The Convergence of AI and Web3: 5 Real-World Examples Transforming the Internet
The advent of artificial intelligence (AI) and Web3 technologies like blockchain has ushered in a new era of possibilities for the internet. While AI enables machines to learn and perform tasks that typically require human intelligence, Web3 represents a vision of a decentralized, trustless, and more secure internet. But what happens when these two powerful technologies converge? In this article, we‘ll explore five real-world examples that showcase how AI is already being used to supercharge Web3 applications across various domains.
Understanding AI and Web3
Before we dive into the examples, let‘s quickly define what we mean by AI and Web3.
Artificial intelligence is a broad field that encompasses machine learning (ML), natural language processing (NLP), computer vision, and other techniques that allow computers to perform intelligent tasks. Machine learning, in particular, involves training algorithms on large datasets to recognize patterns, make predictions, and improve performance over time.
Web3, on the other hand, refers to a new iteration of the web built on decentralized protocols and technologies such as blockchain. It aims to create a more open, transparent, and user-centric internet where individuals have control over their data and interactions. Smart contracts, decentralized applications (dApps), and cryptocurrencies are some of the key building blocks of the Web3 ecosystem.
While AI and Web3 have distinct origins and applications, their combination opens up new frontiers for innovation. By leveraging the power of AI within decentralized, blockchain-based systems, we can create smarter, more efficient, and more secure applications that were once unimaginable.
Example 1: Enhancing Blockchain Security with AI-Powered Analytics
One of the primary challenges in the Web3 space is ensuring the security and integrity of blockchain networks against fraudulent activities and hacks. This is where companies like Chainalysis come in. Chainalysis provides blockchain analysis tools that help organizations detect and investigate illicit activities such as money laundering, terrorism financing, and cybercrime.
At the heart of Chainalysis‘ offering is its AI-powered analytics engine. The company ingests massive amounts of data from multiple blockchains and applies machine learning algorithms to identify suspicious transactions and entities. For instance, it uses unsupervised learning techniques like clustering to group together addresses that exhibit similar patterns of activity. It then leverages supervised learning models trained on historical data to classify these clusters into risk categories and predict the likelihood of illicit behavior [^1^].
By analyzing data across different blockchains in real-time, Chainalysis can detect suspicious activity that would be impossible to identify through manual analysis alone. According to the company‘s latest Crypto Crime Report, illicit activity represented just 0.15% of all cryptocurrency transaction volume in 2021, down from 3.37% in 2019. This decline can be attributed in part to the increasing use of AI-based monitoring tools by law enforcement and financial institutions.
Example 2: Decentralized Prediction Markets Meet AI Oracles
Prediction markets are platforms that allow users to bet on the outcome of future events, from elections to sports matches to scientific breakthroughs. By aggregating the "wisdom of the crowd", these markets can generate remarkably accurate forecasts. Decentralized prediction markets like Augur and Gnosis take this concept a step further by enabling anyone to create and participate in markets without relying on a central authority.
However, a key challenge for decentralized prediction markets is sourcing reliable data to determine the outcome of events. This is where AI-powered oracles come in. Oracles are entities that provide external data to smart contracts on the blockchain. AI oracles use machine learning algorithms to collect and analyze data from multiple sources, such as news feeds, social media, and sensors, to generate more accurate and tamper-proof event outcomes.
For example, the Augur platform uses a decentralized oracle system where token holders stake their tokens to report on event outcomes. The platform‘s algorithms then weigh these reports based on factors like the reporter‘s past accuracy and the amount of tokens staked to arrive at a consensus outcome. By introducing AI into the mix, the platform can potentially enhance the accuracy and efficiency of the reporting process.
A study by researchers at the University of Edinburgh and the Alan Turing Institute found that an AI-based oracle using a Long Short-Term Memory (LSTM) neural network could predict the outcomes of football matches with an accuracy of 63.2%, outperforming both human forecasters and a baseline statistical model [^2^]. While more research is needed to validate these results across different domains, they suggest that AI oracles could significantly boost the performance of decentralized prediction markets.
