New EPA regulations give enterprises the freedom to choose their own preferred leak detection technologies to crack down on excess emission. Industry leaders would benefit from following the regulators’ lead and deploying computer vision models of their own to detect unseen leaks.
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.
Concession and retail sales are a particular area where vision AI can make a huge impact for sports fans and other stadium patrons. With vision AI technology and a rich stream of visual data, spectators can expect a next-generation experience.
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.
Plainsight Deepens Relationship with Google Cloud to Empower Enterprises with Production-ready Computer Vision Solutions
Google Cloud customers can now centralize complete, end-to-end control and management of their vision AI workflows with fully-integrated capabilities in one intuitive interface and unmatched Google Cloud scalability and security for reliable and consistent vision AI solutions.
Converging concepts of edge computing, IoT, machine learning (ML), AI, and computer vision, are working in concert to unlock the long-held promise of digital transformation across markets.
In this post, we’ll define vision AI, what it means for enterprises in different industries.
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