AI News Archive: June 4, 2026 — Part 10
Sourced from 500+ daily AI sources, scored by relevance.
- Mero
AI made shipping fast. Mero makes deciding fast.
- BlogWorkflow AI
Reviewable, structured steps from brief to publish-ready.
- Vireel AI Video Extender
AI-powered video and image generation platform.
- LuminaMind
Multiple AI video models, one platform.
- AI Lip Sync.com
Bring any portrait to life with AI lip-sync.
- AI Thesis Writer
AI-powered thesis writing for students.
- Csong AI
Create original songs from text and lyrics with AI.
- RespondPilot Pro
Auto reply to YouTube comments using AI.
- AI Image Combiner
Instantly merge multiple photos into one seamless image.
- MAI-Thinking-1
MAI-Thinking-1
- Prophetic Dual
Prophetic Dual
- scRNA-seq and genomics analyses reveal key mechanisms of inverted papilloma-associated sinonasal squamous cell carcinoma malignant transformation
Sinonasal inverted papilloma (IP) carries a 10% risk of malignant transformation to IP-associated sinonasal squamous cell carcinoma (IP-SNSCC), yet the molecular and immune drivers of this progression remain poorly defined. This study integrates single-cell RNA sequencing, multiplexed spatial proteomics, whole-exome sequencing, and functional assays across IP and IP-SNSCC cohorts to define mechanisms of malignant transformation. Respiratory epithelial basal cells are identified as the putative cell of origin, with progression marked by recurrent CDKN2A loss and TP53 mutations. Spatial profiling reveals immune reorganization at the lesional interface in IP-SNSCC, characterized by enrichment of alternatively activated M2 macrophages. CXCL14 is shown to directly induce an immunosuppressive myeloid phenotype that suppresses T-cell IFN{gamma} production through an IDO-pathway-dependent mechanism. Integration of these multimodal datasets defines a previously unrecognized CXCL14-IDO mechanism that constrains anti-tumor immunity at the tumor-stroma interface. These findings establish IDO-targeted immunomodulation as a rational adjuvant strategy and provide a comprehensive molecular framework for understanding IP-SNSCC pathogenesis.
- CLASH (Chromatin Loop Across-sample Score Harmonizer) quantifies the relative contributions of genetic variation, methylation, and CTCF occupancy on chromatin loop strength across individuals
Three-dimensional genome organization constrains the regulatory interactions that govern vital cellular processes. Chromatin loops are key features of genome folding, yet it is unclear how genetic and epigenetic variation influences differential loop formation across individuals. Loops primarily form between two CTCF binding proteins, which recognize a specific motif at loop anchors. CTCF binding site motifs are frequently altered by base substitutions, structural variation, and 5-methylcytosine (mC) CpG methylation, yet no study has comprehensively profiled this variation across diverse individuals. Moreover, existing approaches relying on binary loop calls fail to capture subtle changes in genetic and epigenetic features, as well as CTCF occupancy, that drive variation in loop strength. Here, we combined high-resolution Hi-C, Fiber-seq, near telomere-to-telomere phased assemblies, and mC methylation maps across five lymphoblastoid cell lines to quantify how genetic and epigenetic variation shape genome folding. We used DiffHiC to identify 367 differential pixels and found that sequence variation, chromatin accessibility, and mC CpG methylation are each significantly associated with differential chromatin contacts. Next, we developed CLASH (Chromatin Loop Across-sample Score Harmonizer) to harmonize loop calls across samples and enable robust comparisons of loop strengths across individuals. CLASH substantially improved loop calls and loop score calibration with respect to the classification boundary over existing methods and confirmed a significant relationship between CTCF occupancy and loop strength. We then characterized independent contributions of sequence and epigenetic variation to differential loop formation, demonstrating that 57% of sequence variation- and 40% of methylation-associated effects on loop formation acted through CTCF occupancy. Together, we present a multimodal dataset and computational approach to facilitate the study of 3D genome structure across human populations.
