With the advent of an exciting technological era on the horizon, experts widely anticipate a transformative shift in a substantial portion of the world’s software, where AI and machine learning (ML) will serve as the fundamental building blocks.
From cloud computing to networking, ML is revolutionizing our approach to the essential components of software infrastructure. The advent of Web3, which represents a decentralized and open version of the World Wide Web, is no exception. As Web3 gradually becomes more mainstream, machine learning is crucial in advancing AI-based Web3 technologies.
Nevertheless, integrating AI into Web3 comes with various technical challenges and obstacles. Therefore, to fully unlock the potential of AI and web3, it is imperative to identify the barriers that hinder this convergence and devise innovative solutions to overcome them.
Traditionally, AI-based solutions have been centralized, but as we delve into the decentralized realm of Web3, a critical question arises: what is the future AI and web3 together can bring, and why AI in web3 is revolutionizing, shedding its inherent centralization tendencies?
What is Web3?
Web3 refers to the decentralized and open version of the World Wide Web, transforming how we interact with the internet. Unlike the traditional Web2, where intermediaries and centralized platforms dominate, Web3 aims to empower individuals, promote privacy, and enable peer-to-peer transactions. It is built on blockchain technology, providing transparency, immutability, and security.
Web3 encompasses emerging technologies like decentralized applications (dapps), smart contracts, and cryptocurrencies. Its core principles include decentralization, user control over data, and the elimination of middlemen. Web3 fosters a more democratic and inclusive digital environment, enabling users to engage directly with decentralized networks and participate in the value they create.
What is AI?
AI, an acronym for Artificial Intelligence, pertains to the domain of computer science dedicated to creating intelligent machines with the capacity to carry out tasks typically associated with human intelligence. AI systems are designed to analyze data, learn from it, and make decisions or take actions based on the acquired knowledge.
These systems can simulate human cognitive abilities, such as understanding natural language, recognizing images, solving problems, and exhibiting creativity. AI encompasses various subfields, including machine learning, natural language processing, computer vision, and robotics. It has applications in diverse domains, from healthcare and finance to transportation and entertainment, revolutionizing industries and enhancing human productivity.