AI set to take advantage of blockchain-based data infrastructure
The rise of ChatGPT has been nothing short of spectacular. Within two months of launch, the artificial intelligence (AI)-based application reached 100 million unique users. In January 2023 alone, ChatGPT recorded around 590 million visits.
In addition to AI, blockchain is another disruptive technology with increasing use. Decentralized protocols, applications and business models have matured and gained market traction since the Bitcoin (BTC) white paper was published in 2008. Much needs to be done to advance both of these technologies, but the zones of convergence between the two will be exciting to see.
While the hype is around AI, there is a lot going on behind the scenes to create a robust data infrastructure to enable meaningful AI. Low-quality data that is stored and shared inefficiently will lead to poor insights from the intelligence layer. As a result, it is critical to look at the data value chain holistically to determine what needs to be done to obtain high-quality data and AI applications using blockchain.
The key question is how Web3 technologies can exploit artificial intelligence in areas such as data storage, data transfers and data intelligence. Each of these computing functions can benefit from decentralized technologies, and companies are focusing on delivering them.
Data storage
It helps to understand why decentralized data storage is an important building block for the future of decentralized AI. As blockchain projects scale, any centralization vector may come to haunt them. A centralized blockchain project may suffer from governance breakdowns, regulatory issues, or infrastructure issues.
For example, the Ethereum network “Merge”, which moved the chain from proof-of-work to proof-of-stake in September 2022, could have added a centralization vector to the chain. Some have argued that large platforms and exchanges such as Lido and Coinbase, which have a large share of Ethereum’s stake market, have made the network more centralized.
Another centralization vector for Ethereum is its reliance on Amazon Web Services (AWS) cloud storage. Therefore, storage and processing power for blockchain projects must be decentralized over time to reduce the risk of a single centralized point of failure. This provides an opportunity for decentralized storage solutions to contribute to the ecosystem, providing scalability and stability.
But how does decentralized storage work?
The principle is to use several servers and computers around the world to store a document. A document can be easily shared, encrypted and stored on different servers. Only the document owner will have the private key to retrieve the data. On retrieval, the algorithm pulls these individual parts to present the document to the user.
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From a security perspective, the private key is the first layer of protection, and the distributed storage is the second layer. If a node or server on the network is hacked, it can only access parts of the encrypted data file.
Major projects within decentralized storage include Filecoin, Arweave, Crust, Sia and StorJ.
However, decentralized storage is still in a nascent state. Facebook generates 4 petabytes (4,096 terabytes) of data daily, but Arweave has only handled about 122 TB of data in total. It costs about $10 to store 1TB of data on AWS, while on Arweave the cost is about $1350 at the time of publication.
Undoubtedly, decentralized storage has a long way to go, but high-quality data storage can boost AI for real-world use.
Data transfer
Data transfer is the next key use case on the data stack that can benefit from decentralization. Data transfers using centralized application programming interfaces (APIs) can still enable AI applications. However, adding a centralization vector at any time to the data stack would make it less efficient.
Once decentralized, the next element in the data value chain is the transfer and sharing of data – primarily through oracles.
Oracles are devices that connect blockchains to external data sources so that smart contracts can connect to real-world data and make transactional decisions.
However, oracles are one of the most vulnerable parts of the computing architecture, with hackers targeting them extensively and successfully over the years. In a recent example, the Bonq protocol suffered a loss of $120 million due to an oracle hack.
In addition to smart contracts and cross-chain bridge hacks, oracle vulnerabilities have been low-hanging fruit for cybercriminals. This is mainly due to a lack of decentralized data transmission infrastructure and protocols.
Decentralized oracle networks (DONs) are a potential solution for secure data transfer. DONs have multiple nodes that provide high-quality data and establish end-to-end decentralization.
Oracles have been widely used in the blockchain industry, with various types of oracles contributing to the data transfer mechanism.
There are input, output, cross-chain and data-enabled oracles. Each of them has a purpose in the data landscape.