- Bayesian generative modeling reveals a multi-modal hierarchical architecture in the mouse functional connectome
Understanding the principles governing large-scale functional organization of the brain remains a central challenge in systems neuroscience. Despite convergent findings, substantial variability across analytical approaches suggests that functional networks may not admit a unique partitioning. Here, we propose that this variability reflects an intrinsic property of the connectome itself: its organization may be fundamentally multi-modal rather than singular.To test this hypothesis, we employ a Bayesian generative modeling framework based on stochastic block models, enabling principled comparison of competing organizational principles and characterization of the full posterior distribution over network partitions. Applying this framework to resting-state fMRI data in mice, we find that a non-degree-corrected hierarchical architecture provides the most parsimonious description of the functional connectome. Importantly, the inferred posterior landscape is not dominated by a single configuration, but instead comprises multiple distinct and co-dominant organizational schemes.At the mesoscale, these hierarchical communities are anatomically grounded yet systematically reorganize canonical resting-state networks: primary sensory systems remain cohesive, whereas higher-order association networks are fractionated into multiple interacting sub-circuits. This global structural variation is driven by structured variability at the community level, where integrative systems exhibit variable regional affiliations while sensory systems act as structurally stable anchors.Together, these findings suggest that the resting-state connectome is best described as a distribution over alternative, yet co-dominant, organizational configurations. This perspective reconciles inconsistencies across previous studies and supports a view of brain organization as inherently degenerate, providing a latent repertoire of network configurations that may underlie adaptive information routing and dynamic functional reconfiguration.
- Vibe Coding Specificity Foundation Models
Molecular recognition - the determination of which agent binds which target - governs adaptive immunity, gene regulation, signal transduction, RNA silencing, enzyme catalysis, and the selectivity of therapeutics. Determining binding specificity remains dependent on experimental screening or domain-specific computational tools that do not generalize across binding modalities. Transformer softmax attention is mathematically identical to the Boltzmann distribution governing molecular binding. This identity, together with five conditions of molecular recognition systems, prescribes a single neural network architecture for cross-modal binding prediction: dual sequence encoders, symmetric contrastive learning, and a learned physical temperature. A Specificity Foundation Model (SFM) is an instance of this physics-derived, sequence-to-sequence architecture that maps any agent-target sequence pair to a binding compatibility score, enabling bidirectional retrieval across molecular recognition domains without requiring structural information. The first SFM for antibody-antigen binding demonstrated ~100,000-fold greater data efficiency than comparable vision-language models. Here we report six SFMs across six molecular recognition domains - transcription factor-DNA, enzyme-substrate, peptide-MHC, CRISPR gRNA-off-target genomic DNA, microRNA-mRNA target, and small molecule drug-target protein - using the identical architecture without modification and trained using publicly available data only. Evaluated by cross-modal retrieval from pools of 512 candidates (random baseline 0.2%), in-distribution R@1 ranges from 27.7% to 98.0% across the six domains. mir-SFM retrieves miRNA targets at 98.0% R@1, including the ~80% of validated interactions that seed-matching tools cannot find. mhcSFM achieves 95.4% R@1 on held-out rare HLA alleles absent from training. Applying crisprSFM to CRISPR off-target prediction improves precision to 94.0% compared to 33.2% from Hamming distance alone. All six SFMs were built by a domain expert with no programming experience using vibe coding - natural-language-directed AI coding agents - with numerical claims independently verified by an orthogonal AI auditor. These results establish SFMs as a physics-derived, sequence-native class of model that augments experimental and computational workflows across molecular recognition domains.
- R-loop Prediction Reveals Generalization Limits of DNA Foundation Models Beyond Regulatory Genomics
DNA foundation models are increasingly proposed as general-purpose representations for genomic prediction and design, yet their evaluation remains largely centered on conventional regulatory tasks. This leaves a critical question unresolved: do DNA foundation models generalize to sequence biology beyond conventional gene regulation? To answer this question, we introduce RloopBench, a systematic benchmark for R-loop-forming sequence prediction as a biophysically distinct, genome-stability-associated task. We compare rule-based methods, task-specific models, classical sequence encodings, and foundation model representations across in-distribution, cross-platform, consensus-level, and cross-species evaluations. Foundation models achieve strong performance when positive and negative sequences are compositionally separable, but this advantage does not consistently transfer to cross-platform and cross-species settings, where they are often comparable to classical k-mer representations. Unexpectedly, a one-hot classifier baseline shows the strongest overall sensitivity to R-loop-forming sequences, exceeding more complex models across several generalization tests. Rule-based and task-specific models also exhibit limited transfer outside their original training regimes. Performance is further shaped by sequence properties, negative-control design, experimental platform, and species-specific genomic context. Together, RloopBench establishes genome-stability-associated sequence prediction as a complementary direction for DNA foundation model development and evaluation, while underscoring that simple sequence encodings remain necessary baselines for assessing model generalization beyond conventional regulatory tasks.
