Allora: a Self-Improving, Decentralized Machine Intelligence Network

Abstract

Recent advances in data access and computing power have enabled the first forms of machine intelligence capable of offering meaningful insights. However, the tremendous resources required have caused these solutions to be closed and siloed by industry monoliths. To achieve the full potential of machine intelligence, the data, algorithms, and participants must be maximally connected. Network solutions are needed, and the decentralized nature of blockchain technology is ideal for solving this problem. We introduce Allora, a self-improving, decentralized machine intelligence network that surpasses the capabilities of its individual participants by design. Allora achieves this through two main innovations. First, it lets network participants forecast each other's performance under the current conditions, thereby creating a form of context-awareness that enables the network to achieve the best inferences under any circumstances. Second, it introduces a differentiated incentive structure that rewards network participants for their unique contribution to the network goal, tailored to their specific task and purpose, avoiding any distracting incentives. We show that these innovations make Allora's inferences considerably more accurate than before. With its versatility and accessibility, Allora paves the way for machine intelligence to become fully commoditized and integrated with the economy, technology, and society.