How Web3 technologies can utilise artificial intelligence in fields like data storage, data transfers, and data intelligence is the crucial question. Let's find out.
ChatGPT's growth has been nothing less of remarkable. The artificial intelligence (AI)-based application reached 100 million unique users two months after its launch. ChatGPT recorded around 590 million visitors in just January 2023.
Blockchain is another innovative technology that is gaining popularity in addition to AI. Since the Bitcoin white paper was published in 2008, decentralised protocols, apps, and business models have developed and acquired commercial momentum. Both of these technologies still have a long way to go, but it will be fascinating to see where they merge.
Despite the excitement around AI, much work is done behind the scenes to build a solid data infrastructure that will support useful AI. Poor intelligence layer insights would result from poorly stored and communicated low-quality data. So, it is crucial to examine the data value chain as a whole in order to ascertain what must be done in order to obtain high-quality data and AI applications utilising blockchain.
How Web3 technologies can utilise artificial intelligence in fields like data storage, data transports, and data intelligence is the crucial question. Decentralized technologies may be advantageous for each of these data capabilities, thus businesses are concentrating on providing them.
Understanding why decentralised data storage is a crucial component of the future of decentralised AI is helpful. Any centralization vector might come back to hurt blockchain initiatives as they grow. A centralised blockchain project can have infrastructural problems, a breakdown in governance, or regulatory restrictions.
For instance, the "Merge" of the Ethereum network, which switched the chain's algorithm from proof-of-work to proof-of-stake in September 2022, may have introduced a centralization vector. Others claim that the network has become increasingly centralised as a result of well-known platforms and exchanges like Lido and Coinbase, which control a significant portion of the Ethereum staking market.
Ethereum's dependency on cloud storage provided by Amazon Web Services (AWS) is another centralization factor. Hence, in order to reduce the hazards of a single centralised point of failure, storage and processing capacity for blockchain applications must gradually become decentralised. Decentralized storage options now have a chance to improve the ecosystem by adding reliability and scalability.
The idea is to keep a document on several servers and PCs throughout the world. Simply said, a document may be divided, encrypted, and kept on many servers. The private key to access the data will only be available to the document's owner. The programme extracts each of these components upon retrieval and displays the document to the user.
Private keys serve as the first line of defence in terms of security, followed by distributed storage. Just a portion of the encrypted data file is accessible if one node or server on the network is compromised. Filecoin, Arweave, Crust, Sia, and StorJ are significant efforts in the area of decentralised storage.
Yet, decentralised storage is still in its infancy. Daily data generated by Facebook amounts to 4 petabytes (4,096 terabytes), yet Arweave has only processed roughly 122 TB of data overall. On Amazon, storing 1TB of data costs roughly $10, but at the time of publishing, Arweave costs about $1,350. Decentralized storage undoubtedly has a long way to go, but high-quality data storage can advance AI for practical applications.
The next important use case in the data stack that can gain from decentralisation is data transmission. AI applications can still be made possible via data transfers utilising centralised application programming interfaces (APIs). It would become less efficient, though, if a vector of centralization were added at any stage of the data stack.
After decentralisation, the transfer and exchange of data, mostly through oracles, is the next step in the data value chain. Oracles are organisations that link blockchains to other data sources so that smart contracts may access other data sources and make choices about transactions.
Oracles, on the other hand, are one of the most exposed components of the data architecture, and throughout time, hackers have successfully and frequently targeted them. One recent instance was the loss of $120 million caused by an Oracle breach to the Bonq protocol. Oracle vulnerabilities have been low-hanging fruit for attackers in addition to smart contracts and cross-chain bridge breaches. Decentralized data transport infrastructure and protocols are mostly absent, which is the major cause of this.
Secure data transit may be possible using decentralised oracle networks (DONs). DONs establish end-to-end decentralisation and have several nodes that offer high-quality data. Several kinds of oracles have been employed widely in the blockchain business, contributing to the data transmission process.
There are oracles that support input, output, cross-chain, and computation. They all serve a certain function in the data ecosystem. For usage by a smart contract, input oracles transport and validate data from off-chain data sources to a blockchain.
