Exploring The Synergies Between Blockchain And AI

What is Blockchain?

A blockchain is an immutable ledger technology that is used to store all types of data. Blockchain uses a network of blocks which are chained in a chronological way to store data.

Being immutable the data on the blockchain network is irreversible and tamper-proof. The transaction or data entry on the blockchain network is verified by multiple nodes on the network. Even if someone attempts to hack into the blockchain, he needs to hack all the impossible nodes. Moreover, if someone succeeds in altering the data in the blocks, the blockchain is designed in a way that it will automatically reject the altered data.

Apart from this, enterprise blockchain solutions also use smart contracts to automate processes. Smart contracts simplify complex processes through digital contracts that execute themselves once the terms and conditions are met.

Credit to these qualities of blockchain technology, businesses are getting blockchain development services to streamline their processes.

Most importantly, businesses with the help of blockchain can introduce data integrity, high security and automation into their models. Further, blockchain is helping companies achieve high levels of transparency and trust.

What is AI?

AI or Artificial Intelligence uses supercomputers, big data and machine learning to mimic human intelligence. The core purpose of AI is to analyze data to make predictions, reports, decisions and classifications. Moreover, the best thing about AI is that it trains itself from past data analysis and its outcomes.

Artificial intelligence mimics the decision-making and problem-solving capabilities of the human mind with greater efficiency and accuracy.

Companies are using AI solutions to automate repetitive tasks, prevent human error and cut down costs. Moreover, corporates are also using AI bots and robots to offer personalized customer services and enhance the overall customer experience.

Get quality AI solutions from an AI development company to upgrade your business model. AI will help you make informed decisions via big data analysis, reduce employee workload, and reduce human errors to achieve higher efficiency and performance.

Present Day AI Landscape

As we know that AI synthesizes data to act and rationale like human beings. For this AI uses machine learning and deep learning mechanisms.

Today, experts prefer deep learning over any other machine learning method. It is because the Deep Learning algorithm model is inspired by the human brain.

Deep learning involves training artificial neural networks, which are computational models that are composed of interconnected nodes or “neurons.”

Through the use of large datasets and iterative training, deep learning algorithms can learn to recognize patterns, make predictions, and perform complex tasks. Deep learning has been particularly successful in applications such as image recognition, speech recognition, natural language processing, and robotics.

What are the major challenges in AI technology currently?

Firstly, the problem with AI systems today is that the data they are using are all centralized. Big tech companies control and have authority over AI systems.

We do not know what type of data is fed into the AI system. All the companies hold their algorithms and data instead of making it open-source. Thus, there is no trust among the customers regarding the data and algorithms used to train the AI.

Secondly, the challenge with AI technology is that it requires extensive data to improve its performance. The more data an AI system can learn from, the better its output is likely to be. However, acquiring such large amounts of data can be costly and resource-intensive, making it difficult for small companies to compete.

At present, the advantage lies with large companies that have the necessary resources and expertise to develop effective training procedures for their AI systems. Additionally, these companies can afford to hire top talent to help develop new technologies.

Currently, dominant players such as Amazon, Google, and Facebook hold top positions in the AI field. Therefore, any new company entering the field must be able to at least match these powerful competitors to succeed.

Integrating blockchain technology into AI systems can resolve all major technical and cost issues.

Building an Ideal Blockchain AI Model

In recent years, research has revealed a link between blockchain and AI. With data handling being a crucial consideration in business operations, the combination of blockchain and AI can have a significant impact on data management and addressing related issues. Blockchain breaks data into smaller parts and distributes them across a network, with no central authority governing the data.

Every computer in the network maintains a complete copy of the ledger, ensuring that data is not lost. If you desire a private data ecosystem that is processed with AI and offers a superior user experience, blockchain is a good choice.

The integration of blockchain and AI can have far-reaching effects in various industries, providing dynamic solutions for privacy, energy, data control, security, and scalability.

While AI leverages machine learning to improve data performance, efficiency, and accuracy, blockchain provides the necessary power and energy to run a network of computers. Together, the combination of blockchain and AI can streamline the process and make it more viable.

Benefits Of Interlinking Blockchain and AI

• Reliable data source

A significant amount of data is required to train an AI application, and blockchain is an ideal source for refined data due to its transparency. With the traceability of nodes, the source of data can be easily verified.

• Autonomous system

Decentralized ledger technology ensures that no single server handles all the operations of the AI application, promoting an autonomous system that manages AI training and operations without supervision.

• Privacy protection

Cryptographic techniques used in blockchain enhance privacy throughout the network used for AI training and operations. A robust privacy system allows for competitive and highly complex AI systems to be trained and deployed.

• Distributed computing power

To train and maintain AI, significant computing power is required, and blockchain technology provides the necessary support. It takes care of hardware and software space requirements, storage, and maintenance costs.

• Security

While blockchain’s smart contracts are not entirely secure, AI can be used to generate more secure smart contracts, minimizing vulnerabilities and enhancing security.

