AI and blockchain are transforming the enterprise, serving as catalysts for innovation across several industries. And now, the combination of the two technologies is expected to revolutionize aspects of the digital landscape as we know it today, said Ganesh Prasad Kumble, author of the new book Practical Artificial Intelligence and Blockchain.
Kumble's book serves as a guide to integrating AI and blockchain technologies and provides detailed insight on how organizations can benefit from it. He also lays out the basics of each technology in earlier chapters to ensure readers can fully grasp their value.
"This book is for blockchain and AI architects, developers, data scientists, data engineers and evangelists who want to bring the power of AI to blockchain applications," he said.
The following is an excerpt from Chapter 9 of Practical Artificial Intelligence and Blockchain, which gives readers a look into what the future of integrating AI and blockchain technologies looks like and how they will be used in tandem across several industries from healthcare to supply chain management.
The Future of AI with Blockchain
"Let's walk the talk with blockchain and AI!"
In this final chapter of the book, we will take a peek at the future of both AI and blockchain technologies. We will examine how these technologies could be used together to solve some of the biggest problems affecting many industries and our planet. In this chapter, I have also made multiple suggestions that could be used as new ideas for academic projects by interested students and faculties. If you are a working professional or an enthusiast, you could still consider using the ideas as a side project and work with people who would like to brainstorm on new ideas in their leisure time.
In this chapter, we will cover the following topics:
- The convergence of AI and blockchain
- The future of converging AI and blockchain
- Converging AI and blockchain in enterprise
- Converging AI and blockchain in government
- Converging AI and blockchain in financial services
- Converging AI and blockchain in human resources
- Converging AI and blockchain in healthcare
- Converging AI and blockchain in supply chain management
- Converging AI and blockchain in other domains
This chapter requires a basic conceptual understanding of blockchain and AI, articulated in Chapter 1, Getting Started with Blockchain, and Chapter 2, Introduction to the AI Landscape, respectively. This chapter also requires you to brainstorm on new ideas and speculate on outcomes that can be observed by reading Chapter 3, Domain-Specific Applications of AI and Blockchain, and Chapter 5, Empowering Blockchain using AI.
This chapter also requires you to reapply the design patterns learned from Chapter 7, Development Life Cycle of a DIApp. Finally, this chapter can help you to dive into blockchain and AI development by suggesting ideas in terms of building POCs that can address a number of real-world challenges. It would also be beneficial if you are able to build an intelligent application using blockchain and AI based on the steps articulated in Chapter 8, Implementing DIApps.
The convergence of AI and blockchain
AI and blockchain technologies are no longer buzzwords given that you have arrived at the end of this book. Billions of dollars' worth of assets are managed by innovative financial instruments on public blockchain networks such as Ethereum. AI is used in predictive healthcare, cancer research, and contact-tracing COVID-19 infections. Both technologies are serving humans in an indirect manner. As explained in Chapter 5, Empowering Blockchain using AI, the combined application of AI and blockchain is helping to solve a number of critical use cases today.
Now that you are equipped with the basic concepts and hands-on skills of both technologies, let's go through some ideas on converging AI and blockchain to address a number of real-world problems.
The future of converging AI and blockchain
Several experiments are being undertaken on building new waves of digital solutions where AI and blockchains can co-exist to deliver optimal solutions that enable faster decision making and provide the desired transparency to all stakeholders. Some companies have already released products and commercial solutions offering the convergence of both technologies to consumers and enterprise businesses.
It is important we understand that this convergence is just beginning, as we are yet to explore the best of AI techniques to blend with viable blockchain and decentralized storage networks.
In the following diagram, I have provided a general representation of the hybrid solution architecture of a DIApp:
Fig 9.1: Reference solution architecture for a DIApp
The preceding diagram is a pictorial representation of reference architecture for the majority of DIApp solutions. I have identified all the major resources and stakeholders involved in the solution across five layers. I have depicted this in a layered format, similar to the Open Systems Interconnection (OSI) model to provide an understanding of the concept. We have understood how to use several blockchain platforms and AI techniques in Chapter 8, Implementing DIApps. In this chapter, we are presenting the same knowledge in terms of a solution involving all common stakeholders and major technical components.
OSI is a network model conceptualized in the late 1970s. It was published by the International Standards Organization (ISO) in 1984. The ISO model is made up of seven layers: Physical, Data link, Network, Transport, Session, Presentation, and Application.
Before we explore the real-world challenges, let's understand all five layers of the solution architecture from the preceding diagram.
These layers are as follows:
- Application layer: The application layer consists of end users and client software installed on mobiles, laptops, and devices. Users will sign transactions through the middleware and access data through reporting tools from the presentation layer. The application layer also represents a wider range of deployment facilities, and administration of the DIApp through special tools such as an Identity and Access Management (IAM)
- Presentation layer: The presentation layer consists of backend functionalities manifested in the clients, which are not visible to end users. This layer includes all the supporting tools and software required to enable blockchain functionalities in an application such as signing a transaction, propagating a signed transaction, and receiving results. The layer also contains tools required to enable AI-related functionalities that can help users gain insights from the application, such as visualization and reporting.
- Network layer: As the name suggests, the network layer consists of service networks consisting of blockchain validator nodes running a software bundle designated for verifying user transactions and blocks formed by other nodes in the blockchain network. Similarly, the network layer also consists of several AI services based on machine learning (ML) algorithms and deep learning (DL) Some of these AI services may also use artificial neural networks (ANNs), convolutional neural network (CNNs), and so on.
- Data layer: The data layer defines, persists, and provides the interface for applications to access user data, network data, and other processed data. The data layer is a connecting layer between the network layer and the physical layer. Applications, validation software, and AI models will access the critical information from this layer through proper authentication methods configured by administrators.
- Physical layer: The physical layer represents all the Graphics Processing Unit (GPU) nodes, virtual machines (VMs), and storage nodes used to store the data and perform complex computations. This layer also addresses the core management of infrastructure, through a variety of DevOps practices.
Now that we understand the reference architecture in granular detail, let's go through some of the real-world challenges in the following sections. I will be providing ideas in these sections and you can use them to build POCs.
Converging AI and blockchain in enterprise
The global market size of Enterprise Resource Planning (ERP) software is expected to reach around USD 70 billion over the next 5 years. Over the past few years in the last decade, leading companies in the ERP software market, such as SAP and Oracle, have consistently pushed for the use of cloud and other emerging technologies. Several pilots were also launched in the interest of reducing costs and increasing overall productivity on the floor across various solution spaces, including Customer Relationship Management (CRM) and Supply Chain Management (SCM). There are a few niche use cases that could leverage the best of AI and blockchain technologies, along with other peripheral technologies, such as decentralized data management and Decentralized ID (DID) management.
Let's now explore some of the use cases that could enhance current enterprise software with blockchain and AI technologies.