AI News Archive: July 16, 2026 — Part 18
Sourced from 500+ daily AI sources, scored by relevance.
- SLT 2026 REAL-TSE Challenge: Real-world Target Speaker Extraction from Conversational Recordings
We introduce the REAL-TSE Challenge, an IEEE SLT 2026 satellite challenge on target speaker extraction~(TSE) from real conversational recordings. Given a multi-speaker mixture and one or more enrollment utterances from a target speaker, participating systems must recover only the target speech. Unli...
- LLM-Based Re-Ranking for Real Estate Search
QuintoAndar Group operates the leading housing marketplace in Latin America for both rentals and sales. The platform replaces traditionally paper-heavy workflows with a fully digital experience, making housing transactions faster and more accessible to tenants, buyers, and landlords in the region. F...
- SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation
Complex knowledge base question answering (KBQA) is commonly approached through either information retrieval over a question-specific subgraph or semantic parsing into an executable logical form. We study the latter paradigm. Recent large language model agents make semantic parsing interactive: they...
- CoSimRec: Measuring Coordinated-Content Penetration in Recommender Feedback Loops
Recommender systems increasingly shape which content reaches users, making it important to understand whether coordinated activity is amplified beyond the accounts that initiate it. Existing robustness evaluations largely focus on static target-rank changes and do not capture how coordinated interac...
- Accelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap
Online controlled experiments are the gold standard for hypothesis testing in online platforms. Notwithstanding their ubiquity, they are notoriously expensive to run, and issues of variance hamper statistical power in assessing treatment effects. While standard variance reduction techniques leverage...
- Impact of Expert-Following Strategies in Financial Asset Recommendation
Financial institutions hold rich transaction histories, yet delivering recommendations that simultaneously maximize investment returns and ensure preference alignment remains a significant challenge. Existing approaches, namely return-based and preference-based strategies, each optimize a single obj...
- The evolutionary processes of bacterial aromatic polyketide ketosynthases
Background: The biosynthesis of bacterial aromatic polyketide polyketides (type II polyketides, T2PKs) employs a single set of catalysts (ketosynthases, KSs or KS, with chain length factors, CLFs or KS{beta}) and iteratively assembles a carbon backbone with precise chain length control. Considering the increasing number of T2PKs discovered in laboratory settings, it is necessary to understand the evolution trajectories of KSs and CLFs. Results: We employed our recently developed algorithm, MAAPE, based on large protein language model (PLM) to glean insights into the evolution process of KSs and CLFs. Our findings indicated the evolutionary history of KS and CLF domains from bacterial T2PKSs and identified a shared ancestral cluster (Cluster A), supporting a common origin. Despite structural homology, KSs and CLFs followed distinct evolutionary paths, shaped by coevolution and early horizontal gene transfer. Conclusions: Understanding the evolutionary lineage of these enzymes will illuminate the natural optimization processes of their functions and present opportunities for the rational design of novel polyketides with enhanced efficacy.
- What Do Generative Models Learn About Adaptive Immune Receptor Repertoires? A Benchmark Study
Generative models are increasingly used to model adaptive immune receptor repertoire (AIRR) sequence distributions, promising to decode the sequence diversity shaping immune responses and accelerate the design of therapeutic antibodies and T-cell receptors. Yet it remains unclear whether these models produce biologically meaningful outputs or merely capture surface-level sequence statistics while missing features driven by receptor generation and selection. Rigorous evaluation is needed, but the field lacks established standards, as existing machine learning metrics do not all translate directly to the AIRR domain, given the complex structure of the data and the lack of biological ground truth. Consequently, researchers face difficulties in evaluating the models and selecting appropriate ones, which can critically affect downstream clinical applications. Here, we apply a suite of evaluation metrics tailored to AIRR sequence data and present a systematic comparison of popular generative model families proposed for the AIRR field, including variational autoencoders, long short-term memory networks, antibody language models, selection models, and simple statistical baselines. We focus specifically on the task of learning individual-specific immune receptor repertoires, a clinically relevant challenge with direct implications for personalized immunotherapy, disease monitoring, and vaccine response studies. By analyzing the sequences generated by each model, we identify memorization risks, innovation capabilities, and sensitivity to hyperparameter tuning. Taken together, these results advance the understanding of how current generative models reproduce the biology of individual immune repertoires and lay the groundwork for more principled model development and evaluation.
