When AI Meets Blockchain, It’s Intelligence Meeting Trust

Blockchain networks and artificial intelligence systems are central to the current technological revolution.

Blockchain provides decentralized and secure distributed ledgers for transactions, while artificial intelligence (AI) empowers machines with the ability to learn, reason, and make decisions.

The meeting of AI and blockchain has the potential to unlock new possibilities across various industries — far beyond cryptocurrencies and financial services.

The key is that both make use of data, in different ways but ones that complement each other. In short, blockchain records reality, and AI can act on that trusted data.

“AI needs blockchain. This is where intelligence meets trust,” Naseem Naqvi, Founder and President of the British Blockchain Association, stated at the recent Fintech Connect conference in London.

“The safety crisis in AI is essentially a governance crisis,” Naqvi said.

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How Blockchain Brings Data Transparency to AI

To understand the convergence of AI and blockchain, we must look at what these technologies are trying to do with our data.

Naqvi said:

“Data is the most precious asset that we have. This data is not just a digital identity. It includes our communication, our history, our entitlements, digital contracts, social context, money. It includes everything.”

“The whole idea with AI is that it takes the data and then trains itself. It learns from what we ask it to learn—what we call machine learning.

“Machines learn, and then they give us some intelligent answers — hopefully better than humans, or at least equivalent to humans.

“Blockchains are also taking data from us and giving us certain answers. With data on the blockchain, we know the inputs and the outputs. There is an audit trail, there is immutability. There is transparency. And at the moment, we don’t have any of that in the AI systems.

“AI systems have a long-standing problem of explainability of proof of truth of data.”

When data is stored on a blockchain, it has additional value in that it is organized.

As AI systems extract vast amounts of data, blockchains can ensure that the data is transparent, auditable, and that there is some provenance to it.

Blockchains provide a record of who accesses the data and for what purpose. This data can then be used to extract some meaningful insights.

Naqvi continued:

“Blockchains are deterministic; we know exactly what’s going to happen.

“With AI, it’s a guess—they are probabilistic or pseudo non-deterministic, which means that the AI systems are guessing reality.

“When you have blockchain helping AI, you get decentralized AI; when you have AI helping blockchain, you get intelligent projects.

 

“Blockchains are not guessing anything—blockchains are simply recording reality.

 

“This means that AI systems bring intelligence to the data. And blockchains bring trust to the data… we can then combine these two for our benefit.”

This convergence is a top priority for policymakers and regulators.

The UK Parliamentary Group on blockchain technologies has established a working group to explore where blockchain and AI meet. The European Commission is also interested in the opportunities created by integrating AI and blockchain with the Internet of Things (IoT) devices.

“AI and blockchain can potentially disrupt a wide range of sectors and will likely play central roles in the success of Europe’s green and digital transitions and in strengthening its technological sovereignty,” according to a Commission report.

Decentralized On-Chain AI Apps

The convergence of blockchain and AI can pave the way for decentralized AI models.

While AI is being developed based on open-source software models — a step towards transparency — AI is still centralized and being developed by private companies.

With blockchain networks, instead of relying on centralized servers, AI models can be distributed across a network of nodes, enhancing scalability and reducing the risk of system failures.

This decentralized approach aligns with the principles of trust and transparency inherent in blockchain. The decentralized nature of blockchain also reduces the risk of a single point of failure, making it more resilient to cyber-attacks.

This convergence is facilitating the development of new applications. For instance, the SingularityNET platform is a decentralized blockchain network that allows developers to build and launch AI applications and receive the native AGIX cryptocurrency token used to pay for transactions within the platform.

Similarly, Fetch.ai is a blockchain platform that allows anyone to build and deploy AI services.

Hanson Robotics and SingularityNet Foundation (SNET) have created SophiaVerse, a Web3 metaverse to develop sentience in Sophia, a humanoid robot, and partnered with Twin Protocol, a data storage platform leveraging blockchain technology.

The partners aim to create digital twins that can act as an avatar in the metaverse, carrying out shopping, booking, and personal assistant tasks. Twin’s blockchain storage and ecosystem will ensure the data is used efficiently as SophiaVerse scales.

The combination of technologies also creates possibilities for AI apps that generate art that can be stored in smart contracts on a blockchain and sold for cryptocurrency tokens.

And smart contracts could go further, setting rules for how language model agents interact with the real world when they fulfill certain criteria. This could address concerns about the governance of AI applications.

The Bottom Line

To understand the convergence of AI and blockchain, it is important to consider what these technologies are trying to do with our data. While AI is opaque in the way that it processes data for machine learning, blockchains are transparent and give users self-sovereignty over their data.

Blockchains have the potential to transform AI and create trust, auditability, and provenance in the way AI systems interact with the real world.

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Nicole Willing

Nicole Willing has two decades of experience in writing and editing content on technology and finance. She has developed expertise in covering commodity, equity, and cryptocurrency markets, as well as the latest trends across the technology sector, from semiconductors to electric vehicles. Her background in reporting on developments in telecom networking equipment and services and industrial metals production gives her a unique perspective on the convergence of Internet-of-Things technologies and manufacturing.