AI News Today — Part 25
May 7, 2026 · Sourced from 500+ daily AI sources, scored by relevance.
- saro
Ship startup MVPs with AI automation
- PureScan AI: Clean Toxin free shopping
Know what’s really inside your products and how it effects.
- OggyCloud
Cloud, SaaS, and LLM cost intelligence for engineering teams
- APE: Predictive EdTech Architect
Transform static LMS into predictive learning ecosystems.
- ResolveAI
Turn your website into AI support & lead conversion engine
- Vibe Coding Universal
Stop fighting AI over UI. One spec, first try.
- Resume AI
Solving Every Job Hunter's Problem
- StudyPlanner AI
Conquer your exams with smart AI-driven study schedules
- Amatum
EU AI Workforce Intelligence Platform
- Houdiny AI
AI-Powered B2B Lead Generation & Outreach
- Deepfake Detection by Modulate
Detect voice deepfakes in 2.1 seconds.
- Saveto AI | TikTok Transcript Generator
Get 99.9% accurate TikTok transcripts in seconds.
- AirMusic - AI Music Video Generator
Turn any song into viral music videos.
- BankConv
Convert PDF bank statements to CSV instantly.
- Multiagent Stochastic Shortest Path Problem
We introduce and study the multi-agent stochastic shortest path (MSSP) problem, in which $k$ agents strive to reach a target state, aiming to minimize the expected time to reach the target by any agent. We analyze the computational and strategy-complexity of the problem in both autonomous and coordi...
- BioResearcher: Scenario-Guided Multi-Agent for Translational Medicine
Translational medicine turns underspecified development goals into evidence synthesis that must combine literature, trials, patents, and quantitative multi-omics analysis while preserving identifiers, uncertainty, and retrievable provenance. General-purpose foundation models and off-the-shelf tool-a...
- Auto Research with Specialist Agents Develops Effective and Non-Trivial Training Recipes
We study auto research as a closed empirical loop driven by external measurement. Each submitted trial carries a hypothesis, an executable code edit, an evaluator-owned outcome, and feedback that shapes the next proposal. The output is not a generated paper or a single model checkpoint, but an audit...
- Task-Aware Answer Preservation under Audio Compression for Large Audio Language Models
Large audio language models (LALMs) are increasingly used to reason over long audio clips, yet deployment often compresses audio before inference to reduce memory and latency. The risk is that compression can leave aggregate accuracy acceptable while sharply degrading answers for a deployment-critic...
- Predictive-Generative Drift Decomposition for Speech Enhancement and Separation
We propose a plug-and-play framework for speech enhancement and separation that augments predictive methods with a generative speech prior. Our approach, termed Stochastic Interpolant Prior for Speech (SIPS), builds on stochastic interpolants and leverages their flexibility to bridge predictive and ...
- NDF+: Joint Neural Directional Filtering and Diffuse Sound Extraction
Recently, neural directional filtering (NDF) has been introduced as a flexible approach for reconstructing a virtual directional microphone (VDM) with a desired directivity pattern for spatial sound capture. Building on this idea, we propose NDF+, which enables joint neural directional filtering and...
- X-Voice: Enabling Everyone to Speak 30 Languages via Zero-Shot Cross-Lingual Voice Cloning
In this paper, we present X-Voice, a 0.4B multilingual zero-shot voice cloning model that clones arbitrary voices and enables everyone to speak 30 languages. X-Voice is trained on a 420K-hour multilingual corpus using the International Phonetic Alphabet (IPA) as a unified representation. To eliminat...
- Optimal Transport Audio Distance with Learned Riemannian Ground Metrics
In audio generation evaluation, Fréchet Audio Distance (FAD) is a 2-Wasserstein distance with structural constraints for both primitives: the cost is a frozen embedding pullback whose invariance set hides severe artifacts, and the coupling is a Gaussian fit that dilutes rank-1 contamination relative...
