Integration of Blockchain and Artificial Intelligence
A Blockchain is a shared database or ledger where pieces of data are stored in data structures known as blocks. So, we can say that Blockchain is the distribution storage used for data transmission technology. Artificial Intelligence is the self-learning process of finding and analyzing significant data patterns. It is also used to make decisions between supervised and unsupervised learning. Integrating Blockchain and Artificial Intelligence is based on how both can be combined in the future and benefit the people and businesses.
Blockchain and Artificial Intelligence are combined for financial security, health record sharing, food supply chain logistics, etc. Integrating Artificial Intelligence and Blockchain is used in security to provide double protection against cyber-attacks.
Artificial Intelligence is used to extract the data from the vast dataset and create new scenarios and patterns from that extracted data. Now the extracted data are being verified and authorized by the Blockchain. The data verified by the Blockchain are used to create marketing automation with the help of Artificial Intelligence.
Artificial Intelligence and Blockchain are widely used in extensive data, financial economy, internet of things, cloud computing and edge computing. On the other hand, Artificial Intelligence is promoting the use of intelligent development in different industries. In this article, we will learn the three-dimensional point of view on integrating Blockchain and Artificial Intelligence.
Blockchain technology
Blockchain is the distributed technology that stores the data in a chain data structure.
The characteristics of Blockchain are as follows:
1. Multicenter
It can adopt distributed decentralized storage since there is no centralized hardware, so all the Blockchain nodes can be arranged accordingly.
2. Transparency
The data of the Blockchain needs to be transparent. So the actual data of the system should be transparent in nature.
3. Autonomy
The Blockchain has many participants, and they have to perform each automatically. So the system needs to be autonomous.
4. Traceability
Each node of the Blockchain system has to save the history of the record of each node.
5. Programmability
The nature of Blockchain technology is that it provides a trusted environment for intelligent coding. Users can customize the code according to their needs.
How can Artificial Intelligence and Blockchain be mixed up?
Artificial Intelligence and Blockchain can create the world's most reliable decision-making technology. It is virtually tempered proof. There are several benefits such as:
- Globalization of verification system.
- Improvement of the business data model.
- Transparent governance.
- Intelligent predictive analysis.
- Smarter finance.
- Innovative audit system.
The Technical feature that Artificial Intelligence has
1. Security
With the implementation of the security feature of Artificial Intelligence, the Blockchain has become safer by making a secure application. The algorithm of Artificial Intelligence is used in the decision-making of financial transactions to stop fraud.
2. Efficiency
With the help of Artificial Intelligence, we can reduce the minor calculation load, resulting in low network latency. Artificial Intelligence can reduce the carbon footprint of Blockchain technology. The cost and energy can be reduced by Artificial Intelligence when the AI can replace those machines. Also, Artificial Intelligence's data pruning technology can delete the data which will not be used in future. Artificial Intelligence can introduce new learning technology that makes the system more efficient.
3. Trust
The cast record of Blockchain is considered its USP. With the help of Artificial Intelligence, the user can clear the history which follows the thinking process. This helps the bots to trust each other and increases the interaction between machines. It allows the system to share and coordinate the data between the system and bots.
4. Better Management-
Humans can be better in coding by practice. Machine learning can eliminate the requirement of human experience because the system may have sharpened these skills. It also additionally helps to manage the Blockchain.
5. Privacy and new market
It secures the data in the data market by making it private. With the help of Homomorphic encryption, Blockchain privacy can be increased easily. A homomorphic algorithm is used for the encryption of data directly.
6. Storage
Blockchain is best for storing compassionate and personal data that Artificial Intelligence systems can smartly manage. An example of an intelligent storage process is the diagnosis of healthcare based on medical records and scans.
Application of Blockchain in Artificial Intelligence
1. Data sharing
Data is the essential resource of Artificial Intelligence. The accuracy of data in Artificial Intelligence may be affected by the quantity and quality of the data. After data collection, these are trained by the shareholder, and the shareholder cannot trust each other. So, it is challenging to verify the data authorization. To overcome this issue, there is a SecNet architecture which secures the data sharing through Blockchain. SecNet node consists of an access control module and a data storage module. When the data is ready to share, it is registered with the Blockchain and the parties who can access the data are also verified and recorded through the Blockchain.
An IoT architecture is designed based on Blockchain and Artificial Intelligence, known as BlockIoTIntelligence. This framework consists of four-layer which are edge intelligence (EI), Cloud Intelligence (CI), Device Intelligence (DI) and Fog Intelligence (FI). Data is transmitted between various IoT devices with the help of Blockchain.
2. Privacy preserving
It is also a vital issue. It is so challenging to protect such sensitive data during sharing. DeepLinQ is a distributed multilayer ledger used to enable the privacy-preserving of data share. This framework consists of four-layer, and these layers are as follows.
- Addition of trustworthy validators.
- Employee efficient protocol.
- Creation of trusted branches.
- Use subgroup signature.
By taking the example of medical information, this architecture shows the management of patients. It helps to decrease the privacy enclosure and enables sharing of these patients' data among various hospitals.
This framework also proposes implementing a different machine learning algorithm on the Blockchain, where the Blockchain nodes calculate the whole machine learning task.
Now taking another example of the innovative home environment, the data of the IoT devices may collect the daily activity to predict the user's activity. For instance, a user who enters a specific room automatically turns on the light. The configuration file of the users is extracted from the hash value, which contains the information about the device's settings. The user can store and sell their biological data to different research companies with the help of Blockchain.
3. Trusted Artificial Intelligence decision
Machine learning and Artificial Intelligence model are used in different organizations. Sometimes improper outcomes arise due to the failure to pay attention to the model. Also, the trained model has some restrictions. The users cannot trust all the above operations. Therefore we need an Artificial Intelligence model mechanism to create data for the entire process. Blockchain is the only platform which provides participants with trustworthy sharing of data. Transparency and tamper-proofing are the main characteristics of this model. These characteristics are suitable for records of data in machine learning. This model proposed a wireless sensor-based Blockchain and reinforcement learning. These records are stored in various nodes of the Blockchain. Before the data is transmitted from one node to another, the data needs to be verified first. Then each node collects the user information in this model. With this model's help, the military operation commander takes the right decision at the right time based on the intelligence source. For the process of Artificial Intelligence training, so much computing data is needed.
4. Decentralized intelligence
Many IoT data have been created with the help of IoT devices. With massive IoT data, we can obtain learning results from Artificial Intelligence. There are two facts in this model.
- Different IoT devices need to share the complete data for analysis.
- Also, IoT devices need to share their learning model for federated learning.
Federated learning is a technology of machine learning which has privacy-preserving capacity. This model shares the learning data instead of sharing original data. The data holder and computing node are the two main participants of this learning model. Finally, the global model is trained by work of learning model together. The whole training process is divided into three parts. These parts are as follows.
- The initialization of Blockchain and P2P networks is established.
- The computation of local gradient.
- The aggregation of a local gradient.
The validation of the previous block was checked before the new node was created. There is a secure learning system called BEMA, which is based on Blockchain.
Real-life applications
1. Finalize
Finalize is a software platform which uses Artificial Intelligence and Machine Learning to improve civil infrastructure. Its aim is to make crucial work more efficiently.
2. BLACKBOX Artificial Intelligence
This is an Artificial Intelligence tool used in an emergency. It is developed with the help of BlackBox infrastructure. It works with the power of Machine Learning and Artificial Intelligence.