The article also highlights the use of large language models (LLMs) in generating synthetic text-based data, which can be beneficial in fields like healthcare where training data is difficult to obtain. Furthermore, it discusses the potential of quantum generative models in generating high-quality synthetic data from limited datasets. The author advises enterprises to choose the right generative model for their use case, train it on their proprietary data, and continuously validate the model outputs for accuracy.
Key takeaways:
- Generative AI can enhance data analytics by filling in gaps and generating statistically accurate scenarios and simulations, especially in cases where data is difficult or impossible to collect.
- Synthetic data capabilities have improved significantly due to advances in AI and modeling, and by 2024, it's predicted that 60% of all data used for developing AI and analytics will be synthetically generated.
- Large Language Models (LLMs) can be used to generate synthetic text-based data, which can help in areas like healthcare where biases in training data are a significant concern.
- Generative AI models based on quantum statistics, but running on classical hardware like GPUs and CPUs, can provide an advantage today for synthetic data use cases with limited training data.