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 ↗5935 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
Effect of Ironing Parameters on Surface Quality and Energy Demand of FDM ‐Fabricated Proximal Humerus Plates: A Box–Behnken and Machine Learning Study ↗
ABSTRACT This study investigates the effects of ironing parameters on surface quality and energy demand in FDM fabrication of proximal‐humerus‐inspired PLA plate‐like demonstrator, aiming to quantify how ironing settings influence surface roughness (Ra) and electrical energy consumption (EC), thereby revealing the quality–efficiency trade‐off in the finishing stage. Four ironing related process variables including ironing flow (IF), ironing line spacing (ILS), ironing speed (IS), and ironing inset (II) were investigated at three levels using a BBD. Experimental results showed that Ra varied be…
- OpenAlex · Published · First archived
A Survey of Code-to-Code Translation: From Statistical Rule-Based Systems to LLM Agents ↗
Code-to-code translation aims to automatically convert a program written in one programming language into a semantically equivalent, executable program in another language, and is a key enabling technology for legacy-system modernization, cross-platform migration and software supply-chain security. The field has evolved from handwritten rules and statistical machine translation, through pre-trained model fine-tuning and unsupervised back-translation, to LLM prompting, execution-feedback repair and multi-agent collaboration. This survey reviews work from 2013 to 2026. Using a literature search…
- OpenAlex · Published · First archived
Evaluating Retrieval-Augmented LLM Tutoring with Synthetic Learners: A Factorial Simulation of Newtonian Misconception Revision ↗
Retrieval-augmented generation (RAG) is increasingly used to ground large language model (LLM) tutors, but comparing tutoring architectures is expensive, because every design decision ordinarily requires a fresh study with students. We present a reproducible simulation harness that evaluates tutoring architectures against LLM-based synthetic learners, and we use it to run a full factorial comparison. Five learner profiles, parameterized from Conceptual Change Theory and the Force Concept Inventory literature, were crossed with four tutoring conditions (knowledge-graph-augmented RAG, BM25 RAG,…
- OpenAlex · Published · First archived
Machine Learning-Based Evil Twin Attack Detection and Attacker Position Classification Using an IoT Sensor Network in Non-Urban Terrain ↗
The basis for this work is the growing prevalence of the Evil Twin attack on IEEE 802.11 networks, as it is the initial link in downgrade attacks that force the security protocol from WPA3 to WPA2 in transition mode, enabling handshake interception and an offline dictionary attack on the network password. For the study, a Wi-Fi access point and a device simulating the Evil Twin attack were deployed, and a multi-sensor Internet of Things (IoT) infrastructure was developed to record the received signal strength indicator (RSSI) from the access point. The resulting RSSI dataset was used to train…
- OpenAlex · Published · First archived
Machine learning prediction of strain dependent soil damping improves seismic analysis of bridge foundations ↗
Conventional empirical soil damping models often neglect strain-dependent, site-specific variability in heterogeneous alluvial deposits (sands, silts, gravels), leading to uncertainty in seismic bridge foundation design. This study develops a Random Forest (RF) model to predict strain-dependent damping from routine geotechnical parameters (SPT-N₆₀, D₅₀, uniformity coefficient, curvature coefficient, fines content, and plasticity index) and integrates it into nonlinear soil-pile-structure interaction (SPSI) simulations. A physics-informed dataset of 2,340 soil-layer records from ten bridge foun…
- OpenAlex · Published · First archived
Using Artificial Intelligence to Help Human Commanders Fight Future Indo-Pacific Wars ↗
Military forces are beginning to use artificial intelligence (AI) in warfighting. One of the first AI uses is in decision support systems (DSSs), computerised tools that assist commanders in understanding the battlefield, planning future courses of action and directing their forces. The use of such AI-enabled DSSs by humans creates a shared cognition. The use of DSSs in Gaza and Ukraine has revealed both their strengths and shortcomings, and suggested ways that the nature and character of war may change. These various aspects reflect that AI brings a new collective intelligence to the waging o…
- OpenAlex · Published · First archived
Forgetting, Remembering, and Memorializing: Cultivating Mnemonic Phronesis for AI‐Mediated Learning in Business Education ↗
ABSTRACT This paper develops the Mnemonic Phronesis Framework, a pedagogical model of AI literacy that aims to help business students recognize and counteract potential distortions generative AI outputs introduce into collective memory. Drawing on Halbwachs' theory of collective memory and Shneiderman's Human‐Centered AI (HCAI) framework, we investigate how mechanisms of algorithmic forgetting, remembering, and memorializing operate when students rely on large language models to understand historical environmental crises and organizational sustainability efforts. We argue that generative AI sy…
- OpenAlex · Published · First archived
Multimodal Gene Expression Deep Learning for Predicting Sentinel Lymph Node Macro-metastasis in Early Breast Cancer: Development and Validation in the SCAN-B Cohort ↗
Abstract Background: This study evaluates DL using GEX and preoperatively available clinical data (PreopClinic) to predict SLNM, and explores their potential for guiding axillary surgery and prognostic assessment. Methods: We retrospectively included 6,836 clinically node-negative T1-T2 patients with invasive breast cancer who underwent primary surgery from the SCAN-B. Three DL models—a multilayer perceptron, a pathway-informed sparse neural network, and a transformer—were developed using the development set (n=4,625) and evaluated against XGBoost in the independent test set (n=2,211). Results…
- OpenAlex · Published · First archived
Evaluating generative large language models as zero-shot semantic odor descriptor generators with applications to odor sensor prediction ↗
Abstract Predicting odor quality from molecular structure remains a fundamental challenge in sensory science. In this study, we evaluated generative large language models (LLMs) as tools for olfactory prediction across multiple tasks. By assigning LLMs distinct cultural personas through role-play prompting, we demonstrate that odorant concentration modulates LLM-generated odor quality outputs in a zero-shot setting. This represents a form of contextual sensitivity absent in structure-based approaches. A systematic evaluation of prompt design revealed that the chemical name is the primary carri…
- OpenAlex · Published · First archived
MSAFP-Net: An Adaptive Fusion-Driven Multi-Scale Neural Network for Peak Production Forecasting in Gas Wells ↗
This study seeks to overcome the limitations of traditional gas well production peak forecasting approaches, particularly in the context of handling multi-dimensional features, complex dynamic behaviors, and non-linear relationships. Accordingly, a deep learning-based multi-scale adaptive fusion perception network is proposed in this work. Specifically, a time-series processing framework based on multi-scale decomposition is first constructed, where wavelet transform is employed to perform multi-scale decomposition of the gas well production data, thereby effectively extracting both long-term…
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.