AI News Archive: May 19, 2026 — Part 19
Sourced from 500+ daily AI sources, scored by relevance.
- Fast Multichannel NMF with Block-Diagonal Spatial Covariance Matrices for Efficient Blind Source Separation Using Distributed Microphone Arrays
Distributed microphone arrays composed of multiple subarrays enable blind source separation over a wide spatial area. Directly applying fast multichannel nonnegative matrix factorization (FastMNMF) to all subarrays can exploit observations from all subarrays, but it requires repeated inversions of l...
- Divergence Meets Consensus: A Multi-Source Negative Sampling Framework for Sequential Recommendation
Negative sampling is significant for training sequential recommendation models under implicit feedback. The predominant strategy, self-guided hard negative sampling, selects negatives based on the model's current state but suffers from three limitations: (1) the coupling between sampling and model u...
- Understanding Wacky Weights: A Dissection of SPLADE's Learned Term Importance
Learned sparse retrieval models such as SPLADE combine the effectiveness of neural architectures with the efficiency of inverted indices. As these models assign weights to terms from a fixed vocabulary, interpretability is often touted as a major benefit of these models. However, the emergence of wa...
- The cost of efficiency in flexible neural representations
Working memory depends on the flexible representation of stimulus information in neural activity, which changes dynamically depending on task. Stimulus transformations are thought to be efficient in use of neural resources and optimal for task performance. However, these transformations are often opaque, and efficiency may conflict with optimal performance. Here we show that in a working memory task requiring selective recall of one of two stimuli based on a context cue, the prefrontal cortex of two male monkeys prioritized efficiency by overwriting information within a shared neural subspace rather than maintaining distinct subspaces for each stimulus. In neural activity and recurrent neural networks such efficiency incurs a cost, in that efficient representations are more prone to errors. Conversely, stimulation of the cholinergic forebrain which improves behavior altered this default mechanism by encoding distinct contexts in higher dimensions. These findings demonstrate a fundament
- Developing a multi-modal neuroimaging-based BrainAge model across childhood
BrainAge models hold promise as a clinical biomarker for developmental brain health, especially in childhood when there is the potential for early intervention. To distinguish between normative developmental variance and pathological divergence, BrainAge models should reflect the dynamic and diverse neurodevelopmental processes that occur in distinct developmental windows across childhood. We utilized multi-modal neuroimaging data from three pediatric cohorts covering ages 4 to 13 years (n = 1005, 2126 scans), split into Train and Test datasets. Twelve sex-stratified BrainAge models were built stratified by type and different combinations of neuroimaging features. Model types were 'Full-Span' models covering the full age range, and 'Phase-Specific' models split into early- and late-childhood. We first compared BrainAge estimates in the Test dataset amongst our candidate models, then benchmarked the best-performing model against published pre-trained models and DNA-based biological age
- ProtmRNA: Cross-Modal Knowledge Transfer from Proteins to Messenger RNA
Motivation According to the central dogma of molecular biology, messenger RNA (mRNA) sequences are directly translated into amino acid sequences, positioning mRNA as the fundamental intermediary between genetic information and functional proteins. This natural correspondence suggests that mRNA sequence analysis could greatly benefit from the rich evolutionary and functional representations learned by large-scale protein language models. Results ProtmRNA repurposes the pre-trained ESM-2 protein language model for mRNA sequence processing via cross-modal transfer learning. Evaluated on mRNA- and protein-related datasets, along with eight additional benchmarks compiled in this study, ProtmRNA achieves performance comparable or superior to state-of-the-art mRNA language models while using less than half the pre-training computational resources. This work establishes the potential of cross-modal transfer learning between biological sequences by demonstrating that protein-derived knowledge c
- Real-World Validation of Machine Learning Models for HIV Treatment Adherence Prediction and Care Gap Quantification: A Multi-Country Analysis of 192,732 Clinical Records
Delayed diagnosis and poor antiretroviral therapy (ART) adherence remain primary drivers of HIV-related morbidity in low-resource settings, yet real-world AI validation at scale is lacking. We conducted a retrospective validation study using two publicly available, de-identified datasets: a Quality of Care cohort of 27,288 HIV-positive patients on ART across multiple healthcare facilities, and the CEPHIA multi-country assay database comprising 165,444 specimen records from six countries. Four machine learning classifiers were evaluated using 10-fold stratified cross-validation with SMOTE applied strictly to training folds. Explicit data leakage prevention, ablation analysis, calibration assessment, and bootstrap confidence intervals were applied. Economic projections used one-way sensitivity analysis. This study adheres to TRIPOD reporting guidelines. Random Forest achieved AUC-ROC of 0.9753 (95% CI: 0.970-0.975), sensitivity 87.3% (95% CI: 86.4-88.2%), specificity 95.7% (95% CI: 95.2-
