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Human Protocol introduces new blockchain coordination layer for data contribution

Blockchain

Human Protocol introduces new blockchain coordination layer for data contribution

Users receive rewards for contributing data on the Human Protocol, which can be used as an initial point of learning for algorithms.

On Thursday, decentralized infrastructure project Human Protocol said it was launching a new blockchain coordination layer to handle routing functionality among third-party vendors to power data contribution on the network. The feature, known as the Routing Protocol, sits atop Human to enable the discovery of network generators, fee agreements, consensus job standards, proof of balance and governance support for network upgrades.

Human Protocol started via an on-chain bot blocker called Captcha, which rewards users for solving CAPTCHAs and gradually became a broader solution for tokenizing contribution. Human Protocol expects the community-developed, open-source Routing Protocol to simplify the steps of operating a network entity such as an Exchange Oracle. This stems from Routing Protocol’s ability to coordinate oracles, job exchanges, layer-1 integrations for job listings and work pool operators.

As an end goal, the Human Protocol network seeks to leverage the peer-to-peer consensus mechanism inherent in blockchain design to resolve automation tasks that cannot be performed without initial human assistance. One example of such a value proposition is in the realm of AI e-commerce marketing. Without an initial “training” data set, a machine-learning algorithm cannot effectively suggest ads relevant to each web user’s particulshopping behaviors.

But by using the Human Protocol, network clients can post smart bounties for consumer reviews and reward users for their input via the HMT token. The development team’s vision is to create a decentralized platform that rewards those who supply their data to the clients who require it. It seeks to meet the objective of facilitating direct, globally-mapped connections at the intersection of workers, companies and machine learning, all at scale.

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