- Spatio-temporal 3D Mapping of Mouse Cerebellar Vascularization during Embryonic Development
Despite major advances in the study of cerebellar neurogenesis, cerebellar angiogenesis during embryogenesis remains poorly described. Recent advances in tissue clearing, light-sheet microscopy, and artificial intelligence have increasingly enabled detailed 3D modelling of cerebellar vasculature at early developmental stages. Here, vascular networks in mouse embryos from E11 to birth (P0) were labelled with podocalyxin, aSMA, and PECAM-1 antibodies together with the nuclear marker TO-PRO-3 iodide, cleared, imaged by light-sheet microscopy, and finally modelled and quantitatively analyzed using Imaris and VesselVio. Our mapping reveals that the three main paired cerebellar arteries, the superior (SCA), anterior inferior (AICA), and posterior inferior (PICA) cerebellar arteries, emerge sequentially between E11 and E13 and display significant topographical variability comparable to that observed in humans. Morphometric analysis demonstrates distinct developmental dynamics, with SCA growth proportional to cerebellar expansion, whereas the AICA and PICA exhibit accelerated extension during later embryonic stages. Interestingly, the PICA does not reach the cerebellum before birth, highlighting the question of its contribution to embryonic cerebellar vascularization. The intrinsic vascular network evolves from a rudimentary bilayer at E11 into a highly branched architecture organized around radial penetrating vessels, giving rise to collaterals that progressively colonized the cerebellar parenchyma during foliation and lobulation. These vascular changes temporally coincided with the successive stages of cerebellar neurogenesis, supporting an interplay between vascular and neuronal development. Together, our findings provide the first spatio-temporal three-dimensional atlas of cerebellar vascularization during mouse embryogenesis, establishing a reference framework for investigating cerebellar angiogenesis in developmental and pathological conditions.
- SHINE: Decoding transcriptional-metabolic microenvironments through higher-order spatial integration
Spatial omics technologies are expanding to co-profile transcriptomics and metabolomics on the same tissue slide, providing complementary views of gene expression and biochemical activity to reveal molecular programs within native tissue microenvironments. However, integrating the transcriptome and metabolome remains technically challenging due to spatial misalignment, resolution disparity, and higher-order cross-modality interactions. Here, we present SHINE, a hypergraph-based computational framework for the joint analysis of spatial gene expression and metabolic networks derived from the co-profiling slide, focusing on representation learning and cross-modality interaction. Across multiple datasets, SHINE consistently outperformed existing methods for domain segmentation and biomarker co-localization and provided interpretable insights into metabolic-transcriptional microenvironments. Specifically, in Parkinson's disease mouse models, SHINE accurately delineates dopaminergic neuron-depleted regions and reconstructs coherent dopamine-associated axes. In human lung and breast cancers, SHINE resolves tumor-associated spatial regions and identifies spatially organized gene-metabolite programs associated with the tumor microenvironment. SHINE enables scalable spatial multi-omics integration across diverse biological systems.
- Evaluating the use of non-linear models in data-driven rescoring of peptide-spectrum matches
In mass spectrometry (MS)-based proteomics, computational tools match acquired tandem MS spectra to peptides from a sequence database. Machine learning increasingly supports this task through peptide-spectrum match (PSM) rescoring, in which a classifier, typically a linear semi-supervised model, refines the initial matching score. However, Mokapot allows the user to choose among different machine learning algorithms of increasing complexity, from the default linear support vector machine (LSVM) to random forest and XGBoost. Here, we use an entrapment approach to assess the effect of this increasing complexity on PSM identification and the accuracy of the estimated false discovery rate (FDR). We show that, while more complex models increase the number of identified PSMs at a fixed FDR threshold, this gain reflects a bias towards random matches from the target proteome database rather than genuine identifications. Indeed, for the most complex model, the entrapment FDR reaches 6.3% instead of the estimated 1% decoy FDR. This bias thus yields overly optimistic FDR estimates, indicating that model complexity in PSM rescoring must be carefully balanced against this overfitting risk.