- Satellite imagery encodes features predictive of regional mortality and life expectancy
Background Increasingly accessible satellite imagery provides scalable measures of the built and natural environment relevant to population health. However, whether such imagery can capture subnational variation in mortality and life expectancy remains unclear. We therefore assessed its predictive value for regional mortality and life expectancy across OECD regions. Methods We conducted an ecological, cross-sectional prediction study using 2023 data from OECD Territorial Level 3 (TL3) regions. Annual cloud-masked composites from the Harmonized Landsat and Sentinel-2 collection were processed in the Google Earth Engine, tiled at 224 x 224 pixels, and encoded with the pretrained Prithvi foundation model to derive region-level satellite embeddings. For each outcome, we trained LightGBM regressors for a country-only baseline, a satellite-only model, a combined model (country + satellite), and a final contextual model that additionally included prespecified socioeconomic and environmental c
- Predicting Intensive Care Readmission Among Hospitalized Children
Objective: Readmissions to the PICU are associated with increased morbidity and mortality. A prediction model that can identify children at risk of readmission at the time of transfer can allow providers to intervene and potentially improve patient outcomes. The objective of this study was to derive and validate machine learning models to predict PICU readmission at the time of transfer. Design: Retrospective observational cohort study Setting: Three quaternary care PICUs in the city of Chicago Patients: All children admitted to the PICU between 2012 and 2019. Measurements: The primary outcome was unplanned readmission to the PICU within 48 hours of transfer to the inpatient ward. Predictor variables included vital signs, patient characteristics, and laboratory results. We developed and externally validated four models to predict PICU readmission: logistic regression, elastic net, random forest, and XGBoost. Main Results: This study included 35,601 patients, with readmission rates rang
- Predicting Distant Melanoma Metastasis at Diagnosis Using Machine Learning
Distant melanoma metastasis at the time of diagnosis is uncommon, but has major implications for patient prognosis and treatment selection. However, few tools can reliably predict the risk of distant metastasis at initial presentation. Here, we developed and evaluated machine learning models to predict distant melanoma metastasis using routinely captured clinicopathologic and demographic variables across all histologic subtypes. Using the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) program from 2010-2022, we identified adults aged 20 to 90 years with melanoma as the first and only primary malignancy (n=51,285). Explainable Boosting Machine achieved a strong balance of discrimination and precision (AUROC = 0.947, AUPRC = 0.610, Precision = 0.793, Brier = 0.015). At 90% sensitivity, specificity was 0.843 with consistent performance across cross-validation folds. Clinicopathologic variables, including T stage, Breslow thickness, ulceration, and mitotic act
- How to use Google’s new AI agents to go beyond your standard searches
Google is launching AI-powered “information agents” that can monitor topics in the background and proactively alert users to updates and changes.
- An interpretable and interactive clinical AI agent for personalized anti-infective decision support in carbapenem-resistant Gram-negative bacterial infection
Carbapenem-resistant Gram-negative bacteria (CRGNB) infections remain difficult to manage because treatment decisions must balance heterogeneous patient risk, limited antibiotic options, potential toxicity and emerging resistance. Clinical care in this setting requires not only single-endpoint risk prediction, but also decision-support frameworks that can jointly enable prognosis assessment, result interpretation, and individualized treatment comparison. Here we present Dr.BUG, an interactive clinical AI agent for personalized decision support in CRGNB infection. Dr.BUG integrates stable feature-set selection, multi-task prognostic modelling, interpretability analysis and model-based simulation of antibiotic regimen recommendation into a unified workflow. Using a development cohort, a temporally independent validation cohort, and external cohorts from the MIMIC-IV dataset, we developed and validated models for four clinically relevant tasks: clinical efficacy, survival outcome, polymyx
- With Gemini 3.5 Flash, Google bets its next AI wave on agents, not chatbots
Google launched Gemini 3.5 Flash, its most powerful coding and agentic AI model yet, at the company's annual developer conference. It is capable of autonomously executing complex tasks and building software from scratch.