- General prediction of T cell receptor antigen specificity from sequence using AlphaFold 3
The Major Histocompatibility Complex (MHC):peptide:T Cell Receptor (TCR) complex is the most diverse trimolecular interface known in nature and a key trigger for adaptive immunity. TCRs are now routinely sequenced at scale, however decoding their specificity for antigens remains a bottleneck. While in silico approaches have advanced considerably, none enables prediction against 'unseen' epitopes, limiting their applicability to a tiny fraction of cases. Here, we show that AlphaFold 3 (AF3) predicts the structures of MHC:peptide:TCR triads with unprecedented accuracy. By applying AF3 to >9,000 TCRs mapped to >1,000 distinct epitopes restricted by >70 MHC class I / II alleles, we identify features that distinguish cognate triads from controls. The resulting model achieves median AUCs of 0.81-0.92 on validation triads unseen by AF3 or during feature selection. Our results reveal that generalized prediction of TCR specificity from sequence is possible, with the potential to greatly accelerate the decoding of immune responses.
- VLab4Mic: prediction of structural resolvability in super-resolution microscopy
Determining whether a microscopy experiment can resolve a specific feature of a protein assembly remains difficult because researchers must balance imaging modality, labelling strategy, and probe choice. We present VLab4Mic, a simulation platform that predicts structural resolvability before experiments. Starting from atomic models from the PDB or AlphaFold predictions, VLab4Mic places antibodies, nanobodies, chemical linkers, or fluorescent proteins on epitopes, applies stochastic labelling and steric constraints, and generates virtual samples for widefield, confocal, AiryScan, Stimulated Emission Depletion (STED), and Single-Molecule Localisation Microscopy (SMLM). Comparisons with nuclear pore complex data show realistic agreement across modalities. Case studies show that HIV capsid appearance depends strongly on orientation, and that STED and SMLM distinguish domed from flat clathrin lattices, whereas confocal and AiryScan struggle. VLab4Mic thereby helps researchers predict which biological questions are experimentally tractable with a given imaging configuration before spending time finetuning imaging parameters at the microscope.
- Precision Imaging to Evaluate Kaposi Sarcoma (PRIME-KS): protocol for a multicountry novel artificial intelligence-based imaging device
Abstract Background: Kaposi sarcoma (KS) is the most common cancer among men in several Eastern African countries, yet treatment monitoring relies on imprecise, time-consuming ruler-based measurements defined by the AIDS Clinical Trial Group (ACTG). This method suffers from inter-observer variability, fails to capture lesion height or true geometric area, and performs poorly on dark skin. SkinScan3D (SS3D) is a portable, low-cost, AI-enabled 3D imaging device that provides objective measurements of KS skin lesion area, height, volume, and color. The Precision Imaging to Evaluate Kaposi Sarcoma (PRIME-KS) study evaluates whether SS3D provides more reproducible and accurate lesion measurements than the standard method, and validates its integration into routine clinical workflows in Kenya and Uganda. Methods: PRIME-KS is a multicountry prospective mixed-methods study with two clinical objectives. Objective 1 is a cross-sectional diagnostic accuracy study comparing SS3D with ruler-based measurement in 50 adults with KS (150 lesions) across sites in Kenya and Uganda. Two clinicians independently measure three lesions per participant using both methods. The primary outcomes are concordance correlation coefficient (CCC) for inter-rater reproducibility, and co-efficient of determination for accuracy. Objective 2 is a non-randomized before-and-after pilot study in 100 patients at three sites, evaluating device usability, acceptability, appropriateness, and feasibility using validated instruments, along with time-and-motion studies and activity-based micro-costing. Prior to these clinical objectives, a formative study used focus group discussions, discrete choice experiments, and human-centered design workshops to refine the SS3D device and protocols with end-user input. Discussion: PRIME-KS will provide the first rigorous evaluation of a 3D imaging device for monitoring KS treatment response in routine clinical settings. If SS3D demonstrates superior reproducibility and clinical utility, it could reduce unnecessary chemotherapy exposure and associated toxicities by enabling earlier, more objective assessment of treatment response. Trial registration: ClinicalTrials.gov NCT06898203, registered 27 March 2025. Pan African Clinical Trials Registry PACTR202603523439856. Keywords Kaposi sarcoma, SkinScan3D, 3D imaging, treatment monitoring, diagnostic accuracy, implementation science, usability, human-centered design, Kenya, Uganda