- Biological Continued Pretraining Reshapes the Capability Profile of a Foundation Model Without Catastrophic Forgetting
It is widely assumed that continued pretraining (CPT) on a narrow, out-of-distribution corpus such as raw biological sequence must trade away a general-purpose model's broad competence --- the "alignment tax" or catastrophic-forgetting intuition. We test this directly, without any new training, by re-analyzing three checkpoints from a single lineage of a 26B-parameter Mixture-of-Experts model (Gemma-4-26B-A4B): the instruction-tuned base, the same model after biological CPT (8.7B tokens of DNA, protein, and biomedical text), and after subsequent supervised fine-tuning (SFT). Across three independent capability axes --- general knowledge/reasoning (MMLU, ARC, HellaSwag), code generation (MBPP), and biomedical knowledge (BixBench) --- we find that biological CPT does not degrade the model; it lifts it: MMLU +13 points, MBPP pass@1 nearly doubles (0.33 to 0.63), and BixBench discrimination rises sharply (MCC 0.23 to 0.92). The single measured regression is truthfulness (TruthfulQA -8.8 points), a small and interpretable domain drift. A clean vocabulary-expansion ablation (<0.4 pt on every general metric) confirms the gains are attributable to CPT, not tokenizer changes. Crucially, subsequent SFT narrows the model back: all three axes fall to near-base levels, revealing a consistent division of labor --- CPT re-organizes and lifts the shared capability substrate; SFT cashes it out onto target tasks. We argue this reframes biological sequence not as a competitor for a foundation model's capacity but as a form of structured scientific data that reshapes its capability profile, and that CPT and SFT should be budgeted as complementary rather than substitutable stages. All checkpoints, evaluation code, and per-example outputs are public.
- Reconstructing sequence-grammar trajectories enables interpretable and tunable cis- regulatory element design
Designing synthetic cis-regulatory elements (CREs) with cell-type-specific activity is critical for precision gene and cell therapies, but remains challenging because optimization is often treated as a black box, obscuring how regulatory grammar emerges and how failed trajectories can be corrected. Here, we present GO-CRE (Guided Optimization of Cis-Regulatory Elements), a deep learning framework that integrates efficient sequence generation, predictor-guided reinforcement learning, and trajectory-level interpretation. GO-CRE reconstructs iterative sequence changes in a shared sequence-grammar landscape and identifies coordinated update programs corresponding to search, commitment, and optimization. In HepG2, GO-CRE revealed a low-complexity polyG trap and redirected optimization toward functional motif programs by introducing a polyG penalty. Final designs in HepG2 and K562 progressively acquired cell-type-associated grammar while retaining sequence diversity. Lentiviral massively parallel reporter assays validated cell-type-specific activity in K562 and HepG2 and showed higher average activity of HepG2 designs than endogenous CREs. Together, these findings establish sequence-grammar trajectory reconstruction as a basis for interpretable and tunable synthetic CRE design.
