AI News Archive: July 21, 2026 — Part 12
Sourced from 500+ daily AI sources, scored by relevance.
- Effects of AI-driven Lifestyle Intervention on Psychological Well-Being and Body Image Among Young Adults In Malaysia
Background: University students experience substantial psychological well-being and body-image concerns, while scalable, personalized digital support remains underexamined in Malaysia. Artificial intelligence chatbots may deliver repeated lifestyle guidance, but the incremental value of personalization over structured chatbot support is uncertain. Objectives: This study evaluated changes in psychological well-being and body appreciation following a 12 week personalized AI-powered lifestyle intervention, NExGEN, among Malaysian university students. Methods: A two-arm, controlled, quasi-experimental pre-post study allocated 140 students aged 18 to 35 years by matched blocks to NExGEN (n = 70) or a structured-prompt ChatGPT control (n = 70). NExGEN generated adaptive weekly lifestyle actions from a 47-item onboarding assessment, whereas control participants received standardized weekly prompts covering the same lifestyle domains. Psychological well-being and body appreciation were assessed at baseline and week 12 using the World Health Organization-Five Well-Being Index and Body Appreciation Scale-2. Intention-to-treat linear mixed models estimated adjusted within-group changes and between-group differences in change, with Holm adjustment for the co-primary outcomes. Results: Week-12 assessments were completed by 121 participants (86.43%). In NExGEN, psychological well-being improved by an adjusted 8.68 points (95% CI, 6.22 to 11.14), z = 6.91, p < .001, and body appreciation improved by 0.17 points (95% CI, 0.10 to 0.24), z = 4.82, p < .001. However, between-group differences in change were not statistically significant for psychological well-being (2.87 points; 95% CI, -0.48 to 6.23; z = 1.68; Holm-adjusted p = .093) or body appreciation (0.10 points; 95% CI, 0.00 to 0.19; z = 1.99; Holm-adjusted p = .093). Median platform logins were 68.00 in NExGEN and 58.50 in control; mean acceptability scores were 3.92 and 3.59, respectively. Conclusions: NExGEN participation was associated with significant within-group improvements in psychological well-being and body appreciation, but personalized guidance did not demonstrate superiority over structured chatbot guidance. Because allocation was quasi-experimental, causal attribution remains limited. Randomized component-level trials are needed to determine whether personalization provides incremental benefit.
- Context-dependent facial-expression patterns during affective film viewing in patients with bipolar depression
Background: Emotion dysregulation is a core feature of bipolar disorder (BD), yet its behavioral expression during depressive episodes, and potential differences between its types, BD-I and BD-II, remain unclear. This study used automated facial-expression analysis during naturalistic affective film viewing to examine subtype-specific and context-dependent emotional responding in bipolar depression. Methods: The sample included 135 participants: 69 healthy controls and 66 patients with BD (BD-I, 23; BD-II, 43). Participants viewed nine emotionally evocative film clips spanning negative, positive, neutral, and socially threatening contexts, while their facial expressions were continuously recorded and quantified using computer vision-based facial-expression analysis. Results: Patients with BD-I showed a distinct, context-dependent facial-expression profile, characterized by greater negative responses across multiple contexts than other groups. Specifically, they showed increased sadness during sad, reward, and amusing clips, and elevated anger during sad and neutral clips. In socially threatening contexts, BD-I participants showed a multivalent pattern of elevated anger, fear, and joy, suggesting poorly coordinated or context-incongruent affective expression. In contrast, BD-II participants did not differ significantly from healthy controls on any emotion, despite depressive symptom severity comparable to BD-I participants. Conclusions: These findings suggest that facial-expression patterns in bipolar depression differ across subtypes. BD-I may be characterized by heightened negative reactivity and altered context-appropriate modulation of emotional expression, whereas BD-II may not show comparable alterations in overt facial output. Automated facial-expression analysis during naturalistic stimulation may provide a useful behavioral marker for characterizing subtype-specific affective disturbance in bipolar depression and related psychopathology.
