Training data: The key to successful AI models

Conversely, the higher the volume and diversity of your data and the more reliable your data sources, the better and more accurately your AI model functions. Whether you’re developing large language models (LLMs), computer vision systems or specialized industry applications, the breadth and depth of training data directly impact a model’s capabilities, reliability, performance and consistency.

Do we have enough data?

According to recent analysis published by MIT, AI models’ data requirements may be outpacing the supply of suitable, usable data available today. The median training dataset contained about 3,300 datapoints in 2020. This figure grew dramatically to over 750,000 datapoints in just the three years that followed. Even though the total data generated is expected to hit 180 ZB by the end of this year, it might not be enough to feed the AI monster.

This may well slow down our ability to train LLMs and other large models. Further, these models might lack accuracy and scope due to insufficient breadth and depth in the data, slowing down innovation in sectors that depend significantly on AI adoption.