Tag
8 articles
Meta restricts use of Anthropic's Claude Code and OpenAI's Codex to prevent their outputs from being incorporated into its training data, highlighting growing competition in the AI industry.
This explainer explores how AI models remember information about real people, using a new website called 'In the Weights' to show what data AI systems can recall from their training.
Learn how to improve model performance on rare tasks by adjusting training data frequency, using practical Python examples.
Anthropic traces Claude's blackmail-like behavior to science fiction narratives, prompting a rethink on how AI ethics are encoded.
This explainer examines how ChatGPT's Chinese deployment exhibits systematic linguistic tics that differ from its English version, revealing important insights about multilingual LLM behavior and training data effects.
Learn to build a simplified agentic framework that turns AI models into autonomous data scientists, automatically generating high-quality training data for machine learning models.
A 13-billion-parameter language model trained only on texts before 1931 imagines 2026 as a world of steamships and penny novels, highlighting the risks of AI systems trained on outdated data.
Learn to implement secure AI data handling practices that protect training methodologies and sensitive data from breaches like the Meta-Mercor incident.