We are very excited to announce that Zaggy AI has been accepted to the Microsoft for Startups Founders Hub! We look forward to working with the team at Founders Hub to explore the many services and solutions available in the Microsoft Azure ecosystem, as well as back office and productivity tools.
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Generating Smoke and Mirrors
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.
PashehNet v0.1.1 Released
Nothing too crazy to report in this patch-level release: Latest package has been published on PyPi and latest docs are up. Let us know if you have any issues or feature requests on our GitHub project page!
Introducing RAICE: The Racing AI for Crew Enablement
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.
PashehNet v0.1.0 Released
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.
Edge Computing: The Future of Processing and Why It’s Important
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.
Comparing and Contrasting Supervised, Unsupervised, and Self-Supervised Deep Learning
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.
