Upbeat energy permeated EthCC, the Ethereum-focused conference in Paris last week, with the buzz of two hundred and fifty side events and beyond-capacity participation. Spurred on by a positive shift in the crypto-sphere, attendees seemed hopeful, pondering if the bear market tide was finally receding. Notable buoying factors included Ripple‘s recent partial victory against the U.S. Securities and Exchange Commission, which incited price rallies from XRP token to altcoins, including ether. Concurrently, news of bitcoin ETF applications submitted by esteemed global asset managers animated the digital-asset sector, albeit indirectly related to Ethereum.
Artificial Intelligence (AI), the event’s hot favorite topic, was widely deliberated on its confluence with blockchain technology. Opinions diverged broadly but optimism was unanimous. Ken Timsit from Cronos Labs envisions AI as a productivity amplifying tool in crypto financial markets. The potent combination could pave the way for capabilities like analytical chatbots on exchanges, leveling the playing field by providing the advanced technology historically exclusive to high-frequency trading houses.
Along similar lines, Upshot utilizes AI in valuating non-fungible tokens (NFTs), eying the prospect of trillions of dollars’ worth of assets like art, luxury goods, and eventually, real estate, on Web 3. However, these unique assets demand extensive human effort to facilitate, and AI could potentially resolve this by devising pricing mechanisms and clearing markets.
Yet, skepticism remains as the exact use cases remain ambiguous. One noteworthy area of contention surrounds zero-knowledge proofs (ZK), a cryptographic method that can validate a statement without any additional information disclosure. Eli Ben-Sasson, Co-Founder, and President of StarkWare, argues that while blockchain is adept at social coordination and privacy, these attributes don’t correspond with the requirements of machine learning.
Perhaps, counter-intuitively, a venture capitalist argued that blockchain is best employed in tracking data used in AI systems training, a computationally intensive task. Monitored algorithms could be critical. Yet, logistical issues persist – ZK proofs still entail sluggish computing power, performance, and usability. Companies such as Manta Network and Fabric Cryptography strive to address these concerns.
In spite of potential bottlenecks, various projects clearly hint towards capitalising on the synergies of AI and blockchain to seamlessly aggregate computational power required for training and operating machine-learning models.
As for the practical outcomes of these ambitious intersections, the jury is still out. Fascinating discussions ensued in the backdrop of picturesque Paris, over croissants, champagne, and stunning Eiffel Tower views. Only time will reveal where these promising dialogues will lead.
Source: Coindesk
