Introduction
In recent times, blockchain technology has become increasingly popular. People can engage directly with each other through a highly secure and decentralized system, without the need for a middleman, thanks to this technology. Machine learning, in addition to its own strengths, can assist in overcoming many of the restrictions that blockchain-based systems face. These two technologies (Machine Learning and Blockchain Technology) when combined can produce high-performing and valuable solutions.
Blockchain Technology
Blockchain technology’s primary concept is to decentralize data storage such that it cannot be possessed or managed by a single entity. A transaction sheet can be used to update it, however, once a transaction is recorded in the sheet, it cannot be changed. As a result, the impending transaction must be validated by a trustworthy entity before being entered into the sheet. The only difference is that the new set of records is reviewed by the node architecture, which is decentralized. There is no requirement for a centralized entity to verify the records.
Although the mechanics of blockchain technology is intricate, it may be considered as a set of interconnected blocks that allow data to flow freely. The current block in this chain holds the hash of the previous block, and so on. The blockchain mechanism, when used in this way, becomes traceable in terms of data and transactions. Instead, they are resistant to modifications in which the older blockchain cannot be updated but there are still any changes made to the block, implying that their hash has changed.
Machine Learning in Blockchain-Based Applications
Machine learning algorithms offer incredible learning potential. These features can be used to make the blockchain sharper than it was previously. This connection may aid in the enhancement of the security of the blockchain’s distributed ledger. Additionally, the computational power of ML may be leveraged to reduce the time it takes to determine the golden nonce, as well as to improve data exchange pathways. Furthermore, the decentralized data architecture aspect of blockchain technology allows us to create much better machine learning algorithms.
Machine learning models can make predictions or conduct data analysis using the information held in the blockchain network. Consider any smart BT-based application in which data is collected from various sources such as sensors, smart devices, and IoT devices, and the blockchain in this application functions as an integral part of the application, allowing a machine learning model to be applied to the data for real-time data analysis or predictions.
Storing data on a blockchain network reduces ML model failures because the data in the network does not contain missing values, duplicates, or noise, which is a key prerequisite for a machine learning model to achieve greater precision. The architecture for machine learning adaption in a BT-based application is depicted in the graphic below.
Benefits of the Machine Learning Integration in Blockchain-Based Applications
Using machine learning models in blockchain technology has a number of advantages, some of which are stated below:
- When attempting to make modifications to the blockchain, any authorized user can easily authenticate themselves.
- We can use machine learning to make BT give a high level of security and trust.
- Integration of machine learning models can aid in the long-term viability of previously established terms and conditions.
- We can create an updated ML model based on the BT chain environment.
- Models can assist in the extraction of useful data from the user’s end. Which may be calculated on a continuous basis, and on the basis of which we can award prizes to the user.
- We may also analyze the hardware of various servers using BT’s traceability so that ML models do not deviate from the learning path that they are allotted in the environment.
- In the blockchain ecosystem, we may construct a real-time, trustworthy payment mechanism.
Use Cases of Machine Learning with Blockchain Technology
In today’s world, there are numerous large and small businesses that have incorporated both strategies, either in conjunction with one another or in distinct parts of a system that is working to produce a single output. Here are a few examples of how machine learning and blockchain technology might be used together:
- IBM has introduced a blockchain-based microfinancing solution for food merchants in conjunction with Twiga Foods. They’ve successfully integrated certain machine learning algorithms. where data obtained from mobile devices using blockchain is processed using machine learning techniques to establish credit ratings and predict the creditworthiness of individual people. So that lenders can use blockchain technology to make lending and repayment easier.
- Porsche, a well-known automobile manufacturer, is one of the early users of technology that combines machine learning and big data to increase vehicle capabilities and safety. By permitting parking, charging, and third-party temporary access to their car, the company employs blockchain technology to trade data more securely, giving its users peace of mind.
- A blockchain-based invention is also being used by a New York-based startup to enable energy creation and trade for local communities. To track and control energy transfers, the technology employs microgrid smart meters that leverage machine learning algorithms and smart contracts based on blockchain.
Other food corporations, such as Unilever and Nestlé, are employing blockchain and machine learning to cope with food disasters such as food waste and contamination in order to keep their supply chains running smoothly.