- Multi-Modal Toxicological Evaluation of NiO nanoparticles and ionic nickel in a Human Lung-Cardiac Co-Culture System
Nickel is a widespread environmental and occupational contaminant associated with respiratory and cardiovascular toxicity, yet the mechanisms linking pulmonary exposure to adverse cardiac effects remain poorly understood. This study aimed to establish and evaluate a human in vitro lung heart coculture model for investigating cardiovascular responses following pulmonary exposure. Human alveolar epithelial A549 cells were exposed at the air liquid interface to different concentrations of NiO nanoparticles or NiCl2 for 4 and 24 hours. Following cloud exposure, A549 cells were co-cultured with human induced pluripotent stem cell derived cardiomyocytes (hiPSC CMs). Cytotoxicity, metabolic activity, cytokine release, DNA damage, epigenetic alterations, and cardiac electrophysiological function were assessed. Nickel translocation across the epithelial barrier was quantified to facilitate interpretation of downstream cardiomyocyte effects. Exposure to both nickel forms induced cytotoxicity and resulted in measurable nickel translocation into the basolateral compartment. NiCl2 exhibited a time-dependent increase in basolateral nickel concentrations, whereas NiO translocation remained relatively stable over time. Cytokine profiling revealed selective induction of IL8 and IL18, with no significant changes in IL1b, IL6, IL10, or TNFa. Genotoxicity analyses demonstrated cell type specific responses, characterized by delayed DNA strand breaks in A549 cells and early but transient DNA damage in hiPSC CMs. Oxidative DNA damage was particularly pronounced in hiPSC CMs following NiCl2 exposure. Global DNA methylation was reduced in hiPSC CMs without corresponding changes in DNA methyltransferase activity. Electrophysiological assessment showed transient increases in conduction velocity, while beating frequency and field potential duration remained largely unaffected. Overall, the lung heart co-culture model successfully captured both pulmonary and cardiac responses to nickel exposure and provided evidence for direct and indirect mechanisms of cardiotoxicity. Nickel translocation across the epithelial barrier, together with inflammatory and oxidative stress related signalling, may contribute to downstream cardiac effects. These findings highlight the utility of this human relevant platform for investigating systemic cardiovascular consequences of inhaled toxicants.
- Integrating suboptimal secondary structures, AI-assisted genomic synteny, and evolutionary conservation to identify bacterial ncRNA homologs beyond sequence similarity
A bioinformatic approach for genome-wide identification of homologs of bacterial non-coding RNAs (ncRNAs) integrating structural similarity, genomic synteny, and evolutionary conservation is presented. The structural similarity is detected using an algorithm for genome-wide identification of loci in genomic intergenic regions (IGRs) containing sequences capable of adopting secondary structures similar to that of the query ncRNA. The algorithm scans IGR sequences using a sliding window with a predefined step. For each window, suboptimal secondary structures are predicted and compared with the template structure to compute structural similarity scores. These scores are evaluated statistically on a genome-wide scale to infer homology of the RNAs represented by the predicted structures. Loci encoding statistically significant structures are further filtered using genomic synteny of the query ncRNAs inferred from genomic annotations. ChatGPT was used to assist in identifying literature-supported biological relationships between genes with distinct functional annotations. Syntenic loci with the structures are then examined for homologs in related species, as evolutionary conservation among related species is a common feature of ncRNAs Using this approach, we predicted novel homologs of the spot42 RNA-encoding spf gene in Glaciecola and Pseudoalteromonas genomes, and ms1 RNA genes in Frankia and Bifidobacterium genomes, where previous homology searches had failed.
- Multi-Agent Dynamic Refinement Outperforms Static RAG in Clinical Reasoning for Complex Nephrology Cases
Background: Large language models (LLMs) struggle with dynamic, longitudinal clinical reasoning. We developed a Multi-Stage Iterative Clinical Reasoning Agent framework to address this gap and systematically decouple the clinical efficacy of static retrieval-augmented generation (RAG) from dynamic self-refinement. Methods: Ten complex longitudinal nephrology cases, rigorously selected via a modified Delphi consensus technique, were blindly evaluated by four board-certified nephrologists and a multi-model AI panel. We compared three architectures across nine cognitive steps: (Model A) a baseline frontier LLM, (Model B) an LLM augmented with static guideline-based RAG, and (Model C) our proposed multi-agent framework featuring RAG integrated with iterative self-critique and refinement. Results: In human evaluations (20-point scale), Model C (mean 17.2, SD 1.2) significantly outperformed both Model A (16.1, 1.3) and Model B (16.2, 1.2) (P < 0.001). Implementing static RAG (Model B) yielded no significant improvement over the baseline. Automated AI evaluations (15-point scale) corroborated these findings: Model C (14.7, 0.6) outscored Model A (14.2, 0.9, P < 0.001) and Model B (14.3, 0.9, P = 0.01). While monolithic models exhibited severe score degradations in planning-heavy tasks such as dynamic differential diagnoses, the multi-agent framework effectively intercepted error cascades, achieving significantly higher diagnostic accuracy (mean 17.6, P = 0.019) and therapeutic management scores (17.3, P = 0.002). Conclusions: Static knowledge retrieval alone fails to enhance frontier LLM performance in longitudinal medical reasoning. Distributing clinical workflows into a multi-agent dynamic refinement pipeline significantly improves reasoning completeness, intercepts error cascades, and safely resolves planning bottlenecks in complex patient care.
