In recent years, AI and blockchain have evolved significantly. This article explores their potential synergy, focusing on blockchain’s data integrity and the combined applications in sectors like supply chain management, Smart Cities, and healthcare. It discusses challenges and opportunities, offering a promising vision of the tech future.
SwissCognitive Guest Blogger: Meike Krautscheid – “The Synergy of AI & Blockchain – What are the Use Cases?”
The world of technology has experienced a whirlwind in recent years, driven by two overwhelming forces: the hype surrounding artificial intelligence and the meteoric rise in the realm of cryptocurrencies and blockchain. While the AI hype has been accompanied by rapid technological advancements and mass adoption through platforms like ChatGPT, the blockchain world, particularly the realm of cryptocurrencies, has seen its share of ups and downs. However, crypto enthusiasts eagerly await the next upswing.
While the once-dominant cryptocurrency hype was initially overshadowed by the unstoppable wave of artificial intelligence, a question arises:
How can the worlds of AI and blockchain harmoniously converge and potentially unleash synergy?
Within the blockchain community, the belief is widespread that the true magic of the technology unfolds when combined with other groundbreaking technologies. Alongside the Internet of Things (IoT), sensors, Smart Contracts, and Ricardian Contracts, Artificial Intelligence is coming under the spotlight. But before we delve deeper into the potential synergy between AI and blockchain, it is crucial to establish a foundation by understanding what blockchain is and what makes it unique.
Blockchain technology has revolutionized the way we can store and manage data. As early as the late 1980s, scientists Scott Stornetta and Stuart Haber recognized that the increasing flood of data would pose a challenge: the need to determine the time of data creation, authenticate it, and verify it to prevent fraud, such as tampering with transactions by backdating and editing.
The scientists’ approach was to use a kind of mathematical “blender” (cryptographic hash function) to generate a unique serial number, known as a hash, which is as unique as a fingerprint, for each file. This makes even the slightest change in a file detectable. Documents previously encrypted in data blocks using hash values and timestamps and chained together are resistant to retroactive alterations; only new data can be easily added.
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Stornetta and Haber have been offering this system through their central company since the 1990s, allowing users to timestamp their files with a digital timestamp that proves the authenticity and integrity of the file at a specific time. This is a crucial tool for securing the integrity of electronic documents and data.
The innovation of a central timestamp system developed by Stornetta and Haber served as the template for the decentralized system in the Bitcoin blockchain. In Bitcoin, timestamping occurs in a decentralized network without the need for a central authority. Each transaction is hashed and protected by cryptographic keys. A protocol and consensus mechanism ensure that coins cannot be double-spent, and data cannot be retroactively manipulated. The order of transactions and blocks in Bitcoin is secured through the Proof-of-Work mining process. Even if multiple actors in the Bitcoin network fail, falter, or attempt dishonest actions, the system remains robust and continues to be a trusted, decentralized, and secure method for transferring value and data.
A decentralized blockchain is crucial for data integrity because it ensures that no central authority has the ability to retroactively manipulate data. Similar to a global accounting system, the blockchain updates its records simultaneously and decentralizes the origin of data. Moreover, it enables transparent tracking of changes to the data, including the detection of manipulations.
How can these advantages of data security through blockchain now intersect with Artificial Intelligence?
High-quality datasets are essential for developing powerful AI models. AI entities require high-quality data to learn patterns and make accurate predictions or decisions. For example, when the Retrieval Augmented Generation (RAG) framework is employed to retrieve results from an internal source, a blockchain safeguard can be used to verify that the data assets returned are authentic and that the content extracted from these assets aligns with the original consensus against these assets. However, it’s important to note that this is not meant for everyday use, as it is highly costly and is suitable for specific critical cases, such as mortgage documents and financial statements. Think of it as two databases converging: the vectorized database from RAG and the blockchain decentralized database using a consensus mechanism that is widely accepted as the standard. Therefore, the synergy with blockchain could improve the reliability of training data for AI models and enable more effective use of AI in various applications.
With the rise of generative AI-generated digital content, the boundary between reality and fiction is growing increasingly ambiguous. It’s becoming difficult to determine which images and videos are genuine, technically manipulated, or entirely AI-generated. However, a potential solution arises: we can label media content, including Deep Fakes, with universal indicators and facilitate the verification of the authenticity of such content through a blockchain by storing a simple hash of the content. This technology can confirm that the content remains unaltered and genuine, whether it is stored or indexed in the blockchain, and it is verifiable by anyone.
