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

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.

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.

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.

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.

In the rapidly advancing field of artificial intelligence (AI), unsupervised and self-supervised learning is emerging as a transformative paradigm that promises to reshape the landscape.

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.