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.Comment: 17 pages, 5 figure