The potential of the alliance between AI and blockchain can also be explored in areas such as the Internet of Things (IoT), financial markets, Smart Cities, supply chain management, personalized medicine, and more.
In the field of Supply Chain Management, the combination of Artificial Intelligence and Blockchain technology could enable the analysis of data while ensuring a seamless tracking of the origin and the entire product supply chain. Usually, such data is centrally stored in data lakes, and when it is, there is a risk of data manipulation or the possibility that information does not reach relevant stakeholders in the supply chain in real-time.
AI algorithms can validate data before it’s entered into the blockchain to ensure it meets predefined criteria and standards. Real-time fraud detection is also made possible as AI models continuously monitor transactions for anomalies, with the transparency of the blockchain ensuring secure recording. Furthermore, AI data analysis facilitates informed decision-making, providing valuable insights and predictive analytics. This benefits supply chain quality assurance and empowers consumers to verify the quality and authenticity of products – provided that producers grant access to this data.
In Smart Cities, AI agents (AIAs) or Convolutional Neural Networks (CNNs), in conjunction with data stored on the blockchain, could enable a more economical and resilient urban economy. This combination allows for real-time data processing, crucial for urban emergencies, traffic control, and improving citizens’ quality of life. Convolutional Neural Networks (CNNs) are relevant for analyzing visual data in Smart Cities, including traffic pattern recognition, environmental monitoring, and security applications, while AI agents can recognize patterns and make intelligent decisions, such as resource allocation.
Similarly, Blockchain and AI offer numerous advantages in the healthcare sector. Firstly, blockchain allows the decentralized storage and secure encryption of health data, protecting it from hackers and unauthorized access. Patients have control over who can access their data, and with the help of Zero-Knowledge Proofs (ZKPs), patients can share their data without revealing their identity and compromising their privacy. AI agents can then access this data, identify patterns, and make informed decisions. For example, if DNA data is available, it can be used to detect rare genetic diseases.
Furthermore, imagine an AI trading bot that operates on the blockchain without revealing its detailed workings but proves its effectiveness through Zero-Knowledge Proofs. The combination of machine learning (ML) and yield farming also takes place on-chain, with crucial parts of the process remaining confidential. Blockchain enables verification and transparency of information, with critical parameters protected by ZKPs.
It’s worth noting that all transactions occurring on the blockchain can be traced using analytics tools. For example, the blockchain intelligence company Gray Wolf Analytics provides a tool that uses artificial intelligence to understand on-chain and off-chain activities. If fraudulent transactions are detected, financial and cybercriminal activities can be prevented or traced by relevant authorities.
There will also be a revolution in the software sector, as modern NoCode super-app builder platforms will be used to create apps, APIs, and websites with the help of AI. While AI initiates software creation, the use of blockchain creates a secure and verifiable environment for bug-free versions that can be verified by any user.
In another scenario, AI could serve as a “sheriff” monitoring punctuality to meetings. If someone arrives late, the AI triggers a Smart Contract on the blockchain, resulting in a donation from the tardy person to charitable projects. However, there is a certain risk associated with the use of these technologies, especially in authoritarian states concerning the monitoring of legal violations, as individuals’ identities could potentially be listed on a social rating or blacklist on the blockchain, leading to significant restrictions.
Blockchain technology could potentially address issues that come with the use of AI. In the context of Generative Artificial Intelligence (GAI), a challenge is that it might use copyrighted content to generate new content, potentially leading to conflicts with copyright owners. By utilizing digital signatures and hash functions, data integrity is significantly improved, allowing for cryptographic verification that a data record existed at a specific point in time and remained unchanged.
In a later article, we will delve deeper into how blockchain ensures transparent tracking of the creation and modification of content, addressing the legal aspects related to GAI and potential copyright infringements.
We can expect that in the future, blockchain will help distinguish between good and bad data. While blockchains offer ideal attributes for storing critical data, which can be a valuable data source for AIs, it’s important to consider that models like the Generative pre-trained Transformer (GPT-3) were trained on approximately 45 terabytes of text files – a massive amount of data. Given that storing data on the blockchain incurs monetary costs, it’s likely that only indexes like pointers or the most essential data will be directly stored on the blockchain for use as data sources for AIs. Economic and other incentives will be crucial with blockchain usage. Beyond a minimum of revenues that must occur, there are additional challenges to overcome, including scalability, interoperability, and legal issues like GDPR.
It’s worth noting that both AI and blockchain technologies are still in their developmental stages, but we can anticipate exciting developments and innovations on the horizon, with numerous opportunities yet to be explored.
