แปลง Raven Protocol RAVEN เป็น Augur REP

Raven Protocol's specific use case is to perform AI training where speed is the key. We're taking a 1M image dataset that takes 2-3 weeks to train on AWS down to 2-3 hours on Raven. AI companies will be able to train models better and faster. Raven Protocol is creating a self-sustaining and dynamic ecosystem for: Customers who want to train their AI engines; and/or Contributors who would like to share their compute resources in the form of Computers, Smartphones, or even a server rack. Raven Tokens (RAVEN) will work as the common ground to facilitate a secure transaction that will take place inside our ecosystem. Enterprise clients who want to rent compute power will do so with RAVEN and contributors of the compute power will be rewarded in RAVEN. Raven is creating a network of compute nodes that utilize idle compute power for the purposes of AI training where speed is the key. A native token is the key to bootstrapping a nascent network. We want to incentivize and reward people all over the world to contribute their compute power to our network. Additionally, we will reward token holders for running masternodes which will be responsible for orchestrating the training of various deep neural networks. Our consensus mechanism is something we call Proof-of-Calculation. Proof-of-Calculation will be the primary guideline for the regulation and distribution of incentives to the compute nodes in the network. Following are the two prime deciders for the incentive distribution: Speed: Depending upon how fast a node can perform gradient calculations (in a neural network) and return it back to the Gradient Collector. Redundancy: The 3 fastest redundant calculation will only qualify for receiving the incentive. This will make sure that the gradients that are getting returned are genuine and of the highest quality.

Augur’s Reputation token (REP) คือโทเค็นอีเธอร์เรียมที่ออกแบบมาเพื่อการรายงานและโต้แย้งผลลัพธ์ของเหตุการณ์ในตลาดเพื่อการพยากรณ์ ผู้รายงานจะได้รับผลตอบแทนสำหรับการรายงานผลลัพธ์ของเหตุการณ์ที่ถูกต้อง