Blockchain and Artificial Intelligence: How Blockchain complements Artificial intelligence

Technology has indeed come a long way, with the introduction of blockchain and Artificial intelligence which some schools of thought argue to be the most revolutionary technologies of the 21st century.

These technologies have transformed the digital space from how we work, interact, and collaborate.

Evolving rapidly blockchain technology and artificial intelligence hold so much promise for the future, with each having its strengths and applications.

The integration of Artificial intelligence with blockchain technology can unlock enormous potential particularly as it has to do with data sharing and development of IA Models.

In this article, We explore how blockchain technology enhances artificial intelligence, emphasising the special interplay between the two and how their union can go over significant obstacles encountered separately.

An Overview of Blockchain Technology and Artificial Intelligence.

Before we dive deeper, to better put in perspective the full potential of blockchain and artificial intelligence. It will help to give a brief explanation of each technology.

Blockchain Technology

In 2008, a blockchain was created by a person or persons with the pseudonym Satoshi Nakamoto to serve as a public ledger for Bitcoin cryptocurrency. Since then the Bitcoin design has inspired other applications. Hence, blockchain is a decentralised and distributed digital ledger (DL) that securely stores records and transactions across a network of nodes (computers). Records and transactions on blockchain are transparent, immutable ( can not be altered), and resistant to tampering.

How Blockchain Technology Works

In blockchain transactions are usually grouped in “blocks”, each block is linked to the previous one through cryptographic hashes, which form a chronological chain. Once a block is added it cannot be altered, this ensures data integrity.

Normally each block contains some data, the block hash, and the hash of the previous block.

Data stored in a block varies from one network (either Bitcoin, Ethereum or Solana, etc.) to another. Each block also has a hash, which can be compared to a fingerprint or a unique signature used to identify a block and its contents. Once a block is created, its hash is calculated, changing something inside the block will cause the hash to change. if the hash of a block changes it longer the same block. Finally, the hash of the previous is what links these blocks for a chain, hence the name “block-chain”.

In the diagram above we have a chain of four blocks, each having a hash and hash of the previous block. Block number 4, points to block number 3, and number 3, points to block 2, and block to points to block 1. The first block cannot point to the previous block because it is the first one, usually referred to as the genesis block. If the hash of any block is changed, it affects the entire block following it, thereby rendering them invalid.

This is just a simple overview of the blockchain technology, to explore further you can look at other resources such as Wikepedia, Investopedia and Builtin. They did a good job explaining the technology. Next, let's take a brief look at artificial intelligence.

A Brief Overview of Artificial Intelligence (AI)

AI generally is thought to refer to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgement and intention.” Source: brookings.

To simplify this, imagine a world where computers don't just follow programmed or pre-determined set of instructions but have brains of their own. Meaning they can think, learn and make informed decisions just as humans.

Artificial intelligence is indeed a game changer. It is changing the way we interact with technology and transforming industries, from education, to healthcare, to finance and so many more.

Main Types of Artificial Intelligence

Artificial intelligence comes in different forms, each with unique characteristics and capabilities depending on the use case, such as reactive machines, theory of mind, limited memory, and self-awareness.

Application of Artificial Intelligence.

Artificial intelligence has been integrated and deployed into several sectors, significantly improving productivity. Some of these industries are finance, Security, transportation, criminal justice, health care, and smart cities. Artificial intelligence, while a work in progress, holds a lot of promise for the digital world.

“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.”

—Larry Page

If you wish to explore further into the fascinating world of artificial intelligence, here are insightful resources I recommend as a good place to start: Caltech, Brookings, and Britannica.

Machine Learning and Deep Learning

Key areas of Artificial Intelligence (AI) are Machine Learning (ML) and Deep Learning (DL). With machine learning (ML), systems can learn patterns from data and make predictions or judgments without the need for explicit programming. It involves methods like reinforcement learning, in which agents learn through a reward system, supervised learning, in which models learn from labelled data, and unsupervised learning, in which patterns are found in unlabeled data. As a subset of machine learning, deep learning (DL) is concerned with multilayer neural networks that are capable of complicated pattern identification and performing well in tasks like speech recognition, image recognition, and natural language understanding.

Unlike ML, which frequently relies on human feature engineering and can handle smaller datasets, DL automatically extracts features even though it requires larger datasets and greater processing capacity. Both strategies are essential for advancing AI in the present day.

How Blockchain Technology Fits Into Artificial Intelligence.

