In this article, I’m about to drop some knowledge bombs on you. Let me start with a fair warning: this isn’t your run-of-the-mill newbie-friendly stuff. Familiarity with the tech landscape will be your trusty guide. Brace yourself for a mind-melting exploration of two seismic innovations that are reshaping the very fabric of the tech world. I’m talking about the awe-inspiring might of Artificial Intelligence (AI) and the unshakable fortress of reliability that is blockchain.

AI is like giving machines and computers a taste of human-like thinking and decision-making capabilities. It’s a whole new level of intelligence that’s taking industries by storm. We’re talking revolution here, folks!

Blockchain is not just a buzzword. It’s a distributed ledger that’s changing the game when it comes to storing data. This is all about decentralization and trust. It keeps your information locked up tight and secure, like Fort Knox on steroids. Get ready, my friend, because the future just got a whole lot more interesting.

In the past decade, blockchain has emerged as one of the most highly anticipated technological innovations, gaining momentum as it found applications in various fields. Since its inception in 2008, blockchain has been recognized as a disruptive technology with the potential to revolutionize data storage, information exchange, and transactional processes.

A key feature of blockchain is its cryptographic signature mechanism, which ensures that every transaction on the blockchain is securely signed. Mining nodes, which hold a copy of the entire ledger of chained blocks, verify each transaction. This process results in the creation of synchronized, secure, and shared timestamped records that are virtually impossible to alter. This immutability and transparency make blockchain an effective option for eliminating the need for a central authority to govern and verify transactions and interactions among network users.

In today’s technical landscape, the exponential growth of data generated by IoT devices, smartphones, social media, and web applications has played a pivotal role in the advancement of artificial intelligence (AI). AI systems heavily rely on vast amounts of data, utilizing deep learning and machine learning techniques to perform complex analytics.

Currently, many AI models follow a centralized approach where a group of servers trains a specific model against a large dataset and validates the learning using another dataset. Storing a substantial amount of data is crucial for effective AI model training, leading major tech organizations and development teams to accumulate extensive datasets to achieve optimal performance and results.

While centralization has brought success to the AI industry, it comes with significant drawbacks. Centralized data storage poses risks of data tampering and corruption, making it vulnerable to malware and cybersecurity attacks. Moreover, when dealing with large datasets, ensuring the authenticity and provenance of the data sources becomes a challenging task. Incorrect training due to unverified data sources can lead to inaccurate and potentially harmful outcomes.

To address these challenges, blockchain technology has gained prominence in the field of AI. Decentralized AI aims to leverage blockchain to enable secure, trusted, and decentralized data storage and transaction processes without relying on external third-party resources.

Blockchain’s distributed ledger offers an ideal solution for storing AI data. The data is securely stored and verified through digital signatures and shared among participants on the blockchain network. By utilizing blockchain smart contracts, the network can govern transactions and decision-making processes among data generators and users. Autonomous applications and machines powered by blockchain smart contracts can adapt over time, make accurate decisions, and ensure outcomes are verified and validated by the blockchain network’s mining nodes.

Several shortcomings of the artificial intelligence and blockchain industry can be addressed efficiently by combining both technical systems.

By combining the technical systems of the artificial intelligence and blockchain industry, we can address several shortcomings. Blockchain stores and sends data that has been verified by mining nodes of the network. The networks store data in such a way that it is almost impossible to alter the data. This is the foundation of trust for the outcome of machine learning algorithms that make decisions using smart contracts. Using blockchain networks with AI technologies can help to create decentralized, immutable, and secure systems for highly sensitive data that can be collected, processed, and utilized by AI-powered applications.

The integration of blockchain and AI can bring about revolutionary applications across various industries, particularly in sensitive sectors such as healthcare, finance, and defense. Here are some key benefits of combining AI and blockchain:

Blockchain is renowned for its robust security measures, providing a highly safe and secure method for storing information on the web. By leveraging blockchain, sensitive and critical data can be stored as digitally signed records accessible only through private keys. This capability enables AI models to work with sensitive data, providing more accurate and trustworthy information.

In a technical ecosystem, effective coordination among applications and tools is crucial for achieving goals with maximum efficiency. Blockchain systems offer decentralized and distributed solutions for decision-making algorithms, eliminating the need for a central authority. By removing the central authority, robots and AI systems can internally discuss problems, vote on issues, and reach conclusions through majority consensus.

By utilizing blockchain, data can be securely stored in an unalterable manner, ensuring data quality throughout the training process. This leads to highly accurate training data, ultimately increasing the accuracy of the AI model.

Business processes involving multiple users often suffer from inefficiencies due to the need for numerous authorization steps. Implementing blockchain and smart contracts enables the use of Decentralized Autonomous Agents (DAOs). These agents automatically, efficiently, and swiftly validate data or asset transfers among various stakeholders, streamlining the process.

