Supercharging AI with Data Enrichment
Artificial intelligence has rapidly captured the collective corporate imagination, leveraging advanced algorithmic techniques, natural language processing, and human-computer interaction to ignite transformative change in advanced problem-solving. Base models for Generative AI systems are trained to support various applications such as market forecasting, customer behavior predictions, and risk assessments. However, the quality of these models is inextricably linked to the quality and diversity of the data they’re trained on.
In this webinar, we explore the pivotal role of data enrichment in molding AI models with a specific focus on the intricacies of how data enrichment contributes to the training processes. Data enrichment, in which existing datasets are infused with additional context and relevant information from trusted third-party sources, helps AI models surpass their current capabilities, enabling them to understand complex nuances and generate contextually attuned responses. A core component of our discussion will revolve around leveraging trusted third-party datasets. We will discuss how integrating these datasets with AI models can lead to a more comprehensive understanding of intricate patterns and a heightened ability to generate insightful outputs.
Viewers will learn about:
- Establishing levels of trust in third-party datasets
- How data curation by domain experts improves data enrichment
- Methods of data enrichment
- The dependence on trusted third-party data for enhancing the quality and relevance of AI model outputs