AI-CHAIN NETWORK is a project aimed at creating a concept and theorization of the connection of artificial intelligence technology with the Block chain. We will present and propose with plans and considerations that could change the way of development of both the fastest-growing technology industries, ensuring their correlation.
A feature of blockchain smart contracts provides the ability to program the blockchain to manage transactions between participants involved in making decisions or generating and accessing data. Smart contract-based autonomous systems and machines can learn and adapt to changes over time, and make reliable and accurate decisions which are verified and validated by all mining nodes in the blockchain. Such decisions cannot be overturned and can be tracked, followed and verified by all participating entities. AI techniques using blockchain can offer decentralized decentralized learning to facilitate trust-based and secure sharing of knowledge and decision outcomes by a large number of autonomous agents who can contribute, coordinate, and vote on subsequent The concept of AI-Chain Network can be summarized as follows: – We provide an overview of the basics of blockchain and its key features and how these features can be used in AI. – We discuss how the integration of AI and blockchain can help create a new ecosystem for a decentralized economy.
In addition, we outline the key benefits of this integration. – We present a detailed taxonomy of blockchain platforms, architecture and infrastructure types, and consensus protocols, along with existing decentralized AI AI applications. – We present and discuss many practical use cases of AI AI applications and implementations using blockchain in various vertical domains. – We identify and describe open research challenges in adoption and use of blockchain in AI development and adaptation.
AI algorithms rely on data or information to learn, infer, and make final decisions. The machine learning algorithms work better when data are collected from a data repository or a platform that is reliable, secure, trusted, and credible. Blockchain serves as a distributed ledger on which data can be stored and transacted in a way that is cryptographically signed, validated, and agreed on by all mining nodes. Blockchain data are stored with high integrity and resiliency, and cannot be tampered with.
When smart contracts are used for machine learning algorithms to make decisions and perform analytics, the outcome of these decisions can be trusted and undisputed. The consolidation of AI and blockchain can create secure, immutable, decentralized system for the highly sensitive information that AI-driven systems must collect, store, and utilize . This concept results in significant improvements to secure the data and information in various fields, including medical, personal, banking and financial, trading, and legal data.
A blockchain-based approach would create a number of voting transactions transactions sent by robots to their neighbors in the swarm and all these votes are stored in the blockchain. This approach has proven that a blockchain-based swarm ecosystem preserves the integrity of transactions and provides a direct interface to securely store event records in a decentralized log The approach also demonstrated that transactions can be verified even when some swarm members get lost or leave the swarm. In a blockchain-based network, keys, which are publicly available, are the most important available information for an agent so that it can transmit information in a secure manner In the case of swarm robots, a robot can send information to a specific robot, and only the robot that has the matching private key will be able to read the message; In this way, the possibility of data breach can be prevented. Digital blockchain cryptography ensures that robots are allowed to use their private keys to encrypt messages. Other robots can then decrypt the message using the the sender’s public key . Digital signature cryptography can provide authentication of the origin of information and entity authentication between different robots in a swarm, and improve security during information exchange.
The purpose of our consideration of the system is not to make a profit for the creators: the goal is to create valuable shared resources. It is possible for creators to make a financial gain (depending on the incentive mechanism), but this is mainly the result of mechanisms mechanisms designed to punish authors who submit bad data. The dataset is also public because it can be found in the transaction history of the blockchain in the transaction history or through emitted events (as long as this functionality is available within the blockchain). Artificial intelligence is currently in a phase of narrow practical applications – so-called narrow AI. This means that practical applications of artificial intelligence are perfect for the narrow fields, some of which I have listed above.
But let’s not kid ourselves, in the privacy of well-guarded corporate and government labs, work is underway to create general AI that will have autonomy and the ability to generalize problems and abstract them to higher levels, similar to that of humans. This will completely change the balance of power and the nature of the global economic order to a far greater extent than covid-19 did, although the pace at which this change is implemented will be much smaller and more controllable. This transformation is probably inevitable and of considerable concern to most of us.
We are, after all, as a species, accustomed for millennia to firm intellectual dominance over the rest of the species living on earth. The prospect of the emergence of a creature that can intellectually surpass us is deeply disturbing.
We believe in developing dual AI using blockchain technology – so to bear witness to our ideals we created the Ai token. This token, which utilizes automation mechanisms using Smart Contract code technology, can be a testimony to our belief in the future concept of both technologies. Get to know the basic features of our token and join us – enthusiasts and promoters of the movement for the development of AI evolution in the crypto world.
Forreseeable challenges related to the unication and integration of both technologies are listed below:
Blockchain public ledgers enable secure and and authentic data processing, with the data collected being publicly available and accessible to all readers. This website may be an invasion of privacy and a cause for concern. Furthermore, IoT's ubiquitous sensing systems constantly collect personal and sensitive consumer data, and posting this data on open ledgers can lead to privacy issues. privacy issues. Using blockchain private ledgers, data privacy data privacy can be ensured by enabling encryption and allowing controlled access to the ledgers. However, such private blockchain platforms will limit access and exposure of large amounts of data that may be necessary for AI to process and make accurate and make accurate and correct decisions and analyses.
Side chains (known also as side channels) are used to accelerate the performance of blockchains, in which transactions are settled between parties in a quick manner outside the main chain, and settled only once per day on the main chain . Many new emerging types of blockchains improve signi cantly the consensus algorithms of mining nodes. Challenges For example, platforms like Algorand and IoTA can provide substantially better performance than that of Ethereum and Hyperledger blockchains.
Blockchain platforms face this problem to a lesser extent, because consensus protocols are predetermined between parties. In addition, the execution environment of mining nodes mining nodes is not protected, especially in the case of private blockchain platforms with several mining nodes, as in the case of the Hyperledger platform, in which the execution results can be manipulated. Which can be tampered with. To address this problem, emerging blockchain platforms are equipped to offer execution in trusted execution environments (Trusted Execution Environments)
This may pose a key challenge for decentralized AI, in which decision-making algorithms based on AI and machine learning AI and machine learning-based decision-making algorithms are executed as intelligent contracts by mining nodes, in which the execution results results are typically not deterministic, but rather random, unpredictable, and most often approximate. This site requires a novel solution to deal with approximate computations and to develop consensus protocols for node mining nodes to agree on results with a certain degree of certainty, accuracy, or precision, and with input data that can be highly variable, as in the case of IoT and sensory readings. readings.
Fog computing is a newly emerging computing paradigm that allows for localized computing and storage close to the source of data being generated by customers or IoT devices. Fog nodes are typically used to augment the long delay incurred by computing and storage at the cloud environment. Fog nodes can be thought of as a local small-scale cloud. In the context of AI and blockchain, future fog nodes have to be equipped with AI and machine learning capabilities as well as enabled with blockchain interface, whereby localized management, access, and control of data are performed by the fog nodes.
It is predicted that future quantum computing will be able to crack public-key encryption encryption in which private keys can be specified. The current blockchain relies on digital signatures that use public-key encryption. Many experts believe that quantum computing could make the basic security of blockchain by 2027. This requires serious research into a quantum secure and blockchain that will be resilient to such a breach, while also would guarantee high performance and scalability. In addition, involves robust migration and interoperability plans with quantum-resistant blockchain platforms.
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