The conventional approach to AI development is usually centralised and dependent on huge datasets owned by a few number of strong organisations, such as tech companies or academic institutions. This presents a number of difficulties, such as the monopolisation of AI capabilities, trust, and data protection. The decentralised nature of blockchain technology has the potential to change this narrative by creating a transparent, secure, and open ecosystem for data and AI model Sharing. On the other hand, artificial intelligence can significantly improve blockchain capabilities, particularly as it has to do with how data is stored, analysed and used. That being said, here are some of the vital ways blockchain and artificial intelligence complement each other:

Economic incentives and monetization: It is a known fact that artificial intelligence depends heavily on data, and most of this data is sourced from internet users. Blockchain offers financial frameworks to encourage data exchange and artificial intelligence advancement. By obtaining tokens or other digital assets, users of a decentralised AI marketplace can profit from their data, algorithms, and processing power. This concept creates a more dynamic and diversified ecosystem for AI creation by encouraging broader engagement in addition to providing contributors with fair compensation.
Security and Privacy: As great as artificial intelligence is, data sharing in traditional AI models is fraught with risks, such as breaches, unauthorised access, and misuse. The decentralised and cryptographic nature of Blockchain provides robust security measures that can safeguard sensitive data, thereby greatly mitigating these risks. Technologies such as homomorphic encryption and zero-knowledge proofs (ZKPs) can allow AI models to learn from data without exposing it. A typical use case is the Ocean protocol.

Built on blockchain technology, Ocean protocol enables a decentralised marketplace where data owners, consumers, and AI developers can exchange datasets securely.

Facilitates decentralised AI marketplaces: Blockchain enables decentralised artificial intelligence markets. With smart contracts these platforms allow developers to keep control over their products while deploying AI algorithms and services. This promotes a cooperative ecosystem and democratises access to AI technologies.

Improved AI data quality: When these technologies are combined, datasets used for AI training can have better data provenance. Blockchain ensures that data is reliable and sourced ethically by tracking the history and place of origin of datasets used in AI training. This strengthens confidence in results produced by AI.
Decentralisation and Data Democratisation: To solve the problem of data monopoly and control by tech giants and other big institutions, blockchain enables the creation of a decentralised marketplace where data and AI models can be shared across a distributed network of users. Thereby democratising access to datasets and computational resources, giving smaller companies, startups, and researchers an opportunity to contribute and benefit from these shared resources. SingularityNET is a typical use case. " Built on blockchain, SingularityNET is a decentralised platform that enables AI developers to create, share, and monetize AI services on a global scale. It allows any AI algorithm to interoperate with other algorithms, creating a network of AI agents that can collaborate and contribute to more complex AI systems."
Enhanced Functionalities: Blockchain functioning is improved by AI through the facilitation of sophisticated data analysis and prediction capabilities. Blockchain operations like consensus procedures and transaction verification can be made more efficient and less expensive by integrating AI algorithms. AI, for example, can improve performance by dynamically modifying parameters based on real-time data, streamlining the mining process in blockchain networks.
Improve Blockchain Security: Blockchain is an evolving technology. A lot of effort and resources has been put in place to improve blockchain security. This can be done using AI. By identifying irregularities and fraudulent activity within blockchain networks, machine learning algorithms offer an extra line of defense against cyberattacks. This proactive strategy contributes to the preservation of blockchain systems' integrity.

The Future of Blockchain and AI Integration

Blockchain is well known for its capacity to handle important issues like data integrity and trust by offering an unchangeable, safe record for transactions. AI, on the other hand, is superior at processing data and identifying patterns, which enables businesses to extract valuable information from enormous databases. The integration of artificial intelligence and Blockchain is still in its early stage. Combining these holds a lot of promise for the future, particularly in areas such as:

Federated Learning: Federated learning is a sub-field of machine learning which enables multiple entities (clients) to collaboratively train a model while ensuring their data remains decentralised. Each participant (client) trains a local version of an AI model using its own data in a decentralised approach known as federated learning. A central server, also known as a coordinator, receives these local models regularly and combines the model parameters, such as weights and gradients from every client that is involved to produce a global model.

Blockchain technology offers a decentralised, immutable infrastructure that enhances security and trust, which can greatly improve federated learning. With blockchain technology, federated learning participants can work together without having to put their trust in a central authority. Smart contracts handle the aggregation and reward processes, guaranteeing transparency and equity.

Every contribution like model updates can be documented on the blockchain, generating an unchangeable audit trail that improves accountability and makes it easier to identify fraudulent activity or problems with performance. Blockchain also brings tokenized incentive structures, which encourage wider engagement by rewarding individuals with digital tokens for high-quality contributions. Because it is decentralised, blockchain technology improves security by lowering the risks involved with using a single server for model aggregation.

Furthermore, combining federated learning with blockchain and secure multi-party computation (SMPC) enables secure data sharing across multiple parties, improving data privacy and security in sensitive industries like healthcare and finance.

AI Governance: Blockchain has the potential to be extremely important in regulating AI algorithms and making sure they are open, equitable, and responsible. Overseeing the creation and application of AI systems may be done through decentralised autonomous organisations (DAOs.).

To Wrap up

Blockchain and AI are two transformative technologies that, when integrated, offer unprecedented opportunities for innovation. The combined strengths of these technologies can lead to groundbreaking advancements characterised by greater security, efficiency, and transparency. As both fields continue to develop, the potential applications and implications of their integration are likely to expand, ushering in a new era of technological innovation.

Navigating the challenges of this integration and optimising its advantages for society as a whole will require a collaborative strategy that incorporates the ideas of multiple stakeholders.

As these technologies continue to evolve, they will undoubtedly reshape the way we build and deploy AI, ushering in a new era of decentralised intelligence.