Web3 Challenges and Opportunities for the Market
Authors: Dan Sheridan (College of Computing and Software Engineering, Kennesaw State University, GA, USA), James Harris (College of Computing and Software Engineering, Kennesaw State University, GA, USA), Frank Wear (College of Computing and Software Engineering, Kennesaw State University, GA, USA), Jerry Cowell Jr (College of Computing and Software Engineering, Kennesaw State University, GA, USA), Easton Wong (College of Computing and Software Engineering, Kennesaw State University, GA, USA), Abbas Yazdinejad (Cyber Science Lab, School of Computer Science, University of Guelph, Ontario, Canada)
Abstract: The inability of a computer to think has been a limiter in its usefulness and a point of reassurance for humanity since the first computers were created. The semantic web is the first step toward removing that barrier, enabling computers to operate based on conceptual understanding, and AI and ML are the second. Both semantic knowledge and the ability to learn are fundamental to web3, as are blockchain, decentralization, transactional transparency, and ownership. Web3 is the next generational step in the information age, where the web evolves into a more digestible medium for users and machines to browse knowledge. The slow introduction of Web3 across the global software ecosystem will impact the people who enable the current iteration. This evolution of the internet space will expand the way knowledge is shared, consumed, and owned, which will lessen the requirement for a global standard and allow data to interact efficiently, no matter the construction of the knowledge. The heart of this paper understands the: 1) Enablement of Web3 across the digital ecosystem. 2) What a Web3 developer will look like. 3) How this alteration will evolve the market around software and knowledge in general.
Explore the paper tree
Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant
Look for similar papers (in beta version)
By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.