- ReCo: a self-configuring and self-extending agentic framework for biomedical research
This study presents ReCo (Research Cosmos), a self-configuring and self-extending agentic research framework for the biomedical domain. ReCo is orchestrated by a large language model that interacts with native computing tools, bundled Model Context Protocol (MCP) servers, structured skills, persistent project memory, and a desktop interface. Its bundled MCP servers provide biomedical analysis capabilities while serving as implementation paradigms for integrating new computational and AI frameworks. Structured skills encode procedures for environment configuration and framework ingestion, enabling ReCo to inspect repositories, manuscripts, or local codebases; identify dependencies and execution patterns; create isolated runtime environments; design and implement MCP interfaces. Self-extension was evaluated using five heterogeneous systems: the Merlin computed tomography foundation model, MAISI-v2 medical image synthesis framework, asari liquid chromatography-mass spectrometry workflow, DosimeTron agentic radiation-dosimetry platform, and Orthanc DICOM server. ReCo successfully operationalized all five systems and completed predefined functional evaluations. Re-hosted DosimeTron outputs demonstrated near-perfect agreement with the reference pipeline across 651 organ observations (Pearson correlation and Lin concordance correlation coefficient, 0.99999; mean absolute percentage difference, 0.37%). Notably, ReCo configured Orthanc as a PACS-like coordination layer, integrated it with DosimeTron, Merlin, and TotalSegmentator, and orchestrated data retrieval, analysis, and return of valid DICOM RTSTRUCT, RTDOSE, and Structured Report. ReCo provides a unified environment for configuring, documenting, and operationalizing heterogeneous biomedical frameworks, reducing technical barriers to the adoption and integration of emerging computational and AI methods. The official open-source ReCo GitHub repository is available at: https://github.com/eltzanis/ReCo
- Multimodal gene prioritization reveals nonlinear regulatory architecture in childhood-onset asthma
Asthma is a heritable complex disease that disproportionately burdens minority and admixed populations in the US. However, the causal genes and regulatory mechanisms governing inherited risk remain largely unresolved. We performed a European-ancestry meta-analysis of 141,894 cases and 1,361,846 controls drawn from the Trans-national Asthma Genetic Consortium (TAGC) and Global Biobank Meta-analysis Initiative (GBMI), yielding an estimated h2SNP of 0.056 (SE = 0.0038) and 275 independently associated loci. To enhance mechanistic inference beyond variant-level associations, we developed a multimodal framework to predict asthma risk integrating GWAS summary statistics, bulk tissue expression quantitative trait loci (eQTL) data from the Genotype-Tissue Expression (GTEx) project, and single-cell gene eQTL data from the OneK1K Project. We performed transcriptome-wide association studies (TWAS) and subsequently applied probabilistic fine-mapping with FOCUS to prioritize putative causal genes expressed in bulk tissues and higher resolution immune cell populations. Fine-mapping asthma-associated genes implicated barrier-immune and metabolic-endocrine tissues alongside adaptive T-cell subsets as the primary mediators of asthma genetic risk, resolving canonical CD4+ Th2 effector genes including IL1RL1, TSLP, STAT6, and GATA3. Using these prioritized genes, we constructed a polygenic transcriptome risk score (PTRS) using random forest to integrate gene-level effects across critical tissues and cell types. Evaluated in two ancestrally distinct pediatric asthma cohorts, the Childhood Asthma Management Program (CAMP) and the Genetics of Asthma in Costa Rica Study (GACRS), our PTRS demonstrated improved transferability over the standard variant-level and gene-level baseline models. While modest common variant heritability limits the discriminative power of our models, we estimated a theoretical maximum achievable area under the receiver operating characteristic (AUROC) curve of 0.64. Our integrative nonlinear model of PRS-CSx and cross-modal (bulk tissue and single cell) FOCUS PTRS resulted in the best cross-cohort performance (CAMP AUC = 0.632, sd = 0.04, 3.55 case/control odds ratio in top vs. bottom quartiles), representing an increase of +0.118 AUC over PRS-CSx, +0.067 AUC over tissue-specific TWAS pruning and thresholding, and +0.041 AUC over cell-type-specific FOCUS PTRS. Our results demonstrate that modeling nonlinear interactions between variant- and gene-level effects across both bulk tissue and single cell eQTL data improves our ability to determine high-risk individuals and to explain the likely mechanisms driving genetic susceptibility of childhood-onset asthma.