- Developing a Heart Failure Readmission Model From Inpatient Electronic Medical Record Data
Importance: Heart failure readmissions remain common following hospitalization, but accurately identifying which patients will be readmitted after discharge remains challenging. Improved prediction could support targeted transitional care interventions and more efficient allocation of clinical resources. Objective: In this study we attempted to improve readmission prediction after heart failure hospitalization by using variables chosen through a modified Delphi process, and using inpatient Electronic Medical Record (EMR) data, focusing on clinical notes. Design: This prognostic study developed competing risk survival models to predict readmission after heart failure hospitalization. Variables were chosen using a modified Delphi process, and extracted from EMR notes using various natural language processing techniques or from other EMR elements where appropriate. Patients were admitted between 2011 through 2019, and at least one year of follow-up was available for all patients. Models were evaluated using C-statistics, as well as sensitivity, specificity, positive and negative predictive values. Setting: During the study period, all acute-care facilities in Calgary, Alberta used the same EMR system, from which patients were selected. Participants: Patients were 18 years or older, resided in Alberta, and were admitted to a Calgary hospital. All corresponding admissions with a most responsible diagnosis of heart failure were included (n=15,160). Main Outcomes and Measures: The main outcome of interest was readmission within 30 days, though 90- and 365-day time frames were also analyzed. Death was treated as a competing risk and analysed at those time frames as well.
- Aligning Reinforcement Learning with Clinical Practice for Safe Decision Support in Pediatric Sepsis
Offline reinforcement learning (RL) has emerged as a promising framework for clinical decision support in sepsis, yet most existing studies focus exclusively on adult populations, leaving pediatric care largely unexplored despite important physiological and treatment differences. In this work, we develop offline RL policies for pediatric sepsis management in the Pediatric Intensive Care Unit (PICU) using a retrospective cohort of 2,229 episodes from Great Ormond Street Hospital (GOSH), formalized as finite horizon Markov Decision Process (MDP) with joint intravenous fluid and vasopressor actions. To better capture pediatric organ dysfunction dynamics, we incorporate Phoenix 8, a recently proposed pediatric sepsis severity score, as an intermediate reward shaping signal in addition to terminal 90 day mortality. We systematically vary the time step size (4, 8, and 12 hours) and reward structure (terminal 90 day mortality, with and without Phoenix 8 based intermediate shaping), and compare Double Deep Q Networks (DDQN), Conservative Q Learning (CQL), and a behavior cloning (BC) model of clinician practice. CQL consistently exhibits stable learning dynamics and favorable Fitted Q Evaluation estimates, while DDQN is prone to overestimation and instability, particularly at finer temporal resolutions and with dense rewards. CQL policies achieve high action-level agreement with historical clinician decisions for both fluids and vasopressors and reproduce clinically plausible escalation patterns across sepsis severity strata, whereas DDQN policies diverge more frequently toward implausible dosing. Temporal aggregation emerges as a key regularizer: moving from 4 hour to 8 hour bins shortens horizons, smooths reward noise, and improves stability without erasing clinically meaningful dynamics, with 8 hour binning providing the best trade off between policy performance and granularity. Our findings highlight time step size as a core design choice in offline RL for healthcare and provide empirical evidence that alternatives beyond the conventional 4 hour setup can enhance stability and safety while preserving clinical interpretability.
- Gradient-guided adapter merging for neuroimaging vision-language models
Automated interpretation of neuroimaging studies requires simultaneous assessment of multiple imaging evidence variables, each tied to distinct anatomical structures. Vision-language models (VLMs) offer a unified framework for multi-task analysis, but adapting pre-trained VLMs remains challenging. Full fine-tuning is computationally prohibitive, and joint multi-task training requires simultaneous access to all task data, which is often infeasible in clinical settings. Although model merging enables multi-task composition without joint re-training, existing methods focus on post-hoc algorithms with limited extension to VLMs and minimal application to neuroimaging. Here, we present GRadient-guided Adapter Merging (GRAM), a layer-selective low-rank adaptation (LoRA)-based fine-tuning and merging framework for multi-task neuroimaging visual question-answering (VQA). GRAM uses a gradient ratio that contrasts class-specific gradients to identify task-discriminative layers, and applies subspace-constrained projected gradient descent to restrict LoRA updates to directions consistent with the geometry of the pre-trained model. We leveraged a structured VQA benchmark, developed from the National Alzheimer's Coordinating Center (NACC) dataset, that pairs multi-sequence brain MRI studies with question-answer pairs across clinically relevant imaging evidence variables. Experiments on the VQA benchmark showed that GRAM outperformed or matched all-layer LoRA fine-tuning and a standard merging baseline while reducing inter-task interference during merging, and approached or surpassed the performance of joint multi-task training without joint re-training.