- Real-time AI integration for MR to detect artifacts and guide pulse sequence adaptations
Purpose: To present a first-of-its-kind artificial intelligence (AI-)integrated MR pulse sequence that detects out-of-voxel (OOV) artifacts in real-time (within-TR) and responds prospectively by updating the crusher gradient scheme. Methods: Per Excitation Real-time Execution & Guided Responses with Integrated Neural-network Evaluation (PEREGRINE), developed for deployment of deep learning models and sequence updates, operated time-domain (TD) and frequency-domain (FD) convolutional autoencoders that detect OOV artifacts. Scans without (AI-off) and with (AI-on) updates were collected from the prefrontal cortex of healthy volunteers using edited MRS. The degree of OOV contamination (OOV score) was quantified per transient based upon the prevalence of OOV signals in the TD and FD data. OOV scores above a user-defined threshold triggered an update of the gradient scheme, iterating through 48 permutations (6 axis transpositions x 8 polarity flips). Results: Within each 2-second TR, PEREGRI
- Multimodal profiling reveals the centromedian nucleus of thalamus as a dynamic hub orchestrating staged consciousness recovery
Consciousness recovery from general anesthesia represents a fundamental brain state transition. The absence of objective, continuous metrics to monitor the shift from unconsciousness to full awareness has left its dynamic, multidimensional nature unresolved, fracturing consciousness theoretical framework and posing clinical risks due to unreliable assessment of emergence. To address this, we developed a multiscale framework integrating AI-driven behavioral analysis, continuous neuro-physiology recording, and whole-brain functional imaging. This decodes recovery into three hierarchical stages, reflex restitution, level restoration, and content re-establishment, each with unique multimodal signatures. We identified heart rate stabilization as a potential noninvasive biomarker for the restoration of consciousness level. Crucially, the centromedian nucleus of thalamus acts as a dynamic hub, actively orchestrating staged whole-brain reconfiguration via stage-dependent network routing. These
- Image-Conditioned Diffusion for Privacy-Preserving Synthetic Medical Images
Medical imaging models depend on large, shareable datasets, yet privacy constraints limit data dissemination. Current text-conditioned diffusion models fail to preserve subtle, distributed clinical signals, such as continuous physiological biomarkers, rendering synthetic data insufficient for robust downstream physiological modeling. Here, we evaluate image-to-image (I2I) diffusion as a tunable, privacy-preserving transformation that produces a synthetic counterpart of real images while preserving downstream-relevant information. We fine-tune Stable Diffusion with low-rank adapters on retinal fundus photographs and chest radiographs, assessing fidelity, clinical signal preservation, cross-site transfer, and empirical re-identification risk. I2I consistently outperforms text-to-image generation in image fidelity and in preserving biomarker information. In cross-cohort transfer to an external retinal dataset from the UK Biobank, pretraining on I2I synthetic data performs comparably to re
- Robotic perturbation proteomics and AI agents enable scalable drug mechanism discovery
Large-scale mass spectrometry-based proteomic screening could reveal cellular mechanisms of drug action at systems resolution but remains limited by experimental complexity and the difficulty of extracting insight from high-dimensional datasets. Here, we describe an end-to-end platform that combines semi-automated sample preparation, rapid LC-MS/MS, and AI agent-based data analysis to enable scalable proteomic screening. In a screen of 172 compounds in HepG2 cells, we generated 1,232 proteomes with more than 8,700 quantified proteins in approximately three weeks. Agentic AI reduced data analysis and interpretation time to less than one day while translating proteomic measurements into structured mechanism-oriented summaries and experimentally testable hypotheses. Guided by this framework, we validated: (1) a cholesterol-lowering effect of methylene blue in vitro and (2) an association between loratadine exposure and increased circulating iron in matched electronic health record analyse
- Virtual brain twins guide personalized treatment decision in schizophrenia
Schizophrenia is a complex psychiatric disorder whose pathophysiology spans multiple spatial and temporal scales. Structural and functional neuroimaging studies have identified a broad range of disease-associated markers encompassing cortical atrophy, white matter disruptions, and aberrant functional connectivity patterns. Their application to personalized diagnosis and treatment selection has remained elusive. Here, we introduce the first Virtual Brain Twin (VBT) pipeline that integrates individual connectome-based network models with multimodal neuroimaging data, incorporating patient-specific structural connectivity, cortical thickness, and resting-state fMRI features to construct personalised whole-brain dynamical models. Dopaminergic and serotonergic signaling pathways are embedded within a mean-field framework, and simulation-based inference (SBI) is used to recover key pathophysiological parameters from individual patient data. The validity of this inference is first established
- ChatIBD: design, safeguards, and early international use of a guideline-grounded generative AI tool for inflammatory bowel disease (IBD) professionals
Objectives To describe the design, operational safeguards, and early use of ChatIBD, a specialty-specific generative AI platform for inflammatory bowel disease (IBD), during its first 6 months of live deployment. Methods ChatIBD is an online question-answering platform that uses retrieval-augmented generation over a curated corpus of IBD guidelines. Queries undergo hybrid semantic and keyword retrieval with query expansion and reranking, and the model is instructed to answer only from retrieved material and return linked citations. Safeguards include fixed medication dosing information from European Medicines Agency (EMA), user feedback capture, and clinician review of flagged outputs. We performed a descriptive service evaluation of aggregated, de-identified platform metrics collected between 1 October 2025 and 1 April 2026. Results During the study period, ChatIBD registered 913 users and processed 7,222 messages across 3,855 conversations. Activity was recorded across 69 countries a
