AI News Archive: May 5, 2026 — Part 18

Sourced from 500+ daily AI sources, scored by relevance.

• Beyond Similarity Search: A Unified Data Layer for Production RAG Systems

  Retrieval-Augmented Generation (RAG) systems have become the standard architecture for grounding large language models in organizational knowledge. Yet production deployments consistently expose a gap between clean prototype performance and real-world reliability. This paper identifies three root ca...

  📄 ResearchMay 5, 2026http://arxiv.org/abs/2605.03275v1
• Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems

  Reasoning-intensive retrieval aims to surface evidence that supports downstream reasoning rather than merely matching topical similarity. This capability is increasingly important for agentic search systems, where retrievers must provide complementary evidence across iterative search and synthesis. ...

  📄 ResearchMay 5, 2026http://arxiv.org/abs/2605.04018v1
• Domain-Adaptive Dense Retrieval for Brazilian Legal Search

  Brazilian legal retrieval is heterogeneous, covering case law, legislation, and question-based search. This makes training dense retrievers a trade-off between stronger domain specialization and broader robustness across retrieval types of search. In this paper, we explore this trade-off using three...

  📄 ResearchMay 5, 2026http://arxiv.org/abs/2605.04005v1
• Aspect-Aware Content-Based Recommendations for Mathematical Research Papers

  Content-based research paper recommendation (CbRPR) has seen advances in computer science and biomedicine, but remains unexplored for mathematics, where paper relatedness is more conceptual than explicit textual or citation-based similarity. Mathematics papers may be connected through shared proof t...

  📄 ResearchMay 5, 2026http://arxiv.org/abs/2605.03861v1
• Revisiting General Map Search via Generative Point-of-Interest Retrieval

  Point-of-Interest (POI) retrieval aims to identify relevant candidates from massive-scale POI databases, serving as a cornerstone for diverse location-based services. However, in general map search scenarios, conventional POI retrieval methods are increasingly challenged by underspecified user queri...

  📄 ResearchMay 5, 2026http://arxiv.org/abs/2605.03397v1
• Comparative single-cell transcriptomic roadmap of mammalian fetal ovarian development

  Early ovarian development establishes the cellular basis of female reproduction, yet its cellular composition and developmental dynamics remain poorly characterized across mammalian species. Here, we generated a cross-species single-cell transcriptomic atlas of early female gonadal development in cattle (E38-E112), human (PCW6-16), and mouse (E11.5-E18.5), integrating 107,930 cells across comparable developmental windows, spanning sex determination and early ovarian differentiation. We identified 11 shared gonadal cell types across three species, including germ cells and granulosa cells, and uncovered a previously undescribed bovine-specific cell population with steroidogenic features, suggesting species differences in ovarian somatic lineage development. Epigenetic regulators exhibited dynamic activation in germ cells and granulosa cells. Developmental trajectory analysis revealed shared and species-specific genes associated with germ cell and granulosa cell development, including con

  📄 ResearchMay 5, 2026https://www.biorxiv.org/content/10.64898/2026.05.01.721341v1?rss=1
• immuneKG: An Immune-Cell-Aware Knowledge Graph Framework for Target Discovery in Immune-Mediated Diseases

  Biomedical knowledge graphs have emerged as foundational infrastructure for AI-driven drug discovery, yet their translational impact on novel target identification in immune-mediated diseases remains limited. Here we present immuneKG, a multimodal knowledge graph centred on autoimmune diseases, constructed through biologically meaningful feature reprogramming of disease nodes to enable deep mechanistic modelling of immune-related disorders. immuneKG introduces a new entity class immune_cell, and four original directed relation types, together adding 9,105 novel triples absent from all existing biomedical KG schemas. Disease nodes are endowed with three novel modal feature sets quantifying immune homeostatic imbalance: autoantibody profiles, cytokine signatures, and HLA genotypes, complemented by systemic involvement scores and genetic features. The graph encompasses over 407,000 training triples across 7,287 entities and 32 relation types. Applied to inflammatory bowel disease (IBD), i

