Artificial intelligence (AI) is the engineering of computer programs that are capable of intelligent decision making. This is realized through the accelerated analysis of data sets. Well-known examples include Google’s search results, which are determined by the firm’s relevancy algorithm, and IBM’s Watson supercomputer. These AI systems deliver different results, yet both aim to replicate – if not surpass – human reasoning. Combining this technology with a blockchain’s ability to securely store large data sets could lead to self-learning AI solutions that can proactively sense and react to patterns, such as market trends.
As a distributed database, blockchain would act as the facilitator for AI solutions, providing easier access to more data in a secure manner. In April 2016, Deloitte created a blockchain-based warranty solution that utilized Facebook’s AI chatbot. The solution helps customers log their warranty receipts by sending a picture of the receipt through Facebook’s chatbot. The image is then digitally stored on a secure blockchain, where it is easy to retrieve if the purchased product were to break. Although this is a simple example of pairing the two technologies, it demonstrates how, in the future data, stored through blockchain will allow artificial intelligence solutions to self-adjust based on patterns drawn from customer data archives.
State Street is leveraging both blockchain and AI to create new revenue streams. In an interview with CoinDesk, the bank’s EVP of Global Exchange, Lou Maiuri, explained how establishing permission from other banks’ clients to access transaction data is currently a very labor-intensive process that needs simplifying. The goal of this State Street’s blockchain and AI testing is to design an algorithm that can seek out patterns in consumer data while maintaining clients’ privacy through the blockchain’s cryptographic barrier. Maiuri hopes to use data from State Street clients to build a blockchain index that would allow for the identification of patterns, such as the percentage of mutual funds being bought and sold in a particular geographic region.