- Neuroimaging Summary Scores Predict Trajectories of Psychotic-Like Experiences in Youth
Objective. Persistent, distressing psychotic-like experiences (PLEs) are associated with neurobiological alterations and increased psychosis risk. We combined individual-level neuroimaging measures with effect sizes from large neuroimaging studies to create a summary score ('Psychosis Neuroscore') reflecting neuroanatomic liability for psychosis, and examined its ability to predict PLE trajectories in young adolescents. Method. Using latent growth mixture models, we estimated PLE trajectories from four annual visits of the Adolescent Brain Cognitive Development Study (N=9584, ages 9-10 at baseline). Using baseline T1-weighted and diffusion-weighted imaging data, we calculated Psychosis Neuroscores, as well as Neuroscores for two psychiatric disorders with late adolescent/adult onset (Major Depressive Disorder, Bipolar Disorder). We compared Psychosis Neuroscores to i) other psychiatric Neuroscores, ii) modifiable risk factors, and iii) established risk factors in predicting trajectory membership. Results. We identified four trajectories of distressing PLEs: Persistent Elevated (N=1,968, 21%), Gradual Decreasing (N=3,424, 36%), Rapid Decreasing (N=1,593, 17%) and Low/No Distress (N=2,599, 27%). Adolescents with Persistent Elevated PLEs had significantly higher Multimodal (combined T1 and diffusion-weighted) and T1-weighted Psychosis Neuroscores than all other trajectories (Odds Ratios [ORs] 1.27-1.34,pFDR<.01). Bipolar Disorder Neuroscores showed a similar pattern (ORs 1.16-1.23,pFDR<.01). Psychosis Neuroscores showed comparable associations with established risk factors in predicting trajectory membership, but smaller associations than modifiable risk factors, including screen time, physical activity, and sleep disturbances. Conclusion. Psychosis Neuroscores differentiate youth with persistent PLEs from those with decreasing, remitting or low PLEs, demonstrating their potential utility for early risk stratification. Integration with established risk factors may enhance psychosis risk prediction in youth.
- KESOZI Digital Twin: Physics-Informed Neural Network for Independent Estimation and Prediction of Childhood Diarrheal Disease Burden in Kenya, Somaliland, and Zimbabwe
Childhood diarrheal disease remains a leading cause of morbidity and mortality among children under five years in sub-Saharan Africa, particularly in settings affected by inadequate sanitation, climate variability, malnutrition, and limited healthcare access. Conventional forecasting approaches are often constrained by sparse surveillance data, weak spatial representation, and limited incorporation of mechanistic disease dynamics. This study presents a Physics-Informed Multimodal Artificial Intelligence Digital Twin framework that integrates Physics-Informed Neural Networks, Graph Neural Networks, diffusion-reaction epidemiological modeling, multimodal fusion learning, and Digital Twin simulation to estimate and predict childhood diarrheal disease burden in Kenya, Somaliland, and Zimbabwe. Using public epidemiological, environmental, climate, sanitation, and synthetic proof-of-concept datasets, the framework modeled temporal disease dynamics, spatial transmission, pathogen-attributed burden, and outbreak trajectories while enforcing epidemiological consistency through physics-informed optimization. Results demonstrated robust forecasting performance, enhanced spatial transmission modeling, uncertainty-aware predictions, and realistic outbreak simulations across the three countries. Rotavirus, Shigella, and Cryptosporidium were identified as major contributors to modeled mortality burden, while unsafe water exposure, poor sanitation, malnutrition, and climate-sensitive transmission substantially increased disease risk. Compared with a Bayesian baseline model, the multimodal framework achieved superior nonlinear risk characterization, geospatial learning, and temporal prediction. These findings highlight the potential of scientific machine learning and digital twin systems for infectious disease surveillance, outbreak forecasting, climate-health analytics, and evidence-based public health decision-making in low-resource African settings. Keywords: Physics-Informed Neural Networks, Graph Neural Networks, Digital Twin, Childhood Diarrheal Disease, Epidemiology, Kenya, Somaliland, Zimbabwe, Scientific Machine Learning, Spatial Epidemiology, Multimodal Fusion
