AI and Blockchain have emerged as two of probably the most groundbreaking technical innovations in recent times.

• Artificial Intelligence (AI): Enables machines and computers to emulate human considering and decision-making processes.
• Blockchain: A distributed and immutable ledger that securely stores data and knowledge in a decentralized and trusted manner.

Recently, scientists have delved into exploring potential applications of those technologies across various sectors. In this text, we’ll provide a transient overview of how blockchain may be integrated with AI, an idea that may be coined as “decentralized AI”. Let’s dive in.

Decentralized AI: An Introduction to Blockchain in AI

Up to now decade or so, blockchain has been one of the crucial hyped innovations, and it began to achieve momentum when it found its application in other fields. Ever since its inception in 2008, it continued to emerge as a disruptive technology that had the potential to revolutionize the best way we store or exchange data or information, and revolutionize the best way we trace & track transactions or automate them. 

One of the vital talked about points of blockchain is that each blockchain transaction is signed cryptographically, and the mining nodes that hold a reproduction of your complete ledger of chained block of all transactions verifies each such transaction that leads to the creation of synchronized, secure, and shared timestamped records which are unimaginable to change. Resultantly, blockchain may be an efficient choice to eliminate the requirement for a government to confirm & govern the transactions & interactions between users on the network. 

Moving along, the technical industry has been producing and generating an enormous amount of knowledge because of technical innovations like IoT devices, smartphones, social media, and web applications which have contributed significantly within the rise of AI because to perform effectively & efficiently, AI systems often utilize a considerable amount of data using deep learning and machine learning practices to perform different analytics. 

Even today, an enormous chunk of machine learning and deep learning techniques for AI models depend on a centralized model that trains a gaggle of servers that run or train a particular model against training data, after which verifies the educational using validation or training dataset. The high requirement to effectively train an AI model is the rationale why major tech organizations and development teams often store a considerable amount of data to coach their models for the very best possible results and performance. 

Most AI models and practices today are centralized, and although centralization has brought a variety of success to the AI industry, there’s a serious drawback with centralized data storage for AI models. When your complete data is stored in a centralized manner, the potential for data tampering, or data corruption increases as centralized data storage is all the time a subject to malware and cybersecurity attacks. Moreover, when coping with a considerable amount of data, it’s a difficult task to confirm the authenticity & provenance of the information source will not be guaranteed which can lead to fallacious training of the model that may further lead to unwanted, inaccurate, and even dangerous outcomes. 

The challenges with data storage for AI models is the key reason behind the usage of blockchain in AI and the event of decentralized AI. The first aim of decentralized AI is to enable a process and perform decision making or analytics using a digitally signed, secured, and trusted shared data that has been stored & transacted on the blockchain network in a decentralized or distributed manner without using external Third-Party resources. 

AI models have the status of often working with a considerable amount of data, and scientists have already predicted blockchain to be the longer term of knowledge storage. Moreover, blockchain have smart contracts that allow users to program the blockchain network to manipulate transactions amongst the participants involved in generating or accessing the information, or decision-making. Autonomous applications and machines based on blockchain smart contracts can learn and adapt to changes as time progresses, they usually also can make accurate and trusted decisions, outcomes verified and validated by the mining nodes of the blockchain network. 

How Blockchain can Transform Artificial Intelligence?

Several shortcomings of the substitute intelligence and blockchain industry may be addressed efficiently by combining each the technical systems. Blockchain acts as a distributed ledger that stores and transmits data in a cryptographically signed method that’s agreed and verified by the mining nodes of the network. Blockchain networks store data with high resilience & integrity that makes it almost unimaginable to tamper with the information which is the key reason why the consequence of machine learning algorithms once they make decisions using blockchain smart contracts can’t be disputed, and may be trusted. The usage of blockchain networks with AI technologies might help in creating decentralized, immutable, and secure systems for highly sensitive data that may be collected, processed, and utilized by AI-powered applications. The safety and security offered by way of blockchain in AI can have revolutionary applications across industries, especially the more sensitive ones like healthcare & hospitals, finance, defense, and more. 

Moving along, a number of the distinguished advantages of integrating AI and blockchain are listed below. 

A significant reason behind blockchain’s immense popularity is that it offers a highly protected & secure method to store information on the net. Blockchains offer an alternative choice to store sensitive and demanding information on disks, which is by storing digitally signed data that may be accessed only through the use of private keys. Hence, using blockchain to store data for AI algorithms can allow AI models to work with sensitive data, thus leading to more accurate & trusted information. 

