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AI-Enhanced Blockchain: The Role of AI in Optimizing Blockchain Systems
Artificial intelligence and blockchain are two of the most used buzzwords in the last 12-24 months. For a time (and probably still today), all a company had to do was mention one of two words to its investors to spark interest and send stock prices soaring.
But as tempting as it is to be cynical about zeitgeists like these, there are valid reasons why the invocation of both concepts is so powerful. It’s because there’s a basis for the hype.
Then, of course, the AI-powered blockchain session of London Blockchain Conference 2024 he was one of the top registrants on day two. Two leaders took the stage to discuss the transformative potential of integrating AI into blockchain operations: Ijeoma Okoli, director of the Digital Economy Initiative, joined moderator Stefania Barbaglio, founder of Cassiopeia.
Okoli is a former financial lawyer with a wealth of regulatory experience in the US and UK, and this is the perspective from which he evaluates both of these technologies. Barbaglio starts from the bottom: how can artificial intelligence help blockchain be more efficient?
“Sometimes people talk about these emerging technologies as solutions in search of a problem,” Okoli said. “This is true to some extent, but in other ways it is not true.”
“We know that blockchain has been around for a while and that traditional financial services are adopting it beyond the native cryptocurrency space. But with the birth of ChatGPT and the explosion of AI in the world in 2022, people are looking at the problems within blockchain and how AI can help solve some of these problems.”
One of these problems concerns scalability. The speed of transactions limits the growth of blockchains, Okoli says. Watching BTC and Ethereum: BTC, on average, processes 7 TPS, while ETH processes 30 TPS. Financial services payment providers dwarf this figure. View (NASDAQ:V) processed, for example, 6,800 TPS. This is a huge disparity. Not only that, the transaction speed of currencies like BTC and ETH typically slows to a crawl during periods of high congestion.
Okoli says that, in light of AI’s ability to analyze data and then use it to make predictions, this shows that the technology could be used to project periods of congestion within blockchain ecosystems.
As another example, Okoli considers Ethereum side-chain solutions, generally referred to as one of two types of “rollups”: zero-knowledge rollups and optimistic rollups. The first sees transactions validated off-chain and then incorporated into the blockchain using zero-knowledge proofs. The latter form considers all transactions to be authentic unless a validator disputes their validity. Such validations, Okoli says, can take up to two weeks. Outsourcing this validation to AI can dramatically reduce this turnaround.
Okoli also says that in addition to analyzing data on-chain (or waiting to be entered), it could also carry out audits blockchain applications for vulnerabilities. Instead of waiting for software to be released to the world and then relying on black or white hats to uncover any exploits, AI could get the job done quickly and efficiently.
The same can be said for fraud detection. Okoli cites last year’s Ethereum hack to illustrate this. In April there was a exploit on Ethereum blockchain where validators manipulated a vulnerability to steal $25 million. What was important in this case was that it was the first exploit of its kind, highlighting a vulnerability in the post-merge validation process that was only discovered at the time it was exploited. So, Okoli says, AI could have been used to test and detect the vulnerability before bad actors did, but it would also have aided detection once the exploit started being used to steal money.
Next, Barbaglio asks: what about the challenges of integrating AI with blockchain?
Perhaps unsurprisingly, given Okoli’s background, his answer was: regulation. However now, as both of these technologies have already begun to make their way into the consciousness of legislators and regulators, the issue is more grappling with the numerous regulatory frameworks that have become or are becoming law in the last 12 months.
Okoli points out that just this week, the news Artificial Intelligence Act entered into force in the European Union. He calls it “the first of its kind in the world, focused specifically on artificial intelligence.”
“To the extent that people look to incorporate AI into blockchain systems, it’s not just the [EU and UK Markets in Financial Instruments] and the Securities Exchange Act, now you need to think about the EU AI Act and the obligations it imposes on you.”
“It’s a risk-based model,” he explains, “that prohibits certain things. But it has extraterritorial effects. So just because you’re not in the EU doesn’t mean you shouldn’t pay attention.”
Okoli explains that the law requires risk assessments, disclosure and cooperation with authorities. He also warns that while this is the first legislation focused on artificial intelligence, it won’t be the last.
Okoli also highlights this sound regulation of AI and blockchain share one of the main challenges that these same technologies face: interoperability.
“Whenever we find ourselves in a situation where there is something new and regulators around the world have to act, to the extent that they are racing against time, what we tend to find is that there are inconsistencies. This has an effect where things are global, i.e. the activities intended to impact are not limited to the territorial waters of a particular jurisdiction. These are global transactions.”
For artificial intelligence (AI) to function within the law and thrive in the face of growing challenges, it must integrate an enterprise blockchain system that ensures the quality and ownership of data input, allowing it to keep it secure while ensuring immutability. of data. Check out CoinGeek’s coverage about this emerging technology to learn more because enterprise blockchain will be the backbone of artificial intelligence.
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