AI and Blockchain: A New Paradigm in Distributed AI

AI and blockchain, two of the most transformative technologies of our time, are converging to create a new paradigm in distributed AI. While blockchain has been overshadowed by AI hype in recent years, both technologies have deep historical roots. AI can be traced back to ancient concepts like the Golem, while blockchain's origins lie in hashing functions and distributed programming, with roots dating back over 40 years.

Distributed computing, a necessary condition for decentralization, requires a time order and a way to create a version of truth from a set of computers. Independent governance of distributed machines gives us decentralization, which is based on the nature and spread of entities controlling the infrastructure. However, even Bitcoin, often considered decentralized, is controlled by a few mining pools and large institutions.

The integration of blockchain into AI can help address several challenges, including data privacy, energy use, and the availability of siloed data. One startup, Modelx.ai, is pioneering a solution in the realm of federated AI. The company aims to keep model data private while maintaining open access to the model and its weights.

AI has traditionally been controlled by single entities, with deep learning-based large language models, image generation, and audio-to-text models dominating the landscape. However, the data-heavy approach has its limitations, including the risk of AI consuming its own output and degrading into bias and ineffectiveness. Additionally, privacy concerns arise when AI models are trained on publicly available data that was never intended for such use.

Open-source models like DeepSeek have shown comparable performance without large amounts of data or extensive training. Modelx.ai's product addresses the privacy conundrum by enabling hospitals to train AI models on private data without sharing the data itself. The model is trained serially on each hospital's private data, with the model weights hashed and put on a federated ledger at each training step. This approach ensures privacy while allowing for improvements in the AI model and fair compensation for contributors.

The convergence of AI and blockchain is poised to revolutionize the way we approach distributed AI, addressing challenges related to data privacy, energy use, and the availability of siloed data. As these technologies continue to evolve, they will likely shape the future of AI and create new opportunities for innovation and collaboration.