Blockchain and Machine Learning
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Blockchain and Machine Learning (ML) both have made an uproar in recent years, but not so much together. To get a clear answer to the question as to how the two technologies together can disrupt businesses, knowing how machine learning capabilities can be integrated into a blockchain-based system will be helpful.
Machine learning is a subdiscipline of artificial intelligence. Artificial intelligence is a subject dedicated to the study of machines’ ability to mimic intelligent human behavior. They are used to solve complex problems as we humans do.
Blockchain, on the other hand, is a distributed ledger that can handle almost any type of transaction that exists. The blockchain speeds up and simplifies the process of recording transactions. This means the transaction of any asset such as cryptocurrency is transparent with the use of this completely decentralized system.
The key difference here is that no intermediaries such as the government, banks, or even technology companies are involved. Instead, it’s a massive collaboration with some fantastic code that significantly reduces settlement and clearing times to seconds.
Machine learning algorithms can be integrated with a distributed ledger to make it smarter. This integration may aid in the enhancement of the security of the blockchain’s distributed ledger. Also, the computation power of ML can reduce the time required to find the golden nonce, and improve data sharing routes. Furthermore, we can create many better machine learning models by utilizing blockchain’s decentralized feature.
How does blockchain affect business?
Well, before we dwell on how Blockchain and Machine Learning in industries can change the way they process let us look at how a public transaction ledger functions. Blockchain technology tracks transactions from start to finish without the need for human intervention or central authority. It can on its own preserve the transaction or encrypt the data, and since the cryptographic ledger is immutable, this data is safe. It also provides transparency into what has happened in the history of the transactions.
In layman’s terms, it means that due to blockchain technology, the transaction of money from one end to another is faster. Also, due to zero participation of a third party, it accelerates the process involved in taking a loan or buying a house.
The blockchain does have a good side but while we derive all the good changes it can bring about, we should know that it also has the power to bring down organizations. This technology both digital ledger and machine learning (ML) together is so powerful that it has the potential to completely transform how governments and businesses operate. We will study three different types of businesses to determine how both modern tools can bring about changes for such businesses.
Target industries that might transform due to integration of Blockchain and Machine learning in Industries
Now that we know both breakthroughs, blockchain and machine learning, let us look at some examples to get a clearer view of how they both can change the functioning of industries:
The manufacturing companies
One of the foremost examples that we can consider is the manufacturing sector. Companies have begun to rely on bitcoin blockchain-based processes and smart contracts. They employ mainly as part of the manufacturing process to enable production, transparency, and security. ML predictive algorithms can create flexible timely plans for maintenance. Quality control and product testing have also become increasingly automated. They make use of adaptive and computer vision algorithms to detect good and faulty products. Many of the top Machine Learning consulting firms are assisting such businesses with their blockchain and machine learning implementations.
The adoption of both the technology seems farfetched at this moment but Blockchain and Machine Learning are developing at a very fast rate. Hence, its effect on the manufacturing companies seems plausible. Many of the big companies have already started adopting it but its effect on the full scale will be possible only when the companies start competing on the basis of its usage. One thing is for sure, the manual labor in such manufacturing companies will become very less.
The food industry
The integration of Blockchain and machine learning is also increasingly helping the food industry. For instance, the technology together is reducing end-to-end supply chain challenges in the food industry. The key to increasing revenue is knowing the quantities and what foods to offer on your restaurant menu. Because customer and market expectations are rapidly evolving, staying one step ahead of the competition has never been more crucial.
Again, the most valuable thing in the food business is to define the most prevalent tastes and preferences. With Blockchain and Machine learning working together it is now possible to trace food sources. It is also easy to manage related financial transactions using blockchain. The algorithms can monitor and check the AI food delivery and commodities monitoring process at every stage, making it safer and more transparent. It also creates pricing and inventory projections, preventing additional costs. Companies like IBM, Unilever, and Nestlé are already considering blockchain innovations.
The energy sector
Blockchain technology is assisting in the facilitation of energy exchanges in the Energy and Utilities industry sector as well. The technology provides disintermediation, transparency, and tamper-proof transactions. It also provides creative ways for consumers and small renewable providers to participate more actively in the energy market and profit from their assets. In the energy sector, blockchains have enabled sharing-economy applications.
For example, IOTA, an energy-based company, is implementing peer-to-peer blockchain energy production and consumption.
Concluding remarks
As you can see, most companies are already rooting for a major transformation in the way they process due to the integration of Blockchain and Machine Learning in industries. However, the above-mentioned facts are for information purposes only. We cannot purely emphasize the fact that the two technologies will bring either be the downfall or upstage it. There are still many obstacles to overcome for this technology, such as security, interoperability, and regulation. In saying so, we can also add that it might take years for such a huge transformation.
