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 ...

When you set out to create a specialized AI model, the first step is to break down your challenge into smaller, manageable pieces.

Big tech companies — and startups — are increasingly using synthetic data to train their AI models. But there's risks to this strategy.

Organizations that want to harness generative artificial intelligence (AI) more effectively should use their own data to train AI systems, using foundation models as a starting point. Doing so can ...

Synthetic data provides a way around issues like intellectual property litigation but comes with its own risks.

Generative AI models are now widely accessible, enabling everyone to create their own machine-made something. But these models can collapse if their training data sets contain too much AI ...

2. Not Using A Diverse Data Set One key mistake organizations make in training AI models is failing to use a diverse set of data. This can lead to biased results. To avoid this, organizations ...

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