AI and blockchain are two of the major technologies that are catalyzing the pace of innovation. It is also undeniable that it is introducing radical shifts in every industry. The blockchain is extremely powerful but it has its own limitations as well. Some of the limitations are technology-related and some come from the old-minded culture that is inherited from the financial services sector. But all of these limitations can be affected by AI in one or the other way.

Here we will see the following two cases-

How AI can change Blockchain & How Blockchain can change AI

How AI can change Blockchain-

Scalability-

At a steady pace of 1MB every 10 minutes, the blockchain is growing and it already adds up to 85GB. The blockchain pruning i.e., deleting unnecessary data about fully spent transactions in order to not hold the entire blockchain on a single laptop can be a possible solution but AI can introduce new decentralized learning systems such as federated learning, for example, or new data sharding techniques to make the system more efficient.

Security-

Blockchain’s further layers and applications are not so secure even if it is almost impossible to hack. DAO, Mt Gox, Bitfinex, etc. are some applications that are not so secured. AI guarantees the security of blockchain.The incredible progress made by machine learning in the last two years makes AI a fantastically for the blockchain to guarantee a secure applications deployment, especially given the fixed structure of the system.

Energy Consumption-

AI has already proven to be very efficient in optimizing energy consumption. As we know very well mining is an incredibly hard task that requires a ton of energy and money to be completed. As AI is already proven its efficiency in optimizing energy consumption, it can be used in the blockchain. This would probably also result in lower investments in mining hardware.

How Blockchain can change AI-

Reduce the catastrophic risks scenario-

An AI coded in a DAO with specific smart contracts will be able to only perform those actions, and nothing more (it will have a limited action space then).

Help AI explaining itself-

The AI black-box suffers from an explainability problem. Having a clear audit trail can not only improve the trustworthiness of the data as well as of the models but also provide a clear route to trace back the machine decision process.

Increase AI effectiveness-

Secure data sharing means more data (and more training data), and then better models, better actions, better results…and better new data. The network effect is all that matter at the end of the day.



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