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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 ↗5931 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
Cryptanalysis and Improvement of Medical Image Cryptosystem via Fractional-Order Memristive Hopfield Neural Network ↗
Chaos-based image cryptosystems often produce noise-like ciphertexts, but dynamical complexity does not establish security. This paper presents a structural cryptanalysis of the fractional-order memristive Hopfield neural network (FMHNN)-based medical-image cryptosystem proposed by Sun et al. For fixed parameters and dimensions, the published data path is formalized as a plaintext-independent permutation followed by a fixed exclusive-or (XOR) mask. Chosen-plaintext attack (CPA), known-plaintext attack (KPA), chosen-ciphertext attack (CCA), and fixed-key differential analyses then derive equiva…
- OpenAlex · Published · First archived
Embodied Musical Gesture Analysis from Audio Representations: A Review of Datasets, Audio Descriptors, and Deep Learning Approaches ↗
Machine learning approaches have enabled increasingly powerful analysis of musical audio, allowing computational models to capture complex acoustic patterns related to musical structure, performance, and expression. However, whether learned audio representations preserve information related to the bodily actions underlying musical sound production remains an open research question. This review examines how machine-learned audio representations encode information associated with embodied musical processes by connecting perspectives from embodied music cognition, computational music analysis, an…
- OpenAlex · Published · First archived
Peak-aware short-term household electricity consumption forecasting using deep learning and transformer-based architectures ↗
Abstract Accurate short-term prediction of domestic electricity consumption is a prerequisite for smart grid management, demand response, and peak-load reduction. This work presents a detailed deep learning analysis of the UCI household power consumption dataset, which includes minute-level measurements from French households over four years. The data is resampled to hourly and undergoes an intensive feature engineering pipeline and then used with different neural architectures: a standard long short-term memory (LSTM), bidirectional attention LSTM, hybrid CNN-LSTM, temporal fusion transformer…
- OpenAlex · Published · First archived
Bounded Autonomy and Verifiable Safety for Agentic AI Enabled Automation ↗
Agentic AI-enabled automation cannot be safely deployed in high-stakes environments on probabilistic reasoning alone. A recurring risk is epistemic drift: as reasoning deepens, system behavior may move away from subject-matter-expert constraints for safe operation. This paper presents BRaVeS, a bounded reasoning and safety-governance framework termed the Defensible Next-Gen Reasoning System (DNRS). BRaVeS encodes SME-defined constraints as invariant anchors, proposes MoDA-Style (Mixture of Depths Attention) depth-aware access as a candidate mechanism for keeping these anchors visible during in…
- OpenAlex · Published · First archived
Active machine learning using MCMC Bayesian neural networks ↗
Background Standard neural networks require large labelled datasets for strong generalization, which is often costly to annotate. Active learning (AL) reduces annotation costs by selecting the most informative samples for labelling based on model uncertainty, and Bayesian methods are a natural source of such uncertainty estimates. Approximate Bayesian methods such as Monte Carlo Dropout (MC Dropout) are scalable but can introduce systematic approximation errors in low-data regimes, whereas Markov Chain Monte Carlo (MCMC) provides higher-fidelity posterior estimates but is typically too computa…
- OpenAlex · Published · First archived
Markerless motion capture-based machine learning models for low back pain classification: a proof of concept investigation ↗
Low back pain affects up to 70% of individuals globally, posing significant health and economic challenges. Traditional methods for classifying low back pain, including radiological imaging and functional tests, are limited by the substantial costs and limited utility via radiological imaging, as well as the inherent subjectivity of functional tests. To address these limitations, we propose a movement-based screening framework that integrates deep-learning-based “Ergo” 3D markerless motion capture with machine learning for objective LBP classification during an overhead squat test. Seven machi…
- GitHub · Repository created · First archived
scarletkc/Variora ↗
Different models, the same brief - a collection of demos built from shared prompts, with source, screenshots, and notes.
- GitHub · Repository created · First archived
bvolpato/kevala ↗
Decision models (Laya, Kev) in any web page: a zero-dependency Rust engine compiled to WebAssembly, with WebGPU kernels. One import, no server.
- GitHub · Repository created · First archived
pipecat-ai/nemo3-battleships ↗
Open-source repository in HTML.
- Hacker News · Posted on Hacker News · First archived
Security auditing in the age of (good enough) AI ↗
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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