In this work, we propose a straightforward method to derive Elliptic Curve Digital Signature Algorithm (ECDSA) key pairs from embeddings created using Deep Learning and Metric Learning approaches. We also show that these keys allows the derivation of cryptocurrencies (such as Bitcoin) addresses that can be used to transfer and receive funds, allowing novel Blockchain-based applications that can be used to transfer funds or data directly to domains such as image, text, sound or any other domain where Deep Learning can extract high-quality embeddings; providing thus a novel integration between the properties of the Blockchain-based technologies such as trust minimization and decentralization together with the high-quality learned representations from Deep Learning techniques.
As we mentioned in the previous section, there are very few published works related to AI Machine Learning and Blockchain cryptocurrencies. In @cite_23 , the author explores some Blockchain applications in a highly-speculative manner. One of the applications that the author cite is the usage of the Blockchain by AI entities as a mean to provide a permanent transparent public record that can be reviewed and inspected.
@cite_23
2803148772 2891067152 2624953603 2788246026
Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market. Blockchain's vast applications in different industries have drawn several researchers to pursue extensive research in securing blockchain technologies. In recent times we could see several institutions coming together to create consortium based blockchain networks such as Hyperledger. Although for applications of blockchain such as Bitcoin, Litcoin, etc. the majority-attack might not be a great threat but for consortium based blockchain networks where we could see several institutions such as public, private, government, etc. are collaborating, the majority-attack might just prove to be a prevalent threat if collusion among these institutions takes place. This paper proposes a methodology where we can use intelligent software agents to monitor the activity of stakeholders in the blockchain networks to detect anomaly such as collusion, using supervised machine learning algorithm and algorithmic game theory and stop the majority attack from taking place. In the Internet of Things (IoT) scenario, the block-chain and, in general, Peer-to-Peer approaches could play an important role in the development of decentralized and dataintensive applications running on billion of devices, preserving the privacy of the users. Our research goal is to understand whether the blockchain and Peer-to-Peer approaches can be employed to foster a decentralized and private-by-design IoT. As a first step in our research process, we conducted a Systematic Literature Review on the blockchain to gather knowledge on the current uses of this technology and to document its current degree of integrity, anonymity and adaptability. We found 18 use cases of blockchain in the literature. Four of these use cases are explicitly designed for IoT. We also found some use cases that are designed for a private-by-design data management. We also found several issues in the integrity, anonymity and adaptability. Regarding anonymity, we found that in the blockchain only pseudonymity is guaranteed. Regarding adaptability and integrity, we discovered that the integrity of the blockchain largely depends on the high difficulty of the Proof-of-Work and on the large number of honest miners, but at the same time a difficult Proof-of-Work limits the adaptability. We documented and categorized the current uses of the blockchain, and provided a few recommendations for future work to address the above-mentioned issues. Using blockchain technology, it is possible to create contracts that offer a reward in exchange for a trained machine learning model for a particular data set. This would allow users to train machine learning models for a reward in a trustless manner. The smart contract will use the blockchain to automatically validate the solution, so there would be no debate about whether the solution was correct or not. Users who submit the solutions won't have counterparty risk that they won't get paid for their work. Contracts can be created easily by anyone with a dataset, even programmatically by software agents. This creates a market where parties who are good at solving machine learning problems can directly monetize their skillset, and where any organization or software agent that has a problem to solve with AI can solicit solutions from all over the world. This will incentivize the creation of better machine learning models, and make AI more accessible to companies and software agents.