- Comparison of the Mini Parasep SF, ParaPak SpinCon, and Paradevice fecal filtration and concentration devices for microscopic and AI-assisted detection of intestinal parasites
Effective filtration and concentration of stool specimens is an essential pre-analytical step for reducing fecal debris and improving organism recovery using microscopy-based ova and parasite (O&P) examination. This study evaluated three commercially available fecal sedimentation-based filtration/concentration systems, ParaPak SpinCon (Meridian Bioscience), Mini Parasep SF (Apacor), and the newly-available ParadeviceReingenuity), for qualitative parasite detection and workflow logistics using conventional and artificial intelligence (AI)-assisted microscopy. Forty clinical stool specimens (20 parasite-positive and 20 parasite-negative) were processed with the 3 devices, and the resultant 120 wet mount and 120 trichrome stained smear preparations were examined using conventional microscopy. Trichrome-stained slides were also scanned at 40x magnification using a Hamamatsu NanoZoomerS360 flatbed digital slide scanner and images were analyzed using the Techcyte Fusion Human Fecal Trichrome AI algorithm. Positive and indeterminate digital findings were confirmed by conventional glass slide microscopy. Slides and digital images were reviewed in a blinded manner. Concordance was assessed among the 360 initial evaluations (microscopy and AI-assisted), and discrepant parasitology results were resolved through re-review and specimen reprocessing as needed. Final qualitative agreement across slide/image evaluations using all three concentration systems was 100%. Minor discrepancies in protozoan and white/red blood cell detection/identification were noted in 5 and 7 cases, respectively, and likely reflected sampling and observer variability. While the three concentration systems produced equivalent qualitative results, the Paradevice and Mini Parasep SF offered the most streamlined workflows. These findings support the Paradevice and Mini Parasep SF as efficient, analytically equivalent systems that are compatible with traditional and AI-assisted O&P workflows.
- Prototyping a Generative AI-powered Person-centered Digital Health Tool to Mitigate Risk of Preventable Adverse Drug Events
Objectives: Older adults with comorbidities and polypharmacy have disproportionately high risk of hospitalization as well as readmission from adverse drug events (ADEs), of which 28%-71% are preventable (pADEs). This paper introduces an LLM application, CommunicADE, designed to support risk-mitigation of pADE-related readmission for the aforementioned population. We aim to evaluate CommunicADE's technical performance with OpenAI's HealthBench criteria: accuracy, completeness, communication quality, context awareness, and instruction following. Materials and Methods: Our technical validation study used an LLM (KimiK2.5) to simulate interviews between CommunicADE and nine high-fidelity synthetic patients hospitalized and at increased risk for pADE-related readmission (65+ years, comorbidities, 5+ medications). Some pADE risk mechanisms clues were visible to CommunicADE in patient H&Ps, but most mechanisms were solely discoverable in interviews. Two pharmacists evaluated CommunicADE's interview questions and EHR notes with HealthBench-informed variables. Analyzes used descriptive statistics. Results: For 35 mechanisms across 9 patients (avg=3.89 mechanisms/patient), CommunicADE's precision and recall were 0.92 and 0.63, respectively. Hallucinations were absent. Coherence and person-centeredness scored 4.28 and 4.44 on a 5-point scale (5=highest). On average, communication was at a 5th grade level and objective for 78% of patients. Most patient-reported quotes included in notes (92%) supported detected mechanisms. CommunicADE followed all instructions regarding interview length and patient approvals. Discussion: CommunicADE's strongest performance was in accuracy (precision, hallucinations), communication quality (coherence, readability), context awareness (person-centeredness). Completeness (recall) and instruction following (objectivity, pADE mechanism/quote alignment) show room for improvement. Conclusion: Findings suggest technical readiness for a feasibility pilot with real-world patients, and key areas for performance improvement.