  📄 ResearchMay 5, 2026https://www.biorxiv.org/content/10.64898/2026.04.30.721823v1?rss=1
• Transfer Learning Enables Drug-Target Interaction Prediction in Data-Scarce One-Carbon Metabolism

  Predicting drug-target interactions (DTIs) with deep learning offers opportunities to accelerate drug discovery, yet performance is constrained by the scarcity of target-specific training data. This is a particular challenge for mitochondrial one-carbon (1C) pathway enzymes, which are attractive therapeutic targets but remain pharmacologically understudied. Mitochondrial 1C metabolism supplies glycine, reducing equivalents, and 1C units critical for nucleotide synthesis, and has emerged as a key pathway in cancer and fibrosis. SHMT2 and MTHFD2, two key 1C enzymes, support collagen production in fibroblasts, blocking either prevents TGF-{beta}-induced glycine and collagen accumulation. Here, we developed transfer learning-based deep learning models to predict interactions between approved drugs and SHMT2 or MTHFD2 despite minimal target-specific training data, pre-training on large datasets from related enzymes before fine-tuning to these targets. Virtual screening of the DrugBank libra

  📄 ResearchMay 5, 2026https://www.biorxiv.org/content/10.64898/2026.04.30.721937v1?rss=1
• Machine learning approaches for the identification and analysis of enterotoxin genes in Staphylococcus aureus genomes

  Staphylococcus aureus produces a broad range of enterotoxins that act as superantigens, disrupting host immune responses and resulting in a myriad of clinical symptoms. However, large-scale analyses determining enterotoxin gene diversity, lineage structure and isolate metadata remain scarce. We analysed 15,887 S. aureus RefSeq genomes using a machine learning pipeline combining profile Hidden Markov Model-based enterotoxin gene identification, lineage typing, gene profile-based strain clustering and association rule mining using a broad range of gene and metadata features. This approach identified 35 distinct enterotoxin genes and five variant forms, including two putative novel enterotoxin genes, sel34 and sel35. HDBSCAN clustering distinguished 45 enterotoxin gene profile groups, revealing strong associations between the two major egc enterotoxin gene cluster variants (OMIWNG and OMIUNG) and Clonal Complex membership: CC5, CC22 and CC45 with OMIWNG; CC30 and CC121 with OMIUNG. Integr

  📄 ResearchMay 5, 2026https://www.biorxiv.org/content/10.64898/2026.05.01.722155v1?rss=1
• DOMINO: Learning Domain Co-occurrence for Multidomain Protein Design

  Multidomain proteins arise through the reuse and recombination of structural domains, yet natural architectures represent a sparse, structured sample of the possible domain-combination space. Here, we introduce DOMINO, a two-stage framework that learns domain co-occurrence from TED-annotated multidomain proteins and uses the learned patterns to generate new multidomain sequences. DOMIN, a contrastive retrieval model, embeds domains into a latent compatibility space and retrieves candidate partners for a query domain from a TED-derived domain pool, including pairings not observed in the TED-derived co-occurrence set. DOMO, a conditional autoregressive sequence model, converts each retrieved domain pair into a full-length protein sequence by jointly generating the specified domain regions and the non-domain sequence context between and around them. DOMIN recovers hierarchical patterns of natural domain co-occurrence and expands the observed CATH homologous-superfamily co-occurrence netwo

  📄 ResearchMay 5, 2026https://www.biorxiv.org/content/10.64898/2026.05.01.721929v1?rss=1
• Functional Cell-Type Identification in Neuronal Networks Using High-Density Microelectrode Arrays

  The reliable identification of neuronal cell types - in particular, the distinction of excitatory (E) and inhibitory (I) neurons on the basis of extracellular recordings without post-hoc immunostaining or genetic labeling - remains a key challenge in neural-circuit analysis. High-density microelectrode arrays (HD-MEAs) have emerged as a powerful tool to address this issue, enabling simultaneous single-cell and network-level electrophysiology. Here, we present two complementary strategies for establishing cell-type ground truth based on HD-MEA recordings: (i) chemogenetic interneuron activation to label putative inhibitory neurons according to their functional response, and (ii) controlled mixing of excitatory and inhibitory hiPSC-derived populations at defined ratios. A classifier combining action potential waveform morphology and autocorrelogram-based discharge dynamics achieves robust cell-type discrimination in in vitro recordings of rat primary cortical cultures and hiPSC-derived n

