Solving the Generative AI Data Problem

Dealing with structured, numerical and timestamped data is a huge challenge for individual and businesses. AI models urgently need to rise above their limitations and analyse data to create profound, actionable insights.

The disparity between the potential of generative AI and its practical application in real-world scenarios poses a significant challenge, impacting the functionality of AI solutions and limiting the insights that can be gleaned from rich, structured datasets. As a result, there is a critical need for more capable models to drive the next wave of AI innovation.

This article considers some of the challenges of current language models, covering topics, such as:

• How to solve the Generative AI data problem
• Garter: Top 10 data and analytics trends
• 3 most common problems with Small Language Models
• Data decision-makers find AI explainability 'challenging'
• LLMs are not a panacea: Challenges, concerns, and shortcomings of the technology