ABCD: The roots of DeFi
At the heart of DeFi are four technologies best characterized by the abbreviation “ABCD,” which stands for Artificial Intelligence, Big Data, Cloud and DLT (including blockchain and smart contracts). Although many people will be familiar with these notions, we’ll give a quick overview of the underlying technologies to help you understand the policy implications of DeFi.
Artificial intelligence
AI is based on the idea of creating software that replicates human cognitive capabilities like “learning” and “problem-solving.” The greater the volume of data, the more insightful and accurate the inferences derived from the data. Similarly, the greater the volume of data, the more insightful and precise the inferences drawn from the data.
Machine learning is a subset of AI that employs statistical, data-based methods to gradually improve the performance of computers on a particular activity without requiring humans to reprogram the system. In reality, learning is accomplished by considerable “practice” and repeated feedback rounds in which the machine is informed whether it has succeeded or failed at a job. In this context, artificial intelligence for blockchains involves a digital ledger that intelligent digital agents govern.
The vast amount of public data generated around financial transactions is one of DeFi’s primary advantages. This massive amount of financial data can train and develop artificial intelligence models like arbitrage bots, which try to maximize profits on expected asset price movements.
Current AI applications in DeFi technologies are just scratching the surface, especially as the amount of data accessible grows and the sorts of DeFi services proliferate.
Fetch.ai is an example of AI in DeFi. A Cambridge-based machine learning lab, Fetch.ai is developing a decentralized artificial intelligence platform based on a distributed ledger that allows for secure worldwide data sharing, connection and transactions.
Intellectual property (IP) security is critical to the success of any financial technology (fintech) company, including the rapidly growing industry of DeFi. While there are a variety of techniques to safeguard AI developments, one popular strategy is to keep the underlying data used to train AI models a trade secret. For many firms, the data used to train AI models is one of their most important pieces of IP.
When the underlying data is publicly available and has most certainly already been used by competitors to train their own AI models, businesses must rely on additional methods to protect their intellectual property. For example, techniques for obtaining or pre-processing blockchain data before AI training, for example, could be protected as trade secrets or patents.
Big data
Data is at the heart of all of DeFi innovation, which is the outcome of the digitalization of an ever-widening variety of processes: the concept of the “digitization of everything,” which underpins fourth industrial revolution theories.
Traditional data analytics and big data techniques are both supported by the growing volume of data. The collection and analysis of data sets that are too massive or complicated for typical data processing programs are referred to as big data analytics.
Big Data applications analyze large amounts of data and use modern data analytics methods to find unexpected correlations, test expected correlations for causality, or calculate the likelihood of a predetermined pattern. Therefore, DeFi data analysis refers to the act of identifying, interpreting and communicating relevant patterns in DeFi protocol data, as well as the process of using such patterns to make better decisions.
Big Data Protocol is a commercial data and service exchange platform. It gathers commercial data from professional data suppliers, tokenizes it and makes it liquid on Uniswap using the DeFi protocol and the Web 3.0 marketplace. By offering liquidity to data tokens, users can earn data.
