Introduction to Artificial Intelligence and its Risks
Artificial intelligence needs no introduction, driving new innovation and transforming the way people work. But the adoption of AI and our increasing reliance on it also raises questions about the centralised nature of the infrastructure it runs on, and the risks that poses.
The Importance of Decentralisation
Cryptocurrencies have taught us of the importance of decentralisation, and the dangers of concentrating compute resources and data in a small handful of facilities. While such an approach may seem efficient, it also introduces critical vulnerabilities and concerns over access and governance.
Risks of Centralised AI Systems
Centralised AI systems are incredibly vulnerable, as the big server farms that run them represent a single point of failure that could bring hundreds of applications crashing down. Data centres that power AI models like ChatGPT pose a tempting target for hackers, too, due to the enormous amounts of data they possess.
Regulation and Monopolisation
Centralised servers also mean more headaches in terms of regulation. When an AI system is located in a single country, it falls under that nation’s governance, which can cause problems for users in other territories subject to different data sovereignty and privacy rules. Of course, centralisation also means monopolisation, and we already have plenty of evidence of this with the likes of OpenAI, Google, and Anthropic being extremely secretive about how they train their most advanced AI models.
Decentralised AI: The Solution
Fortunately, there is a ready-made solution to these problems in the shape of decentralised AI. With a decentralised AI, the infrastructure that powers models can be distributed in a wide network of users, eliminating the risks associated with centralisation. Decentralisation means no single point of failure, more transparency and user control, and access for everyone.
Core Characteristics of AI Blockchains
The convergence of blockchain and AI holds plenty of promise due to the way they complement one another. Blockchain’s immutability can ensure integrity and trust in the data that powers AI systems, while AI can bring enhanced automation and intelligence to blockchain-based systems.
Transparent Data Attribution
A key capability of AI blockchains is transparent data attribution, which uses “proof-of-attribution” consensus mechanisms to identify and credit the source of data used by AI systems, increasing fairness. It provides visibility into who provided the data, how it contributed to the AI’s outputs, what value did it add, and how much should the provider of the data be compensated.
AI Royalties and Monetisation Layer
Let’s imagine someone poses a question to a decentralised chatbot, and it responds by drawing on what it finds in a post on Substack or Medium. The system would record the fact that the model used this information to inform its response, and using smart contracts, it would automatically process the payment of tokens to the creator of that content.
Decentralised Model Lifecycles
Another key difference is that the entire development process of blockchain-based AI is open, from the initial proposal, to the model training and, finally, its deployment. It supports a more collaborative environment for the creation of community-owned models that are controlled by their users, using democratic governance processes, where token holders vote on the new features they want to see added.
Efficient, Scalable Infrastructure
AI blockchain run on decentralised infrastructures that are provided by their users. For instance, Render Network has built up a network of GPUs, but they’re not hosted in a centralised data centre. Instead, network participants rent out the idle GPU capacity of their laptops and desktops, and these resources are pooled and made available to AI applications that need processing power.
Why Do AI Blockchains Matter?
The vast majority of AI services in use today live in centralised “black boxes” that are incredibly opaque, revealing next to nothing about how they work or the data they use. They’re owned by a handful of powerful organisations, and this concentration of control undermines the democratizing potential of AI technology.
We Cannot Let AI Be Monopolised
The existing AI landscape holds just as much peril as it does potential. The technology has advanced so much in such a short space of time that there’s a very real danger of monopolisation, and with that comes the risk of it being misused.
Conclusion
AI blockchains are the only way to prevent this, serving as a foundation for freely accessible and decentralised AI systems that will be developed in a collaborative way, with checks and balances in place to prevent any abuse. Building this decentralised future for AI requires coordination at every layer, from the data being used to the model training processes and the infrastructure that hosts it.
FAQs
Q: What is decentralised AI?
A: Decentralised AI refers to the distribution of AI infrastructure and data across a network of users, eliminating the risks associated with centralisation.
Q: How do AI blockchains work?
A: AI blockchains use blockchain technology to ensure the integrity and trust of the data that powers AI systems, while AI brings enhanced automation and intelligence to blockchain-based systems.
Q: What are the benefits of decentralised AI?
A: The benefits of decentralised AI include transparency, user control, and access for everyone, as well as the prevention of monopolisation and the risks associated with centralisation.
Q: What is transparent data attribution?
A: Transparent data attribution is a key capability of AI blockchains that uses “proof-of-attribution” consensus mechanisms to identify and credit the source of data used by AI systems, increasing fairness.
