Staffordshire student confronts lecturer for using AI-generated slides – video

The article discusses the SURF method, a new approach to validating generative models in jet tagging. It highlights the importance of understanding the upper performance limits of jet tagging algorithms. By using generative surrogate models, the SURF method enables exact Neyman-Pearson tests, demonstrating that modern jet taggers may be operating near their statistical limits. The study specifically applies the EPiC-FM generative model as a valid surrogate reference for JetClass jets.

The study introduces the Large Shape and Textures dataset (LAS&T), a comprehensive collection of diverse shapes and textures extracted from natural images. This dataset is utilized to evaluate the performance of leading Large Vision-Language Models (VLMs) in recognizing and representing shapes and textures in various contexts. Results indicate that VLMs still lag behind human capabilities in shape recognition, particularly when variations in orientation, texture, and color are present.

Students at the University of Staffordshire expressed feelings of being 'robbed of knowledge and enjoyment' after discovering that a coding module they enrolled in was largely taught by AI. Signs of AI involvement included suspicious file names and an unusual voiceover accent. The course was part of a government-funded apprenticeship program aimed at helping students transition into careers in cybersecurity or software engineering.

The article introduces 'Spot The Ball', a benchmark designed to evaluate visual social inference in vision-language models (VLMs) using sports imagery. The task involves localizing a missing sports ball in images from soccer, basketball, and volleyball. The study compares human performance against four advanced VLMs, revealing that humans are significantly more accurate, achieving 20-34% accuracy compared to the models' maximum of 17%. This highlights the limitations of current AI in understanding complex visual cues.