With the boom in cryptocurrencies over the past few years. Lots of technological advancements have been carried out to secure the system and maximize mining rewards. Such include the integration of Machine learning algorithms in a blockchain.
Machine learning and blockchain integration will help address many limitations of blockchain-based systems. In addition, machine learning will provide a double shield against cyber attacks on the blockchain.
Hence, In this piece, we will learn what machine learning and blockchain technology are about and examine how machine learning algorithms can be integrated into a blockchain technology-based system. We will also discuss some important applications and advantages of this integrated strategy.
What is Machine Learning?
Machine Learning is an artificial intelligence (AI) development that enables systems to understand and enhance automatically from experience without explicit programming.
Therefore, Machine learning concentrates on developing computer protocols that allow them to obtain and use data to learn on their own.
The learning procedure commences with statements or data, such as instances, true experience, or instructions for searching for patterns in data and making better decisions in the future depending on the examples we provide.
Hence, The main goal is to authorize computers to learn automatically without interference or human help and modify activities accordingly.
Why is machine learning important?
Machine learning is essential because it offers companies a vision of business trends and models, as well as the advancement of new products.
Most present companies such as Facebook, Google, and Uber make machine learning the fundamental part of their activities. Since It has become a distinctive feature of significant competitors of many organizations.
Also, Machine Learning facilitates the analysis of huge amounts of data. Although it typically offers faster and more accurate results to identify cost-effective opportunities or dangerous threats. Yet, it can also demand more time and resources for proper training.
The combination of Machine Learning coupled with Artificial Intelligence and cognitive technologies can be even more effective in the treatment of large amounts of information.
Types Of Machine Learning?
Machine learning is frequently classified according to how an algorithm learns to come to be more precise in its forecasts. Data scientists use an algorithm that hinges on the kind of data to be forecasted.
There are four basic approaches:
- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
- Reinforcement Learning.
#1. Supervised machine learning algorithms
This allows the system to apply what it has learned in the past to current data by using classified illustrations to foretell future occurrences.
The system can obtain new entry marks after adequate training. The training algorithm can also relate its outcome to the expected correct output and locate errors, all to enhance the model consequently.
#2. unsupervised machine learning algorithms
These are employed when the training data is neither tagged nor classified. The system does not discern the correct output.
However, it can inspect the data, depict abstractions from the dataset, and interpret the hidden structure of the unlabeled data.
#3. Semi-supervised machine learning algorithms
It falls somewhere between supervised and unsupervised machine learning. Indeed, the training uses both classified and unclassified data.
Usually a lesser proportion of classified data and a massive quantity of unclassified data. Systems using this strategy can significantly enhance learning precision.
Besides, Semi-supervised machine learning is selected when the tagged data compels capable and applicable resources to train/learn.
#4. Reinforcement machine learning algorithms
This is a learning technique that communicates with its environment, generating activities and detecting errors or dividends.
Test and error exploration and constrained rewards are the most appropriate features of reinforcement learning. This technique allows the system to determine ideal behaviors in a specific context to maximize its performance.
Reasonable rewards are needed for the agent to find out what actions are the best; This is known as a reinforcement indication.
The fundamental notion behind Blockchain’s technology is to decentralize data storage. As a result, a particular actor can not control or manage it.
A major disparity here is the fact that there is no interest of intermediaries, such as a government, banks, and even technology companies.
Blockchain technology is so disruptive that it can change the way governments and businesses work. However, there are still many obstacles to overcoming safety, interoperability (because there will probably be more blocks), and regulation.
Why integrate Machine Learning into Blockchain?
Machine Learning is a computer program that improves when learning from new sets of data or information. Since the program adapts on its own, it is not mandatory to manually develop new rules.
An excellent instance is spam detection in which the program or software constantly enhances its capacity to recognize junk e-mail with time. It studies the structure of the algorithm for learning and data forecasts.
Once machine learning (ML) is integrated into the blockchain, the latter take advantage of machine learning ability to expedite the analysis of large amounts of data. The combination of the two can create a whole new model.
You can also dramatically improve security by using Machine Learning to dominate the chain. Additionally, Machine Learning likes to process large amounts of data, so it takes advantage of the decentralized nature of blockchain (which makes it easy to share data). As a result, enabling the opportunity to develop alternatives to build better models.
A common ledger system with two ML use case patterns that can benefit other industries well beyond finance and insurance is:
- Model chains: Targets the entire chain or segment.
- Silo ML and predictive models: Addresses a particular portion of the chain.
Oftentimes, when all the data of silos coincide, you can end up with a new and better set of data. As a result, there will be the formation of a qualitative model in which new intuitions can be obtained. This can, in turn, give new possibilities for creating subsequent standard commercial applications.
