Baldominos Alejandro, Saez Yago
Computer Science Department, Universidad Carlos III of Madrid, Leganés, 28911 Madrid, Spain.
Entropy (Basel). 2019 Jul 25;21(8):723. doi: 10.3390/e21080723.
One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and establishing the concept of "blockchain" as a distributed ledger. As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies. However, many of them share one common feature: they require proof-of-work to support the generation of blocks (mining) and, eventually, the generation of money. This proof-of-work scheme often consists in the resolution of a cryptography problem, most commonly breaking a hash value, which can only be achieved through brute-force. The main drawback of proof-of-work is that it requires ridiculously large amounts of energy which do not have any useful outcome beyond supporting the currency. In this paper, we present a theoretical proposal that introduces a proof-of-useful-work scheme to support a cryptocurrency running over a blockchain, which we named Coin.AI. In this system, the mining scheme requires training deep learning models, and a block is only mined when the performance of such model exceeds a threshold. The distributed system allows for nodes to verify the models delivered by miners in an easy way (certainly much more efficiently than the mining process itself), determining when a block is to be generated. Additionally, this paper presents a proof-of-storage scheme for rewarding users that provide storage for the deep learning models, as well as a theoretical dissertation on how the mechanics of the system could be articulated with the ultimate goal of democratizing access to artificial intelligence.
十年前，比特币问世，成为第一种加密货币，并确立了“区块链”作为分布式账本的概念。截至目前，有许多不同的加密货币在区块链上运行，采用了不同的方法和理念。然而，它们中的许多都有一个共同特征：它们需要工作量证明来支持区块生成（挖矿），最终支持货币生成。这种工作量证明方案通常在于解决一个密码学问题，最常见的是破解哈希值，而这只能通过暴力破解来实现。工作量证明的主要缺点是它需要消耗大量荒谬的能量，这些能量除了支持货币之外没有任何有用的产出。在本文中，我们提出了一个理论方案，引入了有用工作量证明方案来支持在区块链上运行的一种加密货币，我们将其命名为Coin.AI。在这个系统中，挖矿方案需要训练深度学习模型，并且只有当该模型的性能超过阈值时才会挖出一个区块。分布式系统允许节点以一种简单的方式（肯定比挖矿过程本身高效得多）验证矿工提供的模型，从而确定何时生成一个区块。此外，本文还提出了一种存储证明方案，用于奖励为深度学习模型提供存储的用户，以及一篇关于如何构建系统机制以实现人工智能普及这一最终目标的理论论述。