- EEG-Derived Proxies of Cortical Excitability in Epilepsy: Group Discrimination, Temporal Stability and Medication Sensitivity
Rationale: Reliable electroencephalography (EEG) biomarkers of cortical excitability could improve diagnosis and longitudinal monitoring in epilepsy, yet it remains unclear which metrics best balance sensitivity across individuals with intra-individual stability over time. Methods: We analyzed scalp EEG recordings from the open-access Temple University Hospital EEG Epilepsy Corpus, comprising 1,404 recordings from 96 individuals with neurologist-confirmed epilepsy and 85 healthy controls across multiple sessions. Eight global measures were computed: aperiodic exponent and offset, sample entropy, detrended fluctuation analysis exponent and derived index, spatial gamma-band phase consistency, and absolute and relative alpha power. Group differences were assessed by permutation tests with false discovery rate correction at recording, session, and subject levels. Associations with antiseizure medication burden, temporal stability, and cross-metric correlation structure were evaluated as secondary analyses. Results: Aperiodic parameters showed the most robust case-control separation, remaining significant after subject-level averaging (exponent: median difference = 0.20, q = 0.010; offset: median difference = 0.25, q = 0.011). Entropy and alpha power distinguished groups at the recording and session levels, while gamma-band phase consistency was significant at the session level only; none of these survived subject-level averaging, suggesting greater state-dependency. Higher medication burden was associated with reductions in alpha power and detrended fluctuation analysis, and adjusting for it substantially attenuated group differences, though residual effects in the aperiodic exponent persisted. Cross-metric correlation structure was preserved between groups but modestly reorganized by medication burden. Conclusions: Aperiodic spectral parameters are the most robust EEG markers of epilepsy, reflecting stable trait-like network properties. Complexity and synchrony measures capture complementary, state-sensitive dimensions. Medication burden substantially influences multiple metrics, underscoring the need to account for pharmacological effects when interpreting EEG biomarkers in epilepsy.
- A single-nucleus transcriptomic atlas of human basal ganglia during development forwarding diagnosis and therapy of pediatric movement disorders
Gene therapy is rapidly emerging as a transformative treatment for monogenic neurological disorders, including pediatric movement disorders such as aromatic L-amino acid decarboxylase (AADC) deficiency. However, its success critically depends on defining target cells and windows for therapeutic intervention. Here, we present an open-access single-nucleus transcriptomic atlas of the human basal ganglia spanning a therapy-relevant window from second/third trimester to the perinatal period and adulthood. Across 35,755 nuclei, we identify major (non-)neuronal cell types, retrace developmental trajectories, and characterize gene-regulatory networks. We identify so far unrecognized human-specific expression of key neuronal signaling genes, including GNAO1 and ADCY5, and discuss the implications for targeted gene replacement therapies. Unexpectedly, we found that the Huntingtin gene (HTT) is already expressed during prenatal stages of human brain development, supporting a previously proposed neurodevelopmental component of Huntington's disease, which should be considered in diagnostic and therapeutic strategies. Moreover, FOXG1 expression and regulon activity are predominantly located in a prenatal time window, suggesting constraints on the effectiveness of postnatal interventions. Our findings highlight the importance of datasets capturing human brain development in real time and provide a publicly available resource to guide precision gene therapy strategies in the future.
- DeepSeek in talks to raise $7 billion from Tencent, CATL and other investors
Chinese AI startup DeepSeek is poised to raise around 50 billion yuan ($7 billion) in its first external funding round, with backing from major investors including Tencent Holdings and battery giant CATL, according to sources familiar with the matter cited by Reuters. The financing would value DeepSeek at between 350 billion yuan and 400 billion […]
- 'World-first' vaccine designed by artificial intelligence
Cambridge scientists say they have, for the first time, tested a vaccine designed by AI.
- Dreaming: Better memory for a more helpful ChatGPT
ChatGPT introduces a new memory system to better remember preferences, keeping context fresh and relevant across conversations.
- remio: Your Personal ChatGPT
Get Tailored Answer with Your Personal ChatGPT
- Google's AI race gets costlier as Alphabet upsizes equity raise to $84.75 billion
Google parent Alphabet has increased its planned equity raise to nearly $85 billion. The massive investment will help fund spending on data centres, chips and AI infrastructure.