• Efficient data storage

Blockchain applications have limitations in their data storage modes, sacrificing reading efficiency for a more write-intensive approach. However, with the use of AI, data storage methods can be enhanced to improve the speed of data queries.

• Authenticity

Blockchain’s digital record provides insights into the AI framework and the provenance of the data used, addressing the challenges of explainable AI. It enhances trust in data integrity and the recommendations AI provides by providing an audit trail, which pairs blockchain and AI to enhance data security.

• Augmentation

AI reads and processes data at a high speed, bringing higher intelligence to blockchain-based business networks. When access to larger data is provided, blockchain enables AI to scale and provide more actionable insights, manage model sharing and data usage, and create a transparent and trustworthy data economy.

• Automation

The combination of automation, AI, and blockchain creates new value for business processes that span different parties, removing friction, and increasing speed and efficiency. AI models embedded in smart contracts executed on the blockchain, resolving disputes and selecting the most sustainable shipping methods.

Creating Decentralized AI Systems

The concept of a decentralized AI system is to provide processor independence without sharing aggregate data, enabling users to process information independently across different computing devices. This approach can yield diverse findings, leading to fresh solutions for problems that a centralized system might not be able to solve.

Decentralized AI systems have promising applications in science, enterprises, and public services. They allow devices to overcome challenges through trial-and-error, reasoning, and recording results, resulting in a clear and consistent understanding of how things function.

By leveraging blockchain technology, AI can facilitate secure knowledge sharing and build trust in decision-making processes for decentralized learning. It can enable significant autonomous contributions, coordination, and voting in future decision-making processes. However, realizing the full potential of decentralized AI requires systems with significant computing power, rapid connectivity, and storage.

A decentralized AI platform can benefit from various security methods to ensure the privacy and confidentiality of data.

• SMPC (Secure Multi-Party Computation)

SMPC enables computations on sensitive data without revealing the data itself. By using techniques such as homomorphic encryption, the public function can be computed with private data while keeping the inputs hidden.

It will allow the development of AI models without disclosing datasets to external parties. Blockchain technologies such as Enigma utilize SMPC to enhance the privacy of their systems.

• GAN cryptography

GAN cryptography is a novel AI-based method for securing communication channels between parties. It involves the use of neural networks to create a new type of encryption and decryption that can withstand attacks from malicious actors. Google has developed this technology, and its potential benefits for decentralized AI platforms are being explored.

• Homomorphic encryption

Homomorphic encryption is an encryption technique that enables computation on encrypted data. Unlike traditional encryption methods, it does not require a secret key to perform computations, and the output is encrypted as well.

It is a significant advancement in cryptography and has potential applications in decentralized AI platforms. With homomorphic encryption, users can submit data to train models while keeping it private from other parties.

By utilizing these security methods, decentralized AI platforms can ensure the privacy and confidentiality of data while enabling the development of powerful AI models.

Top 5 AI Blockchain Projects

• SingularityNET

SingularityNET is a leading AI blockchain project that facilitates the development of a decentralized AI marketplace, which can aid in the creation and funding of AI projects. Its main objective is to function as a worldwide network for AI algorithms.

The marketplace enables developers and businesses to create and sell their AI projects, including tools, data, services, and algorithms. One noteworthy aspect of SingularityNET is its use of smart contracts.

• Thought AI

Thought AI is an innovative example of AI in blockchain that combines analytics, embedded data, and blockchain to create a unique approach that allows digital information to act independently without needing the application layer. This approach helps scale AI across businesses, govern AI, and generate meaningful value, making it one of the top AI blockchain projects.

• DeepBrain Chain

DeepBrain Chain is a decentralized AI computing platform that uses blockchain to provide a cost-efficient and privacy-protected network for AI development. It aims to replace the current NEP-5 chain and supports secure data trading while maintaining data privacy and value.

• BlackBird.ai

Blackbird.AI is a leading project that combines AI and blockchain technology for content analysis, particularly in verifying the credibility of content and efficiently filtering out fake news.

Through the use of AI, Blackbird.AI classifies and filters content based on specific credibility indicators to determine if a piece of news contains hate speech, misinformation, or satire.

• Matrix AI

Including Matrix AI in a list of top AI-blockchain projects is essential due to its ability to leverage the capabilities of AI-based technologies like Natural Language Processing (NLP) while offering basic blockchain functionalities.

Matrix AI is known for its dynamic delegation networks, auto-coding intelligent contracts, adaptive blockchain parameters, AI-based blockchain security, and other notable features. Its AI-based secure virtual machine is particularly noteworthy for its ability to identify potential vulnerabilities and malicious intent, ensuring a robust and secure system.

Future Approach

The combination of artificial intelligence and blockchain can lead to the creation of a secure and decentralized system that is immutable. This innovative approach can result in significant advancements in data and information security across various industries.

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