attention. Triadic neuronal-level modulation loops among questions ($Q$), clues (keys, $K$), and hypotheses (values, $V$) enable diverse, deep, parallel reasoning chains at the representation level and allow a rapid shift from initial biases to refined understanding. This leads to orders-of-magnitude faster learning with significantly reduced computational demand (e.g., fewer heads, layers, and tokens), at an approximate cost of $\mathcal{O}(N)$, where $N$ is the number of input tokens. Results span reinforcement learning (e.g., CarRacing in a high-dimensional visual setup), computer vision, and natural language question answering.

Body Dysmorphic Disorder (BDD) is a highly prevalent and frequently underdiagnosed condition characterized by persistent, intrusive preoccupations with perceived defects in physical appearance. In this extended analysis, we employ multiple machine learning approaches to predict treatment outcomes -- specifically treatment response and remission -- with an emphasis on interpretability to ensure clinical relevance and utility. Across the various models investigated, treatment credibility emerged as the most potent predictor, surpassing traditional markers such as baseline symptom severity or comorbid conditions. Notably, while simpler models (e.g., logistic regression and support vector machines) achieved competitive predictive performance, decision tree analyses provided unique insights by revealing clinically interpretable threshold values in credibility scores. These thresholds can serve as practical guideposts for clinicians when tailoring interventions or allocating treatment resources. We further contextualize our findings within the broader literature on BDD, addressing technology-based therapeutics, digital interventions, and the psychosocial determinants of treatment engagement. An extensive array of references situates our results within current research on BDD prevalence, suicidality risks, and digital innovation. Our work underscores the potential of integrating rigorous statistical methodologies with transparent machine learning models. By systematically identifying modifiable predictors -- such as treatment credibility -- we propose a pathway toward more targeted, personalized, and ultimately efficacious interventions for individuals with BDD.

This paper addresses the problem of satellite inspection, where one or more satellites (inspectors) are tasked with imaging or inspecting a resident space object (RSO) due to potential malfunctions or anomalies. Inspection strategies are often reduced to a discretized action space with predefined waypoints, facilitating tractability in both classical optimization and machine learning based approaches. However, this discretization can lead to suboptimal guidance in certain scenarios. This study presents a comparative simulation to explore the tradeoffs of passive versus active strategies in multi-agent missions. Key factors considered include RSO dynamic mode, state uncertainty, unmodeled entrance criteria, and inspector motion types. The evaluation is conducted with a focus on fuel utilization and surface coverage. Building on a Monte-Carlo based evaluator of passive strategies and a reinforcement learning framework for training active inspection policies, this study investigates conditions under which passive strategies, such as Natural Motion Circumnavigation (NMC), may perform comparably to active strategies like Reinforcement Learning based waypoint transfers.

Robotic dentistry is becoming a reality. Your dentist may remind you to brush, floss and mouthwash – but what is the "right" order to do it? While all steps of oral hygiene can benefit dental health, Dr. Mike Wei, DDS, of New York City, shared with Fox News Digital that he'd recommend the below order. Starting with floss helps to remove food debris and plaque between the teeth and along the gumline, which a toothbrush "may not reach effectively," according to Wei. Dr. Ellie Phillips (not pictured) recommends using xylitol gum and mints to promote healthy salivary flow.

"Hum," Helen Phillips's third novel, begins with a needle being drawn, steadily and irreversibly, across a woman named May's face. She is participating in a paid experiment in "adversarial tech," undergoing a procedure that will ever so slightly alter her features, making her harder for surveillance cameras to identify. As the book opens, May is mid-op, the needle advancing its "slender and relentless line of penetration" across her temple, toward the skin of her eyelid. What lies on the other side of the surgery? "Some sort of transformation, undeniable but undetectable," Phillips writes.

Concern about doctored or manipulative media is always high around election cycles, but 2024 will be different for two reasons: deepfakes made by artificial intelligence (AI) and the sheer number of polls. The term deepfake refers to a hoax that uses AI to create a phoney image, most commonly fake videos of people, with the effect often compounded by a voice component. Combined with the fact that around half the world's population is holding important elections this year – including India, the US, the EU and, most probably, the UK – and there is potential for the technology to be highly disruptive. Here is a guide to some of the most effective deepfakes in recent years, including the first attempts to create hoax images. The banana where it all began.

Fox News host Greg Gutfeld gives his take on Deadspin laying off all of its staff on'Gutfeld!' I love every one of you. All right, let's get started. Dylan Mulvaney is trying to switch from transgender influencer to stand-up comic. After the Budweiser fiasco, you got to admit, that takes balls.