Solving Complex Issues with Decentralized AI Technology

Artificial Intelligence has completely revolutionized the way we live in recent years. It’s no longer just an optional addition to our lives but an essential component for countless individuals. AI is seamlessly integrated into our daily routines, from browsing social media to driving our cars, and it easily caters to a massive population.

But AI’s significance extends far beyond mere convenience. It’s fast becoming our silent partner, intuitively understanding our preferences and requirements. It goes above and beyond being a mere tool to becoming a critical element of our existence, suggesting the perfect playlist and ensuring safer road travel.

AI becomes more useful to humans with each click, like, and search. It works by taking in data and generating an output. AI can identify patterns and reconstruct information on the previously used topic by analyzing the input data.

But why are centralized AI’s problematic?

The development of AI systems has long been restricted to a select few tech giants like Google, Microsoft, and OpenAI, who have held an undisputed monopoly over creating infrastructure and tools required for handling large datasets. This centralization has made it imperative for aspiring organizations to have substantial funding to begin creating and processing data, which is now a critical factor for market competition.

Furthermore, the trend towards centralization has led to a lack of transparency in AI development, with technology vendors adopting an opaque testing approach. While this may shield the technical intricacies of AI models from users and regulators, it also limits transparency, making it increasingly difficult to detect faultiness, biases, and misinformation. As a result, the AI landscape has become increasingly challenging, posing a significant threat to the integrity and reliability of AI systems.

Why it has to be Decentralized?

The imperative need for decentralizing AI is driven by the unequivocal necessity to democratize access to AI technologies, ensure unparalleled privacy and security, and foster innovation through diverse participation. Decentralization of anything is the only way to mitigate these risks. For AI, it’s achieved by distributing the control and processing of AI across multiple nodes, thereby ensuring no single entity has complete control.

Studies such as Ben Goertzel’s research prove that decentralized AI networks offer the dual benefits of heightened security and wider participation in AI development and governance. This approach is crucial for creating a more just and inclusive AI creation landscape.

Decentralized AI is pivotal in driving innovation and fostering a competitive ecosystem. A study published in the IEEE Access journal by H. Rashid et al. demonstrates how decentralization can create a more competitive market, eliminate barriers to entry for smaller players, and accelerate AI innovation. This is due to the fact that decentralized frameworks enable the collaborative development of AI models, where developers can share datasets, algorithms, and insights without losing control or compromising privacy. Such an environment promotes rapid experimentation and iterative learning, which are the key drivers of technological progress.

Several projects are addressing the challenge of decentralizing AI at the forefront. OpenMined is an open-source community solely focused on developing privacy-preserving AI technologies. It allows data to remain encrypted during analysis to safeguard user privacy. Spheron Network, on the other hand, offers a decentralized marketplace for AI services, which enables AI developers to publish their AI services on a blockchain-based platform, making AI more accessible and fostering a collaborative development environment. Furthermore, Ocean Protocol provides a decentralized data exchange protocol to unlock AI data while ensuring secure data sharing and preserving privacy and control for data owners. These projects are the epitome of the ongoing efforts to create a more decentralized, accessible, and equitable AI ecosystem.

Are there any Alternatives?

Federated learning uses a decentralized dataset to train two or more AI models on different computers. This methodology is used by organizations looking to build a centralized AI model that makes decisions based on data processed on a decentralized basis to maintain user privacy. It is not the same as decentralized AI, which has no central data processing authority. The organization has centralized control over the AI model used to process the datasets in federated learning. This approach tends to have a centralized model that gets updated based on the learnings of distributed models. In contrast, a decentralized system would have multiple nodes that come to a consensus, with no central model as an authority. It is worth noting that both these distributed forms of AI can be quite challenging to implement correctly, and only large companies such as FAANG, Microsoft, or similar, with the necessary expertise and resources, can do the necessary R&D for reliable at-scale deployment.

Benefits of Decentralized AI

Using a decentralized AI architecture offers some key benefits to AI developers and users. Some of these are:

• Users can benefit from AI-based decision-making without sharing their data;
• More transparency and accountability over how AI-based decisions are made;
• Independent researchers have more opportunities to contribute to AI development;
• Blockchain technology provides new opportunities for encryption;
• Decentralization enables new possibilities for collaborations with Web3 and the metaverse.

Democratizing AI Development

Decentralized AI, despite being in its early stages, has immense potential to democratize the development of AI. This will offer greater opportunities for open-source model developers to interact directly with users, without the need for a centralized authority. If more vendors adopt decentralized AI models, it could drastically reduce the control that proprietary model developers have over the market, while simultaneously increasing transparency over AI development as a whole.

Originally published at https://blog.spheron.network on May 3, 2024.