The predictive model or silos is not new from what we are implementing presently with the accessible data. However, model chains are much more complicated and should learn and quickly adjust dependency to the chain.
Application of Machine Learning in Blockchains
cryptocurrencies and blockchains are becoming more and more exciting and, of course, as Ethereum and Bitcoin hit record prices so far in 2021. The number of applications of blockchain technology is increasing, particularly with the growing popularity of non-fungible tokens (NFTs).
Machine Learning and Blockchain show a strong combination, enhancing every industry in which they are implemented.
Like other technologies, blockchain and cryptocurrencies present issues of security, usability, and efficiency. Beyond all the enthusiasm and interest, efforts are focused on solving blockchain problems. Such problems are:
#1. Trading (Reinforcement Learning)
Cryptocurrency Trading, such as Bitcoin and Ethereum trading, has become an enterprise between retail investors and major economic institutions.
The trading bots used in the stock market are equipped with built-in machine learning algorithms. Hence, it is not surprising that Machine Learning techniques also support the construction of systems that enable cryptocurrency trading.
Thomas E. Koker and Dimitrios Koutmos have written a research paper introducing the usage of direct reinforcement learning to develop cryptocurrency-based active trading models.
Reinforcement learning is a subfield of machine learning common to games and simulation protocols. Reinforcement Learning works through a training program (agent) on the development of optimized strategies (policies) to earn rewards in an interactive setting.
#2. Optimizing Mining Strategies (Reinforcement Learning)
This segment describes research endeavors aimed at optimizing mining efforts by the application of Machine Learning techniques and also to avoid the diversion of mining resources.
Conventional reinforcement learning algorithms design ways agents can maximize reward achievement in their domain. However, the blockchain system is a dynamic environment where it is not easy to create a typical model. The authors designed a multidimensional RL algorithm that optimizes cryptocurrency mining using Q-learning (free model algorithm).
The inventors have proven that machine learning technology can solve the advancement of high-performance mining strategies. It is a well-known fact that cryptocurrency mining is a flourishing initiative.
Numerous mining firms such as Argo Blockchain, Hive Blockchain, and Riot Blockchain are said to have mined bitcoins worth millions. These firms may include Machine Learning experts on their technical teams.
#3. Tackling Cryptojacking (Deep Learning)
Another significant use of machine learning in cryptocurrency mining is related to security. Academic institutions and government-sponsored research institutes have significant IT resources and infrastructure. This makes it a major target for “crypto hijackers”
Cryptocurrency hijackers seize computational resources to mine cryptocurrencies. Such attacks have become commonplace and talked about. Thus, some researchers in the United States have worked together to find ways to detect the presence of malware that seeks to hijack computer resources.
The researchers created a system and named it “SiCaGCN”.
SiCaGCN specifies the similarity between a program or code pair based on the distance measurements made in the graphical representation of the program’s control flow.
The SiCaGCN system consists of elements of neural network architecture and techniques based in the fields of deep learning and Machine Learning.
An exciting aspect of the study is the actual application of the SiCaGCN system. Researchers assessed the system’s ability to detect Bitcoin mining code from a collection of scientific source code.
SiCaGCN worked well compared to another graph similarity computation system (SimGNN). It’s okay to assume that the SiCaGCN can be modified to work with a wider range of antivirus systems.
The SiCaGCN system impedes the acrimonious and unauthorised use of computing resources by unfamiliar programs.
Advantages of Machine Learning integration into Blockchain.
Advantages of the application of Machine Learning into Blockchain are as below:
- Better management
- Privacy and new markets
With the execution of Machine Learning, blockchain technology will be more secure with future deployments of secure applications. A good example is a Machine Learning algorithm that is making enhanced verdicts about whether a transaction is a scam and should be prevented or reviewed.
Machine Learning can help optimize analyses to lessen the load of miners and consequent network delays for rapid transactions. Likewise, Machine learning allows you to reduce the Blockchain technology coal track.
The miner’s cost will also be lessened with the energy released if the machines learning replaces the miners.
Since the block data increases every second, the pruning algorithms can also be used for blockchain data. The algorithm will automatically conceal data that are not necessary to be used in the future.
Machine learning can even initiate fresh decentralized learning systems, such as federated learning or new data-sharing methods, which make the system extensively efficient.
The solid records of Blockchain are deemed one of its USPs. When applied in combination with machine learning, users can have a clear record of following the system’s thinking process.
This allows robots to trust each other, increase interaction between machines, and share data to coordinate overall decision-making.
#4. Better Management
About cracked codes, human experts improve with time and practice. However, the programming of machine learning mining algorithms can eradicate the requirement of human experience. Because the system can improve its capabilities if good training knowledge is applied.