This section explores key concepts related to the application of blockchain technologies in AI. The figure below illustrates these concepts and their relevance in AI applications.

While current AI applications operate autonomously to make informed decisions, decentralizing AI applications presents significant challenges. Overcoming these challenges is crucial for achieving the goal of enabling partially or fully autonomous operations, where intelligent agents or computer programs analyze their local environments, preserve internal states, and execute actions accordingly.

AI applications aim to achieve partially or fully autonomous operations. This involves intelligence agents or small computer programs perceiving and analyzing their local environments, maintaining internal states, and executing specified actions. Autonomic computing plays a key role in realizing this goal.

One of the major features of AI applications is their potential to make the most effective & efficient decisions by filtering a set of ideal solutions amongst all the possible solutions, and it’s possible because of the optimization of AI algorithms and models. Optimization techniques aim to find the best solution to a problem by operating in a constrained or unconstrained environment depending upon the system level and application-level objectives. Decentralized optimization will result in better efficiency & boosted performance.

AI applications make use of planning strategies when collaborating with other applications & systems to solve complex problems in new or challenging environments. Planning strategies play an important role in maintaining the resilience & efficiency of AI models. Using blockchain for planning strategies can result in devising more immutable and critical strategies used for mission-critical systems and strategic applications.

Knowledge Discovery and Knowledge Management

AI applications have a reputation for working with a large amount of data, and their reliance on centralized data processing systems. With the use of decentralization, the knowledge discovery and knowledge management processes will be able to provide personalized knowledge patterns that consider the needs of all the stakeholders involved.

Learning

At the heart of AI applications sits the learning algorithms that enable the knowledge discovery & automation processes. There are different kinds of learning algorithms like supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, ensemble, deep learning models, and much more that solve different machine learning problems. The use of decentralized learning models can result in highly autonomous learning systems that support local intelligence across different verticals in AI systems.

Decentralized AI Operations

AI models and algorithms often train, test, and validate a large amount of data to make better, and more versatile decisions. However, using centralized data storage solutions like data centers, clouds, and clusters acts as a major hurdle in developing highly secure AI applications that preserve the privacy of their users. Here are some of the top blockchain implementations that can be adopted by numerous AI applications.

Decentralized Storage

Centralized data storage solutions are highly susceptible when it comes to security and privacy as these data storage solutions involve a user’s personal and sensitive data along with their locations, health records, activities, and financial information. Blockchain offers decentralized and cryptographically secure storage solutions across the participating applications & networks. Decentralized data storage solutions use nodes, and each node in the network keeps a client-centric encrypted copy of the database to ensure data availability for clients. Clients are free to use and mine their data as per their needs and requirements.

Two of the most common storage techniques used in decentralized data storage solutions are Sharding and Swarming. Sharding is the process in which you create logical partitions of the databases known as “Shards” where each partition is assigned a unique key that can be used to access the partition. On the other hand, Swarming is a method that uses “Swarms” to enable parallel data access from multiple nodes in the network to reduce the latency in AI applications, and thus resulting in more efficient & smoother performance. The shards are grouped resulting, in germinated storage that is supported in the network by a group of nodes in the form of swarms.

The use of decentralized storage solutions can result in enhanced reliability & scalability of storage because of the multiparty geographical distributions offered by the decentralized storage solutions. Some of the emerging decentralized storage solutions include Storj, Swarm, Sia, FileCoin, IPFS, and more.

Data Management

One of the major requirements of developing an AI application is to manage data in a way that highly accurate, relevant, and complete datasets can be collected from reliable and trusted data sources. Conventionally, AI applications and algorithms have run centralized data management methods like data segmentation, data filtration, and content-aware data storage that are executed across all the nodes in the network. When compared against decentralized data storage offered by blockchain networks, centralized data management fares poorly because not only will the rate of data duplication be high even when only minor changes are made to the data, but the need to transfer similar datasets repeatedly will also be high.

Decentralized data management methods on the other hand have been designed to be deployed at the node levels in the network considering the spatial and temporal attributes in the data. Furthermore, to maintain the provenance and security of the data, decentralized management schemes can put the metadata on the blockchain.

Blockchain-types for AI Applications

Blockchain technology can be grouped into two categories: Permissioned where only authorized users can access the blockchain applications in the cloud-based, consortium, or private settings, and Permissionless where anyone can publicly access the systems using the internet.

Public Blockchains

Public blockchain belongs to the permissionless category of blockchain networks, where users have the freedom to download the blockchain code on their systems, modify the code, and use the code as per their own needs and requirements. Furthermore, public blockchains are often open-source for read & write operations, and easily accessible. Because public blockchains are accessible by everyone, these systems make use of complex protocols for safety, and the identity & transactional privacy information of the users on the network is managed using pseudonymous and anonymous data on the network. For data and asset transfer, each public blockchain network uses native tokens also known as value pointers or cryptocurrencies.