  📄 ResearchMay 5, 2026https://www.biorxiv.org/content/10.64898/2026.04.30.721923v1?rss=1
• Failure detection in medical image classification under realistic distribution shifts: A large-scale benchmark

  Medical images (MI) exhibit variability due to different acquisition protocols, devices, and patient populations, making failure detection at inference time essential for reliable deployment of clinical classifiers. As existing evaluations of failure detection methods use different settings, it is difficult to compare results and identify the best strategy, if any. We present a comprehensive benchmark of eight confidence scoring functions and two score-aggregation strategies across eight MI tasks spanning diverse modalities, backbone architectures, training setups, and failure sources. The confidence ranking ability and classification error mitigation are jointly evaluated. While no single method systematically dominated across settings, aggregation of confidence scores consistently matched or approached the best individual method and substantially reduced silent failure rate. The failure detection performance was strongly correlated with classifier accuracy for all tested settings. Th

  📄 ResearchMay 5, 2026https://www.medrxiv.org/content/10.64898/2026.05.04.26350496v1?rss=1
• Analgesic Equivalence of NSAIDs and a Weak Opioid in Acute Postoperative Pain Following Minimally Invasive Surgery Under Balanced General Anesthesia: A Pilot Randomized Controlled Trial

  Background: Non-steroidal anti-inflammatory drugs (NSAIDs) and weak opioids such as tramadol are cornerstones of multimodal analgesia, particularly in settings with limited access to potent opioids. However, cross-class equianalgesic data comparing these agents remain scarce. This pilot randomised controlled trial aimed to explore the analgesic equivalence of ketorolac, diclofenac, and tramadol administered as premedication in patients undergoing minimally invasive surgery. Methods: In this double-blind, parallel-group pilot trial, 30 patients scheduled for elective minimally invasive surgery (28 laparoscopic cholecystectomies, 2 laparoscopic abdominal wall repairs) under balanced general anaesthesia were randomised to receive intravenous tramadol 150 mg, ketorolac 60 mg, or diclofenac 150 mg 45 minutes before skin incision. The primary outcome was pain intensity measured using the Numerical Rating Scale (NRS, 0-10) at recovery room arrival (T0) and at 30 (T1), 60 (T2), and 90 (T3) min

  📄 ResearchMay 5, 2026https://www.medrxiv.org/content/10.64898/2026.05.03.26352343v1?rss=1
• Solving Emergency Department Triage with Small Language Models

  Emergency department (ED) triage assigns patients a five-level Emergency Severity Index (ESI) score that determines care priority. We investigate the feasibility of au- tomating this process, comparing large commercial models (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, MedGemma) against a purpose-built pipeline combining a small ex- traction model with a deterministic clinical engine, and a 9B-parameter language model trained with structured chain-of-thought supervision and reinforcement learning. Off- the-shelf large models achieve only 45 - 55% exact ESI accuracy while being impractical for clinical deployment due to privacy constraints, cost, and latency. Our specialized BiomedBERT [4] pipeline achieves 88.9% exact accuracy with 97.2% adjacent accu- racy (1 ESI) on a 50-case expert-labeled evaluation set, approaching nurse inter-rater agreement. A Qwen3.5-9B model [16] fine-tuned with chain-of-thought supervision achieves 75.0% exact / 97.2% adjacent accuracy on a 36-case narrative

  📄 ResearchMay 5, 2026https://www.medrxiv.org/content/10.64898/2026.05.04.26352355v1?rss=1
• Extracting adverse event nature, severity, timelines and resulting interventions from clinical notes of patients receiving CAR-T therapy using large language models.