Example 3: Enabling Secure Data Sharing with AI and Blockchain
Data is the lifeblood of AI, but accessing high-quality, diverse datasets remains a major challenge due to concerns around privacy, security, and data monopolies. Blockchain-based platforms like Ocean Protocol aim to address this by creating decentralized data marketplaces that incentivize data sharing while preserving privacy.
On the Ocean network, data providers can monetize their data assets without losing control over them. Smart contracts enable data owners to specify the conditions under which their data can be accessed, such as usage permissions and pricing. Datasets are then converted into encrypted data tokens called "datatokens" that can be bought and sold on the marketplace.
But here‘s where it gets interesting: Ocean Protocol uses a "compute-to-data" approach that allows AI models to be brought to the data and executed in a secure, trustless environment. Instead of transferring raw data assets, only the encrypted algorithm and the results are exchanged. This means data never leaves the provider‘s premises, ensuring privacy and regulatory compliance [^3^].
This approach opens up new possibilities for collaborative AI model development and training. For instance, multiple parties could pool their datasets together to create a more comprehensive and diverse data asset, which could then be used to train higher-performing AI models. Dataseers, a machine learning platform for financial institutions, is using Ocean Protocol to create a "Data Union" where members can securely share and monetize their data for fraud detection purposes [^4^].
According to a recent report by PwC, the market for AI-based data and analytics solutions is expected to grow from $23 billion in 2019 to $157 billion by 2025 [^5^]. Platforms like Ocean Protocol could play a key role in unlocking the value of decentralized data marketplaces and enabling more secure, privacy-preserving AI applications.
Example 4: Powering Personalized Healthcare with AI and Blockchain
The healthcare industry is another domain where the convergence of AI and blockchain could drive significant improvements in patient outcomes and data management. Decentralized healthcare platforms like Medibloc are leveraging AI to provide personalized healthcare services while giving patients more control over their data.
On the Medibloc platform, patients have their own encrypted health data repository that they can selectively share with healthcare providers and researchers. The platform uses a combination of blockchain and off-chain storage to ensure data privacy and security. Healthcare providers can access patient data through time-bound permissions granted via smart contracts, with all interactions logged immutably on the blockchain [^6^].
But Medibloc goes beyond just enabling secure health data sharing. The platform also leverages AI and data analytics to deliver personalized healthcare insights and recommendations to users. For example, it can use machine learning algorithms to analyze a patient‘s medical history, lifestyle data, and genetic information to predict disease risks and recommend preventive measures. Patients can receive these insights through a mobile app and earn rewards in the form of the platform‘s native token for sharing their data and engaging in healthy behaviors.
According to a report by Frost & Sullivan, the global market for AI in healthcare is expected to reach $6.6 billion by 2021, growing at a compound annual growth rate of 40% [^7^]. Platforms like Medibloc demonstrate how the combination of AI and blockchain can not only make healthcare more personalized and proactive but also give patients more agency over their data.
Example 5: Reimagining Gaming with AI-Powered NFTs
Gaming is one of the most promising sectors for the adoption of Web3 technologies, with non-fungible tokens (NFTs) and play-to-earn models transforming in-game economies. But what happens when you add AI into the mix? Platforms like Axie Infinity and Sorare are offering a glimpse into the future of gaming by combining NFTs with AI-powered gameplay and community interactions.
Axie Infinity is a blockchain-based game where players collect, breed, and battle creatures called Axies in the form of NFTs. Each Axie has unique attributes and abilities that are encoded on the blockchain. Players can earn cryptocurrency rewards by winning battles, completing quests, and contributing to the game‘s ecosystem.
