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Decentralized approaches are used in a number of data infrastructure and intelligence use cases to provide AI functions.

ChatGPT's growth has been nothing less of remarkable. The artificial intelligence (AI)-based program gained 100 million unique users two months after its launch. ChatGPT recorded around 590 million visits 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, decentralized protocols, apps, and business models have developed and gained market traction. Both of these technologies still have a long way to go, but it will be fascinating to see where they converge.

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 using blockchain.

How Web3 technologies can utilize 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, so businesses are concentrating on providing them.

Storing Data

Understanding why decentralized data storage is a crucial component of the future of decentralized AI is helpful. Any centralization vector could come back to hurt blockchain projects as they grow. A centralized blockchain project can experience 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, might have introduced a centralization vector. Some claim that the network has become increasingly centralized 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 centralized point of failure, storage and processing capacity for blockchain applications must gradually become decentralized. Decentralized storage options now have a chance to improve the ecosystem by adding reliability and scalability.

How does decentralized storage operate, though?

The idea is to keep a document on numerous servers and PCs throughout the world. Simply put, a document may be divided, encrypted, and kept on various servers. The private key to access the data will only be available to the document's owner. The algorithm extracts each of these components upon retrieval and displays the document to the user.

Private keys serve as the first line of defense in terms of security, followed by distributed storage. Only 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 decentralized storage.

Yet, decentralized 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 publication, Arweave charges about $1,350.

Decentralized storage undoubtedly has a long way to go, but high-quality data storage can advance AI for practical applications.

Data Exchange

The next important use case in the data stack that can gain from decentralization is data transmission. AI applications can still be made possible via data transfers using centralized 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 decentralization, the transfer and sharing of data, mostly through oracles, is the next step in the data value chain.

Oracles are organizations that link blockchains to other data sources so that smart contracts can access other data sources and make choices about transactions.

Yet, oracles are one of the most exposed components of the data architecture, with years of extensive and effective hacking against them. In one recent instance, an Oracle compromise cost the Bonq protocol $120 million.

Together with cross-chain bridge attacks and smart contracts, oracle vulnerabilities have been easy targets for attackers. This is mainly because there is no decentralized infrastructure or protocol for data transit.

A proposed answer for safe data transit is decentralized oracle networks (DONs). DONs have numerous nodes that establish end-to-end decentralization and offer high-quality data.

The blockchain sector has made substantial use of oracles, with several oracle types contributing to the data transfer 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 use off-chain computation to provide decentralized services.

Decentralized oracles are also provided by protocols like Nest and Band, however Chainlink has been a pioneer in developing oracle technology for blockchain data transfer. Platforms like Chain API and CryptoAPI offer APIs for DONs to safely consume off-chain data in addition to pure blockchain-based protocols.

Knowledge-Based Data

All infrastructure efforts to store, share, and process data are realized at the data intelligence layer. AI-powered blockchain applications can still use conventional APIs to source data. Yet doing so would increase centralization, which might reduce the ultimate solution's robustness.

Yet, a number of blockchain and cryptocurrency applications are utilizing machine learning and artificial intelligence.

Investing And Trading

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 prices, macroeconomic trends, and alternative data sources like social media to produce user-specific insights.

The recommendations 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 program 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 use AI to gather information from many sources and manage an investment portfolio much like a hedge fund would.

Market For AI

The network effect between developers creating AI solutions on one end and users and organizations using these solutions on the other is what drives a decentralized AI market. The majority of business interactions and transactions between these parties are automated utilizing smart contracts as a result of the application's decentralized structure.

Through the use of smart contract inputs, developers can customize the pricing method. Payment to them for using their solution may take the form of a flat 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 programs are SingularityNET and Fetch.ai, to name only two examples. A decentralized market place for AI tools is called SingularityNET. With APIs, developers design and publish solutions that businesses and other platform users can use.

Similar to Fetch.ai, it provides decentralized 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 entire data platform enables usage tracking and transaction management for smart contracts.

Metaverse Intelligence And NFT

A promising 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 log in to a metaverse experience using their Bored Ape Yacht Club NFT avatars, organizations like Yuga Labs have gone a step further.

The use of NFTs as virtual avatars will increase along with the metaverse story. But today's digital avatars on metaverses are neither clever nor resemble the personality the user would expect. 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 can design metaverse avatars that are as similar to themselves as possible thanks to its technology.

A decentralized protocol is being developed by Althea AI to produce intelligent NFTs (iNFTs). With machine learning, these NFTs can be taught to react to basic user cues. Developers can use the iNFT protocol to develop, train, and profit from their iNFTs, which would become avatars on its "Noah's Ark" metaverse.

Together with ChatGPT's growth, a number of these AI initiatives have seen a surge in token prices. The essential litmus test, however, is user adoption; only then can we be certain that these platforms actually help users with their problems. Projects involving decentralized data and AI are still in their infancy, but the early signs are encouraging.

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