AI News Archive: May 19, 2026 — Part 15
Sourced from 500+ daily AI sources, scored by relevance.
- 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.
- Trainer
Train AI agents by recording your screen
- 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.
- 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.
- 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.
- 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-
- 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.
- 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
- 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
- Cloudflare says Anthropic's Mythos Preview finds exploit chains that earlier frontier models missed
Cloudflare tested Anthropic's security-focused AI model Mythos Preview across more than 50 of its own code repositories as part of Project Glasswing. The article Cloudflare says Anthropic's Mythos Preview finds exploit chains that earlier frontier models missed appeared first on The Decoder .
- Musk's OpenAI case runs out of time
PLUS: How to 3D model anything with Claude and Blender
- 😸 Elon lost... here's why
PLUS: The calm before Hurricane IO...
- KPMG integrates Claude across its core business and workforce of more than 276,000 in strategic alliance
KPMG integrates Claude across its core business and workforce of more than 276,000 in strategic alliance
- Introducing Claude Managed Agents with Modal Sandboxes
Introducing Claude Managed Agents with Modal Sandboxes
- Are human journalists truly irreplaceable? How to safeguard public interest journalism in the age of AI
Are human journalists truly irreplaceable? How to safeguard public interest journalism in the age of AI reutersinstitute.politics.ox.ac.uk
- Inside the news industry’s efforts to join forces to defend its journalism from AI companies
Inside the news industry’s efforts to join forces to defend its journalism from AI companies reutersinstitute.politics.ox.ac.uk
- Anthropic to Brief Global Financial Watchdog on Mythos Cyber Flaws
Anthropic to brief global financial watchdog on Mythos cyber flaws
- Jury Unanimously Rejects Musk OpenAI Lawsuit, Clearing Path to $1 Trillion IPO
Jury rejects Musk's OpenAI lawsuit, clearing path to IPO
- m3BERT: A Modern, Multi-lingual, Matryoshka Bidirectional Encoder
Embedding models are pivotal in industrial information retrieval systems like search and advertising. However, existing pretrained models often exhibit fixed architectures and embedding dimensionalities, posing significant challenges when adapting them to diverse deployment scenarios with varying bu...
- Base Models Look Human To AI Detectors
As AI-generated text enters the real-world at scale, institutions increasingly use commercial AI-text detectors, especially in education and academic-integrity workflows. We report a surprising empirical finding about such systems: when evaluated by GPTZero and Pangram, generated text from base mode...
- Position: The Turing-Completeness of Real-World Autoregressive Transformers Relies Heavily on Context Management
Many works make the eye-catching claim that Transformers are Turing-complete. However, the literature often conflates two distinct settings: (i) a fixed Transformer system setting, in which a fixed autoregressive Transformer is coupled with a fixed context-management method to process inputs of diff...
- CaMo: Camera Motion Grounded Evaluation and Training for Vision-Language Models
Vision-Language Models (VLMs) achieve strong performance on spatial question answering benchmarks, yet it remains unclear whether such gains reflect genuine spatial intelligence. We show that existing spatial VLMs lack basic camera motion understanding, a key component of spatial cognition. We propo...
- Interpretable Computer Vision for Defect Detection in X-ray Tomography of Aerospace SiC/SiC Composites
Non-destructive testing of aerospace SiC/SiC composites via X-ray computed tomography (XCT) relies on expert visual assessment, with current workflows offering limited traceability for accept/reject decisions. Deep convolutional networks can automate defect detection, yet their black-box nature conf...
- SetCon: Towards Open-Ended Referring Segmentation via Set-Level Concept Prediction
Referring segmentation grounds natural-language queries to pixel-level masks, but extending it to complex scenarios with multiple instances, cross-category groups, or open-ended target sets remains challenging. Previous Large Vision Language Model (LVLM)-based methods represent referred targets with...
- MetaEarth-MM: Unified Multimodal Remote Sensing Image Generation with Scene-centered Joint Modeling
Multi-modal remote sensing images are vital for Earth observation, yet complete paired observations are often scarce in practice. Existing generative methods commonly address this problem through isolated pairwise modality translation, but their versatility and scalability remain limited as the numb...
- X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing
This work proposes a pixel-classification approach for vessel segmentation in x-ray angiograms. The proposal uses textural features such as anisotropic diffusion, features based on the Hessian matrix, mathematical morphology and statistics. These features are extracted from the neighborhood of each ...
- Cardiac fat segmentation using computed tomography and an image-to-image conditional generative adversarial neural network
In recent years, research has highlighted the association between increased adipose tissue surrounding the human heart and elevated susceptibility to cardiovascular diseases such as atrial fibrillation and coronary heart disease. However, the manual segmentation of these fat deposits has not been wi...
- Stage-adaptive Token Selection for Efficient Omni-modal LLMs
Omni-modal large language models (om-LLMs) achieve unified audio-visual understanding by encoding video and audio into temporally aligned token sequences interleaved at the window level. However, processing these dense non-textual tokens throughout the LLM incurs substantial computational overhead. ...