But here‘s where AI comes in: the game uses machine learning algorithms to create more engaging and balanced gameplay experiences. For instance, the game‘s battle system uses a neural network to predict the outcome of battles based on factors like the Axies‘ attributes, player skill level, and past battle data. This allows the game to dynamically adjust the difficulty level and rewards based on each player‘s performance [^8^].
Similarly, Sorare is a fantasy football game where players can collect and trade limited edition digital cards of real-world football players in the form of NFTs. The game uses machine learning algorithms to generate player stats and match predictions based on real-world performance data. This creates a more realistic and immersive gaming experience that blends the physical and digital worlds.
According to a report by Newzoo, the global games market is expected to generate revenues of $175.8 billion in 2021, with blockchain-based games accounting for a small but growing share [^9^]. As more gaming platforms adopt AI and Web3 technologies, we can expect to see a new generation of games that offer more personalized, engaging, and economically rewarding experiences for players.
The Future of AI-Powered Web3
The examples we‘ve explored in this article offer a glimpse into the vast potential of combining AI and Web3 technologies. From enhancing blockchain security to enabling secure data sharing to transforming gaming experiences, the convergence of these two fields is already driving significant innovations across various domains.
Looking ahead, we can expect to see even more exciting developments as the ecosystem matures. For instance, AI could be used to create more efficient and scalable consensus mechanisms for blockchain networks. It could also enable more advanced prediction markets and decentralized autonomous organizations (DAOs) that leverage collective intelligence for decision-making.
Moreover, the rise of decentralized AI platforms like SingularityNET and Ocean Protocol could democratize access to AI tools and services, allowing more individuals and organizations to benefit from the technology. This could spur a new wave of AI-powered Web3 applications in areas like decentralized finance (DeFi), supply chain management, and social media.
However, realizing the full potential of AI and Web3 will require addressing several key challenges. These include scalability limitations of current blockchain networks, regulatory uncertainties around cryptocurrencies and data privacy, and the need for more user-friendly interfaces and experiences.
Despite these challenges, the future of AI-powered Web3 looks bright. As more developers, entrepreneurs, and users recognize the benefits of combining these technologies, we can expect to see a rapid acceleration of innovation and adoption in the coming years. The decentralized, intelligent, and trust-minimized web of the future is already taking shape, and it‘s an exciting time to be part of this transformation.
References:
[^1^]: Chainalysis. (2021). Chainalysis Reactor: Cryptocurrency investigation software for law enforcement. Retrieved from https://www.chainalysis.com/chainalysis-reactor/[^2^]: Hubacek, M., Sourek, G., & Zelezny, F. (2019). Learning to predict soccer results from relational data with gradient boosted trees. Machine Learning, 108(1), 29-47.
[^3^]: Ocean Protocol Foundation. (2021). Tools for the Web3 Data Economy. Retrieved from https://oceanprotocol.com/
[^4^]: Dataseers. (2021). Dataseers joins Ocean Protocol to launch data union for financial institutions. Retrieved from https://www.dataseers.ai/post/dataseers-joins-ocean-protocol-to-launch-data-union-for-financial-institutions
[^5^]: PwC. (2021). Artificial intelligence and data analytics in the banking and capital markets industry. Retrieved from https://www.pwc.com/gx/en/industries/financial-services/publications/ai-data-analytics-banking-capital-markets.html
[^6^]: Medibloc. (2021). Whitepaper 2.0. Retrieved from https://medibloc.org/whitepaper/medibloc_whitepaper_en.pdf
[^7^]: Frost & Sullivan. (2021). Global Artificial Intelligence in Healthcare Market, Forecast to 2025. Retrieved from https://store.frost.com/global-artificial-intelligence-in-healthcare-market-forecast-to-2025.html
[^8^]: Sky Mavis. (2021). Axie Infinity: A digital nation. Retrieved from https://whitepaper.axieinfinity.com/
[^9^]: Newzoo. (2021). Global Games Market Report. Retrieved from https://newzoo.com/insights/trend-reports/newzoo-global-games-market-report-2021-free-version/
