Applications of Blockchain Technology with Artificial Intelligence

Source: Artificial Intelligence on Medium

Blockchain together with AI

Simply said, blockchain is concerned with keeping accurate records, authentication, and execution; while AI helps in making decisions, assessing, and understanding certain patterns and datasets, ultimately making autonomous interaction. Blockchain and AI are able to work together and potentially produce seamless interactions, because they share several characteristics:

I. Data Sharing Requirement

Both blokchain and AI require some forms of data sharing. For blockchain, A decentralized database emphasizes the importance of data sharing between multiple clients on a particular network, where the data shared can be authenticated by different parties to ensure trust and security. AI relies greatly on Big Data, specifically, data sharing. With more open data to analyze, the predictions and assessments of machines are considered to be more accurate and correct, and the algorithms generated using the shared data sets are more reliable.

II. Security

In the financial sector, and of course not only limited to the financial sector, there is a big need for security when dealing with high-value transactions on the blockchain network. This is enforced via the existing protocols within the blockchain technology. For AI, the autonomous nature of the machines also requires a high-level of security in order to reduce the probability of a falsely made algorithm or data set that can result in catastrophic occurrences.

Some might say that AI requires access to several data sets, and it may compromise privacy issues. The blockchain technology is able to allow you to restrict or license the access to certain data, then what about AI? There is a technology within the AI field called Federated Learning, which is a machine learning technique that trains an algorithm across multiple decentralized devices or servers holding local data samples, without uploading or exchanging their data samples to other servers. This approach is in contrast to the traditional centralized machine learning techniques where all data samples are uploaded to one server. It enables multiple actors to build a common, robust machine learning model without sharing data, thus addressing critical issues such as data privacy, data security and data access rights.

III. Trust

The advancement and adoption of any widely-accepted technology require the element of trust, and neither AI nor blockchain are excluded. To facilitate machine-to-machine communication and data sharing in AI, there is an expected level of trust. Also, to execute certain transactions on the blockchain network, trust is required.