Blockchain and AI
Blockchain and AI: Is it a good combination?
While blockchain and AI are different technologies, they complement each other. For instance, AI can read thousands of TBs of data, bringing new and more intelligent insights to blockchain-based businesses.
What's more, AI needs quality data to create better prediction models. With non-redundant, unchangeable information stored in its blocks, blockchain can act as a good source for data. Quality data enables companies to create more accurate production models.
Furthermore, Chung Dao, Co-founder and CEO of Orichain — a decentralized app and smart contract— believes that people don't understand blockchain fully: they think of blockchain as a secure world where financial transactions occur digitally. However, that's only one aspect of it. Here's what he has to say in response to Cointelegraph.:
AI can read thousands of TBs of data, bringing new and more intelligent insights to blockchain-based businesses.
"AI must come to help blockchain recognize real-world utility, expand its applicability and enable intelligent decision-making. Both technologies are in their early stages, but not 'very early.' There are many successful AI solutions that recognize patterns better than humans, and there are no doubt many advantages of automation in a wide range of businesses."
All of the above examples translate to one thing, and that is: Blockchain and AI are definitely a good combination. Let's now learn how AI will impact blockchain.
Getting Smarter, safer and more efficient
Impact of AI on the decentralized ledger
1. Enhance Data Security
Thanks to the inherent encryption, blockchain is a reliable storage place for personal information such as medical reports or payment data. Presently, the data must be decrypted to be analyzed by organizations, which is potentially risky.
However, organizations are formulating ways for AI to use encrypted data securely for analysis, which will be monumental for data security.
Furthermore, while blockchain, at its base, is secure, blockchain apps are still at risk. Bitfinex scam is an example. However, machine learning (a subset of AI) can reliably notice illicit patterns and predict data fraud, enhancing data security.
2. Improving Smart Contracts
This pointer is an extension of the above.
Blockchain does come with some technical flaws concerning smart contracts that hackers exploited. Simply put, smart contracts aren't that smart after all. They release and transfer funds when the programmed conditions are met, and a network consensus is reached.
This is when things get shady. As the code for smart contracts is public, anyone can review it line by line, looking for flaws or loopholes. Fortunately, AI can help verify smart contracts and predict data vulnerabilities that could be exploited.
3. Improving Efficiency of Blockchains
Traditional computers were fast and stupid at the same time. For instance, the hashing algorithms used for mining blocks on Bitcoin use a "brute force" approach, i.e., trying every possible combination of characters until the correct one is found to verify the transaction, which is inefficient.
However, if AI is fed the right data, it can crack that code using its intelligence within minutes or seconds. And this can improve the overall efficacy of blockchains.
4. Solving the Issue of Fragmented Liquidity: Probably the Biggest Contribution of AI
While blockchain has become popular in the past few years, most individuals still don't want to opt for blockchain and crypto. Why? Because of the poor liquidity of the crypto ecosystem.
Unlike their digital counterparts, such as bonds, stocks, commodities, and futures, digital assets are not yet accepted widely, which means only a limited number of people own them.
What's more, the trade volume at most exchanges is relatively lower than in the financial markets, despite the volatility of the crypto market. This impacts the trading experience leading to people dumping their holdings at high discounts and crashing the prices. And as the demand drops, the market becomes less liquid, making it hard to trade.
Furthermore, most blockchain networks operate individually and aren't interoperable. While thousands of cryptocurrencies exist, most of them stay confined in their own ecosystem. These cryptocurrencies existing in isolation and catering to a limited number of investors lead to liquidity fragmentation.
All this becomes a bottleneck for people who wish to trade crypto.
Pseudo or Half Solutions
Liquidity aggregators tried to solve the liquidity problem by connecting different crypto exchanges and creating a pool of tokens, making it easy to trade.
However, they were able to address only half of the problem. Because most of these liquidity aggregators could only combine the exchanges on a particular blockchain, creating a heterogeneous silo.
For instance, an aggregator deployed on Ethereum can aggregate liquidity from crypto exchanges that exist on the same chain.
Ahamed Ismail, the president and CEO of Fluid - an AI quant-based monetary platform – believes that Blockchain and AI are used by liquidity aggregators to understand the various phenomena and could be used to deal with fragmented liquidity. Here's what Ismail said:
"The mix can assist customers in capitalizing on one of the best alternatives. What this actually interprets into is an ultra-low latency and ultra-low-cost resolution to fragmented liquidity — a multitrillion-dollar downside that plagues the digital belongings market at the moment."
In a nutshell, blockchain and AI can tackle the problem of fragmented or low liquidity and do so with low latency and cost-effectiveness, generating greater interest and inspiring trust to trade crypto.