- In Silico Trial Simulation with Artificial Intelligence-Generated Synthetic Control Cohorts Reproduces Results of a Randomized Controlled Trial in Acute Myeloid Leukemia
Rising costs, slow accrual and molecular substratification of cancers necessitate novel clinical trial designs. We demonstrate that artificial intelligence-generated synthetic patients can replace real controls to reproduce results of the SORAML trial. Using external multimodal data from 1,377 acute myeloid leukemia (AML) patients from previous trials and a real-world registry, we fine-tuned a tabular foundation model to generate synthetic patients, reproducing clinical and genetic features and outcome associations. Synthetic patients were then matched to the original SORAML intervention group using Cox risk scores, replacing the original control and reproducing the original trial result with near-identical median event-free survival (EFS) and treatment effect (original hazard ratio [HR] 0.64, 95%-confidence interval [CI] 0.47-0.87, p=0.004; with synthetic control HR 0.66, 95%-CI 0.48-0.90, p=0.009). Our findings demonstrate that AI-generated synthetic patients can serve as statistically rigorous controls supporting novel trial designs.
- Multi-matrix copper exposure is associated with reduced olfactory bulb volume and odor sensitivity in adolescents
Copper (Cu) is an essential metal involved in neurobiological processes including energy metabolism and neurotransmission, yet dysregulated Cu levels may adversely affect brain health and olfactory performance. Although olfactory dysfunction has primarily been studied in older adults and neurodegenerative disease, adolescence is a critical period of brain maturation during which the olfactory system may be particularly vulnerable. This cross-sectional study examined associations between Cu exposure, olfactory bulb (OB) volume, and olfactory performance in 200 adolescents and young adults (64% female; ages 13 - 25) from the Public Health Impact of Metals Exposure cohort. Cu concentrations in blood, urine, hair, and saliva were measured using inductively coupled plasma mass spectrometry. T2-weighted magnetic resonance imaging scans estimated left, right, and total OB volumes using a three-stage deep learning pipeline. Olfactory performance was assessed using the Sniffin Sticks test. Weighted quantile sum regression evaluated associations between a Cu mixture index and OB outcomes, while standard linear regression models assessed individual Cu biomarkers. Models were adjusted for age and sex. A higher Cu index was associated with reduced left (Beta= -0.72, 95% CI [-1.42, -0.02]), right (Beta = -0.79, 95% CI [-1.43, -0.15]), and total OB volume (Beta= -1.55, 95% CI [-2.85, -0.25]), as well as lower odor threshold scores (Beta = -0.23, 95% CI [-0.42, -0.03]). Individual biomarkers were not independently associated with outcomes. These findings suggest that Cu exposure may adversely affect olfactory neurodevelopment during adolescence and highlight the importance of studying environmental exposures relevant to long-term neurological health.
