The impact of AI on Blockchain technology can be felt everywhere, from decentralized applications, investment projects, smart contracts, blockchain scalability, and several other key areas. AI has strengthened Blockchain technology with enhanced stability, scalability, and security.
Artificial Intelligence and Blockchain initially developed as separate technologies in their individual capacity. However, artificial intelligence is increasingly impacting blockchain technology with the growing convergence of multiple technologies. AI-based projects like AI Dapps and AI Cryptocurrencies are receiving greater attention, especially from renowned fund managers, due to their advantages over others.
Foundations of AI and Blockchain
The initial foundation of AI was laid in the 1950s when scientists tried to understand the information processing capacity of humans. The most influential event in the growth of AI was a conference held at Dartmouth College. It was essentially a brainstorming session.
Whereas blockchain technology finds its root in Bitcoin, peer-to-peer electronic cash. Bitcoin was a cryptocurrency that solved the famous double spending problem. However, there were also notable cases of earlier digital currencies such as Bitgold, Hashcash, and several others. Cryptographer Nick Szabo introduced programmable blockchain technology in 1996.
Sectors with the Highest Impact of AI on Blockchain
There are several sectors within Blockchain technology where AI has impacted blockchain technology. Some key impacts were felt in the following:
- Blockchain Security
- Blockchain Scalability
- Decentralized Applications
- Smart Contracts
- Predictive Analysis
- Trading and Investment
The current article is limited to core blockchain aspects like Dapps, Smart Contracts, Security, and Stability.
Improving Blockchain Security with Artificial Intelligence
Like any other system, blockchain requires a network to operate with other network participants called nodes. These nodes are independent contributors to a blockchain and verify each other’s transactions to check validity.
Theoretically, these nodes can identify false transactions by consensus. However, if a majority(more than 50%) of nodes are hacked or illegally accessed, the attacker can easily write manipulative information on the blockchain, such as fake transactions.
Artificial Intelligence can be used to enhance the security of blockchain networks.
Suspicious Behavior Detection
Past malicious behavior can be analyzed to carry out smart contract audits. Typical smart contracts are huge in size, and auditing each part individually becomes highly time-consuming. AI would make fraud detection much easier as it can easily analyze bulk data and provide critical insights.
Consensus Mechanism Optimization
The consensus mechanism is a process by which blockchains agree on the validity of a transaction. Data from similar other consensus mechanisms can be used to analyze further improvements in a particular consensus mechanism.
This exercise would help reduce the intrinsic vulnerabilities of the blockchain.
AI can be used to detect suspicious behavior in the nodes of a blockchain. If any blockchain deviates from its usual behavior, an AI model would be the fastest way to detect such subtle changes. The node activity can be verified by using multi-layer authentication or through Zero Trust methods.
Blockchain Scalability with AI-assisted Sharding
The impact of AI on blockchain technology would be best felt in sharding, one of the latest methods of increasing network capacity. A network is split into smaller pieces that parallelly process transactions and act as multiple separate networks.
Ethereum implements sharding via its EIP-4844 Protocol.
AI can assist with sharding by using machine learning algorithms to dynamically allocate the shard assignments and manage the distribution of transactions across the different shards.
For example, AI algorithms can analyze the transaction and usage patterns of the blockchain and help distribute loads. Dynamic transaction allocation would be easy to achieve with AI-based sharding. If a particular shard is experiencing high traffic, the transactions would be reallocated to other shards.
AI-Powered Decentralized Applications
Dapps are increasingly being used by users who take great care of their privacy but also need personalized recommendations. AI recommender algorithms have been proven in several key areas, including YouTube, Google News, Amazon Shopping, Instagram ads, Website ads, etc.
However, current AI-based recommender systems use usernames, customer names, mobile numbers, or IP addresses for identifying persons. This can be replaced by “wallet addresses,” which are unique yet do not reveal any personal information about the user.
A few Dapps are already using AI, such as Decentralized AI. It allows anyone to use AI without much knowledge.
For example, the dapp can be used for semantic segregation of people to identify different types of professionals present in a gathering.
Cryptocurrencies have the largest felt impact of AI on blockchain technology. There are several cryptocurrencies that use some degree of AI. Cryptoslate lists 68 AI-based cryptocurrencies with varied levels of Artificial Intelligence in them.
The most popular one of them and also the largest one is The Graph. It is used to query data from public blockchains like Ethereum and create custom visual statistical graphs. A user can also create suitable APIs using The Graph.