• Collective Decision Making

In a technical ecosystem, the involved applications or tools must work in coordination with one another to realize the goal with maximum efficiency. Blockchain systems offer decentralized and distributed solutions for decision making algorithms that may replace the requirement for a government. Eliminating the central authority will allow the robots to debate the issue internally, vote on any issue, and resolve the matter with majority until a conclusion is agreed upon. 

• Enhanced Trust on Robotic Decisions

Blockchain stores the information in a highly secure way that can’t be altered with which ensures the standard of the information throughout the event of the training process. In consequence, the model will train on highly accurate data that may ultimately assist in increasing the accuracy of the mode. 

One among the key the explanation why business processes that always involve multiple users like multiple shareholders or stakeholders, governmental organizations, and business firms are sometimes inefficient is because of diverse authorization of business transactions. Using blockchain and smart contracts will enable DAOs or Decentralized Autonomous Agents that may validate data or asset transfers amongst different stakeholders robotically, efficiently, and quickly. 

Taxonomy of Blockchain in AI

On this section, we shall be talking about a number of the key concepts utilized in the appliance of blockchain technologies for AI applications which are mentioned within the figure below. 

Decentralized AI Applications

Current AI applications generally operate in an autonomous manner to execute informed decisions using different planning, searching, optimizing, learning, knowledge recovery and management strategies. Nonetheless, decentralizing AI applications is a difficult and difficult task for varied reasons. 

One among the key goals of AI applications is to enable partially or fully autonomous operations where quite a few intelligence agents or small computer programs will perceive & analyze their local environments, preserve their internal states, and execute specified actions accordingly.

One among the key features of AI applications is their potential to make probably the most effective & efficient decisions by filtering a set of ideal solutions amongst all of the possible solutions, and its possible due to optimization of AI algorithms and models. Optimization techniques aim to search out the very best solution to an issue by operating in a constrained or unconstrained environment depending upon the system level and application level objectives. Decentralized optimization will lead to higher efficiency & boosted performance. 

AI applications make use of planning strategies when collaborating with other applications & systems to resolve complex problems in latest or difficult environments. Planning strategies play a crucial role in maintaining the resilience & efficiency of AI models. Using blockchain for planning strategies may end up in devising more immutable and demanding strategies used for mission critical systems and strategic applications. 

• Knowledge Discovery and Knowledge Management

AI applications have a status of working with a considerable amount of data, and their reliance on centralized data processing systems. With the usage of decentralization, the knowledge discovery and knowledge management processes will find a way to supply personalized knowledge patterns that considers the needs of all of the stakeholders involved. 

At the guts of Ai applications sits the educational algorithms that enable the knowledge discovery & automation processes. There are different sorts of learning algorithms like supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, ensemble, deep learning models, and rather more that solve different machine learning problems. The usage of decentralized learning models may end up 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 on a considerable amount of data to make higher, and more versatile decisions. Nonetheless, using centralized data storage solutions like data centers, clouds, and clusters act as a serious hurdle in developing highly secure AI applications that preserve the privacy of its users. Listed below are a number of the top blockchain implementations that may be adopted by quite a few AI applications. 

Centralized data storage solutions are highly susceptible with regards to security and privacy as these data storage solutions involve a user’s personal and sensitive data together 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 every node within the network keeps a client-centric encrypted copy of the database to make sure data availability for clients. Clients are free to make use of and mine their data as per their needs and requirements. 

Two of probably the most common storage techniques utilized in decentralized data storage solutions are Sharding and Swarming. Sharding is the method during which you create logical partitions of the databases often called “Shards” where each partition is assigned a singular key that may be used to access the partition. Then again, Swarming is a technique that uses “Swarms” to enable parallel data access from multiple nodes within the network to scale back the latency in AI applications, and thus leading to more efficient & smooth performance. The shards are grouped together leading to the formation of a collected storage that’s supported within the network by a gaggle of nodes in the shape of swarms. 

The usage of decentralized storage solutions may end up in enhanced reliability & scalability of storage due to multiparty geographical distributions offered by the decentralized storage solutions. A number of the emerging decentralized storage solutions include Storj, Swarm, Sia, FileCoin, IPFS, and more. 

One among the key requirements of developing an AI application is to administer data in a way that highly accurate, relevant, and complete datasets may 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 which are executed across all of the nodes within the network. Compared against decentralized data storage offered by blockchain networks, centralized data management fares poorly because not only will the speed of knowledge duplication be high even when only minor changes are made to the information, but the necessity to transfer similar datasets repeatedly may even be high. 

Decentralized data management methods then again have been designed to be deployed on the node levels within the network considering the spatial and temporal attributes in the information. Moreover, to take care of the provenance and security of the information, decentralized management schemes can put the metadata on the blockchain. 

