Machine learning is the process in which systems autonomously learn from data and information patterns. The process is extremely valuable due to its ability to allow for programs to learn and adapt outside of their initial programming.
The most vital resource to machine learning is information. In the modern world, large scale data collection has allowed for cases of machine learning that are able to surpass anything that was possible with previous technology.
Big data has created a wealth of opportunities for companies to train artificial systems to learn independently by analyzing trends in data.
With the blockchain revolution now among us, it’s worth considering how these emerging technologies can complement one another to create a smarter and more efficient process.
Blockchain & Machine Learning
Blockchain technology and machine learning are concepts that exist entirely separate from one another. However, often separate technologies can be used together to create exciting applications that benefit the world at large.
Since blockchain has many applications for big data, a natural progression involves partnering the technology with machine learning processes.
Blockchain technology is an inherently robust form of information storage due to its nature. Since blockchain works on a peer-to-peer network, it allows for a system of data storage that benefits individual parties working together and eliminates conflict of interest.
Additionally, blockchain technology is highly secure in its ability to verify information through both internal and external systems. This lends itself well to big data and, by extension, machine learning processes.
The most vital factor in large scale information storage is accuracy, as fraudulent data can skew systems and inhibit a program’s ability to learn or develop as intended.
A Real-World Example
To better understand blockchain technology’s potential impact on the world of machine learning, it may be helpful to consider a real world application where the emerging technology is actively being used.
LendingRobot is a real-world example of a company that’s partnering machine learning and blockchain technology in order to deliver their ideal service offerings to their clients.
The service is an automated advisor for peer-to-peer lending; a practice that matches lenders and borrowers online. LendingRobot is using machine learning to automate loan selection and trading, and the process is built on the backbone of blockchain technology.
In this application, blockchain technology is used to add transparency to the platform. LendingRobot’s developers benefit from this in two ways.
The developers benefit from increased public trust, an asset that is in increasingly short supply. By making their public ledger available, they are able to show users exactly how transactions are occurring and eliminate a need for blind trust.
Additionally, in this application blockchain technology benefits the service by improving data integrity. With the public ledger available for scrutiny from both external and internal systems, automated processes help to make the data virtually tamper-proof.
Compromised data leads to machine learning systems that are inherently prone to missteps in development.
Essentially, imagine learning a new skill by reading resources that are incorrect. While you would theoretically still gain knowledge, it would be built on the backbone of corrupt information and would be virtually useless.
This is the same with machine learning, as accurate data is the only resource programs are able to learn from.
As stated earlier, blockchain technology can eliminate parties acting in their own self-interest. Cooperation between the data-collection services could have a huge impact on machine learning.
This is because companies would be able to eliminate redundancy and trade information in a way that would be easy to verify and benefit all parties by increasing access to data for the purposes of machine learning.
Internally, when information is added to a blockchain it is entered in ‘blocks.’ These blocks are verified against pre-existing blocks in the network to corroborate data and confirm its legitimacy before adding it to the ledger.
However, these factors barely scratch the surface about how machine learning and blockchain technology could be used together to create more robust learning systems that benefit from heightened data integrity and access.
We’re already seeing examples in the real world of these emerging technologies working together, and the future promises a seemingly endless array of real-world applications for blockchain technology.
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