Private Blockchains

Unlike public blockchains, private blockchain networks are permissioned systems that are managed by a single organization, and they are designed as permissionless systems where the users or participants are always known within the network, and they have the pre-approval for read and write operations on the network. Private blockchains often offer higher efficiency because the identity of the visitors is known, and they are pre-approved participants of the network to eliminate the need for complex algorithms and mathematical operations to validate any transaction on the network. Additionally, private blockchain networks can transfer any kind of assets, values, or indigenous data within the network.

Just like in public blockchain networks, the approval of a transaction and asset transfers in the private blockchain network is done by multi-party consensus algorithms or voting that not only enable faster transactions but also consume low energy. Astonishingly, the average transaction approval time on a private blockchain network is under a second.

Consortium Blockchain Networks

Consortium Blockchains, also known as Federated Blockchains are operated by a group of organizations where the groups are generally formed based on mutual interest shared by these organizations. Consortium blockchain networks are generally offered by government organizations & bodies, banks, and some private blockchain companies as well.

Just like their private blockchain counterparts, the Consortium blockchain network operates as permissioned systems although a few users on the network have both read and write privileges on the network. Generally, all the users on the Consortium blockchain network have read access, but only a handful of individuals can write data on the network.

Decentralized Infrastructure for AI Applications

Blockchain architectures were traditionally designed by developers as linear infrastructure using a combination of hashing strategies, and linked lists data structures. However, recently, developers have been working on nonlinear infrastructures using queuing information, and graph theory to handle big data, and cater to the requirements of real-time AI-based applications.

Blockchain-enabled AI Applications

Decentralized Data Storage and Data Management with AI

Using Blockchain with AI has allowed developers to work on developing stable systems that support the interaction of different technical innovations, thus providing a platform for secure and safe data management, data transfer, and data storage. The below figure demonstrates the combined features of blockchain and AI technologies for the medical industry including different stages like analytics, diagnosis, validation of medical discoveries & reports, and critical decision-making.

In recent years, handling a large amount of data, increasing the computing power of algorithms & models exponentially, and growing user acceptance of connected systems and applications have been the top priorities in the AI and ML industry. As artificial neural networks often require a large amount of data and computing power for training purposes, it is essential to create powerful data centers to acquire large datasets. During an audit process, blockchain networks can be used to store the data & the query information while achieving a higher level of security and privacy. Furthermore, the integration of AI and Blockchain technologies will provide a strong consensus mechanism that is immutable, robust, and decentralized.

Decentralized Infrastructure for AI

The introduction of the Blockchain network infrastructure added three new characteristics to the traditional distributed architectures: decentralized and shared control of data and assets, native asset exchanges, and immutable audit trails. When the blockchain infrastructure was combined with AI technologies, the infrastructure provided users with new data models and offered shared control of AI models & training data while adding to the trustworthiness of the data. To produce better and more efficient data models, AI models need access to a large amount of data that is provided by blockchain networks.

Decentralized networks like IPFS and Ethereum can handle data storage, and huge computational resources respectively, therefore providing tamper-free records with a high level of privacy. Open-source decentralized AI platforms like ChainIntel aim to get rid of the monopolization of AI services by big companies.

Decentralized AI Applications

Collective decision-making and decentralized intelligence can have numerous applications. For example, the figure below demonstrates the features & benefits of combining Blockchain with IoT and AI technologies to increase the yield in farming fields. IoT sensors can monitor soil’s nutrient levels, and capture images that can help in monitoring the growth of crops over time. AI can make use of the data received from IoT sensors to provide predictive analysis that allows farmers to monitor different conditions. The use of blockchain ensures that every user on the network has access to the transactions which helps in reducing the time spent on logistics.

In this article, we have delved into the realm where the paths of blockchain and AI intertwine. We looked at decentralized storage, where the mighty blockchain holds the key to unlocking solutions for several issues with AI. We navigate the intricate taxonomy of blockchain in AI, exploring its various manifestations and the accompanying technologies that shape its landscape. We went further to address the diverse implementations of blockchain, dissecting their blockchain types, infrastructure, decentralized AI operations, and protocols.

But our expedition did not end there. We also went further into the terrain of practicality, uncovering the various applications of blockchain in AI. We showed the potential of this powerful amalgamation to tackle the pressing challenges that plague the AI industry: safeguarding user privacy, fortifying oracles with unyielding security, bolstering smart contract integrity, forging robust consensus protocols, fostering standardization, and establishing a framework of governance.

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