- Google updates its Gemini app to take on ChatGPT and Claude at IO 2026
The updates signal Google’s push to turn its Gemini app into an all-purpose AI hub rather than a stand-alone chatbot.
- Google introduces Gemini Spark, a 24/7 agentic assistant with Gmail integration, at IO 2026
At the Google I/O developer conference, the company announced a new agentic personal assistant called Gemini Spark, built from Gemini's base models and an agentic harness from Google Antigravity.
- Gemini 3.5 Flash might be fast enough for gen AI to make sense
Google says its more efficient Gemini 3.5 Flash is the key to your agentic AI future.
- Everything Announced at Google I/O 2026: Gemini, Search, Smart Glasses
Google is sprucing up its Gemini models, revamping search, and enabling AI agents in everything. There are also some spiffy new smart glasses coming this fall.
- Gemini Spark Is Google’s Response to OpenClaw’s 24/7 AI Agent
Google’s always-running, data-hungry AI agent is designed to spend your money and send your emails.
- Google I/O 2026 Live Blog: All the Gemini and Smart Glasses Updates as They Happen
Follow our live coverage of Google’s annual developer keynote, where the company will announce updates to its Gemini suite of AI tools and more details about Android XR smart glasses.
- Google’s new Gemini Spark is an always-on AI agent for daily digital tasks
Entering into the artificial intelligence agent era, Google LLC today introduced Gemini Spark, a 24/7 personal AI assistant that can help people navigate their digital lives and do real work on their behalf. Under the hood, Spark runs on the newly released Gemini 3.5 Flash and uses the company’s updated Antigravity platform to orchestrate AI agents. […] The post Google’s new Gemini Spark is an always-on AI agent for daily digital tasks appeared first on SiliconANGLE .
- Google’s new $100 AI Ultra plan just changed the AI race — here's what you get
Google’s new $100 AI Ultra plan just changed the AI race — here's what you get Tom's Guide
- Google unveils Gemini Spark — a '24/7 personal AI agent'
Google unveils Gemini Spark — a '24/7 personal AI agent' Tom's Guide
- Google’s new AI agent can draft your emails, monitor your inbox and eventually spend your money
Google on Tuesday unveiled Gemini Spark , a personal AI agent designed to work around the clock — drafting emails, assembling documents, monitoring inboxes, and eventually making purchases — even when a user's laptop is closed and their phone is locked. The announcement, made at Google I/O 2026 , is the company's most ambitious attempt yet to transform its AI assistant from a tool that answers questions into one that autonomously completes tasks. It also arrives at a moment of extraordinary competition, as Microsoft, OpenAI, Anthropic, and Apple all race to build AI systems that don't merely converse but act — completing multi-step workflows with decreasing human supervision. "We are in that part of the cycle where people want to see real value in the products they use on a day-to-day basis," Sundar Pichai, CEO of Google and Alphabet, said during a press briefing ahead of the keynote address. With Spark, he argued, that value comes from an agent that never stops working. It operates ar
- Google just launched Gemini 3.5 Flash — here's all the upgrades
Google just launched Gemini 3.5 Flash — here's all the upgrades Tom's Guide
- Google Makes It Easy to Deepfake Yourself
Google’s overhaul of its AI creation software, Flow, includes a new video model and a tool for generating selfie videos called avatars.
- Google just gave Workspace a 24/7 AI agent that sends emails and books meetings while you sleep
Gmail Live answers inbox questions by voice, Docs Live turns rambling speech into structured documents, and Google Pics lets you edit one element of an image without touching the rest.
