Two dynamic new technologies you've no doubt been hearing a lot about are Blockchain and Artificial Intelligence, or AI. While each is groundbreaking on its own, the combination of the two is beginning to offer some powerful opportunities for business innovation.
AI is, simply put, the theory and practice of building machines capable of performing tasks that seem to require intelligence. Currently, cutting-edge technologies striving to make this a reality include machine learning, artificial neural networks and deep learning.
Meanwhile, blockchain is essentially a new filing system for digital information, which stores data in an encrypted, distributed ledger format. Because data is encrypted and distributed across many different computers, it enables the creation of tamper-proof, highly robust databases that can be read and updated only by those with permission to do so.
Data held on a blockchain is by its nature highly secure, thanks to the cryptography inherent in its filing system. This makes blockchains ideal for storing the highly sensitive personal data that, when intelligently processed, can unlock value and convenience in our personal and professional lives.
The data fed into these systems—after being collected from us as we browse or interact with services—is highly personal. So a key attribute of blockchain is that it helps ensure that data remains both secure and private because blockchain holds all of that the information in an encrypted state.
AI also brings much to the table in terms of security. An emerging field of AI focuses on building algorithms capable of working with data while it is still in an encrypted state. Because any part of a data process that involves exposing unencrypted data represents a security risk, reducing these incidents makes things considerably safer.
Here's a second advantage: when AI and blockchain converge, the latter benefits from AI’s ability to accelerate the analysis of enormous amounts 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, because ML works best with vast amounts of data, it creates an opportunity to build better models by taking advantage of the decentralized nature of blockchains, which inherently that encourage data sharing.
When all the data from silos converge, what results is a qualitatively new data set that’s greater than the sum of its parts. This often yields new models that provide new insights that, in turn, 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:
·Model chains that address the whole chain or a segment;
·Focused ML and predictive models to address specific segments of the chain.
The predictive model isn’t any different from what we currently do with available data. However, model chains are far more complex and are able to quickly learn and adapt.
Lastly, because computers have been very fast, but largely unintelligent of themselves, explicit instructions on how to perform a task are required; without them computers can’t get them done. This means that, due to their encrypted nature, operating with blockchain data on “stupid” computers requires large amounts of computer processing power.
As an example, the hashing algorithms used to mine blocks on the Bitcoin blockchain take a “brute force” approach – effectively trying every combination of characters until they find one that fits to verify a transaction.
AI is an attempt to move away from this brute force approach, and manage tasks in a more intelligent, efficient manner. Consider how a human expert on cracking codes will, if even minimally proficient, become better and more efficient at code-breaking as he or she successfully cracks more and more codes.
A machine learning-powered mining algorithm tackles its job in a similar way, although rather than having to take a lifetime to become an expert, it could almost instantaneously sharpen its skills if it's fed the right training data.
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