AI News — Friday, May 22, 2026
Spotify and Universal Music Group have partnered to permit fan-created AI covers and remixes, signaling a significant shift in music rights and AI integration.
Google's I/O 2026 keynote introduces the 'agentic Gemini era,' highlighting a future where AI agents powered by Gemini play a more autonomous and integrated role.
Former President Trump has postponed an AI security executive order, citing concerns that its initial wording might hinder innovation.
Salesforce has launched a new Slackbot AI agent, intensifying its competition with Microsoft and Google in the enterprise workplace AI market.
Nous Research has released NousCoder-14B, an open-source coding model positioned as a strong competitor in the rapidly evolving AI code generation space.
Researchers introduce Mega-ASR, a new approach to significantly improve speech recognition in diverse real-world conditions by scaling up acoustic simulation.
AdventHealth is partnering with OpenAI to integrate AI into its healthcare services, aiming to enhance whole-person care delivery.
A new method, Video2GUI, enables the synthesis of vast interaction data to pretrain AI agents for generalized graphical user interface control.
A novel technique allows for the generation of consistent, infinitely long videos without requiring additional training, advancing video synthesis capabilities.
This paper suggests that LLMs can be effectively extrapolated with minimal Reinforcement Learning from Variational Rewards (RLVR) training using rank-1 trajectories.
OScaR introduces an extreme KV cache quantization method for LLMs, significantly reducing memory footprint while maintaining performance.
OpenAI is expanding its presence and services into Singapore, marking a strategic move to foster AI innovation and adoption in the region.
An article details the practical challenges and insights gained from developing an AI-powered tool for database performance testing.
IndusAgent proposes using agentic tools to enhance open-vocabulary industrial anomaly detection, improving robustness and adaptability in manufacturing.
A comprehensive survey explores the current state, challenges, and future directions of Large Audio Language Models, focusing on generalization and trustworthiness.