Smart contracts can transmit data off-chain activities and initiate specific actions thanks to output oracles. As blockchain interoperability advances, cross-chain oracles, which transmit data across two blockchains, may become essential. Conversely, compute-enabled oracles employ off-chain computing to provide decentralised services.
Protocols like Nest and Band also provide decentralized oracles, however, Chainlink has been a pioneer in creating oracle technology for blockchain data transmission. Platforms like Chain API and CryptoAPI offer APIs for DONs to consume off-chain data in addition to pure blockchain-based protocols safely.
Fintech companies have been using machine learning and artificial intelligence for some years to provide investors with robo-advisory functions. These AI applications served as a source of inspiration for Web3. Platforms gather information on market pricing, macroeconomic trends, and other data sources like social media to produce user-specific insights.
The suggestions from the AI platform often fit within the user's risk and return expectations, which are defined by the user. The AI platform uses oracles to source the data needed to give these insights. Examples of this use case for AI are Numerai and the Bitcoin Loophole. A trading programme called Bitcoin Loophole uses artificial intelligence to send trade alerts to platform users. It asserts that its success rate in doing so is over 85%.
To create "the world's final hedge fund," according to Numerai, blockchain and AI will be used. It uses AI to gather information from many sources and manage an investment portfolio much as a hedge fund would.
All infrastructure efforts to store, share, and process data are realised at the data intelligence layer. AI-powered blockchain applications can still use conventional APIs to get data. Yet doing so would increase centralization, which may reduce the ultimate solution's resilience.
Yet, a number of blockchain and cryptocurrency applications are utilising machine learning and artificial intelligence.
The network effect between developers creating AI solutions on one end and consumers and organisations using these solutions on the other is what drives a decentralised AI market. The majority of business interactions and transactions between these parties are automated utilising smart contracts as a result of the application's decentralised structure.
By the use of smart contract inputs, developers may customise the pricing method. Payment to them for utilising their solution may take the form of a set retainer cost for the duration of use, per data transaction, or each data insight. Hybrid price plans are also a possibility, with consumption data being collected on-chain when the AI solution is applied. Smart contract-based payments would be made for using the solution as a result of the on-chain activity.
Such programmes are SingularityNET and Fetch.ai, to name only two examples. A decentralised marketplace for AI tools is called SingularityNET. With APIs, developers design and publish solutions that businesses and other platform users may utilise.
Similar to Fetch.ai, it provides decentralised machine learning options for creating modular, reusable solutions. Peer-to-peer solutions are built by agents on top of this architecture. Using a blockchain for the economic layer throughout the whole data platform enables use tracking and transaction management for smart contracts.
A possible use case involves metaverses and nonfungible tokens (NFTs). Since 2021, many Web3 users who use their NFTs as Twitter profile images have come to regard them as social identities. By enabling consumers to join in to a metaverse experience using their Bored Ape Yacht Club NFT avatars, organisations like Yuga Labs have gone a step further.
The use of NFTs as virtual avatars will increase along with the metaverse story. Yet today's digital avatars in metaverses are neither clever nor resemble the personality the user would anticipate. Here is where AI can be useful. To enable NFT avatars to learn from their users, intelligent NFTs are currently being created.
Two companies, Matrix AI and Althea AI, are creating AI tools to give metaverse avatars intelligence. AvI, or "avatar intelligence," is the goal of Matrix AI. Users may design metaverse avatars that are as similar to themselves as possible thanks to its technology.
A decentralised protocol is being developed by Althea AI to produce intelligent NFTs (iNFTs). With machine learning, these NFTs may be taught to react to basic user signals. On its "Noah's Ark" metaverse, the iNFTs would take the form of avatars. The iNFT protocol allows developers to build, train, and monetize their iNFTs.
Together with ChatGPT's growth, a number of these AI initiatives have witnessed a surge in token values. The essential litmus test, however, is user adoption; only then can we be certain that these platforms actually help users with their problems. Projects using decentralised data and AI are still in their infancy, but the early signs are encouraging.