PashehNet v0.1.1 Released

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.

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.

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.

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.

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.