About the Author:
Meike Krautscheid is an entrepreneur and expert in blockchain-based applications. Her extensive knowledge of blockchain, NoCode, AI and related technologies has established her as a recognized thought leader. Meike is a sought-after keynote speaker at international conferences and events. Furthermore, she shares her expertise and vision through lectures and workshops at renowned universities worldwide. Through her dedication, she engages with a worldwide audience and plays an active role in spreading innovations.
In recent years, AI and blockchain have evolved significantly. This article explores their potential synergy, focusing on blockchain’s data integrity and the combined applications in sectors like supply chain management, Smart Cities, and healthcare. It discusses challenges and opportunities, offering a promising vision of the tech future.
SwissCognitive Guest Blogger: Meike Krautscheid – “The Synergy of AI & Blockchain – What are the Use Cases?”
While the once-dominant cryptocurrency hype was initially overshadowed by the unstoppable wave of artificial intelligence, a question arises:
How can the worlds of AI and blockchain harmoniously converge and potentially unleash synergy?
Within the blockchain community, the belief is widespread that the true magic of the technology unfolds when combined with other groundbreaking technologies. Alongside the Internet of Things (IoT), sensors, Smart Contracts, and Ricardian Contracts, Artificial Intelligence is coming under the spotlight. But before we delve deeper into the potential synergy between AI and blockchain, it is crucial to establish a foundation by understanding what blockchain is and what makes it unique.
Blockchain technology has revolutionized the way we can store and manage data. As early as the late 1980s, scientists Scott Stornetta and Stuart Haber recognized that the increasing flood of data would pose a challenge: the need to determine the time of data creation, authenticate it, and verify it to prevent fraud, such as tampering with transactions by backdating and editing.
The scientists’ approach was to use a kind of mathematical “blender” (cryptographic hash function) to generate a unique serial number, known as a hash, which is as unique as a fingerprint, for each file. This makes even the slightest change in a file detectable. Documents previously encrypted in data blocks using hash values and timestamps and chained together are resistant to retroactive alterations; only new data can be easily added.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Stornetta and Haber have been offering this system through their central company since the 1990s, allowing users to timestamp their files with a digital timestamp that proves the authenticity and integrity of the file at a specific time. This is a crucial tool for securing the integrity of electronic documents and data.
The innovation of a central timestamp system developed by Stornetta and Haber served as the template for the decentralized system in the Bitcoin blockchain. In Bitcoin, timestamping occurs in a decentralized network without the need for a central authority. Each transaction is hashed and protected by cryptographic keys. A protocol and consensus mechanism ensure that coins cannot be double-spent, and data cannot be retroactively manipulated. The order of transactions and blocks in Bitcoin is secured through the Proof-of-Work mining process. Even if multiple actors in the Bitcoin network fail, falter, or attempt dishonest actions, the system remains robust and continues to be a trusted, decentralized, and secure method for transferring value and data.
A decentralized blockchain is crucial for data integrity because it ensures that no central authority has the ability to retroactively manipulate data. Similar to a global accounting system, the blockchain updates its records simultaneously and decentralizes the origin of data. Moreover, it enables transparent tracking of changes to the data, including the detection of manipulations.
How can these advantages of data security through blockchain now intersect with Artificial Intelligence?
High-quality datasets are essential for developing powerful AI models. AI entities require high-quality data to learn patterns and make accurate predictions or decisions. For example, when the Retrieval Augmented Generation (RAG) framework is employed to retrieve results from an internal source, a blockchain safeguard can be used to verify that the data assets returned are authentic and that the content extracted from these assets aligns with the original consensus against these assets. However, it’s important to note that this is not meant for everyday use, as it is highly costly and is suitable for specific critical cases, such as mortgage documents and financial statements. Think of it as two databases converging: the vectorized database from RAG and the blockchain decentralized database using a consensus mechanism that is widely accepted as the standard. Therefore, the synergy with blockchain could improve the reliability of training data for AI models and enable more effective use of AI in various applications.
With the rise of generative AI-generated digital content, the boundary between reality and fiction is growing increasingly ambiguous. It’s becoming difficult to determine which images and videos are genuine, technically manipulated, or entirely AI-generated. However, a potential solution arises: we can label media content, including Deep Fakes, with universal indicators and facilitate the verification of the authenticity of such content through a blockchain by storing a simple hash of the content. This technology can confirm that the content remains unaltered and genuine, whether it is stored or indexed in the blockchain, and it is verifiable by anyone.