Blockchain-types for AI Applications

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

Public blockchain belongs to the permissionless category of blockchain networks, where users have the liberty to download the blockchain code on their systems, modify the code, and use the code as per their very own needs and requirements. Moreover, public blockchains are sometimes open-source for read & write operations, and simply 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 often called value pointers or cryptocurrencies. 

Unlike public blockchains, private blockchain networks are permissioned systems which are managed by a single organization, they usually are designed as permissionless systems where the users or participants are all the time known throughout the network, they usually have the pre-approval for read and write operations on the network. Private blockchains often offer higher efficiency since the identity of the visitors is thought, they usually are pre-approved participants of the network to eliminate the necessity for complex algorithms and mathematical operations to validate any transaction on the network. Moreover, private blockchain networks can transfer any form of assets, values, or indigenous data throughout the network. 

Identical to in public blockchain networks, the approval of a transaction and asset transfers within the private blockchain network is finished by multi party consensus algorithms or voting that not only enable faster transactions but additionally consumes low energy. Astonishingly, the typical transaction approval time on a non-public blockchain network is under a second. 

• Consortium Blockchain Networks

Consortium Blockchains, also often called Federated Blockchains are operated by a gaggle of organizations where the groups are generally formed on the premise of mutual interest shared by these organizations. Consortium blockchain networks are generally offered by government organizations & bodies, banks, and a few private blockchain firms as well. 

Identical to their private blockchain counterparts, the Consortium blockchain network operates as permissioned systems although a couple of users on the network have each read and write privileges on the network. Generally, all of the users on the Consortium blockchain network have read access, but only a handful of people can write data on the network. 

Decentralized Infrastructure for AI Applications

Blockchain architectures were traditionally designed by developers as linear infrastructure using a mix of hashing strategies, and linked lists data structures. Nonetheless, recently, developers have been working on nonlinear infrastructures using queuing information, and graph theory to handle big data, and cater the necessities 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 various technical innovations, and thus providing a platform for secure and protected data management, data transfer, and data storage. The below figure demonstrates the combined features of blockchain and AI technologies for the medical industry that features different stages like analytics, diagnosis, validation of medical discoveries & reports, and demanding decision making. 

Lately, handling a considerable amount of data, increasing the computing power of algorithms & models exponentially, and growing user acceptance of connected systems and applications have been the highest priorities within the AI and ML industry. As artificial neural networks often require a considerable amount of data and computing power for training purposes, it is crucial to create powerful data centers to accumulate large datasets. During an audit process, blockchain networks may be used to store the information & the query information while achieving the next level of security and privacy. Moreover, the mixing of AI and Blockchain technologies will provide a powerful consensus mechanism that’s immutable, robust, decentralized. 

Decentralized Infrastructure for AI

The introduction of the Blockchain network infrastructure added three latest characteristics to the normal distributed architectures: decentralized and shared control of knowledge and assets, native asset exchanges, and immutable audit trails. When the blockchain infrastructure was combined with AI technologies, the infrastructure provided users with latest data models, and offered shared control of AI models & training data while adding to the trustworthiness of the information. To supply higher and more efficient data models, AI models need access to a considerable amount of data that’s provided by blockchain networks. 

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

Decentralized AI Applications

Collective decision making, and decentralized intelligence can have quite a few applications. For instance, the figure below demonstrates the features & advantages of mixing Blockchain with IoT and AI technologies to extend the yield in farming fields. IoT sensors can monitor soil’s nutrients levels, and capture images that might help in monitoring the expansion of crops over time. AI could make use of the information received from IoT sensors to supply predictive evaluation that enables the farmers to observe different conditions. The usage of blockchain ensures that each user on the network has access to the transactions that helps in reducing the time spent on logistics. 

The above image demonstrates blockchain-based systems used for unmanned automated intelligent exploration of the ocean beds. 

The above image demonstrates the usage of Blockchain and AI for financial and banking purposes, and the way blockchain and AI can improve the efficiency, safety & security of the economic system. 

Conclusion

In this text, we have now talked in regards to the application and use cases of blockchain in AI. The article gives an outline of decentralized storage, and the way blockchain may be the important thing to solving several issues with AI. Moving along, we also discussed the taxonomy of blockchain in AI, and the related technologies, and the comparison of blockchain implementations when it comes to blockchain types & infrastructure, decentralized AI operations, and protocols. Finally, we discuss the varied applications of blockchain in AI. 

To sum things up, it might be protected to say that the implementation of blockchain in AI has the potential to handle and solve existing issues within the AI industry related to user privacy, secured oracles, smart contract security, consensus protocols, standardization, and governance.