- Boora, an AI-assisted digital platform for overweight and obesity care in Brazilian primary care: a formative mixed-methods evaluation of perceived usability and acceptability
Objective. To evaluate the perceived usability, acceptability, and user experience (rather than the clinical effectiveness) of Boora, an AI-assisted, human-supervised digital platform prototype for longitudinal overweight and obesity care, among users and health professionals in Brazilian primary care. Design. Convergent mixed-methods formative evaluation. Perceived usability was measured with the System Usability Scale (SUS) and summarised descriptively; semi-structured interviews conducted after hands-on use were analysed with codebook thematic analysis (Braun and Clarke); the two strands were integrated through a joint display. Qualitative reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ). Setting. Primary health care network of Ananindeua, Para, within the Brazilian Unified Health System (January to February 2026). Participants. Fifteen adults with overweight or obesity (BMI at least 25 kg/m2, confirmed via electronic health records) who used the patient application on their own smartphones for 24 hours, and eight primary care professionals (nurses, physicians, and a dietitian) who used the professional dashboard for approximately 20 minutes on predefined tasks with synthetic data. Main outcome measures. SUS scores and qualitative themes addressing usability, acceptability, perceived usefulness, barriers, and perceived clinical and workflow fit. Results. Boora showed good perceived usability in both cohorts (users mean 76.5, SD 10.3; professionals mean 77.5, SD 4.6; both above the SUS normative average of 68). Four themes emerged per cohort. Users valued an accessible interface and visible progress but described daily logging burden, fragile anticipated engagement, and digital-literacy and accessibility barriers. Professionals valued a clear interface and the prospect of panel-managed, proactive follow-up, while requiring training, AI governance, protected time, and interoperability with the national record. Integration indicated that the disengagement users anticipated was the risk professionals perceived the dashboard could help identify, whereas the educational AI assistant was the weakest and most ambiguous component for both groups. Conclusions. Boora was perceived as usable and acceptable, with perceived value concentrated in human-supervised, longitudinal follow-up rather than autonomous self-tracking or AI advice. These findings concern perceived usability and acceptability, not clinical effectiveness or sustained engagement. Real-world adoption would depend on accessibility refinements, electronic-record integration, and clear AI governance aligned with the principles of Brazil's proposed risk-based AI framework and the LGPD.
- PromptForge
Build type-safe AI prompts with TypeScript
- CubeAR
Solve your Rubik's Cube with the camera — free, no ads
- Roblox launches Build, a mobile tab that turns text prompts into playable games
Roblox announced Build on Wednesday, a new creation tab inside the Roblox mobile app that lets anyone turn a text prompt into a basic playable game without touching Roblox Studio or writing a line of code. A creator can describe something like a cozy forest adventure game with environmental obstacles, and Build will generate a […] This story continues at The Next Web
- Roblox announces Build, AI tools that let anyone create games
Roblox is bringing Build, a set of AI tools, to its app in order to help users create games on the platform.
- Roblox will offer AI-generated game creation on mobile later this year
Build starts limited alpha testing later this month.
- Roblox is Adding AI Game Creation to iPhone and iPad
Online game platform Roblox today said it is adding a new Build tool that will let iPhone and iPad users create games using AI. With the mobile-first Build option, Roblox users can write a text-based prompt and have AI turn it into a basic game. A single prompt will create a starting point that can be expanded with playtesting and further commands. The entire game-making process, from creation to uploading on the Roblox platform, can be done on a mobile device. Right now, creating a game in Roblox requires a Mac or PC app, but Build will extend game creation to mobile users too. Roblox is also adding new AI creation tools to Roblox Studio, its desktop game creation software. Content created using the Build tool can be iterated on with Studio. Roblox Build is set to roll out to the Roblox mobile app on July 28, beginning with a public alpha test for users in New Zealand. Build will roll out to additional regions in the coming months as Roblox improves the experience. Roblox has 132 million daily active users, and user-created games like Brookhaven RP, Adopt Me, and Dress to Impress have been wildly popular. Tag: Roblox This article, " Roblox is Adding AI Game Creation to iPhone and iPad " first appeared on MacRumors.com Discuss this article in our forums
- Roblox will let people use AI to make games on their phone
Roblox is about to let people make games with AI right inside its mobile app, which could make a platform that's already filled with content of questionable quality feel even more overloaded. The company has embraced AI with open arms, including a preview of an ambitious take on AI world models similar to Google's Project […]
- Google AI Mode now integrates with Canva, YouTube Music and Instacart
You can now make playlists, design flyers and compile shopping lists using AI in Search.
