According to analysts, a sizable portion of the world’s software will be rewritten with artificial intelligence and machine learning as the primary building blocks. By 2030, artificial intelligence is predicted to add $15.7 trillion to the global economy, equating to a 14% increase in global GDP, according to PWC. AI will continue to advance, as will other technologies in the coming years. Capabilities like databases and identity are also notable when considering the pervasive parts of software applications.
You could say that intelligence is the primary ingredient in today’s most cutting-edge programs. Cloud computing, cyber security, and networking are just a few of the software ideas that are being rethought with the help of ML. The third generation of these software trends is called Web3, and ML (Machine Learning) is expected to play a crucial role in developing artificial intelligence-based web3 technologies.
Web3 is not an exception to the trend of AI impacting other technologies and markets. However, significant technical barriers prevent web3 technologies from fully embracing AI. Therefore, it is essential to determine how AI integration into web3 can be realized and the obstacles that might prevent it. The majority of AI-based solutions today are managed by a single entity. After all the hype, though, the question that needs answering is what place AI will have in the distributed third web. Is there a way to counteract the tendency for AI to centralize? This article provides an extensive discussion of these topics.
What is Web3 and What are its Key Features?
Web3 is an umbrella term for the next generation of the internet. When fully implemented, it will ensure that a small group of tech giants has no monopoly over the internet’s most fundamental features, and Web3 solutions will play a key role in this decentralization process. Decentralization means that everyone involved shares in the benefits, and users will have more privacy because they will have more control over their data. You can post whatever you like, and everyone will get a fair share of the rewards. Web3 has yet to be uniformly defined, but these are some of its defining features.
Features of Web3
- When it comes to web3, decentralization is key. The distributed ledger technology (blockchain) at the heart of Web3 would make it possible to store data in multiple nodes across a network. This would give users more control over the information stored in the massive databases of websites like Google and Meta.
- Web3 is open-source and distributed. Decentralized applications (dApps) are third-generation web applications on blockchain networks.
- For computers to understand information on par with humans, Web3 will implement technologies based on Semantic Web concepts and natural language processing. Machine learning will be used in Web3, too. This subfield of AI utilizes data and algorithms to model the learning process, gradually increasing its precision to levels where it can compete with human beings.
- Through web3, there is greater interoperability between information and content, making it available to a wider range of programs.
AI- What are the Types of AI?
The term “artificial intelligence” (AI) refers to the process by which machines attempt to mimic human cognitive abilities. Expert systems, NLP (natural language processing), speech recognition, and computer vision are all forms of artificial intelligence. AI uses specialized hardware and software to create and refine machine learning algorithms.
There Are Two Types of Artificial Intelligence:
Strong AI: Artificial intelligence that is “strong” allows machines to carry out tasks normally reserved for humans. There is more going on in these systems. These programs are designed to work independently of human input. Strong AI can be seen in places like self-driving cars and operating rooms.
Weak AI: Artificial intelligence that is too simplistic to perform complex tasks. Weak AI systems include those found in video games and personal assistants such as Siri and Amazon’s Alexa. The help desk staff interrogates you to determine the answers to your questions.
What Role Does AI Play in the Development of Web3 Intelligence?
AI can be used to further secure the blockchain by quickly mining data and making predictions about user behavior; this can help identify fraudulent activity and thwart attacks. Blockchain technology will also gain from using AI, which could be implemented as a protocol that uses AI to anticipate transactions and design scalable consensus mechanisms.
Using smart contracts and protocols, the Web3 stack can also incorporate ML capabilities. In the most glaring example of this pattern, we find DeFi.
For the rapid implementation of ML-driven features, decentralized applications (dApps) are predicted to be a top web3 solution. We can see this pattern developing already in NFTs. The next generation of NFTs will evolve from simple images to interactive artifacts. These NFTs can modify their actions depending on the owner’s emotional state.
Why AI in Web3?
Shift from Generalization to Individualism
Over the past decade, big tech has used centralized AI models to mine user data for insights and value. To ensure that all, not just the wealthy, share the benefits of AI, web3 is expanding its capabilities in this area. Each AI model is honed using the creator’s unique information collection, interests, and experiences.
Transfer of Control from Consumers to Entrepreneurs
In the world of web 3, creators own all of their information and creations, including any AI models used in them. Because so few organizations are investing in blockchain infrastructure, content creators retain complete control over their data and can use it for any purpose they see fit, including redistributing it.
From Scarcity to Utility
With the help of social tokens, your AI can give you and your network access to previously inaccessible collaboration spaces, allowing you to generate new, mutually beneficial assets.
From Consumption to Participation
These days, content creators produce their work, and their audiences consume it; the process is a one-way street on today’s platforms. Personal AIs and a decentralized system for exchanging value with social tokens give creators and their communities their platform.
Why does Web3 Follow the Top-Down Adoption of ML Technologies?
Given the web3 intelligence layers, a bottom-up adoption pattern seems likely. Blockchain runtimes will likely develop intelligence, trickling up to affect protocols like NFTs and DeFi. But due to severe technological limitations, web3 has had to adopt ML technologies hierarchically. Since blockchains are built for decentralized computing, these technical hurdles arise when developing new blockchain runtimes.
In contrast to state-of-the-art ML models, which necessitate time-consuming and laborious calculations to prepare and streamline designs, this method emphasizes a unified approach from the start. The native ML capabilities can be consolidated within blockchain runtimes. But we’ll need to refine this a couple of times.
Fewer constraints exist for DeFi protocols to take advantage of ML highlights because they can use external intelligent specialists and oracles. For NFTs and dApps, the limitations are nearly nonexistent. According to this theory, dApps and protocols will be the first to adopt ML capabilities in web3, followed by blockchain runtimes.
Web3 AI represents an emerging industry standard. An abundance of ML platforms, frameworks, and APIs that can bring smart features to web3 solutions have emerged due to the rapid AI development and research over the past decade. There are already intelligent web3 apps out there. We can state categorically that web3 is brilliant, but this intelligence is not uniformly dispersed.