Four Examples of Blockchain-Artificial Intelligence Deployments

four-examples-of-blockchain-artificial-intelligence-deployments

Three of the most notable technological innovations facing enterprises today are artificial intelligence (AI), the Internet of Things (IoT), and blockchain. And while many organizations are awakening to these (and other) technological concepts and capabilities, few implementations are focused on the convergence between them. How will the convergence of emerging data analytics techniques, connected devices/infrastructure, and distributed database architectures manifest? What follows are four examples of companies exploring applications involving the intersection of these technologies.

IBM’s Smart IoT Lifecycles and Cognitive Contracts

IBM is currently working on the intersection between AI, blockchain, and the IoT in projects and experimentations that combine these three areas for comprehensive device lifecycle management. The idea is to use blockchain for device registration, management of updates, user management, compliance, and joint ownership, as well as for permissioned access via smart contracts. This would enable a “single version of the truth” for which manufacturers and service providers could monitor device performance and security, while managing AI software solutions deployed to devices remotely, especially if multiple companies were deploying such services to the same device.

IBM also recently announced plans to launch “cognitive blockchain,” wherein smart contracts become “cognitive contracts,” that learn and adapt over time. Intelligent agents will analyze changes to data, regulations, interactions, suspect activity, etc., and perform tasks and make recommendations for updates to smart contracts, associated analytics, and specific processes based on insights across the broader network.

Colony.io’s Decentralized Autonomous Organization Concept Uses Artificial Intelligence to Connect the Right Skill with the Right Project

Colony.io is a startup that has developed a platform for a decentralized autonomous organization (DAO), in which individuals can collaborate on large-scale projects, called colonies, or business ventures and track collaboration, reputation, manage productivity, and transact payments using their own cryptocurrency. By building colonies, contributors collect unique tokens, which serve as “proof of cognition” and can be traded on the open market for cash. The company uses AI to analyze contributions and reputation in order to direct the right project listings to the most appropriate talent at the most opportune time.

State Street’s Smart, Searchable, and Secure Data Analysis and Index Generation

State Street is a bank that is looking at a way to combine both blockchain and AI to create new revenue streams by securely mining and analyzing client data. Specifically, it wants to use blockchain to streamline the processes (i.e., approvals, possession, mining, securing, etc.) involved in leveraging client transaction data to create new searchable indexes. The key is analyzing client data by sector, country, client type, and global movement of funds, even sentiments, without revealing client identity or holdings. AI would be used to seek out patterns within the data and better utilize unstructured data, while blockchain would serve as an immutable structure to secure the data, remain compliant, and protect sensitive or private client information using cryptography to enable access instead of ownership.

Google (and Others) Working toward Secure Precision Medicine

Google’s DeepMind deep learning platform has been experimenting with distributed ledger technologies to enhance the security of electronic medical records and patient data. In this example, Google is working with the London NHS Trust to build an AI application to identify and potentially preemptively address kidney issues for patients. The U.S. Food and Drug Administration (FDA), interested in similar AI experimentations related to oncology, recently partnered with IBM Watson Health for blockchain architectural guidance. While applying AI and deep learning for image recognition of suspect cells, signals of disease, or genomic indicators could be a powerful tool for doctors (and life-saving for patients), records, rights, and regulatory compliance remain sensitive, but complex issues to protect. To help resolve risks around privacy, security, and auditability, while preserving the opportunity for AI-based precision medicine, more and more companies are looking to blockchain.

The Mutual Benefits of Blockchain and Artificial Intelligence Working Together

What these examples illustrate is the potential for convergence based on the need for checks and balances. Blockchain helps make AI more accountable, while software and hardware intelligence enhance blockchain development, application, and process automation. As connectivity and algorithms infuse every aspect of business, the need for security, trust, access to data, and accountability grows. Blockchain is perhaps most interesting when it is applied as a foundation for a more decentralized economy, where transactions are immutable, reputation and trust are encoded, and scale is not just secure, but smart.

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