The emerging field of decentralized artificial intelligence (AI) is becoming one of the most exciting technology trends of the last few months. A lot has been written about the potential value of the intersection of artificial intelligence (AI) and blockchain technologies and we, this year, we have even entire conferences dedicated to the subject of decentralized AI. However, I feel that a lot of the hype behind decentralized AI fails to highlight some of the key value propositions of the new technology movement that can make it one of the most foundational technology trends of this decade. If you believe in the idea that AI is going to become an increasingly influential factor in our daily lives, I believe decentralized AI will be an essential element to guide the impact that machine intelligence will have in future generations. Let's look at some of the economic dynamics behind decentralized AI to try to clarify our point.
Last week I published a brief analysis of the OpenMined platform as one of the new technologies that is trying to enable truly decentralized artificial intelligence(AI) processes by leveraging blockchain technologies. In the article, I mentioned that OpenMined drew parts of its inspiration from Google's research about federated learning as a mechanism to improve on the traditional centralized approach to train AI models. From my perspective, I consider federated learning is one of the most interesting AI research breakthroughs of the last two years that is already powering mission critical applications.
Last week, I presented a session at the "AI With The Best" conference about one of my favorite topics, decentralized artificial intelligence(AI). The "AI With the Best" conference is notorious for bringing together a rate mix of AI researchers and practitioners as part of the same audience so, as a speaker, you have to have the right balance between deep AI research and practical topics. In the case of my talk, I tried to summarize some of the ideas I've been exploring in the decentralized AI space. The complete slide deck is available below or here. The remaining of this post summarizes some of the most relevant ideas included in my presentation.
In 2017 Google introduced Federated Learning (FL), "a specific category of distributed machine learning approaches which trains machine learning models using decentralized data residing on end devices such as mobile phones." A new Google paper has now proposed a scalable production system for federated learning to enable increasing workload and output through the addition of resources such as compute, storage, bandwidth, etc. The Google paper also addresses various FL challenges, solutions and future prospects. "Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions".
Federated learning is not only a promising technology but also a possible brand new AI business model. Indeed, as a consultant, I have been recently tasked with making recommendations about how a healthcare company could create a "data alliance" with some competitors by creating a Federated Learning framework. The goal of this article is to explain to you how FL might give birth to a new data ecosystem and create data alliances. Without getting too much into technical details, FL could be defined as a distributed machine learning framework that allows a collective model to be constructed from data that is distributed across data owners. The data required by AI projects involve multiple elements.