AI and Blockchain have emerged as two of the most groundbreaking technical innovations in recent times.
- Artificial Intelligence (AI): Enables machines and computers to emulate human thinking and decision-making processes.
- Blockchain: A distributed and immutable ledger that securely stores data and information in a decentralized and trusted manner.
Recently, scientists have delved into exploring potential applications of these technologies across various sectors. In this article, we’ll provide a brief overview of how blockchain can be integrated with AI, a concept that might be coined as “decentralized AI”. Let’s dive in.
Decentralized AI: An Introduction to Blockchain in AI
In the past decade or so, blockchain has been one of the most hyped innovations, and it started to gain 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 way we store or exchange data or information, and revolutionize the way we trace & track transactions or automate them.
One of the most talked about points of blockchain is that every blockchain transaction is signed cryptographically, and the mining nodes that hold a replica of the entire ledger of chained block of all transactions verifies each such transaction that results in the creation of synchronized, secure, and shared timestamped records that are impossible to alter. Resultantly, blockchain can be an effective option to eliminate the requirement for a central authority to verify & govern the transactions & interactions between users on the network.
Moving along, the technical industry has been producing and generating a huge amount of data thanks to technical innovations like IoT devices, smartphones, social media, and web applications that have contributed significantly in the rise of AI because to perform effectively & efficiently, AI systems often utilize a large amount of data using deep learning and machine learning practices to perform different analytics.
Even today, a vast chunk of machine learning and deep learning techniques for AI models rely on a centralized model that trains a group of servers that run or train a specific model against training data, and then verifies the learning using validation or training dataset. The high requirement to effectively train an AI model is the reason why major tech organizations and development teams often store a large amount of data to train their models for the best possible results and performance.
Most AI models and practices today are centralized, and although centralization has brought a lot of success to the AI industry, there is a major drawback with centralized data storage for AI models. When the entire data is stored in a centralized manner, the possibility of data tampering, or data corruption increases as centralized data storage is always a subject to malware and cybersecurity attacks. Furthermore, when dealing with a large amount of data, it is a challenging task to verify the authenticity & provenance of the data source is not guaranteed which may result in wrong training of the model that can further result in unwanted, inaccurate, and even dangerous outcomes.
The challenges with data storage for AI models is the major reason behind the use of blockchain in AI and the development of decentralized AI. The primary 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 reputation of often working with a large amount of data, and scientists have already predicted blockchain to be the future of data storage. Furthermore, blockchain have smart contracts that allow users to program the blockchain network to govern transactions amongst the participants involved in generating or accessing the data, or decision-making. Autonomous applications and machines based on blockchain smart contracts can learn and adapt to changes as time progresses, and they can also 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 artificial intelligence and blockchain industry can be addressed efficiently by combining both the technical systems. Blockchain acts as a distributed ledger that stores and transmits data in a cryptographically signed method that is agreed and verified by the mining nodes of the network. Blockchain networks store data with high resilience & integrity that makes it almost impossible to tamper with the data which is the major reason why the outcome of machine learning algorithms when they make decisions using blockchain smart contracts cannot be disputed, and can be trusted. The use of blockchain networks with AI technologies can help in creating decentralized, immutable, and secure systems for highly sensitive data that can be collected, processed, and utilized by AI-powered applications. The security and safety offered by the use of blockchain in AI can have revolutionary applications across industries, especially the more sensitive ones like healthcare & hospitals, finance, defense, and more.
Moving along, some of the prominent benefits of integrating AI and blockchain are listed below.
A major reason behind blockchain’s immense popularity is that it offers a highly safe & secure method to store information on the web. Blockchains offer an alternative to store sensitive and critical information on disks, which is by storing digitally signed data that can be accessed only by using private keys. Hence, using blockchain to store data for AI algorithms can allow AI models to work with sensitive data, thus resulting in more accurate & trusted information.
- Collective Decision Making
In a technical ecosystem, the involved applications or tools must work in coordination with each other to achieve the goal with maximum efficiency. Blockchain systems offer decentralized and distributed solutions for decision making algorithms that can replace the requirement for a central authority. Eliminating the central authority will allow the robots to discuss the problem 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 data in a highly secure way that cannot be altered with which ensures the quality of the data throughout the development of the training process. As a result, the model will train on highly accurate data that will ultimately help in increasing the accuracy of the mode.
One of the major reasons why business processes that often involve multiple users like multiple shareholders or stakeholders, governmental organizations, and business firms are often inefficient is because of numerous authorization of business transactions. Using blockchain and smart contracts will enable DAOs or Decentralized Autonomous Agents that will validate data or asset transfers amongst different stakeholders automatically, efficiently, and quickly.
Taxonomy of Blockchain in AI
In this section, we will be talking about some of the key concepts used in the application of blockchain technologies for AI applications that are mentioned in 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. However, decentralizing AI applications is a difficult and challenging task for numerous reasons.
One of the major goals of AI applications is to enable partially or fully autonomous operations where numerous intelligence agents or small computer programs will perceive & analyze their local environments, preserve their internal states, and execute specified actions accordingly.
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 its possible because of 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 of 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 considers the needs of all the stakeholders involved.
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 on a large amount of data to make better, and more versatile decisions. However, using centralized data storage solutions like data centers, clouds, and clusters act as a major hurdle in developing highly secure AI applications that preserve the privacy of its users. Here are some of the top blockchain implementations that can be adopted by numerous AI applications.
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 & smooth performance. The shards are grouped together resulting in the formation of a collected 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 multiparty geographical distributions offered by the decentralized storage solutions. Some of the emerging decentralized storage solutions include Storj, Swarm, Sia, FileCoin, IPFS, and more.
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
The Blockchain technology can 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 internet.
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.
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 consumes 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 on the basis of 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 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, and 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 that includes 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, 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 nutrients 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 the farmers to monitor different conditions. The use of blockchain ensures that every 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 use of Blockchain and AI for financial and banking purposes, and how blockchain and AI can improve the efficiency, safety & security of the financial system.
Conclusion
In this article, we have talked about the application and use cases of blockchain in AI. The article gives an overview of decentralized storage, and how blockchain can be the key 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 in terms of blockchain types & infrastructure, decentralized AI operations, and protocols. Finally, we discuss the various applications of blockchain in AI.
To sum things up, it would be safe to say that the implementation of blockchain in AI has the potential to address and solve existing issues in the AI industry related to user privacy, secured oracles, smart contract security, consensus protocols, standardization, and governance.
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