- Elon Musk sued OpenAI and lost. But the core question of the case remains unanswered
OpenAI now has a clear path to take its next big step in the AI race.
- OpenAI avoided a costly court loss to Elon Musk, but neither side is unscathed
Elon Musk lost his case against OpenAI and its top executives in a high-stakes trial that pitted billionaire against billionaires
- Musk’s Failed OpenAI Lawsuit Underscores xAI’s Struggles
Musk’s Failed OpenAI Lawsuit Underscores xAI’s Struggles Time Magazine
- Musk loses $150B lawsuit against OpenAI, Altman
Musk had previously helped start the artificial intelligence company
- OpenAI avoided a costly court loss to Elon Musk, but neither side is unscathed
OpenAI avoided a costly court loss to Elon Musk, but neither side is unscathed The Mercury News
- Jury rules against Elon Musk in his feud with OpenAI
Jury rules against Elon Musk in his feud with OpenAI Dallas News
- OpenAI avoided a costly court loss to Elon Musk, but neither side is unscathed
OpenAI avoided a costly court loss to Elon Musk, but neither side is unscathed AP News
- Google Search is getting AI agents that will monitor the web for you
Google used I/O 2026 to announce a major overhaul of Search, adding background AI agents that monitor the web for you, a redesigned search box, and agentic coding that can build custom apps and dashboards on demand.
- Google is transforming your online shopping cart into something much smarter
Google's 'Universal Cart' hunts deals, tracks availability, and flags bad buys before you check out.
- Google’s Gemini Omni is an all-purpose content generator that wants to replace your entire studio
Gemini Omni doesn't just create AI video, it remembers your edits, understands physics, and lets you direct the whole thing through plain conversation. Here's what it actually does and who can use it today.
- Literary prize winner accused of using AI
Literary prize winner accused of using AI The Telegraph
- Google Search will now tell you if an image is AI-generated and talk about it in detail
Google is expanding its SynthID technology into Search, Chrome, and Android to help users identify AI-generated or AI-edited images more easily.
- Gemini 3.5 Flash is Google’s new default AI model, and it’s built to act, not just answer
Google today announced Gemini 3.5 Flash, its most capable Flash-series model to date. The company says it outperforms Gemini 3.1 Pro on coding and agentic benchmarks and runs at four times the speed of comparable frontier models.
- Google’s new Gemini Spark AI agent can run your errands while you run your life
Google's Gemini Spark is a new AI agent that handles multi-step tasks in the background, works across Google's apps, and keeps going even after you close your laptop.
- Google I/O 2026: What to expect from Gemini, Android 17, and more
Google I/O 2026 is expected to focus heavily on Gemini AI, Android 17, smart devices, and Google’s broader push toward an AI-first ecosystem.
- Gemini’s new Canva integration turns AI creations into fully editable designs
You can now send Gemini images straight into Canva with a simple command.
- Google Search on Android now lets you ask AI about any link you open
So you never have to read through a link again.
- Google turns Gemini into a proactive AI agent with Spark, Daily Brief, and a major redesign
Google announced major design and feature upgrades to the Gemini app at I/O 2026. Here's what's new.
- Gemini 3.5 Flash is here: Google’s smartest speed model promises better coding and agents
Gemini 3.5 Flash beats older Pro models on challenging agentic and coding benchmarks.
- Google announces Gemini Spark to quietly run your digital life for you
Google wants Spark to be the digital equivalent of a very proficient real-life personal assistant.
- CaseGap AI
Find law firm revenue leaks, then fix them
- Google is giving Gemini far more control over your Mac than before
These new Gemini features for Mac are trying to automate the annoying stuff for you.
- Google’s AI subscriptions get a new $100 tier, a price cut, and new features across all plans
Google has announced an overhaul of its AI subscriptions at I/O 2026, adding a new $100/month AI Ultra tier and cutting the price of its top plan from $250 to $200. The update brings fresh models, a smarter Gmail inbox, YouTube Premium perks, and more across all paid plans.
- Google’s newest Gemini Omni model can turn real videos into surreal fever dreams
Gemini Omni gives a whole new meaning to AI hallucinations.