The role of AI in emerging Web3 technologies
Recent advancements in artificial intelligence (AI) technology have broadened its practical applications and allowed the technology to be integrated into diverse and previously unimagined domains.
From curating personalized recommendations that enhance online user experiences to guiding self-driving cars, AI’s influence is undeniable. Deep learning algorithms drive many of AI’s recent breakthroughs. This is because deep learning capabilities allow AI systems to carry out tasks with remarkable efficiency and accuracy by emulating the natural human learning process, which is made possible through their reliance on artificial neural networks.
Unlike traditional rule-based approaches, deep learning algorithms are trained using large quantities of data to refine their performance progressively. This iterative process allows AI applications to discern complex patterns and relationships within the data, ultimately enabling them to perform designated tasks with exceptional precision.
The enhanced capabilities make AI the ideal solution for augmenting Web3 features. So, what exactly is Web3? The term Web3, coined by Ethereum co-founder Gavin Wood in 2014, refers to a transformative concept of the internet built on the principles of decentralization and user empowerment and is underpinned by blockchain technology.
Unlike the current internet setup, often referred to as Web2, which relies heavily on centralized platforms and intermediaries, Web3 is envisioned as a more distributed and user-centric version of the internet.
User ownership and data control take center stage in this novel framework, whose core tenets are upheld by bypassing centralized entities that exploit user data, as is prevalent in the current Web2 internet model.
This level of autonomy also extends to content monetization, where Web3 enables communities to create their own tokenized economies and content creators to own and manage their content directly.
By removing intermediaries, Web3 also allows creators to connect directly with their audiences, set their own monetization rates, and receive full compensation for their work while preserving their artistic sovereignty.
The role of AI in Web3
Presently at the nexus of a technological revolution, AI has the potential to supercharge Web3’s growth. The following is a breakdown of how AI can be used to enhance Web3 environments:
Enhancing user experiences
AI could emerge as a transformative force that revolutionizes user engagements on Web3 platforms by providing users with hyper-personalized experiences. The technology could, for example, be used to deliver news feed content to users based on their individual preferences. This capability would be achieved by leveraging data-driven insights based on user interactions and engagement patterns.
The technology could also be used to develop superior Web3 applications. The applications, powered by AI, would offer next-generation features and functionalities. In Web3 gaming environments, for example, AI-powered non-player characters (NPCs) could be set up to adapt to players’ actions and choices, creating dynamic narratives and more immersive experiences. NPCs are controlled by the game’s software rather than by a player.
AI in content creation
The internet’s very existence hinges on a constant stream of fresh content that fuels engagement by igniting conversations and encouraging discovery. Yet the demands of continuous content creation can be daunting.
Enter generative AI — a game-changer in this area. Generative AI, short for generative artificial intelligence, refers to a class of AI systems that are designed to generate unique content autonomously.
In the context of Web3, generative AI tools can, for example, be used to create unique, evolving artwork, overlays and sound effects. This level of variability and adaptability is made possible through the use of machine-learning frameworks such as generative adversarial networks (GANs).
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Enhancing quality
The quality of content will be a crucial factor in attracting and retaining users on Web3 platforms. This is because high-quality content captivates users, which leads to increased engagement and interactions.
Web3 platforms could leverage AI-powered tools to enhance their content by using the tools to automate editing tasks such as image enhancements and translations.
Furthermore, AI could be integrated into Web3 spaces to detect deepfakes — manipulated and often deceptive media content — by employing sophisticated algorithms and machine learning techniques such as those based on convolutional neural networks (CNNs) to carry out facial analysis and discern this type of media.
AI-driven smart contracts
Smart contracts are autonomously executing snippets of code that run on blockchain systems. The meticulously crafted programs serve as immutable arbiters of trust that execute without the need for intermediaries and get triggered only when predefined conditions are met.
When augmented with AI capabilities, they evolve beyond their traditional scope and can analyze extensive data sets, comprehend complex scenarios, and make adaptive decisions in response to shifting conditions. By making use of AI-driven smart contracts, Web3 platforms can usher in a new era of smart contracts that possess both intelligence and versatility.
In Web3 decentralized finance (DeFi) ecosystems, for example, AI-driven smart contracts could be used to calculate optimal collateral requirements for loans based on real-time market data and risk factors. Such capabilities would help to mitigate liquidity-related issues on DeFi networks.
AI data insights and predictive analytics in Web3
AI technology can be used within Web3 platforms to undertake data analysis with the aim of extracting meaningful insights from decentralized data sets.
Natural language processing (NLP) AI algorithms could, for example, be used to analyze textual data within Web3 platforms to determine aspects such as sentiments, topics and other key information. Such capabilities would be beneficial to understanding user feedback and sentiment.
Beyond text analysis, predictive analytics AI models could be deployed to analyze market trends and token dynamics in order to provide deep insights into tokenomics within Web3 networks. Such features would be particularly beneficial to Web3 investors who wish to understand prevailing market conditions, risks and evolving ecosystem dynamics.
AI for Web3 security solutions
AI Web3 security solutions could be counted on to implement robust security protocols such as digital identity verification. For instance, AI-powered verification systems can be used to confirm the authenticity of users within decentralized networks through the use of techniques such as facial recognition and biometric authentication. Such capabilities can help reduce the risk of impersonation or unauthorized access.
In addition, the technology could be harnessed to implement privacy-preserving techniques such as differential privacy and homomorphic encryption that allow data to be analyzed without compromising user privacy.
Besides the aforementioned security use cases, AI could be utilized in Web3 to detect fake nonfungible tokens (NFTs). AI tools built for this purpose are usually able to scrutinize NFT data, such as timestamps, signatures and unique identifiers, to flag potential fakes. Such technologies would be instrumental in deterring copyminting scams in Web3 decentralized marketplaces.
AI technology for oracles
In the world of decentralized blockchain applications, smart contracts are exceedingly important. However, the self-executing code agreements require accurate and trustworthy external data to work effectively, and they depend on oracles to obtain this information.
While conventional oracles are reliable to a certain extent because they are rule-based and get their data from predefined data sources, they are unable to analyze and interpret information to detect potentially unreliable data that could lead to unprecedented platform risks.
By employing data verification methods such as cross-validation techniques, the AI oracles would ensure that only verified data reaches the smart contracts, thereby mitigating the risk of fraud and manipulation.
Web3 data sovereignty and AI
Traditional AI models, largely relegated to centralized systems, often struggle to reconcile with decentralization ideals. Their reliance on private data stores and proprietary algorithms raises data privacy and control concerns, thereby hindering true decentralization.
The introduction of decentralized AI models has the potential to address these issues. Decentralized AI models, unlike their centralized counterparts, would ideally be developed within open-source communities that uphold transparency and collaboration.
The algorithms, devoid of proprietary limitations, would allow Web3 platforms and their users to benefit from AI technologies without surrendering their data to centralized entities. Advances in such decentralized systems would help to uphold the sovereignty and decentralization of Web3 networks.
