Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data.

A new study by researchers at the University of Toronto suggests that one of the fundamental assumptions of deep learning artificial intelligence models – that they require enormous amounts of ...

If they didn’t, you wouldn’t have a single training run, you’d have 200,000 chips training 200,000 models on their own. That data-sharing process starts with “checkpointing”, in which a ...

Today, LLMs leverage distributed training across thousands of GPUs or specialized hardware such as tensor processing units (TPUs), combined with optimized software frameworks. Innovations in cloud ...

Parallel Domain, a startup developing a platform for synthesizing AI model training data, has raised $11 million. Skip to main content Events Video Special Issues Jobs ...

LinkedIn profiles have the “Use my data for training content creation AI models” setting turned on by default, and it’s been left up to users to turn it off.

Over the past year, many of the most important web sources used for training A.I. models have restricted the use of their data, according to a study published this week by the Data Provenance ...

A technical paper titled “Optimizing Distributed Training on Frontier for Large Language Models” was published by researchers at Oak Ridge National Laboratory (ORNL) and Universite Paris-Saclay.