The Value of Data in Machine Learning

This post is also available in: עברית (Hebrew)

Data is highly valued in today’s society. It can reveal previously unseen patters and behaviors, which can in turn offer insight into how things around us work and function. If we were to highlight one technology that benefits greatly from a vast amount of data, it would be artificial intelligence.

Data plays a huge role in Artificial Intelligence (AI) decision-making. By understanding how individual data sources contribute to technology-based decision-making processes, AI users can expect a more effective and improved experience.

Measuring the value of data enables us to eliminate inputs that might contribute to biased models. Furthermore, understanding the value of data allows us to assign appropriate pricing to data sources, thereby facilitating data sharing. This is particularly important to industries where specific data is difficult to obtain or for small businesses grappling with limited data access.

Assistant Professor Ruoxi Jia in the Bradley Department of Electrical and Computer Engineering at Virginia Tech says that “right now, there is much excitement about Machine Learning and AI, especially after the emergence of ChatGPT.”

“What’s under the hood is a lot of data. That’s what enables this kind of machine, which is why we aim to raise awareness around the value of data,” said Jia.

Jia noted the importance of data quality and how it can impact Machine Learning results. She explained: “If bad data feeds into Machine Learning, you will get bad results. We want to get an understanding, especially a quantitative understanding, of the value of data for data selection.”

As reported by innovationnewsnetwork.com.