- Quantitative Fundus Autofluorescence in Early Dry AMD Using ImageJ: Near-Perfect Interobserver Agreement and Pattern-Specific Intensity Characterization
Purpose: To assess the feasibility of quantitative fundus autofluorescence (FAF) measurement in early age-related macular degeneration (AMD) using the freely available ImageJ software, to characterize signal intensity across FAF patterns, and to evaluate interobserver reproducibility in pattern classification. Methods: Single-center, non-blinded, retrospective, consecutive-case analytical study. FAF images acquired with Spectralis OCT+HRA (Heidelberg Engineering) from patients with early dry AMD seen at a tertiary referral center between January 2010 and September 2016 were analyzed. A standardized 300x300-pixel region of interest (ROI) centered on the fovea was evaluated in ImageJ v2.0.0-rc54/1.51h (Fiji distribution). Mean, minimum, and maximum autofluorescence (AF) pixel intensity were recorded. Each image was independently classified according to the Bindewald classification system by two graders; a third senior grader adjudicated discordances. Cohen's kappa (k) was used to assess interobserver agreement. Results: Of 423 patients with available FAF studies, 107 had dry AMD; 45 met quality and diagnostic criteria for early AMD and were included in the quantitative analysis. Mean age was 73.47 +/- 8.1 years; 62.2% were female. Mean FAF intensity was 120.26 (range 74.76-160.79); mean minimum was 32.07 (range 3-63) and mean maximum was 205.80 (range 125-255). Seven of eight Bindewald patterns were identified; the stippled pattern was absent. The most frequent pattern was minimal changes (31.1%), followed by increased focal (24.4%) and patchy (15.6%). Reticular pattern showed the highest mean AF (143.8), while lace pattern showed the lowest (88.4). Interobserver agreement for Bindewald pattern classification was almost perfect (k = 0.969; 95% CI, 0.908-1.000; p < 0.001). Agreement for lesion extent was moderate (k = 0.531) and for foveal involvement was substantial (k = 0.622). Conclusions: Quantitative FAF evaluation of early AMD using ImageJ is feasible and reproducible. ImageJ represents a cost-free alternative for multimodal retinal image analysis, with potential for automated screening applications in resource-limited settings. Keywords: age-related macular degeneration; fundus autofluorescence; ImageJ; quantitative autofluorescence; image analysis; Bindewald classification; interobserver agreement
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- Multi-model forecasting of respiratory disease activity in Germany during the 2024-2025 season
Respiratory diseases cause considerable morbidity in autumn and winter and are a priority in public health monitoring. In Germany, they are subject to a number of surveillance systems, including both pathogen-specific and syndromic indicators. In this paper we present a collaborative multi-target and multi-model real-time forecasting system rolled out during the 2024/25 season, and discuss differences to earlier efforts carried out during the COVID-19 pandemic. A total of nine models were run to generate forecasts of general practitioner consultations for acute respiratory infections (ARI), hospitalizations for severe acute respiratory infections (SARI) and confirmed cases of seasonal influenza and RSV. As all indicators were subject to retrospective revisions, forecasting models were combined with a nowcasting step. Whenever multiple models were available for the same indicator, we combined them into an ensemble. Nowcasts showed convincing performance, even though for some models Christmas break effects led to an upward bias in early January. Forecasts were overall well-calibrated and most models outperformed simple benchmark models. These improvements were generally more substantial for age-stratified than pooled targets, and concentrated at lead times of two to three weeks. Anticipating the peak timing and magnitude proved to be challenging, with many models predicting too flat curves with a too early turnaround (e.g. already in late January rather than mid-February for SARI). The combined ensemble forecast was among the best-performing approaches, but unlike in previous related projects did not consistently outperform individual models. We conclude by discussing learnings on the organization of collaborative forecasting projects in post-COVID-19 times and the potential of AI-supported modelling.
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- America needs to stop getting shocked by Chinese AI
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- OpenAI says Hugging Face was breached by its pre-release models
OpenAI has come forward to claim responsibility for the Hugging Face breach, saying it was the result of internal testing gone awry.
- OpenAI Says Its Models Accidentally Hacked Hugging Face
OpenAI said its advanced artificial intelligence models inadvertently hacked Hugging Face Inc. in an “unprecedented” incident that prompted fresh calls for curbs on the technology.
