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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Sensor Fusion: An Overview
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.
Untangling the Loops of Human Interaction in Artificial Intelligence
In this blog post, we will explore the concepts of Human-In-The-Loop, Human-On-The-Loop, as well as fully autonomous AI – examining their differences and similarities while focusing on how these models incorporate human input.
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.
Characterization, and why it matters in motorsports
By developing characterization approaches for drivers, vehicles, and tracks, we can better ascertain if each major component is playing its part to perfection.
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!
Unsupervised and Self-Supervised Learning: The Future of AI
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.