- You can now link your favorite apps to AI Mode in Google Search to get things done
Google Search's AI Mode can now connect to apps like Instacart, Canva, and YouTube Music to complete tasks for you.
- Google Search’s AI Mode can now handle tasks beyond the search bar
Why app-hop when Google Search can do the busywork for you?
- Bookcraft
AI BOOK CREATOR
- Google AI Mode adds more Connected Apps, including YouTube Music
Google is updating AI Mode with support for more first and third-party apps, just like the Gemini app.
- Connect more of your apps to Search
Connected apps rendering
- Google Rebrands NotebookLM as Gemini Notebook; Brings Cloud Computing and Search Integration
Google is rebranding NotebookLM to Gemini Notebook, the company announced on Thursday. The AI-powered research and note-taking assistant, which was originally announced as Project Tailwind at Google I/O 2023, has evolved into a standalone research platform in recent years. Google claims it is now used by more than 30 million people and over 600,000 organisations world...
- Google rebrands NotebookLM as Gemini Notebook and opens its search app to third-party integration
Google is renaming NotebookLM to Gemini Notebook and integrating the tool more deeply into its ecosystem. A new feature gives each notebook its own cloud computer that can write and run code, initially for AI Ultra and Workspace customers. Separately, Google Search is getting app connections. The article Google rebrands NotebookLM as Gemini Notebook and opens its search app to third-party integration appeared first on The Decoder .
- NotebookLM is now Gemini Notebook
NotebookLM is now Gemini Notebook
- Google rebrands NotebookLM as Gemini Notebook, focusing on ecosystem and accessibility
Google LLC today announced that its popular NotebookLM, the company’s artificial intelligence-powered research assistant, is being rebranded as Gemini Notebook and is getting upgrades aimed at providing secure cloud computing for every notebook. Launched globally in 2023, NotebookLM, began as an AI note-taking and source understanding application that allowed people to upload large amounts of […] The post Google rebrands NotebookLM as Gemini Notebook, focusing on ecosystem and accessibility appeared first on SiliconANGLE .
- Google Renames NotebookLM to Gemini Notebook
NotebookLM gets a new name and expanded access to native code writing features.
- NotebookLM is now Gemini Notebook, with 3.5 + Antigravity upgrade coming to AI Pro
Google announced today that NotebookLM is now called “Gemini Notebook” and previewed upcoming features that users can soon expect.
- Google is renaming NotebookLM to Gemini Notebook
Google is giving its AI note-taking app a new name. The company announced on Thursday that NotebookLM is becoming Gemini Notebook, but will remain a standalone app even as it integrates more deeply across Gemini and Google Search. Google first revealed Gemini Notebook - then called Project Tailwind - in May 2023 before widely releasing […]
- Google’s handy NotebookLM tool is dropping the worst part of its name
NotebookLM is now Gemini Notebook.
- It's official: EU will force Google to share search data and open up AI on Android
Google says these changes could endanger user privacy and security.
- Google required to open up to AI, search engine rivals under EU-mandated changes
Google required to open up to AI, search engine rivals under EU-mandated changes Reuters
- EU forces Google to share search data, open Android to rival AI companies
In the latest attempt to rein in tech behemoths' deep control of the digital economy, the EU said it will support innovation and diversity in the field by enabling fair access to AI features on Android devices and search engines.
- EU forces Google to share search data and open Android to rival AI companies
EU forces Google to share search data and open Android to rival AI companies Toronto Star
- EU forces Google to share search data and open Android to rival AI companies
EU forces Google to share search data and open Android to rival AI companies AP News
- EU forces Google to share search data and open Android to rival AI companies
The European Union has issued new rules for Google, requiring it to share search data and open its Android system to rival AI companies
- EU Tells Google To Share Search Data, Open Android To AI Rivals
EU Tells Google To Share Search Data, Open Android To AI Rivals Barron's
- Rival AI assistants could soon gain full access to Android features
The EU has mandated Google to open up Android to competing AI assistants.