Plainsight Blog

Deep learning sits within the machine learning subset of AI technologies. Machine learning systems are designed to educate themselves and adapt with or without human intervention. ML systems attempt to learn the same way humans do, through trial and error. Targeted ads, recommended products, and predictive search terms are all the result of successful machine learning.

Taking the first step toward more data-centric AI is as simple as recognizing the immense value your data holds and the value inherent in curating it carefully. But centralizing and systematizing your approach to AI isn’t enough—nor is it enough to focus on updating and testing the models you deploy at the expense of data quality. Even better than a data-centric approach to AI is a data-driven approach, one that focuses on both the quality of the data collected and the efficacy of the models this data is used to train.

By enhancing their computer vision technology and applying AI to visual data like satellite imagery and drone feeds, public and private sector organizations can better recognize wildfire risks, predict the likely path of blazes, and coordinate their fire-fighting efforts to protect communities.