Clearly, Blockchain and ML/AI are two technological trends which, while ground-breaking in their own rights, have the potential to become even more revolutionary when put together. Both serve to enhance the capabilities of the other, while also offering opportunities for better oversight and accountability.
What is Blockchain ?
To put it in simple terms a Blockchain is a decentralized, distributed, oftentimes public, digital ledger that is used to record transactions across many computers so that any involved record cannot be altered retroactively, without the alteration of all subsequent blocks.
Bitcoin is the most popular example of Blockchain.
Check out my article Everything you need to know about Blockchain and Bitcoin ! for further details
What is ML/AI ?
ML focuses on the development of computer programs that can access data and use it learn for themselves.
AI is a distilled concept that machines will be able to execute tasks characteristic of human intelligence.
Check out my article Everything you need to know about AI for further details.
The Magic combination of ML/AI and Blockchain :
When Artificial Intelligence (AI) and blockchain converge, the latter can benefit from AI’s ability to accelerate the analysis of an enormous amount of data. In fact, putting the two together can potentially create a totally new paradigm.
By using ML and AI to govern the chain, there’s also an opportunity to significantly enhance security. Further, as ML loves to work with a lot of data, it creates an opportunity to build better models by taking advantage of the decentralized nature of blockchains (that encourage data sharing).
Sometimes when all the data from silos converge, you might end up with a qualitatively new data set that’s also a better data set. As a result, it will lead to the creation of a qualitatively new model where you can derive new insights which, in turn, can provide new opportunities for building cutting-edge next-gen business applications.
This can be a game changer for the finance and insurance industries as it could be used as a tool to identify fraud. It can also benefit other industries far beyond finance and insurance because of a shared ledger system with two patterns of ML use cases:
- Model chains that address the whole chain or a segment;
- Silo ML and predictive models to address a specific segment of the chain.
The predictive model or silo ML isn’t any different from what we currently do with available data. However, model chains are far more complex and should be able to quickly learn and adapt given the chain dependence.
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