  Chimeric Antigen Receptor T-cell (CAR-T) therapy, where genetically engineered patient T cells target tumor antigens, has transformed care for hematologic malignancies but requires careful tracking of adverse events (AEs) often documented only in unstructured EHR notes. We evaluated a Large Language Model (LLM)-based approach in UCSFs secure environment to extract AEs, dates, grades, and interventions within 30 days post-infusion for six commercial CAR-T products (2012-2023), benchmarking against two evaluators. Using GPT-4-0314 in a zero-shot setting with four prompts (prespecified AEs, non-prespecified AEs, CRS, ICANS), we compared outputs against dual annotations on a random sample of 50 notes using accuracy, precision, recall, F1, and Cohens kappa. From 4,762 progress notes for 293 patients (median age 65.6), CRS occurred in 80.2% (median onset 4 days); neutropenia 70.0% (16 days); neutropenic fever 64.8% (4 days); ICANS in 34.8%. Interventions included tocilizumab and corticostero

  📄 ResearchMay 5, 2026https://www.medrxiv.org/content/10.64898/2026.04.28.26351782v1?rss=1
• Calibration Drift Under Cross-Institutional Deployment: An External Validation Framework for ICU Mortality Prediction Across MIMIC-IV and eICU

  Background: Machine learning models for intensive care unit (ICU) mortality prediction achieve strong internal discrimination yet rarely undergo external validation with calibration assessment - a gap undermining clinical deployment. Calibration, the agreement between predicted probabilities and observed event rates, is prerequisite for threshold-based decisions yet remains underreported. Methods: We conducted a retrospective cohort study using MIMIC-IV (v2.2; n = 52,028 ICU stays) for model development and eICU (n = 114,060) for independent external validation. Logistic regression, random forest, and gradient boosting (XGBoost) were evaluated on first-24-hour clinical variables. Discrimination was assessed via receiver operating characteristic area (AUROC) and precision-recall area (AUPRC); calibration via slope, intercept, and expected calibration error (ECE). Post-hoc logistic recalibration was applied externally. Clinical utility was evaluated by decision curve analysis benchmarked

  📄 ResearchMay 5, 2026https://www.medrxiv.org/content/10.64898/2026.05.03.26352335v1?rss=1
• Multilingual Evaluation of a Large Language Model-Based Primary Care Chatbot

  Pre-visit planning has the potential to reduce EHR documentation burden while improving workflow efficiency, care quality, and patient-provider engagement. Large language model (LLM) chatbots show promise for supporting this task, but while their English-centric development suggests a potential for disparity, the extent to which these concerns translate into performance degradation in multilingual clinical settings remains unclear. In this mixed-methods study, we systematically evaluate the multilingual capabilities of PCP-Bot, an English-developed LLM-based (GPT-4o) clinical chatbot that collects patient concerns and generates structured, physician-ready summaries (~200 words) under structured output constraints. We enrolled 31 bilingual individuals (11 Mandarin, 10 Spanish, 10 Hindi) to role-play as patients to evaluate the PCP-Bot, interacting with it across five synthetic clinical cases in both English and a second language. Participants completed a structured survey comprising bas

  📄 ResearchMay 5, 2026https://www.medrxiv.org/content/10.64898/2026.05.03.26352241v1?rss=1
• Screening for Rheumatic Heart Disease in Asymptomatic Children using Machine Learning from Electrocardiograms

  Early detection of Rheumatic Heart Disease (RHD) is essential in reducing its associated mortality and late complications. In resource-limited settings, automated detection using low-cost electrocardiogram (ECG) sensors can enhance prevention efforts. However, its effectiveness as a potential RHD screening tool in at-risk populations remains unexplored. This study aimed to investigate the utility of machine learning for classifying RHD in a cohort screened for RHD using low-cost ECG devices. The ECGs were collected from 611 at-risk schoolchildren using KardiaMobile, where 47 were confirmed RHD and 564 were healthy. First, the ECG fiducial points were annotated using a publicly available prominence-based delineator. Then, temporal, frequency, wavelet, and visibility graph-based features were extracted from six-leads and fed to the XGBoost classifier. A 10-fold cross-validation was used at different prediction score thresholds to obtain target sensitivity (Se) for screening RHD. Single-l

  📄 ResearchMay 5, 2026https://www.medrxiv.org/content/10.64898/2026.05.03.26352326v1?rss=1
• Disentangling the contribution of disease genes to drug therapeutic and side effects

