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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗6057 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- Hacker News · Posted on Hacker News · First archived
AI tutoring with Khanmigo in a two-year school experiment ↗
Hacker News discussion.
- OpenAlex · Published · First archived
GP-DHT: A dual-head transformer with supervised contrastive learning for shared-gene pretraining and single-cell GRN prediction ↗
Gene Regulatory Networks (GRNs) are crucial for understanding cell fate decisions and disease mechanisms. However, inferring GRNs from single-cell RNA sequencing remains challenging due to noise, data sparsity, and species-specific distribution variations. We propose GP-DHT (Gene Pair–Dual Head Transformer), a single-cell GRN inference framework that pre-trains gene representations on a unified shared gene space using the BEELINE datasets of humans and mice. It models gene-cell expression relationships through a multi-relational heterograph and predicts transcription factor (TF)-target regulat…
- OpenAlex · Published · First archived
CAMEO: Compressive Energy-Aware Multi-Task Deep Inference in Resource-Constrained Energy Harvesting Systems ↗
Multi-task Convolutional Neural Network (CNN) inference on Energy Harvesting (EH) devices has received limited attention, particularly in scenarios where energy availability fluctuates significantly. This poses substantial implementation challenges, especially in deploying and running inference under varying energy conditions while performing multiple correlated tasks. In this work, we present an end-to-end framework that enables multi-task inference to inherently conserve computation under dynamic energy conditions. To facilitate this adaptation in multi-task settings, we propose inter-task a…
- OpenAlex · Published · First archived
Real-time AI-guided planning of fetal femur bone MRI and automatic quantification of growth ↗
Abstract While an important indicator of antenatal growth and routinely assessed by ultrasound, MRI-based femur length measurement remains challenging because of bone-slice misalignment, fetal motion, and manual assessment. We developed and evaluated a fully automated real-time method for acquisition planning and fetal femur length measurement. A low-latency 3D U-Net with transformer-based multi-representation refinement was trained with fractional optimal-transport supervision on 59 fetal MRI scans acquired at 0.55T (18-40 weeks) to localize the proximal and distal femur endpoints. These coor…
- OpenAlex · Published · First archived
Understanding practitioners’ perceived effectiveness of lean tools across non-value adding activities: evidence from ordinal and machine learning models ↗
The precast industry provides standardisation, quality and speed to the infrastructure sector, but there are still many non-value adding (NVA) activities that are causing the industry to struggle to deliver efficiencies, such as waiting time, excess inventory, unplanned maintenance, and workflow disruptions. Even though lean construction is widely used, it’s not well understood how effective it is for each NVA category in precast construction. This research aims to identify practitioners’ perceptions of the effectiveness of three lean tools (Just-in-Time (JIT), Continuous Improvement (CI), and…
- OpenAlex · Published · First archived
Artificial intelligence–assisted transrectal ultrasound for rectal cancer invasion depth classification ↗
Abstract Background Accurate assessment of tumor invasion depth is essential for treatment selection in rectal cancer. Transrectal ultrasound (TRUS) provides high-resolution visualization of rectal wall layers but is highly operator-dependent, limiting reproducibility outside expert centers. Artificial intelligence–based approaches may help standardize TRUS interpretation, particularly for depth-of-invasion assessment. Methods We developed and evaluated a two-stage deep learning model for automated analysis of standard TRUS images. A total of 677 two-dimensional images from 103 patients were i…
- OpenAlex · Published · First archived
Rapid identification of porcine bacterial pathogens using an artificial intelligence Raman microbial analyzer for biology ↗
ABSTRACT Rapid and accurate identification of bacterial pathogens is critical for effective disease control in pig farms. Conventional bacterial culture-based and molecular diagnostic methods are time-consuming and often impractical for clinical decision-making. Raman spectroscopy, combined with artificial intelligence, offers a label-free alternative and provides isolate-level identification within approximately 2 min. The aim of this study was to evaluate the performance of the commercial Artificial Intelligent Raman Microbial Analyzer for Biology (AIRMAB) for the rapid clinical identificati…
- OpenAlex · Published · First archived
Machine-learning-based cross-prediction of ultimate and proximate properties for cleaner biomass fuel assessment ↗
Reliable assessment of biomass fuel quality is essential for cleaner solid-fuel utilisation and thermochemical upgrading, but repeated ultimate analysis, proximate analysis, and higher heating value (HHV) measurement are time-consuming. This study developed a closure-aware machine-learning framework for bidirectional cross-prediction between ultimate and proximate properties of thermally treated biomass and for HHV estimation. A literature-derived dataset containing elemental composition, proximate composition, HHV, temperature, and residence time was curated, normalised for compositional clos…
- OpenAlex · Published · First archived
Designing an AI-Responsive Early Childhood Curriculum: A Conceptual Framework for Supporting Developmental Learning Trajectories in Preschool Education ↗
Background The rapid integration of artificial intelligence (AI) and deep learning technologies into education creates new opportunities for personalized learning in early childhood education (ECE). Yet limited research has examined how educators interpret and enact AI-responsive curriculum decisions in preschool settings, particularly in culturally diverse contexts. Purpose This study explores educators’ experiences in designing and implementing AI-responsive curricula and develops a conceptual framework explaining how AI can inform curriculum decision-making for young children without displa…
- OpenAlex · Published · First archived
Artificial neural network-based prediction of steel-concrete bond shear strength under elevated temperature exposure ↗
This study proposes an artificial neural network (ANN)-based predictive model to estimate the percentage normalised steel-concrete bond stress under elevated temperature conditions. The model was developed using an extensive experimental database comprising 393 data points collected from the published literature, incorporating key governing parameters such as material properties, geometric characteristics, and temperature. The proposed ANN formulation demonstrated high predictive capability, achieving a coefficients of determination of 0.97 for the design dataset and 0.89 for the testing datas…
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How to assess evidence about Artificial Intelligence
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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