In this blog post, we look at the potential of service automation through autonomous economic agents in Blockchain-based systems. Datarella’s partner Fetch.ai has made it their mission to combine intelligent agents with blockchain technology in several use cases. Deep Parking is one of them, and using a specific example from MOBIX here you can get […]

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Earlier this week, together with our partners at fetch.ai we released a driver walkthrough video that lets you come along for the ride during the M-Zone Field trials.  For the first time, Datarella and Fetch.ai have field-tested an AI-powered Deep Parking solution installed at the Connex building complex in Munich. M-Zone provides automated incentives for […]

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We’ve all been there.  It seems like every time you go downtown you end up stuck in traffic and then have to drive in circles for ten minutes searching blindly for a parking spot. Even if you have one of the “digital” parking apps you can only park in a limited number of “in-network” spots. […]

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Datarella, in partnership with Fetch.ai, a Cambridge-based artificial intelligence lab building an open-access decentralized machine learning network for smart infrastructure,  announced today the launch of M-ZONE, their smart city zoning infrastructure trials in Munich, Germany. Using their AI-powered software agents to optimize resource usage and reduce the city’s carbon footprint Datarella and Fetch.ai predict mass implementation […]

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Last October during the Outlier Ventures Diffusion Hackathon, we built a Proof of Concept of a Parking Agent System – Effortless Parking, which could solve the coordination problem of parking in crowded cities. In this post, we will look at the individual components of the promising technology stack upon which we build this project – […]

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