  Most clinical trials fail due to either lack of efficacy or safety concerns. Human genetics can address both failure reasons: disease-associated genes are not only promising therapeutic targets but also predict drug side effects. However, because the same genetic signal underlies both outcomes, we need methods that disentangle which disease genes mediate therapeutic benefit versus adverse side effects. We use DraphNet, our previously developed model that maps drug molecular effects onto disease genes to generate two gene sets per drug: one linked to its therapeutic effects (IND genes) and one linked to its side effects (SE genes). We show that IND and SE genes overlap for 76% of the tested drugs (compared to a null model). We also show that drugs sharing greater IND similarity also have greater SE similarity ({rho}=0.57, p<1e-300). To show how our approach enables insights into drug biology, we construct groupings of drugs based on their IND and SE genes. We find that drugs in the same

  📄 ResearchMay 5, 2026https://www.medrxiv.org/content/10.64898/2026.05.04.26352378v1?rss=1
• Detection of Hepatocellular Carcinoma from B-Mode and Contrast-Enhanced Ultrasound Using a Dual-Path Convolutional Network

  Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, with particularly severe consequences in sub-Saharan Africa where access to advanced diagnostic imaging remains limited. Ultrasound is the most widely available imaging modality in low-resource settings, yet its sensitivity for detecting early-stage HCC remains insufficient when used in conventional B-mode alone.Methods: We present a dual-path convolutional neural network (CNN) that jointly analyzes B-mode and contrast-enhanced ultrasound (CEUS) images for automated HCC detection. The model processes 1,057 labeled liver ultrasound images from 85 patients sourced from The Cancer Imaging Archive, a publicly available single-center dataset. A preprocessing pipeline extracts liver-centered regions of interest from heterogeneous DICOM files, including automatic separation of dual-panel B-mode and CEUS frames. Each imaging modality is processed through a dedicated ResNet-34 backbone initializ

  📄 ResearchMay 5, 2026https://www.medrxiv.org/content/10.64898/2026.05.04.26352359v1?rss=1
• A data-driven Alzheimer's disease progression simulator for retrospective validation and prospective Phase III power design

  Anti-amyloid immunotherapies have recently demonstrated the first significant slowing of cognitive decline in Alzheimer's disease (AD), yet clinical benefit varies markedly across drugs and scales with the completeness of amyloid clearance. Pharmacokinetic/pharmacodynamic (PK/PD) models are currently the standard tool for trial simulation, but they typically operate on single biomarkers and rely on drug-concentration assumptions, leaving the multi-scale cascade from amyloid clearance through tau, neurodegeneration, and cognition largely unmodelled. No existing framework has been jointly validated against the quantitative outcomes of multiple real-world phase III trials, spanning clearance kinetics, multi-modal biomarker trajectories, and statistical power. We present a trial simulation platform based on SimulAD, a disease progression model trained exclusively on longitudinal observational data from ADNI, with no access to trial-arm labels or drug-specific outcomes. SimulAD encodes inte

  📄 ResearchMay 5, 2026https://www.medrxiv.org/content/10.64898/2026.05.03.26352317v1?rss=1
• Neural signatures of real-world turning during naturalistic locomotion in Parkinson's Disease

  Turning is a complex motor behavior that frequently triggers freezing of gait and falls in Parkinson's disease (PD), yet its neural dynamics in naturalistic settings remain unknown. Using chronic at-home intracranial recordings in four subjects with PD, we show that turning is marked by premotor cortical beta desynchronization driven by reduced burst rate. These findings identify a robust signature of ecological turning and implicate beta dynamics in adaptive motor transitions.

  📄 ResearchMay 5, 2026https://www.medrxiv.org/content/10.64898/2026.05.03.26352320v1?rss=1
• AIImageToVideo.video

  Turn any photo into video in seconds.

