Anthropic is making some major changes to how it handles user data. Users have until September 28 to take action.

Summary of Training Data: Training data is the backbone of AI and machine learning systems. The data’s quality, diversity, and volume directly affect the model’s ability to learn and generalize.

The quality, quantity, and diversity of training data are crucial for the accuracy and generalization of AI models. Poor or insufficient data can lead to inaccurate models or overfitting, where ...

Confidentiality, to a lesser extent, and integrity, to the greatest extent, are the most important considerations with AI ...

In July, the European Commission (EC) released a new general-purpose artificial intelligence (GPAI) template. This means that AI providers must disclose the content inputted into the models to train ...

DataSum focuses on ethical guidelines, methodologies and standards to help ensure responsible, transparent and unbiased AI training data.

StayModern reports that effective AI training is crucial for businesses, as it boosts productivity and ensures teams can fully utilize AI tools.

OpenAI declares AI race “over” if training on copyrighted works isn’t fair use National security hinges on unfettered access to AI training data, OpenAI says.

LLMs’ “simulated reasoning” abilities are a “brittle mirage,” researchers find Chain-of-thought AI "degrades significantly" when asked to generalize beyond training.