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

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.

One of the biggest challenges in bringing AI to motorsports hasn’t been the technology itself—it’s been challenging the deeply ingrained mentality of “how it’s always been done.” Many foundational data analysis texts in our industry are more than a decade old, and most analysis software still defaults to the same channels and visualizations that engineers…

Sensor fusion, the process of integrating data from multiple sensors to provide a more comprehensive and accurate view of the environment, has become increasingly significant in various fields. Deep learning, a subset of machine learning characterized by deep neural networks, has shown outstanding capabilities in extracting patterns and information from large datasets. Its integration with sensor fusion can bring transformative benefits.

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.

Hybrid AI aims to combine the best of both worlds to create more robust, reliable, and effective systems by leveraging a blend of both rule-based and learning-based approaches.

We’re excited to introduce LapLabeler, a groundbreaking new tool specifically designed for labeling deep learning datasets in motor sports.