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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Hardware Matters
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.
Introducing Zaggy AI’s LapLabeler
We’re excited to introduce LapLabeler, a groundbreaking new tool specifically designed for labeling deep learning datasets in motor sports.
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.
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.
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.
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.
