MEXC Global and Bybit announce a web3 ecosystem fund of $150 million to support artificial intelligence-based decentralized smart contract infrastructure on Fetch.ai.
Is Machine Learning-Based Blockchain the Next Generation of Cryptography?
MEXC Global and Bybit announced an ecosystem fund of $150 million to support applications on the machine learning-based blockchain ecosystem of Fetch.ai, a Cambridge-based artificial intelligence lab building an open-access decentralized machine learning network that allows for the deployment of smart applications on the AI-based blockchain network. With the rise of DeFi and GameFi applications, there is a new rush for building scalable blockchain solutions that enable the development of advanced decentralized applications. As per academic research, Machine learning (ML) algorithms offer incredible learning potential, which can unleash the potential of DeFi by allowing the creation of intelligent DeFi lending and intelligent Automated Market Maker that can cause reduce market inefficiency such as price slippage and protocol exploitation through flash loans in DeFi.
These features can make the blockchain smarter than the 2nd Generation and the 3rd Generation blockchains by enhancing the security of the distributed ledger by reducing downtimes in settlement and reconciliation of transactions and data flows. Additionally, the computational power of ML can be leveraged to reduce the time it takes to determine the golden nonce, as well as to improve data exchange pathways. Furthermore, blockchain technology's decentralized data architecture aspect allows us to create better machine learning models. Such applications can be used to fight covid-19 by applying. blockchain and AI for covid-19 modelling, managing immunity testing results through blockchain and privacy-preserving and gathering of personal data for collective analysis for medical applications.
Consider any smart blockchain-based application in which data is collected from various sources like sensors, smart devices, and IoT devices such as ArcTouch, Fetch.ai and NetObjex. The blockchain under this application functions as an integral part of the application. Storing data on a blockchain network reduces ML model mistakes because the data in the network does not include missing values, duplicates, or noise, which is a key prerequisite for a machine learning model to achieve greater accuracy. This reduces error margins when collecting medical data from IoT devices when treating patients in hospitals.
ML-based blockchain enables the development of advanced decentralized applications, Layer-1 solutions and programming languages such as the WASM-based smart contract language (Cosmwasm) on the Fetch.ai blockchain, allow for complex cryptography and machine learning logic be deployed on and off-chain, making it an interchain protocol. Layer-1 blockchains like Fetch.ai can operate as layer-2 networks for legacy blockchains and interchain bridges to the outside world. Fetch.ai's toolkits allow developers working in the cosmic ecosystem or Ethereum to develop interchain applications. The interchain applications can create a billion-dollar connected DeFi economy that can unleash new possibilities for blockchain protocols.
Leveraging Machine Learning in Blockchain-Based Applications
Using machine learning models in blockchain technology may have several advantages, including authenticating any authorised user when attempting to modify the blockchain. Developers can scale blockchain technology to give a high level of security and trust via the use of ML. To guarantee the terms and conditions that were previously agreed upon are maintained, ML models may be included in the application. Such as building a decentralized application that focuses on strengthening data security through the use of ML and blockchain for construction, manufacturing, energy and transportation companies.
The machine learning model can be adjusted to reflect the current state of the blockchain's network. Models may aid in the extraction of useful information from the user. For example, building personalized decentralized applications that link to IoT devices that can process agreements faster through smart contracts. We may then reward the user based on this continual computation. By using blockchain's traceability, one can also assess the hardware of various machines to prevent machine learning models from departing from the learning path to which they are allocated.
With the ML models built into the blockchain, businesses all across the globe can speed up their data trade. Where the ML models are responsible for managing the data's trading routes, alternatively, they may be used for other purposes such as data validation and encryption. Due to this, machine learning models can be applied to DeFi and other blockchain-based use cases such as contract tracing to scale and reduce black swan events by making IoT devices smarter and privacy-centric by enabling decentralized and more robust applications.
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Image credits: Brian McGowan and Maximalfocus.
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