Therefore, it contributes to the management of Blockchain.
#5. Privacy and New Markets
Making secure private data unfailingly results in sales, with consistent data markets/model markets.
Markets get a simple and safe data sharing that helps the players to get the confidentiality of Blockchain. This can be increased by performing “homomorphic encryption” algorithms.
The homomorphic algorithms are those that use the operations that can be executed directly on the encrypted data.
Blockchains are excellent for cataloging very sensitive and personal data, which can add significance and suitability when they are treated by Machine Learning.
Smart Health systems for performing specific diagnoses based on medical analyzes and recordings are a good example.
Skills Needed To Apply Machine Learning Into Blockchain.
There are some skills needed by anyone who desires to employ the machine learning algorithm in Blockchains. They are
- Applied mathematics
- Computer science fundamentals and programming
- Machine Learning Algorithm
- Data Modelling and Evaluation
- Natural Language Processing
#1. Applied Mathematics
Mathematics is a fairly important ability in the armory of a machine learning expert. It is likewise one of the fundamental topics that are taught in the school, thus, the first to go to our list.
Furthermore, mathematics has several uses in machine learning. You can use different mathematical formulas and functions to select the appropriate machine learning algorithm for your data. Besides, You can also use mathematics to select parameters, the estimated level of trust.
Most of the Machine Learning algorithms are programs created from statistical modeling methods. Therefore, they are simple to understand if you have a solid footing in mathematics.
#2. Computer Science Fundamentals and Programming
Another essential requirement is to be knowledgeable about various CS notions. Such concept includes data structures (stacks, queues, trees, graphs), algorithms (search, sort, dynamic and greedy programming), spatial and time complexity.
The good thing is that if you have a bachelor’s degree in computer science, you probably know all of these.
You should be familiar with various programming languages such as Python and R for Machine Learning and statistics, Spark and Hadoop for distributed computing, SQL for database management, and Apache Kafka for data preprocessing.
Furthermore, Python is a very prominent programming language, especially in machine learning and data science. Hence, it’s perfect if you’re familiar with its libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, and TensorFlow.
#3. Machine Learning Algorithms
Of course, it is very crucial to understand all the machine learning algorithms. So that you know where to use the algorithms. In detail, Machine Learning algorithms include the Naive Bayes classifier, K means clustering, vector support, linear regression, decision trees, random forests, etc.
It is therefore good that you have an adequate understanding of all these algorithms before journeying to be a Machine Learning specialist.
#4. Data Modeling and Evaluation
As a machine learning expert, you must be competent in data modeling and evaluation of the data. Data modeling includes comprehending the basic data structure, then finding models that are not noticeable to the physical eye.
You must also analyze the data using an appropriate data algorithm. For example, the type of learning algorithms for the machine to use, such as regression, classification, clustering, reducing size, etc. It depends on the data.
The classifying algorithm adapted to the main data and speed can be Naive Beyes. While the regression algorithm can be a random forest.
Similarly, the clustering algorithm for categorical variables is K mode when in the case of probability they are K means. Therefore, You must understand all these facts about different algorithms to effectively subscribe to modeling and evaluating data.
#5. Natural Language Processing
The treatment of natural languages processing is naturally very important as it is the fundamental part of machine learning. Natural language processing strives to educate the human language to computers.
In this way, machines can able to comprehend and decipher the human language to understand human communication.
Different libraries have different functions that enable computers to understand human language. This is done according to its syntax, breaking them into text. Thereby extracting valuable sentences and removing strange words, etc.
Machine learning plays a huge role in the world of blockchain and cryptocurrencies. The applicability of the Machine Learning method goes beyond forecasting cryptocurrency prices and trading.
As many of these technologies are introduced into production and commercialization, the blockchain world may begin to open up to machine learning experts in the coming years.
Frequently Asked Questions
What is AI ML blockchain?
Creating solutions employing artificial intelligence, machine learning, and blockchain technology.
What is the difference between machine learning and blockchain?
Although Blockchain helps to retain unchanged and resistant correct data, machine learning can use these data to indicate models and provide accurate forecasts.
Does blockchain use machine learning?
Machine learning models can utilize the data reserved in the blockchain network to make predictions and analyze the data.
Does Bitcoin use machine learning?
The conventional trading bots used on the stock exchange today come with integrated machine learning algorithms.
- expert.ai – machine learning definition
- searchenterpriseai.techtarget.com – machine learning ML
- intersog.com – what happens when you combine blockchain and machine learning
- geeksforgeeks.org – skills needed to become a machine learning engineer
- geeksforgeeks.org – integration of blockchain and ai
- towardsdatascience.com – machine learning in the world of blockchain and cryptocurrency
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