- A Nash Equilibrium Framework For Training-Free Multimodal Step Verification
Multimodal large language models often generate reasoning chains containing subtle errors that lead to incorrect answers. Current verification approaches have notable limitations. Learned critics need extensive labeled data and show inconsistent performance across different tasks. Meanwhile, existin...
- InterLight: Leveraging Intrinsic Illumination Priors for Low-Light Image Enhancement
Low-Light Image Enhancement (LLIE) has long been a challenging problem in low-level vision, as insufficient illumination often leads to low contrast, detail loss, and noise. Recent studies show that deep learning-based Retinex theory can effectively decouple illumination and reflectance. However, ex...
- Towards Fine-Grained Robustness: Attention-Guided Test-Time Prompt Tuning for Vision-Language Models
Vision-Language Models (VLMs), such as CLIP, have achieved significant zero-shot performance on downstream tasks with various fine-tuning adaptation methods. However, recent studies have proven that adversarial attacks can significantly degrade the inference ability of VLMs, posing substantial risks...
- Gartner Forecasts Worldwide AI Spending to Grow 47% in 2026
Gartner Forecasts Worldwide AI Spending to Grow 47% in 2026 Gartner
- Gartner Survey Shows Just 36% of Chief Procurement Officers Are Very Confident in Ability to Redesign Function for AI
Gartner Survey Shows Just 36% of Chief Procurement Officers Are Very Confident in Ability to Redesign Function for AI Gartner
- Gartner Survey Finds AI Saves Sellers Nearly 5 Hours Per Week, Yet 72% of Sales Organizations Fail to Reinvest Time in High-Value Activities
Gartner Survey Finds AI Saves Sellers Nearly 5 Hours Per Week, Yet 72% of Sales Organizations Fail to Reinvest Time in High-Value Activities Gartner
- Gartner Survey Shows 31% of Chief Sales Officers Cited Difficulty Proving ROI of AI-driven Tools as a Top Challenge for Sales Objectives in 2026
Gartner Survey Shows 31% of Chief Sales Officers Cited Difficulty Proving ROI of AI-driven Tools as a Top Challenge for Sales Objectives in 2026 Gartner
- Gartner HR Research Reveals AI Will Create More Jobs Than It Eliminates Beginning in 2028
Gartner HR Research Reveals AI Will Create More Jobs Than It Eliminates Beginning in 2028 Gartner
- Announcing Claude Managed Agents on Cloudflare
Cloudflare has integrated with Anthropic's Claude Managed Agents to provide a fast, isolated execution environment for autonomous code delivery. This means builders can scale agent workflows globally while strictly controlling access to private backends and easily customizing their agent’s tools and runtimes.
- Google increases SynthID access to identify AI-generated content
Google increases SynthID access to identify AI-generated content The National
- Google SynthID comes to Chrome, Search, and ChatGPT. Users can right-click to check for AI content.
At Google I/O 2026, the company announced it's expanding its SynthID digital watermark, and OpenAI announced it's adopting it, too
- 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.
- Atoms of Thought: Universal EEG Representation Learning with Microstates
Learning universal representations from electroencephalogram (EEG) signals is a cutting-edge approach in the field of neuroinformatics and brain-computer interfaces (BCIs). Conventionally, EEG is treated as a multivariate temporal signal, where time- or frequency-domain features are extracted for re...
- A Methodology for Selecting and Composing Runtime Architecture Patterns for Production LLM Agents
Production LLM agents combine stochastic model outputs with deterministic software systems, yet the boundary between the two is rarely treated as a first-class architectural object. This paper names that boundary the stochastic-deterministic boundary (SDB): a four-part contract among a proposer, ver...
- Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVR
Reinforcement learning with verifiable rewards has made post-training highly effective when correctness can be checked automatically. However, many important model behaviors require satisfying several qualitative criteria at once. Rubric-based rewards address this setting by grading prompt-specific ...
- Less Back-and-Forth: A Comparative Study of Structured Prompting
Large language models (LLMs) are widely used for open-ended tasks, but underspecified prompts can lead to low-quality answers and additional interaction. This paper studies whether structured prompt design improves response quality while reducing user effort. We compare three prompt conditions: a ra...
- Draft Less, Retrieve More: Hybrid Tree Construction for Speculative Decoding
Speculative decoding (SD) accelerates large language model inference by leveraging a draft-then-verify paradigm. To maximize the acceptance rate, recent methods construct expansive draft trees, which unfortunately incur severe VRAM bandwidth and computational overheads that bottleneck end-to-end spe...
- ThoughtTrace: Understanding User Thoughts in Real-World LLM Interactions
Conversational AI has now reached billions of users, yet existing datasets capture only what people say, not what they think. We introduce ThoughtTrace, the first large-scale dataset that pairs real-world multi-turn human--AI conversations with users' self-reported thoughts: their reasons for sendin...
- What Do Evolutionary Coding Agents Evolve?
Recent work pairs LLMs with evolutionary search to iteratively generate, modify, and select code using task-specific feedback. These systems have produced strong results in mathematical discovery and algorithm design, yet a fundamental question remains: what do they actually evolve? Progress is typi...