- Artificial Intelligence Driven Support and Self Care Competence as Determinants of Medication Adherence in Diabetes Care, A Cross-sectional Nigerian Study
Medication adherence among patients with diabetes remains suboptimal in low and middle income countries, including Nigeria. Emerging digital health interventions such as AI powered virtual support may be associated with improved adherence behaviours. This study examined self care competence and perceived AI powered virtual support as predictors of medication adherence among patients with diabetes. A cross sectional survey was conducted among 450 patients recruited through multistage sampling across hospitals in Benue State, Nigeria. Standardised measures of self care competence scale, perceived AI support scale, and medication adherence scale were analysed using correlation and regression analyses. Results showed that, self-care competence significantly predicted medication adherence, although some components (glucose management, physical activity, healthcare use) showed negative associations. Perceived AI-powered support demonstrated stronger predictive power, with social presence and
- Conserved neuroectodermal aging encodes primate health and longevity
Neuroectoderm-derived tissues are highly metabolically active and exhibit minimal regenerative turnover, rendering them uniquely vulnerable to age-related stress while preserving undiluted degenerative signals. Yet aging dynamics in these tissues remain elusive in living primates. Here, we introduce an in vivo neuroectodermal aging clock and trace its trajectory in 66,602 human adults and six rhesus macaques across nine health and disease cohorts using an in situ optical biopsy. Through a digital histology atlas integrated with artificial intelligence, we resolve tissue representations of neuroectodermal aging within the human retina, predominantly localized to the metabolically active ganglion and bipolar cell populations and the photoreceptor complex, while demonstrating their evolutionary conservation across primate species. Neuroectodermal aging predicts health and longevity, scales across space and time, and captures preclinical aging signals within and beyond the neuroectodermal
- Toward Early Diagnosis and Therapeutic Discovery in CLN3 Disease: A Computational Biomarker Discovery Framework
Background CLN3 disease, also known as juvenile neuronal ceroid lipofuscinosis, is a rare and neurodegenerative disorder characterized by the accumulation of lipopigments in the cells, progressive cognitive decline, seizures, and vision loss. Biomarker discovery in CLN3 disease is essential for enabling early and accurate diagnosis, which is critical given its neurodegenerative course. Biomarkers provide objective measures to track disease progression, stratify patients, and serve as surrogate endpoints in clinical trials, thereby accelerating therapeutic development. They also offer valuable insights into underlying disease mechanisms and treatment response, ultimately advancing individualized medicine and improving clinical outcomes. Methods We developed various machine learning models to predict potential protein biomarkers in CLN3 disease using proteomics data and laboratory tests collected from participants in a prospective, observational cohort. To prioritize and evaluate these c
- Generating synthetic tau-PET scans in Alzheimer's disease from MRI, blood biomarkers and demographics with deep learning
Tau protein aggregation in the brain is a hallmark of Alzheimer's disease (AD). Positron emission tomography (PET) is the only in vivo method to visualize tau pathology and estimate both its burden and regional distribution, but the use of tau-PET is constrained by high cost and limited accessibility. Here, we develop a deep learning model to synthesize tau-PET scans from more accessible data: structural magnetic resonance imaging (MRI), demographics, and when available, blood biomarkers. We included 5,191 participants across the AD continuum or with another neurological disorder from 13 cohorts (mean age 70 years, 51% female) and optimized a 3D U-Net neural network with residual and attention units for this task. In held-out test data, synthetic tau-PET reliably modeled tau burden, with correlations of R=0.77-0.86 with true tau-PET across individuals in common AD regions of interest. Spatial similarity between synthetic and true tau-PET was likewise high, with mean regional correlatio
- Natural Language Autoencoders: Turning Claude’s thoughts into text
Natural Language Autoencoders: Turning Claude’s thoughts into text
- Financial stability risks are rising as AI fuels cyber-attacks, IMF warns; oil below $100 on Iran peace hopes – as it happened
Rolling coverage of the latest economic and financial news Climate campaigners attack Shell over ‘windfall’ profits from Iran war The Danish shipping giant Maersk has maintained its profit guidance for the year, even as it reported a spike in fuel costs and warned that traffic through the strait of Hormuz “remains at a near standstill”. The company, which transports goods around the world via sea, road, rail and air, said demand for shipping containers remained strong, but that war in the Middle East was ramping up costs. “The reopening of the strait of Hormuz, whether it happens in the days to come or the months to come, will have limited impact on cargo flows. What really are the most important factors to consider: first is our ability to mitigate the cost increases we have been suddenly faced with. And I would say so far we have been successful with both our cost measures and the revenue, the commercial measures that we have put in place to mitigate the impact of these increases to
- Chinese chatbot maker Moonshot AI secures $2bn new funding
Chinese chatbot maker Moonshot AI secures $2bn new funding verdict.co.uk