The use of Artificial Intelligence, or AI, is slowly spreading, with new applications being discovered for the technology, and some previous ones – discarded. And while some are questioning the real-life applications of AI, others are stressing the vital role it will play in the formation of the internet of the future, or Web3.
However, there are still some misconceptions about Web3, or to be more specific – about the differences between Web3 and Web3.0. The fact of the matter is that both of these technologies have some aspects in common, but are rather different, with AI being the major factor uniting them under the banner of the internet of the future.
Web3 and Web3.0 – Differences and Similarities
On the technological level, Web3 is based on the use of blockchain technology as its core infrastructure, making the internet a fully decentralized environment. While Web3.0 also relies on a decentralized base for its operation, it entails the use of several other technologies that facilitate data exchange, among them being SKOS, SPARQL, OWL, and others. In addition, the Web3 blockchain basis makes all data entered into it immutable and completely secure, while any data input into Web3.0 can be altered with ease.
Better known as the semantic web, Web3.0 is entirely focused on providing efficiency in user interaction and data processing across its many hubs through the use of AI and machine learning. In contrast, Web3 is focused on personal data security and the use of user identity as an access key to various applications and resources. The centralized solid pod of user data storage applied in Web3.0 gives all of its users the ability to interact with third-party applications and information, while Web3 relies on blockchain-based wallets for user identity in the form of private keys.
Ultimately, both Web3 and Web3.0 are focused on the creation of a better and more reliable form of the internet, where the use of AI and machine learning streamline user interaction through speech recognition and data analysis, making communication with the machine basis more accessible and comprehensible. The permissionless and trustless nature of Web3 would make it accessible for any user from any point of the globe with an internet connection, including for developers who will be able to tap into the open-source code of the infrastructure.
The Application of AI in Web3 and Web3.0
Artificial Intelligence is vital for the realization of the potential that Web3 and Web3.0 entail. The use of such technologies that allow for near-instant and effective compilation and analysis of data make it possible for developers to introduce such concepts as natural language processing (NLP) and semantic web algorithms to facilitate integration with users. The use of machine learning will vastly improve data analysis and targeting accuracy by allowing the web to learn from human experience based on the information flows it receives. The end result is higher accuracy in matching user requests with potential solutions and vastly improved targeting of specific interests.
The key advantages in terms of technological improvements that the use AI entails include the following:
The ability to connect vast amounts of information through a single repository and make it accessible for instant analysis is the main advantage of a decentralized framework for Web3 and Web3.0 alike. Accessible to a huge number of applications in real-time makes it possible to connect an almost infinite number of devices, thus fast-forwarding the introduction of the Internet of Things on a massive scale. AI will play a critical role in this regard, analyzing and sorting information for better matching with solutions based on the data provided from machine learning streams.
The decentralized basis of the Web3 and Web3.0 architecture involve the use of autonomous agents – AI-based programs that would improve the operation of blockchain smart contracts. By utilizing such agents, contracts can be processed, executed and negotiated in real time, thus fully personalizing interactions between users and online entities. This paves the way for the use of cryptocurrencies as a means of payment within the system, thus optimizing transaction costs through the elimination of intermediaries in the chain of operation.
The use of AI is instrumental for the introduction of tailored and fully personalized user experiences. By analyzing individual user data and behavior, the AI will be able to filter useful information and formulate patterns to produce recommendations on various types of content. This element vastly improves the overall experience of using Web3 and Web3.0. The use of NLP further enhances that experience by making it possible to interact with the web via direct speech.
Selective data sharing is an interesting concept of Web3 that relies on the use of AI for empowering individuals and giving them the ability to choose which personal details they wish to control and share. Users will have the power to monetize their privacy, as their personal data will belong only to them and will not be publicly available as is the case in Web2. Companies will be willing to pay for that data to improve the targeting and accuracy of their AI and machine learning algorithms – a crucial factor for marketing and advertising.
AI makes it possible to process immense amounts of information, thus making it possible for scientists and even automated algorithms to engage in data mining. With sufficient useful information at their disposal, some entities will be able to engage in predictive analysis, create personalized recommendations based on arrays of statistical data, and even derive trends and insights.
The decentralized basis of Web3 and Web3.0 focuses on the privacy of users and grants control over their personal data to them. The use of AI can further enhance that inherent security by analyzing the system and revealing possible loopholes and vulnerabilities. It can also act as a sentinel and identify malicious behavior, such as phishing. AI is the vanguard of user privacy and the trustless environment of Web3, adding confidence through decentralization.
Web3.0 boasts an enhanced user experience as its key selling point. But that UX will be impossible without machine learning, which leverages data streams to learn and thus improve performance. By analyzing personal data blocks, the system will better adapt to each individual user and cater to their personal needs, both constant and dynamic in real time.
The combination of AI and machine learning make it possible to sift immense amounts of data to identify patterns of user behavior and adapt to them. Thus, by personalizing the entire user experience, Web3.0 will produce extremely accurate search results, effectively curing one of the main disadvantages of traditional search engines. Machine learning and AI will consider various factors when producing search results based on the user’s location, preferences, budgetary expectations, tastes, and much more. This means that users will be receiving tailored recommendations and will be free from having to sift through search results by themselves in search of solutions. It also means that social networks will be free from annoying and irrelevant advertising, while feeds will produce only user-relevant content.
The engagement of users through the display of relevant information is a key advantage of Web3 and Web3.0, which will not only add transparency to data flows, but will also likely result in the elimination of censorship and fake news. By analyzing multiple aspects of a single piece of information, the AI and machine learning algorithms will automatically identify truthful and confirmed facts.
Web3 and Web3.0 are actually two distinct, but complementary parts of a single system. Both are vital for the development and launch of a holistic version of the future form of the internet. The advantages of such a new and fully decentralized system are numerous, since they are all user-centric by nature, focusing on delivering an engaging, secure, interactive, personalized, and enhanced user experience.