- Arctick
Validate, deploy, and scale a trading bot under 1 min
- Talivo 1.0
AI website builder powered by Google Maps
- OpenBudget
Ask AI anything about your finances
- Exitus
AI guide to leaving the US and finding where to go
- MiraeCV
AI resumes tailored to every job in minutes
- Publishers can now opt out of Google AI summaries and training
Online publishers and news organisations will now be able to prevent Google from using their content to train its artificial intelligence (AI) models, or from appearing in the company’s AI search summaries, the UK’s competition watchdog has announced. In October 2025, the Competition and Markets Authority (CMA) classified Google search and search advertising with strategic market status (SMS), a designation that enables it to consider proportionate, targeted interventions to ensure that general search services are open to effective competition. Following a consultation on potential digital market fairness measures launched in January 2026, the CMA has now introduced conduct requirements to give publishers more control and stronger bargaining power over the use of their content. This includes requiring Google to provide “effective tools” that allow publishers to prevent their content being used in the company’s AI features, and allowing publishers to opt out of allowing their content to be used for the “fine-tuning” of AI models. Google must now ensure that publisher content is properly attributed, with clear links displayed in AI‑generated search results. The measures follow complaints from media and civil society organisations that publishers have experienced a drop in click-through traffic to their sites since Google started placing AI-generated summaries at the top of search results. Until now, websites were unable to opt out of their content being scraped for AI overviews without also withdrawing from appearing in traditional Google search results. “Today, we have introduced a world‑first requirement on Google’s search services in the UK, enabling fair treatment, greater transparency and meaningful choice for businesses and consumers,” said CMA chief executive Sarah Cardell. “With features like AI Overviews rapidly reshaping online search, it is crucial that content publishers, including news organisations, have appropriate bargaining power over how their content is used. At the same time, these measures will help tens of millions of UK search users better understand and trust the information presented to them.” The watchdog added while these new requirements are expected to “put publishers, like news organisations, in a stronger position to negotiate content deals with Google”, it will take an “active role” in overseeing how Google implements the measures. “[Google] will have nine months to implement all changes but the CMA expects important parts of the controls to become available to publishers well before that deadline,” said the CMA in a blogpost announcing the measures. “Google will also be required to submit and publish compliance reports, supported by key data and metrics, explaining changes it has made and how it has complied. These are due every six months for the first year, after which the CMA will review the frequency of reporting.” Google has said it would start testing a new control from Wednesday on a subset of UK-based media sites, allowing owners to manage how their links and content appear in its AI search features, with the aim of rolling the controls out globally. A study by search engine optimisation platform Authoritas from July 2025 previously found that a site ranked first in a search result could lose around 79% of its traffic if it was listed below an AI overview. However, a Google spokesperson at the time said in a statement that the study was “inaccurate and based on flawed assumptions and analysis”, using outdated estimations and a set of searches that did not represent all the queries that would generate traffic for news websites. A further study run by the Pew Research Center also showed a big hit to referral traffic from Google AI Overviews, with a month-long survey of almost 69,000 Google searches revealing that users only clicked a link under an AI summary once every 100 times. A Google spokesperson said that study also used “flawed methodology and skewed queryset that is not representative of search traffic”. In a blogpost published on 3 June 2026, Google said it was engaging with regulators such as the CMA “to ensure website owners have the right tools as user preferences evolve”. Blog author Mrinalini Loew, the general manager at Google Search Ecosystem, added the company will begin testing a new tool allowing website owners to manage how their links and content appear in its AI search features, such as AI overviews and AI mode. “We are beginning to roll these features out to a subset of website owners in the UK, allowing for thorough testing before rolling them out to website owners globally,” she said, adding that the controls will not be used as a ranking signal for search results outside the generative AI search features. Responding to the CMA announcement, tech-focused civil society group Foxglove said although it welcomes the regulator new measures, it is concerned that implementation needs to be faster to end