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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 ↗5971 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.
- OpenAlex · Published · First archived
Physics-Informed Machine Learning for Fatigue and Fracture Analysis and Prediction: A Scoping Review ↗
Physics-Informed Machine Learning has emerged as a powerful paradigm for fatigue and fracture analysis, combining the data-driven flexibility of neural networks with the consistency of physical laws. This scoping review maps the extent, range, and nature of research on PIML applied to fatigue life prediction, crack growth and propagation, multiaxial fatigue, damage mechanics, prognostics and health management, and structural health monitoring, covering the literature published from 2018 up to August 2026. A comprehensive search strategy integrated the core terminology of the field with broader…
- OpenAlex · Published · First archived
Agentic AI—Physicist Collaboration in Experimental Particle Physics: A Proof-of-Concept Measurement with LEP Open Data ↗
We present an agentic measurement of the thrust distribution in \(e^+e^-\) collisions at \(\sqrt{s}=91.2\) GeV using ALEPH archived data. This work was carried out through an AI agent–physicist collaboration, using interactive natural-language prompting of Anthropic Claude and OpenAI Codex. Under physicist guidance, the agents wrote and executed all analysis code and drafted the majority of the text included in this paper. A fully-corrected thrust spectrum is obtained via Iterative Bayesian Unfolding and Monte Carlo based corrections. The principal contribution of this work is the demonstratio…
- OpenAlex · Published · First archived
GenAI-Net: A generative AI framework for automated biomolecular network design ↗
Biomolecular networks underlie both natural biological processes and engineered cellular technologies, from intracellular regulation and ecological dynamics to biomanufacturing, smart therapeutics, and cell-based diagnostics. However, designing chemical reaction networks (CRNs) that implement a desired dynamical function remains a challenging task. Although candidate networks can be evaluated by simulation, the inverse problem of discovering networks from behavioral specifications remains difficult. It requires navigating vast spaces of topologies and kinetic parameters governed by nonlinear a…
- OpenAlex · Published · First archived
Stacked Hybrid Recurrent Convolutional Neural Network Reconstruction of X-Ray Influence on 21 cm Brightness Temperature ↗
Abstract X-ray photons substantially affect the thermal and ionization states of the intergalactic medium during the epoch of reionization (EoR), thereby significantly influencing the 21 cm line observables, such as its sky-averaged (global) brightness temperature. Nevertheless, the complicated dependency of astrophysical processes on a broad spectrum of parameters, including X-ray efficiency, spectral characteristics, and gas dynamics, makes precisely simulating the effect of X-ray flux challenging. Traditional approaches, including N -body and hydrodynamical simulations, are computationally…
- Europe PMC · Published · First archived
Artificial intelligence in orthopaedics should be built with surgeons, not just for them! ↗
Surgical artificial intelligence (AI) is typically validated on one patient population and then deployed unchanged into another, without surgeon input into its design. AI in orthopaedics should be built with surgeons, not simply for them: Surgeons should hold shared authority over the clinical question, data curation, validation, implementation, monitoring and retirement of any tool they use. Specialty-tuned models outperform general-purpose systems on some tasks, but the margin is modest and likely to narrow as generalist models improve. The more durable distinction is who controls the update
- Hacker News · Posted on Hacker News · First archived
Inspect: An open-source framework for large language model evaluations ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Councilmember, residents push back on AI 'blight scores' given to homes ↗
Hacker News discussion.
- GitHub · Repository created · First archived
roselis-lab/awesome-agent-hardening ↗
Limiting what AI agents can reach, change, and break.
- Hacker News · Posted on Hacker News · First archived
Your Big Mac might cost more if McDonald's AI thinks people nearby can afford it ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
'AI should assist teachers, not replace them': Union warns of AI marking risks ↗
Hacker News discussion.
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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