21 results for "ai data analysis"
An analysis of Internet Archive data finds that by mid-2025, ~35% of new websites published since ChatGPT's launch in late 2022 were AI-generated or AI-assisted (Matthew Gault/404 Media)
Matthew Gault / 404 Media : An analysis of Internet Archive data finds that by mid-2025, ~35% of new websites published since ChatGPT's launch in late 2022 were AI-generated or AI-assisted — Researche…
Show HN: Ragnerock, an AI data analysis tool
Hi HN, I’m Matt Mahowald, and together with my cofounder John, we’re launching the public beta of Ragnerock today. As a data scientist, you spend the majority of your time wrangling data. Even though …
MAE-Based Self-Supervised Pretraining for Data-Efficient Medical Image Segmentation Using nnFormer
Transformer architectures, including nnFormer,have demonstrated promising results in volumetric medical image segmentation by being able to capture long-range spatial interactions. Although they have …
Topological Data Analysis-friendly CAD/3D point cloud dataset [P]
Hi everyone, I’m looking for a suitable 3D point cloud dataset — or a CAD/mesh dataset from which I can sample point clouds — for a small research/report project. The goal is to compare Topological Da…
MetaGAI: A Large-Scale and High-Quality Benchmark for Generative AI Model and Data Card Generation
The rapid proliferation of Generative AI necessitates rigorous documentation standards for transparency and governance. However, manual creation of Model and Data Cards is not scalable, while automate…
Your Reviews Replicate You: LLM-Based Agents as Customer Digital Twins for Conjoint Analysis
Conjoint analysis is a cornerstone of market research for estimating consumer preferences; however, traditional methods face persistent challenges regarding time, cost, and respondent fatigue. To addr…
An Intelligent Fault Diagnosis Method for General Aviation Aircraft Based on Multi-Fidelity Digital Twin and FMEA Knowledge Enhancement
Fault diagnosis of general aviation aircraft faces challenges including scarce real fault data, diverse fault types, and weak fault signatures. This paper proposes an intelligent fault diagnosis frame…
Analytica: Soft Propositional Reasoning for Robust and Scalable LLM-Driven Analysis
Large language model (LLM) agents are increasingly tasked with complex real-world analysis (e.g., in financial forecasting, scientific discovery), yet their reasoning suffers from stochastic instabili…
IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance
Industrial maintenance environments increasingly rely on AI systems to assist operators in understanding asset behavior, diagnosing failures, and evaluating interventions. Although large language mode…
When AI reviews science: Can we trust the referee?
The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At the same time, modern large language models (LLMs) of…
Information-Theoretic Measures in AI: A Practical Decision Guide
Information-theoretic (IT) measures are ubiquitous in artificial intelligence: entropy drives decision-tree splits and uncertainty quantification, cross-entropy is the default classification loss, mut…
FastOMOP: A Foundational Architecture for Reliable Agentic Real-World Evidence Generation on OMOP CDM data
The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), maintained by the Observational Health Data Sciences and Informatics (OHDSI) collaboration, enabled the harmonisation of el…
Does Machine Unlearning Preserve Clinical Safety? A Risk Analysis for Medical Image Classification
The application of Deep Learning in medical diagnosis must balance patient safety with compliance with data protection regulations. Machine Unlearning enables the selective removal of training data fr…
An Information-Geometric Framework for Stability Analysis of Large Language Models under Entropic Stress
As large language models (LLMs) are increasingly deployed in high-stakes and operational settings, evaluation strategies based solely on aggregate accuracy are often insucient to characterize system r…
RADIANT-LLM: an Agentic Retrieval Augmented Generation Framework for Reliable Decision Support in Safety-Critical Nuclear Engineering
Reliable decision support in nuclear engineering requires traceable, domain-grounded knowledge retrieval, yet safety and risk analysis workflows remain hampered by fragmented documentation and halluci…
Quantifying Divergence in Inter-LLM Communication Through API Retrieval and Ranking
Large language models (LLMs) increasingly operate as autonomous agents that reason over external APIs to perform complex tasks. However, their reliability and agreement remain poorly characterized. We…
The Power of Power Law: Asymmetry Enables Compositional Reasoning
Natural language data follows a power-law distribution, with most knowledge and skills appearing at very low frequency. While a common intuition suggests that reweighting or curating data towards a un…
A Systematic Approach for Large Language Models Debugging
Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based reasoning. However, debugging these models remains…
AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecasting
Accurate long-term time series forecasting (LTSF) requires the capture of complex long-range dependencies and dynamic periodic patterns. Recent advances in frequency-domain analysis offer a global per…
Expert Evaluation of LLM's Open-Ended Legal Reasoning on the Japanese Bar Exam Writing Task
Large language models (LLMs) have shown strong performance on legal benchmarks, including multiple-choice components of bar exams. However, their capacity for generating open-ended legal reasoning in …
A systematic evaluation of vision-language models for observational astronomical reasoning tasks
Vision-language models (VLMs) are increasingly proposed as general-purpose tools for scientific data interpretation, yet their reliability on real astronomical observations across diverse modalities r…