  🧰 ToolsMay 5, 2026https://theresanaiforthat.com/ai/aiimagetovideo-video/
• Comparative analyses of Alzheimers disease blood biomarkers and cognitive domains

  INTRODUCTION: Whether Alzheimers disease (AD) blood biomarker-cognition associations differ across cognitive domains, analytic context, and biomarker modeling strategy in population-based cohorts is unclear. METHODS: In 1,170 older adults from the Health and Retirement Study Harmonized Cognitive Assessment Protocol, we examined cross-sectional (2016) and prospective (2016-2022) associations of blood p-tau181, glial fibrillary acidic protein (GFAP), neurofilament light (NfL), and amyloid-{beta}42/40 with memory, executive function, language, visuospatial ability, and global cognition using individual biomarker, principal components analysis-derived composite, and multibiomarker panel models. RESULTS: Cross-sectionally, NfL and GFAP showed the broadest associations. Prospectively, p-tau181 was independently associated with memory and global cognition, whereas GFAP was associated with executive function, memory, and global cognition. P-tau181 also showed relative memory-versus-executive s

  📄 ResearchMay 5, 2026https://www.medrxiv.org/content/10.64898/2026.05.03.26352316v1?rss=1
• AIProofMe

  Get your free AI-proof career score.

  🧰 ToolsMay 5, 2026https://theresanaiforthat.com/ai/aiproofme/
• WhatToWear | Outfit Generator

  Upload one item, get complete AI outfit ideas.

  🧰 ToolsMay 5, 2026https://theresanaiforthat.com/ai/whattowear-outfit-generator/
• WiseOCR

  Transform receipts into structured JSON effortlessly.

  🧰 ToolsMay 5, 2026https://theresanaiforthat.com/ai/wiseocr/
• GoAISong

  AI songs from ideas, lyrics, or your audio — PDF license included.

  🧰 ToolsMay 5, 2026https://theresanaiforthat.com/ai/goaisong/
• Chattee AI App Builder

  Turn ideas into full-stack apps instantly with GDPR-compliant builder.

  🧰 ToolsMay 5, 2026https://theresanaiforthat.com/ai/chattee/
• remio: Your Personal ChatGPT

  Get Tailored Answer with Your Personal ChatGPT

  🧰 ToolsMay 5, 2026https://theresanaiforthat.com/ai/remio/
• PageLens AI

  AI-powered website audits with instant fixes.

  🧰 ToolsMay 5, 2026https://theresanaiforthat.com/ai/pagelens-ai/
• Quanto

  AI agents for accounting firms

  🧰 ToolsMay 5, 2026https://www.producthunt.com/products/quanto?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+the500feed+%28ID%3A+283491%29
• AI’s hottest private companies have booming crypto shadow market

  AI’s hottest private companies have booming crypto shadow market The Japan Times

  🌐 MovesMay 5, 2026https://www.japantimes.co.jp/business/2026/05/05/markets/ai-companies-crypto-shadow-market/
• Apple will pay $250 million for failing to deliver its AI-powered Siri on time

  The proposed settlement is the result of a class action lawsuit filed in California.

  💰 MoneyMay 5, 2026https://www.engadget.com/2165602/apple-will-pay-250-million-for-failing-to-deliver-its-ai-powered-siri-on-time/
• iOS 27 could let users pick an AI model of their choice for text and image tasks

  For the first time since Apple Intelligence launched, iPhone users may soon decide which AI does their heavy lifting, whether that's drafting emails, editing photos, or powering Siri.

  🌐 MovesMay 5, 2026https://www.digitaltrends.com/computing/ios-27-could-let-users-pick-an-ai-model-of-their-choice-for-text-and-image-tasks/
• OpenAI projects $50 billion spending on computing power this year, Brockman says

  OpenAI projects $50 billion spending on computing power this year, Brockman says Reuters

  💰 MoneyMay 5, 2026https://www.reuters.com/technology/openai-projects-50-billion-spending-computing-power-this-year-brockman-says-2026-05-05/
• Peppr AI

  Real-time AI answers during sales calls

  🧰 ToolsMay 5, 2026https://www.producthunt.com/products/peppr-ai?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+the500feed+%28ID%3A+283491%29
• Apple could let you pick a favorite AI model in iOS 27

  The next update to Apple's operating systems could allow users to choose their preferred AI model for running Apple Intelligence. According to Bloomberg's Mark Gurman, Apple is planning to allow third-party chatbots to power its AI features system-wide in iOS 27, iPadOS 27, and macOS 27, all expected for this fall. In addition to running […]