The potential of the alliance between AI and blockchain can also be explored in areas such as the Internet of Things (IoT), financial markets, Smart Cities, supply chain management, personalized medicine, and more.
In the field of Supply Chain Management, the combination of Artificial Intelligence and Blockchain technology could enable the analysis of data while ensuring a seamless tracking of the origin and the entire product supply chain. Usually, such data is centrally stored in data lakes, and when it is, there is a risk of data manipulation or the possibility that information does not reach relevant stakeholders in the supply chain in real-time.
AI algorithms can validate data before it’s entered into the blockchain to ensure it meets predefined criteria and standards. Real-time fraud detection is also made possible as AI models continuously monitor transactions for anomalies, with the transparency of the blockchain ensuring secure recording. Furthermore, AI data analysis facilitates informed decision-making, providing valuable insights and predictive analytics. This benefits supply chain quality assurance and empowers consumers to verify the quality and authenticity of products – provided that producers grant access to this data.
In Smart Cities, AI agents (AIAs) or Convolutional Neural Networks (CNNs), in conjunction with data stored on the blockchain, could enable a more economical and resilient urban economy. This combination allows for real-time data processing, crucial for urban emergencies, traffic control, and improving citizens’ quality of life. Convolutional Neural Networks (CNNs) are relevant for analyzing visual data in Smart Cities, including traffic pattern recognition, environmental monitoring, and security applications, while AI agents can recognize patterns and make intelligent decisions, such as resource allocation.
Similarly, Blockchain and AI offer numerous advantages in the healthcare sector. Firstly, blockchain allows the decentralized storage and secure encryption of health data, protecting it from hackers and unauthorized access. Patients have control over who can access their data, and with the help of Zero-Knowledge Proofs (ZKPs), patients can share their data without revealing their identity and compromising their privacy. AI agents can then access this data, identify patterns, and make informed decisions. For example, if DNA data is available, it can be used to detect rare genetic diseases.
Furthermore, imagine an AI trading bot that operates on the blockchain without revealing its detailed workings but proves its effectiveness through Zero-Knowledge Proofs. The combination of machine learning (ML) and yield farming also takes place on-chain, with crucial parts of the process remaining confidential. Blockchain enables verification and transparency of information, with critical parameters protected by ZKPs.
It’s worth noting that all transactions occurring on the blockchain can be traced using analytics tools. For example, the blockchain intelligence company Gray Wolf Analytics provides a tool that uses artificial intelligence to understand on-chain and off-chain activities. If fraudulent transactions are detected, financial and cybercriminal activities can be prevented or traced by relevant authorities.
There will also be a revolution in the software sector, as modern NoCode super-app builder platforms will be used to create apps, APIs, and websites with the help of AI. While AI initiates software creation, the use of blockchain creates a secure and verifiable environment for bug-free versions that can be verified by any user.
In another scenario, AI could serve as a “sheriff” monitoring punctuality to meetings. If someone arrives late, the AI triggers a Smart Contract on the blockchain, resulting in a donation from the tardy person to charitable projects. However, there is a certain risk associated with the use of these technologies, especially in authoritarian states concerning the monitoring of legal violations, as individuals’ identities could potentially be listed on a social rating or blacklist on the blockchain, leading to significant restrictions.
Blockchain technology could potentially address issues that come with the use of AI. In the context of Generative Artificial Intelligence (GAI), a challenge is that it might use copyrighted content to generate new content, potentially leading to conflicts with copyright owners. By utilizing digital signatures and hash functions, data integrity is significantly improved, allowing for cryptographic verification that a data record existed at a specific point in time and remained unchanged.
In a later article, we will delve deeper into how blockchain ensures transparent tracking of the creation and modification of content, addressing the legal aspects related to GAI and potential copyright infringements.
We can expect that in the future, blockchain will help distinguish between good and bad data. While blockchains offer ideal attributes for storing critical data, which can be a valuable data source for AIs, it’s important to consider that models like the Generative pre-trained Transformer (GPT-3) were trained on approximately 45 terabytes of text files – a massive amount of data. Given that storing data on the blockchain incurs monetary costs, it’s likely that only indexes like pointers or the most essential data will be directly stored on the blockchain for use as data sources for AIs. Economic and other incentives will be crucial with blockchain usage. Beyond a minimum of revenues that must occur, there are additional challenges to overcome, including scalability, interoperability, and legal issues like GDPR.
It’s worth noting that both AI and blockchain technologies are still in their developmental stages, but we can anticipate exciting developments and innovations on the horizon, with numerous opportunities yet to be explored.
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