ongoing damage to the news industry. It added further action may be needed to ensure effective scrutiny of Google’s compliance. “We’re delighted that the CMA is finally standing up to Google’s theft of journalists’ work,” said Foxglove’s co-executive director Rosa Curling, who added the group has been urging them to do this for the past year. “Google’s AI Overviews are a threat not only to an independent news industry, but to an informed democracy. Google’s AI Overviews don’t only take others’ work without payment. They also make it harder for journalists to directly reach their audience – threatening their survival. Without independent journalism, it becomes far harder to hold powerful governments and corporations to account. “Until now, the only way to stop Google stealing your work was to opt out from being visible at all in Google search. With Google controlling 90% of search, this was akin to removing yourself from the internet.” She added that there are concerns that Google may still be able to “wriggle out” of the obligations imposed by the CMA: “The measures would allow it to mark its own homework, rather than being subject to rigorous, independent audit. The timeframe is too generous – there is no reason to give Google nine months to put a stop to the terrible harm it is causing – which it has, itself, been aware of for years. “The CMA must watch Google like a hawk – both to ensure compliance with these measures, and to act urgently on any harm resulting from its new proposals around new AI features and agents in search.” Read more about artificial intelligence Challenging AI hype narratives with director Valerie Veatch : Computer Weekly speaks with Valerie Veatch, the director of a documentary charting the historical development of artificial intelligence, about the difficulties of challenging hype narratives and the pressing need to build a culture of technological refusal. Google AI engineer claims dismissal for opposing tech sales to Israel : ‘Our work on AI was sold to facilitate genocide’: Artificial intelligence engineer claims Google unfairly sacked them for internally criticising the company’s decision to continue supplying technology to the Israeli military, despite credible claims of war crimes committed in Gaza. UK MoD awards more than two dozen contracts for AI targeting systems : The UK Ministry of Defence is ramping up its investment into military artificial intelligence in a bid to increase the ‘lethality’ of the British armed forces.
- Nvidia CEO Jensen Huang mounts charm offensive in South Korea
South Korea holds a critical position in the AI landscape
- Curata
A shared workspace for AI agents and humans.
- Airbnb CEO Brian Chesky plans to start a new AI company
Airbnb CEO Brian Chesky plans to start a new AI company Fortune
- Brian Chesky in Talks to Back New AI Lab
Brian Chesky in Talks to Back New AI Lab The Information
- Meta rolls out a new AI creator assistant on Facebook
Creators often have to parse through charts and dashboards to understand their performance, but with the new AI assistant, they can get quick answers to questions like "When should I post?" and "What are people saying in my comments?"
- Meta's latest AI tool gives creators a 'brainstorming partner'
Meta's Creator Assistant AI can analyze your past posts to help with future content plans.
- Police have yet to catch a thief who used a Waymo to steal yoga clothes
Believe it or not, this is not the first robotaxi-assisted theft.
- Waymo’s spent robotaxi batteries will be used as grid storage
The company announced a deal with B2U Storage Solutions to repurpose the battery packs as Waymo pulls them off the road.
- Ramp hits $44 billion valuation as companies look to rein in AI spending
Ramp's latest funding round was led by ICONIQ, GIC and the Ontario Teachers' Pension Plan.
- Fintech firm Ramp's valuation surges to $44 billion on AI-driven growth
Fintech firm Ramp secured $750 million in funding, valuing the company at $44 billion. This significant jump highlights investor confidence in AI's potential to transform corporate finance through automation. The deal signals a strong resurgence in the fintech sector. Ramp's platform helps over 70,000 organizations manage expenses and payments efficiently.
- Ramp hits $44 billion valuation in $750 million raise as it bets that AI token spending is the next corporate expense to tame
Two years ago, Ramp was a $7.65 billion corporate card company. On Wednesday, it announced a $750 million Series F that values it at $44 billion, a nearly six-fold increase that makes it one of the most valuable private fintech companies in the world. The round was led by ICONIQ, GIC, and Ontario Teachers’ Pension […] This story continues at The Next Web
- OpenAI and Anthropic Sign Letter to Prevent AI-Developed Biological Weapons
Leading AI labs, executives, and scientists are sending a letter to lawmakers urging them to improve tracking of synthetic DNA sequences that could be used for bioweapons.
- Anthropic says AI labs need coordinated plan to halt development if risks rise
Anthropic says AI labs need coordinated plan to halt development if risks rise Reuters
- Anthropic calls for global freeze in AI development
Anthropic calls for global freeze in AI development The Telegraph