- OpenAI says AI models went rogue during testing, triggering 'unprecedented' breach at startup
OpenAI says AI models went rogue during testing, triggering 'unprecedented' breach at startup Reuters
- Hugging Face Breach Signals A New Era Of AI-Powered Cyberattacks
The Hugging Face breach suggests AI-powered cyberattacks are no longer theoretical. Experts explain what it means for defenders and what's coming next.
- OpenAI says its AI models escaped control and hacked into AI company Hugging Face
OpenAI says its AI models escaped control and hacked into AI company Hugging Face Fortune
- OpenAI Says Its AI Broke Containment, Went to Internet and Hacked Hugging Face
OpenAI Says Its AI Broke Containment, Went to Internet and Hacked Hugging Face The Information
- Hugging Face confirms data breach by AI agent: Why it has sparked a debate on cyber guardrails
Hugging Face confirms data breach by AI agent: Why it has sparked a debate on cyber guardrails
- OpenAI and Hugging Face partner to address security incident during model evaluation
OpenAI and Hugging Face share early findings from a security incident during AI model evaluation, highlighting advanced cyber capabilities and lessons for defenders.
- OpenAI Models Behind HuggingFace Cybersecurity Incident
Link: https://openai.com/index/hugging-face-model-evaluation-security-incident/ From the OpenAI blog post: Last week, Hugging Face disclosed a new kind of security incident after they detected and contained an AI agent that compromised their infrastructure , something we expect to become more commonplace with the proliferation of increasingly cyber-capable models. After investigating, we now know that this particular incident was driven by a combination of OpenAI models — including GPT‑5.6 Sol and an even more capable pre-release model, all with reduced cyber refusals for evaluation purposes — while being internally tested on a benchmark of cyber capabilities. We consider this incident to be an unprecedented cyber incident, involving state-of-the-art cyber capabilities , and are responding accordingly. We are sharing preliminary findings at this stage to help defenders understand what happened and to help calibrate on what models are now capable of. We will continue to conduct a thorough investigation alongside Hugging Face and will share more details on the vulnerabilities, incident, and findings when our investigation is complete. (emphasis added.) Yesterday, OpenAI disclosed that some of their internally models were misaligned. Today, they disclosed that "a combination of OpenAI models — including GPT‑5.6 Sol and an even more capable pre-release model" had compromised HuggingFace infrastructure in the course of running some OpenAI internal cyber evaluations on ExploitGym . These cyber evaluations were supposed to be run in sandboxed environments, with internet access limited to installing packages, then: The models identified a zero-day vulnerability to escape the sandbox, gained privileges in OpenAI research infrastructure, and eventually acquired open Internet access. The models reasoned that ExploitGym test solutions lived on HuggingFace servers. They then stole credentials and used zero-day vulnerabilities to hack HuggingFace's production infrastructure in order to get said solutions. HuggingFace noticed, contained the models, called law enforcement , fixed the vulnerabilities, and then disclosed this incident on July 16th. (The incident happened "earlier in the week"). The exact scope of the incident is unknown Discuss
- OpenAI: We (inadvertently) hacked Hugging Face (sorry)
The company didn't actually say "sorry."
- OpenAI Confirms Its AI Broke Out of a Sandbox and Breached Hugging Face
OpenAI said on Tuesday that two of its AI models, including the flagship Sol, broke out of a secure test environment, gained internet access by exploiting a zero-day vulnerability in third-party software, and hacked into Hugging Face’s production infrastructure. The company called the incident “unprecedented” and said it was sharing preliminary findings to help defenders […] This story continues at The Next Web
- OpenAI says its AI technology acted on its own in an 'unprecedented' hack of another company
OpenAI says its AI technology acted on its own in an 'unprecedented' hack of another company Toronto Star
- OpenAI says AI models went rogue during testing and hacked a startup
Company says models managed to reach the internet and compromise the infrastructure of Hugging Face
- OpenAI: Oops, Our Models Went Rogue, Hacked Hugging Face
OpenAI: Oops, Our Models Went Rogue, Hacked Hugging Face PCMag
- AI Platform Hugging Face Fends Off Hack From... AI
AI Platform Hugging Face Fends Off Hack From... AI PCMag
- OpenAI: Oops, Our Models Went Rogue, Hacked Hugging Face
OpenAI: Oops, Our Models Went Rogue, Hacked Hugging Face PCMag Australia
- OpenAI: Oops, Our Models Went Rogue, Hacked Hugging Face
OpenAI: Oops, Our Models Went Rogue, Hacked Hugging Face PCMag UK
- OpenAI says its own AI models broke out of testing and hacked Hugging Face
OpenAI Group PBC today disclosed that two of its artificial intelligence models broke out of a controlled testing environment and hacked open-source AI platform Hugging Face Inc. to cheat on an internal benchmark in what the company called an unprecedented cyber incident. The two models, OpenAI’s latest publicly available model GPT-5.6 Sol and a more capable […] The post OpenAI says its own AI models broke out of testing and hacked Hugging Face appeared first on SiliconANGLE .