Input oracles carry and validate data from off-chain data sources into a blockchain for use by a smart contract. Output oracles allow smart contracts to transfer data off-chain and trigger certain actions. Cross-chain oracles carry data between two blockchains—which could be fundamental as blockchain interoperability improves—while data-enabled oracles use off-chain data to provide decentralized services.
While Chainlink has been a pioneer in the development of oracle technologies for blockchain data transfer, protocols such as Nest and Band also provide decentralized oracles. Apart from pure blockchain-based protocols, platforms such as Chain API and CryptoAPI provide APIs for DONs to consume off-chain data securely.
Data intelligence
The data intelligence layer is where all the infrastructure efforts for storing, sharing and processing data are carried out. A blockchain-based application using AI can still pull data from traditional APIs. However, it will add a degree of centralization and may affect the robustness of the final solution.
However, several applications make use of machine learning and artificial intelligence in crypto and blockchain.
Trade and investments
For several years, machine learning and artificial intelligence have been used in fintech to deliver robo-advisory capabilities to investors. Web3 has drawn inspiration from these AI applications. Platforms source data on market prices, macroeconomic data and alternative data such as social media, generating user-specific insights.
The user typically sets the risk and return expectations, with the recommendations from the AI platform falling within these parameters. The data required to deliver these insights is sourced from the AI platform using oracles.
Bitcoin Loophole and Numerai are examples of this AI use case. Bitcoin Loophole is a trading application that uses artificial intelligence to provide trading signals to platform users. It claims to have over 85% success rate in doing so.
Numerai claims it is on a mission to build “the world’s last hedge fund” using blockchain and AI. It uses AI to collect data from various sources to manage a portfolio of investments like a hedge fund would.
AI marketplace
A decentralized AI marketplace thrives on the network effect between developers building AI solutions on one end, and users and organizations using these solutions on the other. Due to the decentralized nature of the application, most commercial relationships and transactions between these stakeholders are automated using smart contracts.
Developers can configure the pricing strategy through inputs to smart contracts. Payment to them for use of their solution can be per data transaction, data insight or just a fixed fee for the period of use. There can also be hybrid approaches to the pricing plan, with usage tracked on the chain when the AI solution is used. The activities in the chain will trigger smart contract-based payments for using the solution.
SingularityNET and Fetch.ai are two examples of such applications. SingularityNET is a decentralized marketplace for AI tools. Developers create and publish solutions that organizations and other platform participants can use through APIs.
Fetch.ai similarly offers decentralized machine learning solutions to build modular and reusable solutions. Agents build peer-to-peer solutions on this infrastructure. The financial layer across the entire data platform is on a blockchain, enabling usage tracking and smart contract transaction management.
NFT and metaverse intelligence
Another promising use case is around non-fungible tokens (NFTs) and metaverses. Since 2021, NFTs have been seen as social identities by many Web3 users who use NFTs as Twitter profile pictures. Organizations like Yuga Labs have gone one step further, allowing users to log into a metaverse experience using their Bored Ape Yacht Club NFT avatars.
As the metaverse narrative increases, so will the use of NFTs as digital avatars. However, digital avatars in the metaverse today are neither intelligent nor bear any resemblance to the personality that the user expects. This is where AI can add value. Intelligent NFTs are being developed to allow NFT avatars to learn from their users.
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Matrix AI and Althea AI are two firms developing AI tools to bring intelligence to metaverse avatars. Matrix AI aims to create “avatar intelligence,” or AvI. The technology allows users to create metaverse avatars as close to themselves as possible.
Althea AI is building a decentralized protocol to create intelligent NFTs (iNFTs). These NFTs can learn to respond to simple user signals through machine learning. The iNFTs would become avatars on its metaverse called “Noah’s Ark.” Developers can use the iNFT protocol to create, train and monetize their iNFTs.
Several of these AI projects have seen an increase in token prices alongside the rise of ChatGPT. Nevertheless, user adoption is the true litmus test, and only then can we be sure that these platforms solve a real problem for the user. These are still early days for AI and decentralized computing projects, but the green shoots have emerged and look promising.