  🌐 MovesMay 5, 2026https://www.theverge.com/tech/924515/apple-intelligence-third-party-chatbot-extensions-ios-27
• Mankinds

  Continuous AI testing, with audit ready proof

  🧰 ToolsMay 5, 2026https://www.producthunt.com/products/mankinds?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+the500feed+%28ID%3A+283491%29
• Disputron

  AI small claims court for petty disputes

  🧰 ToolsMay 5, 2026https://www.producthunt.com/products/disputron?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+the500feed+%28ID%3A+283491%29
• Pennsylvania sues Character.AI over claims chatbot posed as doctor

  State officials allege a Character.AI bot claimed to be a licensed psychiatrist and provided a fake state medical license number.

  🌐 MovesMay 5, 2026https://www.npr.org/2026/05/05/nx-s1-5812861/characterai-chatbot-medical-advice-pennsylvania-lawsuit
• OpenAI releases GPT-5.5 Instant, a new default model for ChatGPT

  The company said the model reduces hallucination in sensitive areas such as law, medicine, and finance, while maintaining the low latency of its predecessor.

  🤖 ModelsMay 5, 2026https://techcrunch.com/2026/05/05/openai-releases-gpt-5-5-instant-a-new-default-model-for-chatgpt/
• OpenAI is planning to spend $50 billion on computing in 2026, its president says in court

  Greg Brockman testified that the company's computing costs have climbed from about $30 million in 2017 to tens of billions this year

  💰 MoneyMay 5, 2026https://qz.com/openai-computing-spending-50-billion-brockman-trial-050526
• OpenAI exec says company hopes to burn $50B of somebody else's money on compute this year

  If the numbers are large enough, perhaps we won't question the math An executive for ChatGPT maker OpenAI said in court testimony on Tuesday that the AI model developer expects to burn $50 billion on computing power before the end of the year.…

  💰 MoneyMay 5, 2026https://go.theregister.com/feed/www.theregister.com/2026/05/05/openai_50b_compute/
• Apple settles lawsuit over late Siri AI features for $250 million

  Apple settles lawsuit over late Siri AI features for $250 million Reuters

  💰 MoneyMay 5, 2026https://www.reuters.com/legal/litigation/apple-settles-lawsuit-over-late-siri-ai-features-250-million-2026-05-05/
• Apple reaches $250mn settlement over delayed ‘AI Siri’

  iPhone buyers sued the tech giant for touting features in 2024 that have yet to launch

  💰 MoneyMay 5, 2026https://www.ft.com/content/f2c6a27e-8ed9-487c-9384-7e0f0dca3061
• Apple agrees to $250 million settlement over AI Siri claims

  Apple agrees to $250 million settlement over AI Siri claims Gulf News

  💰 MoneyMay 5, 2026https://gulfnews.com/technology/apple-agrees-to-250-million-settlement-over-ai-siri-claims-1.500530536
• ChatGPT update rolls out GPT-5.5 Instant with fewer hallucinations and more personalized answers

  OpenAI is swapping out ChatGPT's default model for GPT-5.5 Instant. In internal testing, the update produced 52.5 percent fewer hallucinated claims on high-risk topics like medicine and law. A new feature called "memory sources" lets users see which stored context shaped a given response. The model is rolling out to all ChatGPT users right away, though personalization based on past chats, files, and Gmail launches first for Plus and Pro users on the web. The article ChatGPT update rolls out GPT-5.5 Instant with fewer hallucinations and more personalized answers appeared first on The Decoder .

  🤖 ModelsMay 5, 2026https://the-decoder.com/chatgpt-update-rolls-out-gpt-5-5-instant-with-fewer-hallucinations-and-more-personalized-answers/
• GPT-5.5 Instant System Card

  GPT-5.5 Instant System Card

  🤖 ModelsMay 5, 2026https://openai.com/index/gpt-5-5-instant-system-card
• OpenAI rolls out ChatGPT 5.5 Instant as the new default model for everyone

  ChatGPT 5.5 Instant is the new default model for users as of May 5.

  🤖 ModelsMay 5, 2026https://mashable.com/article/openai-chatgpt-55-instant-out-now