Training a single AI model can emit as much carbon as five cars in their lifetimes Deep learning has a terrible carbon footprint.

For training AI, synthetic data uses a base data set of actual historical events or transactions and then creates a synthetic representation of that data and builds upon it.

In the ever-evolving world of artificial intelligence, the Auto Train library from Hugging Face has emerged as a game-changer, enabling users to fine-tune a Llama 2 model with their own data set ...

As the AI-generated information — known as synthetic data — polluted the training set, the model’s outputs became gibberish.

A novel approach from the Allen Institute for AI enables data to be removed from an artificial intelligence model even after it has already been used for training.

Trying to develop your own frontier AI model today? You might as well set a big pile of money on fire, says OpenAI chairman Bret Taylor. IMDB / Warner Bros.© IMDB / Warner Bros.

Training a modern AI system involves ingesting data—sentences, say, or the structure of a protein—that has had some sections hidden.

AI model training is changing, and smaller players are finding ways to compete. But open-source AI alone doesn’t fix the bigger problem I see—centralization.