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70 articles
This article explores the complex legal question of whether training AI models on copyrighted books constitutes copyright infringement, examining the technical mechanisms of AI training and the implications for intellectual property law.
Amazon is using AirTag technology to track and destroy rare books for AI training, sparking ethical concerns over data sourcing and cultural heritage.
This article explains how rare books are being destroyed to train AI models, covering the technical aspects of LLM training, data curation challenges, and the ethical implications of this practice.
Learn how fine-tuning tool-calling language models helps AI systems use specific tools to perform real-world tasks more effectively.
World Labs introduces R2S2R, a simulation engine that trains robot controllers entirely in virtual environments, generating thousands of variations from a single real-world task for robust AI deployment.
This article explains OpenAI's Computer History feature, which records user behavior data to enhance AI responses and discusses its implications for AI training and user privacy.
Learn how reasoning-focused language models are built to think through problems step-by-step, making AI more useful and trustworthy. This beginner-friendly guide explains the process using real-world examples.
Amazon has introduced a new opt-out feature for Twitch streamers, allowing them to prevent their content from being used to train generative AI models. The move reflects growing concerns about data privacy and content usage rights in the streaming industry.
Learn how AI systems use training data from Twitch streamers, why Amazon's default inclusion policy raises privacy concerns, and what this means for the future of AI development.
The FineBooks project from Hugging Face and EleutherAI tested 14 open-source OCR models on more than 2,000 historical book pages, with the top model achieving 97.6% character accuracy. While this is sufficient for AI training data, it falls short of scholarly transcription standards.
Cursor Research has open-sourced Mixture-of-Kittens (MoK), a deterministic MoE training megakernel designed for high-performance computing environments like GB300 NVL72 racks, delivering up to 2.37x faster performance than public baselines.
Former Anduril founders have raised $30 million to build Europe's first synthetic battlefield for defense AI training. The startup aims to create a virtual combat environment for autonomous drones and missiles.
