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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 ↗5965 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
Military AI and autonomous systems: views in The Strategist ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Can AI run an family/organization if so for who? ↗
Hacker News discussion.
- GitHub · Repository created · First archived
autohandai/getzit ↗
Zit: a git extension that lets developers and coding agents change one repository at the same time without stepping on each other's toes, and it's kind to your SSD.
- Hacker News · Posted on Hacker News · First archived
Show HN: The Poteto Frontier for AI Agents ↗
I made this lil meme app based on @poteto (lauren tan)'s amazing talk asking: how many agents do you generally have working on your behalf? her original talk: https://x.com/poteto/status/2102050467505430555 source: https://github.com/transitive-bullshit/poteto-frontier
- OpenAlex · Published · First archived
Kecerdasan Buatan Multimodal dan Robotika: Keterbatasan Model Visi-Bahasa (VLM) dalam Persepsi Visual serta Integrasi Sensorik dan Pembelajaran Mendalam untuk Sistem Robot Cerdas ↗
Integrasi kecerdasan buatan multimodal dalam robotika telah membuka peluang baru bagi sistem robot cerdas yang mampu memahami lingkungan dan berinteraksi secara alami dengan manusia. Namun, keterbatasan mendasar pada Model Visi-Bahasa (Vision-Language Models/VLM) dalam persepsi visual menjadi hambatan signifikan dalam mewujudkan sistem robot yang benar-benar otonom dan adaptif. Penelitian ini mengkaji keterbatasan VLM dalam penalaran spasial dan persepsi visual, serta mengeksplorasi strategi integrasi sensorik dan pembelajaran mendalam untuk mengatasi keterbatasan tersebut. Melalui tinjauan li…
- OpenAlex · Published · First archived
AI-Based Real-Time Person Counting and Security Monitoring System Using YOLO Object Detection and Multi-Object Tracking ↗
AbstractManual counting of people at entrances, exhibitions and access-controlled premises is slow, labour-intensive anderror-prone, motivating automated, vision-based alternatives. This paper presents the design, implementation and fieldtesting of an AI-based counter and security monitoring system that couples a deep-learning object detector (YOLO)with a multi-object tracker to count and monitor people crossing a virtual reference line in live video. Frames capturedfrom a CCTV/webcam feed are processed with OpenCV; a pretrained YOLO model localises every person in theframe, and a tracking sta…
- OpenAlex · Published · First archived
An attention-based CNN–Transformer hybrid framework for osteoporosis and osteopenia classification in knee X-ray images ↗
Osteoporosis is a widespread bone disease characterized by loss of bone strength, and thus, individuals, particularly elderly people and postmenopausal women, are prone to fractures. Plain radiographic images still represent a major clinical challenge in the early identification of osteoporosis, especially in the accurate distinction between osteopenia and osteoporosis due to subtle variations in trabecular and cortical bone structures. The study presents a hybrid deep learning framework that integrates the DenseNet169 model and a Vision Transformer (ViT) through an attention-based feature fus…
- OpenAlex · Published · First archived
Faithfulness-audited LLM alerting for multi-scale SCADA-based anomaly detection and subsystem localization in wind turbines ↗
Supervisory control and data acquisition (SCADA) data enable wind-turbine condition monitoring without additional sensors. However, existing stationarity-based detectors often evaluate each signal over a single fixed window, monitor parameters in isolation, and issue only binary alarms, limiting their deployment value. This paper presents a deployment-oriented energy-informatics framework that extends the augmented Dickey–Fuller stationarity paradigm across temporal scales, physical channels, and alert granularity. A causal multi-scale engine evaluates each signal over several sliding windows,…
- OpenAlex · Published · First archived
Hybrid Intelligence in Blockchain: Toward a Unified Model of AI-Enhanced Smart Contracts ↗
Abstract Blockchain technology and artificial intelligence (AI) were introduced and integrated to develop new possibilities for security, automation, and transparency in digital financial ecosystems. This study proposes a hybrid AI Blockchain framework for automated credit evaluation and identity tokenization, addressing issues with opacity, bias, and data manipulation that affect conventional, centralized credit scoring systems. To predict client creditworthiness from past financial behavior, we trained several supervised machine learning models on the UCI Default of Credit Card Clients datas…
- OpenAlex · Published · First archived
Question Selection and Answer Scoring Shape Revision Risk in Recurrent Language Models ↗
An evaluation of early answers must choose which questions to release and how to read their answers. Using one scoring convention for both choices can make two procedures look similar while concealing their sensitivity to either choice. We measured this dependence on stored trajectories from Huginn and Ouro, using cloze scoring on three filtered benchmarks with 200 questions per task and cohort. The historical cohorts share questions; the three cohorts are not independent replications. At 50% release in the headline windows, changing the selection margin with the answer readout fixed changed r…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
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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