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

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.

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.

Edge computing is transforming the way data is being handled, processed, and delivered from millions of devices around the world. As the next wave in the evolution of internet architecture, edge computing is poised to redefine connectivity and provide new opportunities for businesses and consumers alike. Here’s an in-depth look at what edge computing is and why it is crucial.

Over 300 sensors and 1.5TB of data per car over the weekend, and they had to wait ’til post-race to analyze the data because of time and manpower constraints at the track. The concept for RAICE was born.

By developing characterization approaches for drivers, vehicles, and tracks, we can better ascertain if each major component is playing its part to perfection.

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.