9 stories tagged Synthetic Data
ServiceNow and Hugging Face released AutoSynthData, a tool that automatically generates synthetic training data for enterprise AI agents using large language models, reducing the need for costly real-world datasets.
NVIDIA released NeMo Data Designer (NDD), an open-source framework for multimodal synthetic data generation. It lets users define dataset columns for text, code, images, embeddings, and more via a declarative config format, with a plugin system for extensibility.
Researchers found that AI models can create realistic synthetic consumer responses for market research. This could make testing new products and campaigns faster and cheaper without needing real people.
Researchers have developed a method called activation steering to improve synthetic data generation for languages with limited resources. This approach could make AI models more effective and affordable for less common languages.
Researchers developed CodeAlchemy, a system that generates synthetic code to train AI models. This could make AI coding tools smarter and more versatile for real-world tasks.
Scientists have developed a new way to study how AI models can degrade when trained on synthetic data. Their findings show that this problem spreads between models, much like a contagious disease. This could help prevent future AI systems from becoming unreliable.
Researchers developed a way to generate synthetic factory data for testing AI systems. This could help train AI workers without using real, sensitive factory data.
NVIDIA has open-sourced a new OCR model that supports multiple languages and leverages synthetic data for training. The model is designed for speed and accuracy in text recognition tasks.
Researchers propose a new framework to create synthetic physician-physician case discussions using LLMs, addressing privacy concerns. This could enhance AI agents' clinical reasoning capabilities without compromising patient data.