Zaggy AI is now a part of Microsoft for Startups Founders Hub

I’m sure by now you’ve noticed the oddly abstract post headers we’ve been using in lieu of clip art, stock art, or custom artwork. In the spirit of being an AI-focused venture ourselves, we decided it would be interesting to dabble in generative AI.

Deep learning, a subset of machine learning, has taken the technological world by storm, underpinning the advancements in various applications from autonomous vehicles to drug discovery. Three dominant paradigms within deep learning are supervised, unsupervised, and self-supervised learning. In this article, we will elucidate these methods, noting their similarities and distinctions.

In an era where automation, precision, and real-time monitoring have become paramount, industrial sensors stand as unsung heroes. These small, yet potent devices, have profoundly revolutionized industries, driving operational efficiencies, reducing manual labor, and enhancing product quality. So, what exactly are industrial sensors, and how do they fit into the grand scheme of modern manufacturing?

Nothing too crazy to report in this patch-level release: Latest package has been published on PyPi and latest docs are up. Let us know if you have any issues or feature requests on our GitHub project page!

Leveraging AI at the edge is challenging at best. We see many IoT solutions trying to push AI all the way to the actual sensors, or all the way into the cloud. We think there is a better solution.

Sensor fusion, in its essence, is the combination of sensory data from diverse sources to generate a comprehensive understanding of an environment. This data integration process seeks to produce more consistent, accurate, and useful information than would be possible by relying on a single sensor alone. By merging information from various sensors, sensor fusion can address individual sensor limitations like noise, inaccuracies, or failure.