Academic papers, research findings, and scientific discoveries in AI and computing.
30 articles
A coalition of mathematicians from top institutions has issued a formal declaration urging AI companies to stop using mathematical work without permission, calling for transparency and attribution.
A new Anthropic study reveals that men use AI coding agents more than twice as often as women in social science research, highlighting a significant gender gap in AI tool adoption.
Learn how to detect AI-hallucinated citations using Python. This beginner-friendly tutorial teaches you to identify fake references in academic papers that may mislead clinical guidelines.
An OpenAI model has solved the 80-year-old unit distance problem in discrete geometry, disproving a major conjecture and marking a significant milestone in AI-driven mathematics.
ArXiv, the open-access repository for preprint research, will ban researchers for one year if they submit papers with obvious signs of unchecked AI generation. The move aims to uphold academic integrity and quality in scholarly publishing.
This article explains the concept of 'AI slop' in academic research, how it's detected, and why it threatens research integrity. It covers the technical detection methods and implications for scholarly communication.
AI-generated research papers are becoming increasingly sophisticated, raising concerns about academic integrity and the credibility of scholarly databases. The phenomenon challenges current peer-review processes and citation metrics.
A data scientist's machine learning model reveals that career momentum and team dynamics are more critical to early tech job departures than previously thought.
Researchers have developed Talkie-1930, a 13-billion-parameter language model trained exclusively on pre-1931 English texts, to study historical reasoning and generalization.
Researchers are combining Transformer architectures with NetKet and JAX to solve complex frustrated spin systems, advancing quantum machine learning applications.
A Stanford study reveals that multi-agent AI systems' advantages often stem from increased compute rather than inherent collaboration, with notable exceptions where specialized agents deliver superior results.
MIT research suggests AI's impact on employment will be gradual, not dramatic, with human workers continuing to play crucial roles in the evolving workplace.
