Researchers like Robert Max Williams believe optical illusions could prove a useful resource for their efforts to develop solutions capable of making the same subtle predictions and adjustments as the human eye. If scientists want to achieve “General Vision,” and truly mimic the human eye with artificial intelligence, teaching machines to recognize optical illusions and mimic a human response may play an essential role.

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.

Organizations can’t serve their customers with maximum efficacy unless they’ve got a deep repository of insights into customer behavior and preferences. Custom-built computer vision models like the ones Plainsight develops and deploys can help, empowering businesses to see more and make visual data (like video footage) into a driver of business innovation.

It was only a matter of time before the stats-obsessed sport and computer vision joined forces to create an unprecedented understanding of America’s favorite game.

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.