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

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.

The term “generative AI” (GenAI) is being abused to describe non-generative AI in the current market hype surrounding the technology, even though the lines are admittedly a bit blurred in some cases.

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, 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.

Zaggy AI is proud to announce our first FOSS contribution, PashehNet. PashehNet is a tool for quickly and reproducibly creating simulated sensor networks (SSN) that can publish to a target system.

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.