Decentralized infrastructure project Human Protocol is reportedly releasing a new blockchain coordination layer to enable routing features among third-party vendors, to operate data contribution on the network. 

Specifically, the functionality, going by the name “Routing Protocol”, sits atop Human to allow for the exploration of network generators, fee agreements, consensus job standards, proof of balance and governance support for network upgrades.

Human Protocol reportedly begun through an on-chain bot blocker dubbed Captcha, which offers prizes for solving CAPTCHAs and slowly turning into a larger solution for tokenizing contribution. 

Human Protocol reportedly has an expectation for the community-developed, open-source Routing Protocol to bring simplification to the stages of running a network entity, nominally an Exchange Oracle. 

This is reportedly generated from Routing Protocol’s ability to coordinate oracles, job exchanges, layer-1 integrations for job listings and work pool operators.

The ultimate target for the Human Protocol network is reportedly to utilize the peer-to-peer consensus mechanism visible in blockchain, built to take care of the automation assignments which are unable to be carried out without initial human involvement. 

An instance of that kind of a value proposition is reportedly the sphere of AI e-commerce marketing. Without an initial “training” data set, a machine-learning algorithm cannot effectively recommends ads with relevance to each web user’s specific shopping behaviors.

However, with the involvement of the Human Protocol, network customers reportedly have the ability to post smart bounties for consumer reviews and reward users for their input via the HMT token. 

The idea of the development team is reportedly to form a decentralized platform which rewards the ones supplying their information to the customers that needed it. It works towards the target of satisfying the objective of supporting direct, globally-mapped connections at the intersection of workers, companies and machine learning, all at scale.