Machine Learning is one of the most popular technologies, with incredible possibilities, whereas Blockchain is at the heart of all cryptocurrencies. Blockchain technology is gaining popularity because it allows any user to deal with others directly through a highly secure decentralised system without the need for an intermediary. Machine Learning may be used in conjunction with Blockchain technology to improve its efficiency and effectiveness. In this topic, we will look at how machine learning and Blockchain may be coupled to achieve the best outcomes. Before we begin, let’s review the fundamentals of both technologies.
What exactly is Blockchain?
Blockchain is a shared, unchangeable digital ledger that enables for the storage of transactions and the tracking of assets inside a highly secure network. In this case, the assets can be either physical (a home, a vehicle, cash, or land) or intangible (patents, copyright, brandings, intellectual property). Because blockchain is unchangeable, data once inputted is irrevocable.
Simply said, blockchain is a form of distributed database system that keeps any type of data and is extremely impossible to hack, edit, or cheat. The primary distinction between a traditional database and a blockchain is that a database saves data in tables, but a blockchain stores data in blocks that are linked together.
Blockchain is a decentralised system, which implies it is managed by a distributed network rather than a centralised entity (person, business, or any group).
A blockchain may hold several sorts of data, but it is most commonly used to power cryptocurrencies such as Bitcoin.
What is the Process of Blockchain?
When a transaction takes place, it is recorded as a block on the chain
A new transaction is kept as a block whenever it occurs. The data block can contain information such as Who, What, When, Where, How Much, and any condition, such as the temperature of a food shipment, based on your preferences.
Each block is linked to the ones that come before and after it
Each block is linked to create a chain, and its position changes when ownership changes. Each block certifies the transaction’s precise time and is linked in such a way that no block may be edited or introduced between the two existing blocks.
Transactions are linked in an irreversible chain.
Each newly added block that confirms its predecessor block adds to the overall security of the blockchain. As a result, blockchain becomes immutable, and each transaction becomes irrevocable.
How does Machine Learning enter the picture with Blockchain?
Machine learning is a system that learns from previous data and improves performance with fresh data. As a result, we may call it self-adaptive technology because we don’t have to manually apply new rules. We can comprehend it by using one of the most well-known instances of machine learning, “Spam Detection”. It is software that continuously improves its effectiveness in recognising spam and rubbish emails. It accomplishes this with the assistance of an underlying algorithm that allows it to learn from data and make predictions based on data.
When such machine learning skills are integrated with blockchain, it creates several potential and benefits for its users.
The use of machine learning to control the blockchain can greatly improve the chain’s security. Furthermore, because machine learning works better with large amounts of data, it might provide a wonderful chance to construct stronger models by using the decentralised nature of blockchains.
The integration of these technologies has the potential to transform the banking and insurance sectors’ ability to detect fraudulent transactions.
In a Blockchain-Based Application, Machine Learning
Improved Customer Service
Companies are utilising various ML approaches to improve their customer services since customer happiness is one of the most difficult tasks for any firm. Customer service may be greatly improved by merging Machine Learning with a blockchain-based application.
Observation People are concerned about system security because of the rising crime rate in the current climate. Machine learning and Blockchain technology may be utilised for surveillance, with blockchain used for maintaining continuous data and ML used for data analysis.
Nowadays, smart cities are expanding on a daily basis, assisting individuals to improve their living standards by making their lives easier. A smart city also includes machine learning and blockchain technology, which are critical. A smart house equipped with blockchain and machine learning algorithms, for example, can be readily monitored and can give device customisation to each user.
Trading (Reinforcement Learning)
Because blockchain is the underlying technology of the majority of prominent cryptocurrencies such as Bitcoin and Ethereum. These trading cryptocurrencies are gaining popularity among regular investors as well as huge financial organisations. Traditional trading bots are now outfitted with strong Machine Learning algorithms.
Reinforcement learning is a sort of machine learning that is frequently employed in complicated games and simulation systems. Reinforcement Learning is a feasible strategy for developing effective and adaptable bitcoin trading strategies.
Mining Strategy Optimization (Reinforcement Learning)
The mining process is critical in the blockchain. This procedure entails predicting a collection of values in order to solve a function on a blockchain using various computing resources. The miner that solves the function can add legitimate pending transactions to the blockchain.
Taotao Wang, Soung Chang Liew, and Shengli Zhang published a study in which they demonstrated how reinforcement learning may be utilised to optimise blockchain mining strategies for cryptocurrencies like Bitcoin. In this research, the author demonstrates how to optimise bitcoin mining using a multidimensional RL algorithm that employs a Q-learning method.
Defending Against Cryptojacking (Deep Learning):
Another use of machine learning in blockchain is to make it more secure. Because diverse computing resources are utilised to mine cryptocurrencies, Cryptojackers who hijack these computational resources can target them. These assaults have become more prevalent in recent years, necessitating increased protection. Several researchers have discovered a novel way for identifying the presence of dangerous applications that may steal computer resources. SiCaGCN is one of these approaches.
The researchers developed SiCaGCN, a technique that detects similarities between two lines of code. It is made up of neural network components as well as deep learning and machine learning algorithms.
The Advantages of Combining Blockchain and Machine Learning
Combining Machine Learning with Blockchain can yield huge advantages for a variety of businesses. The following are some of the most common advantages of merging Blockchain with Machine Learning for the Organization:
Data on Blockchain is substantially more secure due to the system’s implicit encryption. It is ideal for storing extremely sensitive personal data, such as tailored suggestions.
Although blockchain is safe at its core, some apps or extra layers that use blockchain may be vulnerable. In such a circumstance, we may make use of machine learning. ML can assist in predicting potential breaches or security issues in blockchain apps.
Controlling the Data Market
Various large firms, such as Google, Facebook, and LinkedIn, contain massive amounts of data or vast data pools, and this data may be quite beneficial for AI processes. Others, however, do not have access to such information.
However, by utilising Blockchain, many start-ups and small businesses may have access to the same data pool and AI process.
Energy Consumption Optimization
Data mining is a high-energy-consuming activity that is one of the primary challenges for several companies. However, with the aid of Machine Learning, Google has mostly resolved this issue. Google does this by teaching the DeepMind AI to minimise energy use for cooling data centres by around 40%.
Putting in Place a Reliable Real-Time Payment Process
The most trustworthy real-time payment procedure may be developed in the Blockchain environment by combining Blockchain with ML.
Based on the foregoing, we may infer that Machine Learning and Blockchain are ideal complements to one another. Both of these technologies have the potential to serve as foundations of future innovation.