- OpenAI says its AI technology acted on its own in an 'unprecedented' hack of another company
OpenAI has disclosed an "unprecedented cyber incident" where its AI system allegedly hacked into another AI company
- Agentic AI attack breaches Hugging Face
Agentic AI attack breaches Hugging Face Computing UK
- OpenAI says its AI technology acted on its own in an 'unprecedented' hack of another company
OpenAI says its AI technology acted on its own in an 'unprecedented' hack of another company San Francisco Chronicle
- OpenAI says its AI technology acted on its own in an 'unprecedented' hack of another company
OpenAI says its AI technology acted on its own in an 'unprecedented' hack of another company AP News
- OpenAI says it accidentally hacked Hugging Face with a new AI system
OpenAI says its AI models mistakenly breached open-source AI platform Hugging Face during internal testing. In a blog post on Tuesday, OpenAI writes that GPT-5.6 Sol and "an even more capable pre-release model" discovered vulnerabilities within their sandboxed testing environment, allowing them to gain access to the internet and target Hugging Face. On July 16th, […]
- OpenAI says its AI technology acted on its own in an ‘unprecedented’ hack of another company
OpenAI says its AI technology acted on its own in an ‘unprecedented’ hack of another company Boston Herald
- US judge approves Anthropic’s $1.5 billion settlement of copyright lawsuit
US judge approves Anthropic’s $1.5 billion settlement of copyright lawsuit
- Anthropic pays $1.5B to settle contentious copyright case
A US federal court has approved Anthropic’s $1.5 billion settlement in a class-action lawsuit in which authors accused the AI company of using their books without permission to train the AI model Claude. This is the largest such settlement to date in a US copyright case, according to Reuters . The dispute is one of several legal cases in which copyright holders sued AI companies over how large language models (LLMs) were trained, and it is the first major AI-related copyright dispute in the US to be resolved through a settlement. A judge had previously ruled that the actual training of AI models using books falls under the “fair use” doctrine in US copyright law. But Anthropic was found to have violated the law by storing more than 7 million pirated books in a central library, regardless of whether they were later used for AI training or not.
- Anthropic’s $1.5bn copyright lawsuit settlement gets approval
Each payout per work will amount to roughly $3,000, to be paid to around 500,000 works. Read more: Anthropic’s $1.5bn copyright lawsuit settlement gets approval
- Judge approves a $1.5B Anthropic settlement over pirated books used to train the Claude chatbot
Judge approves a $1.5B Anthropic settlement over pirated books used to train the Claude chatbot Toronto Star
- Judge Approves Anthropic's $1.5 Billion Copyright Settlement for Authors
Judge Approves Anthropic's $1.5 Billion Copyright Settlement for Authors PCMag Australia
- Judge Approves Anthropic's $1.5 Billion Copyright Settlement for Authors
Judge Approves Anthropic's $1.5 Billion Copyright Settlement for Authors PCMag UK
- Judge approves a $1.5B Anthropic settlement over pirated books used to train the Claude chatbot
Judge approves a $1.5B Anthropic settlement over pirated books used to train the Claude chatbot Chicago Tribune
- Judge approves a $1.5B Anthropic settlement over pirated books used to train the Claude chatbot
Judge approves a $1.5B Anthropic settlement over pirated books used to train the Claude chatbot San Francisco Chronicle
- Judge approves a $1.5B Anthropic settlement over pirated books used to train the Claude chatbot
Judge approves a $1.5B Anthropic settlement over pirated books used to train the Claude chatbot Houston Chronicle