THE INTERNET IS THINKING
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 ↗6025 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
Energy infrastructure for AI data centers: A critical review of grid integration, techno-economic challenges, and future pathways ↗
Artificial intelligence (AI) workloads are rapidly changing the energy landscape of today's data centers, both in terms of total electricity usage and power density at the facility level. The ongoing deployment of large-scale AI training and inference infrastructure is further driving and increasing the demand for electrical reliability, cooling capacity, power quality, grid interconnection, and low-carbon electricity supply. These interdependent issues require coordinated consideration of energy-infrastructure planning that takes into consideration renewable energy generation and storage, ele…
- OpenAlex · Published · First archived
Interpretable multimodal retrieval augmented diagnosis for breast ultrasound with multinational clinical validation and reader study ↗
Breast ultrasound is central to breast cancer diagnosis but suffers from substantial inter-observer variability in Breast Imaging Reporting and Data System (BI-RADS) assessment, and existing deep learning models remain opaque. Multimodal large language models promise interpretable report-based reasoning, yet scarce paired image-report data yields weak visual-semantic alignment. We propose B-RAD, a BI-RADS-aware retrieval-augmented diagnosis framework that mirrors the radiologist workflow of retrieving exemplars, localizing lesions, and determining the BI-RADS category. Its retrieval module lea…
- OpenAlex · Published · First archived
Liquid biopsy, cfDNA, and machine learning: an integrative approach to cardiovascular diseases ↗
Cardiovascular diseases (CVDs) and their complications remain the leading cause of mortality worldwide. However, obtaining diseased cardiovascular tissue is often invasive, costly, and clinically impractical, highlighting the need for noninvasive approaches that can capture dynamic pathological information. Cell-free DNA (cfDNA)-based liquid biopsy has emerged as a promising strategy for the diagnosis, monitoring, and risk assessment of CVDs. As cfDNA is released from injured, dying, or stressed cells, it carries multiple layers of biological information, including concentration changes, tissu…
- OpenAlex · Published · First archived
Trustworthy and physics-informed artificial intelligence for food processing operations: validation, control relevance, and industrial readiness ↗
ABSTRACT Background Artificial intelligence (AI) applications in food processing are mostly limited to data-driven prediction models, while industry needs AI for trustworthy operational use. Much of the evidence still rests on dataset-level performance, whereas validation quality, physical credibility, uncertainty, interpretability, control relevance, and industrial readiness of AI models are examined less consistently. Scope and approach This critical review uses structured evidence mapping strategy to appraise recent reviews, methodological papers, and direct evidence studies across drying,…
- OpenAlex · Published · First archived
Unsupervised machine learning for automated crystal orientation mapping on noisy 4D-STEM data ↗
Abstract Four-dimensional scanning transmission electron microscopy (4D-STEM) is a high-throughput measurement used to comprehensively acquire diffraction patterns across a region of interest in a specimen. While the measurement is useful for identifying crystallographic microstructures, the data analysis requires significant effort owing to the complexity and high dimensionality of the data. Furthermore, the analysis of low signal-to-noise data from beam-sensitive materials such as polymers is particularly challenging. This study proposes an automatic analysis pipeline that leverages denoisin…
- OpenAlex · Published · First archived
A machine learning based investigation of void distribution effects in ductile failure ↗
The overarching goal of this work is to develop a predictive model of fracture resistance in a context of epistemic uncertainty due to analytically intractable mechanisms in metallic materials. The uncertainty associated with the spatial distribution of defects, such as voids, and its conflicting effects on setting ductility limits has hindered robust predictions for decades. An immediate objective is to explore the capabilities of carefully selected machine learning methods in uncovering fundamental evolution laws for the spatial distribution of defects, such as voids, under the finite deform…
- OpenAlex · Published · First archived
Against speech liberalism for generative AI ↗
Abstract Speech liberalism holds that the linguistic output of generative AI is continuous with human speech and should therefore be treated as meaningful in broadly the same way. Strong versions of this view take the fluent behaviour of chatbots to justify attributing genuine linguistic and cognitive agency, while nevertheless advocating caution when questions of moral or legal responsibility arise. I argue that this gives too much evidential weight to successful conversational interaction, where speaker intentions largely recede into the background. Once AI-generated speech contributes to se…
- OpenAlex · Published · First archived
Controllable AI modeling of northern and southern timbercraft convergence in Qing interior canopies ↗
This study examines interior canopies from the Qianlong–Jiaqing era (1736–1820) of the Qing dynasty to model northern–southern timbercraft convergence through controllable AI generation. We constructed northern official and southern garden semantic lexicons, trained dual LoRA modules on Stable Diffusion v1.5, and generated intermediate canopy samples using controlled alpha-based weighting. Structural, ornamental, and semantic indicators were then extracted and compared with historical Yangzhou canopy samples. As southern weighting increased, generated samples showed lower aspect ratios and hig…
- OpenAlex · Published · First archived
Securing the open-source ecosystem: AI-enhanced and explainable supply chain security for reliable software development ↗
Abstract Modern software supply chains face increasing risks from vulnerable and anomalous dependencies, necessitating automated and interpretable detection methods integrated within Continuous Integration and Continuous Deployment (CI/CD) workflows. Open-source software (OSS) ecosystems are increasingly targeted by sophisticated attacks—including AI model poisoning, dependency confusion, contributor hijacking, and transitive dependency anomalies—that often evade traditional CVE-centric tools such as Snyk and Dependabot. These reactive solutions are primarily designed for known vulnerabilities…
- OpenAlex · Published · First archived
Knowledge boundary probing and demand-guided intervention for LLM-based power system code generation ↗
Large language models (LLMs) can turn grid-analysis requests into executable programs for power-system simulation, but utilities and research laboratories often require on-premise deployment. In this setting, first-pass failures frequently arise at an API-knowledge boundary , through hallucinated functions, misused parameters, and mishandled result tables. We present PowerCodeBench , a parameterised benchmark generator released as a frozen 2000-task suite for pandapower , and a deployment-time workflow that requires no weight updates. Documentation-driven L0–L3 probes produce per-model API pro…
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
Follow a thread
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.