Overview

Overcoming information inefficiency with Allora

The biggest challenge in the digital world is efficiently exchanging information. Efficient information exchange enables actors to make informed decisions across domains, from logistics and planning to governance and financial markets. When this information is accessible only to some ecosystem participants, they gain a unique advantage to beat the competition.

At Allora, the intersection between blockchain technology and artificial intelligence (AI) provides an unprecedented opportunity to overcome information inefficiency.

Introducing Allora

The Allora Network is a state-of-the-art protocol that uses decentralized AI and machine learning (ML) to build and deploy predictions among its participants. It offers actors who wish to use AI predictions a formalized way to obtain the output of state-of-the-art ML models on-chain and to pay the operators of AI/ML nodes who create these predictions. Allora bridges the information gap between data owners, data processors, AI/ML predictors, market analysts, and the end-users or consumers who can execute these insights.

The AI/ML agents within the Allora Network use their data and algorithms to broadcast their predictions across a peer-to-peer network, and they ingest these predictions to assess the predictions from all other agents. The network consensus mechanism combines these predictions and assessments and distributes rewards to the agents according to the quality of their predictions and assessments. This carefully designed incentive mechanism enables Allora to continually learn and improve, adjusting to the market as it evolves.

Allora Network Participants

Allora Network participants can fulfill a variety of different roles after any of these participants have created a topic. A topic is registered on the Allora chain with a short rule set governing network interaction, including the loss function that needs to be optimized by the topic network.

Upshot will contribute to the development of the network alongside other external code contributors. Upshot will also participate in the network as a worker by running models. Upshot will contribute as a sales/marketing service provider for Allora.

Workers provide AI/ML-powered inferences to the network. These inferences can directly refer to the object that the network topic is generating or to the predicted quality of the inferences produced by other workers to help the network combine these inferences. A worker receives rewards proportional to the quality of its inferences.

Reputers evaluate the quality of the inferences provided by the workers. This is done by comparing the inferences to the ground truth when available. Reputers also quantify how much these inferences contribute to the network-wide inference. A reputer receives rewards proportional to its stake and the quality of its evaluations.

Consumers request inferences from the network. A consumer uses the native network token to pay for the inferences.

With these participants, Allora achieves a step change in collaborative intelligence. The network can continuously improve through collective predictions (proof-of-work) and objective performance assessment (proof-of-stake). Allora features a unique feedback loop that enables models to learn from one another and represents a pioneering step towards truly decentralized intelligence.