Decentralized and Collaborative AI: How Microsoft Research Is Using Blockchains To Build More-Transparent Machine-Learning Models

The biggest challenge of the next decade of artificial intelligence (AI) is going to be based on determining whether data and intelligence remains a privilege of a handful of large technology companies based in a few countries or if it can be democratized to the rest of the world. The centralized nature of machine learning and AI applications foments a “rich get richer” dynamic in which only the companies with access to high-quality data sets and data-science talent are able to take advantage of AI opportunities. The field of decentralized AI is one of the leading trends that is looking to address this challenge. Although still impractical for many real-world implementations, the decentralized AI space has been steadily gaining traction within the AI community. Recently, AI researchers from Microsoft open-sourced the Decentralized & Collaborative AI on Blockchain project that enables the implementation of decentralized machine-learning models based on blockchain technologies.

From training to optimization, every single step in the lifecycle of machine-learning models can be improved with certain degrees of decentralization. Let’s take the example of a simple prediction model that is designed to forecast sales of a given product. In the traditional centralized approach, we need to implicitly trust a group of data scientists to select the right neural-network architecture, build the correct data sets, train the model efficiently, tune the hyperparameters in order to optimize performance, and a dozen of other tasks. After all that, we can’t really be sure the model is performing optimally. This problem gets even worse once we start introducing new versions of the model because it is nearly impossible to correlate specific changes with a particular performance. Decentralized AI methods look to simplify this problem by enabling transparent accountability and organic collaboration across all stages of the machine-learning lifecycle.

The raise in popularity and maturity of blockchain technologies has been an important catalyzer for decentralized AI architectures. The immutability and distributed consensus models of blockchain technologies intrinsically introduce a level of trust and enables collaborative dynamics in machine-learning applications. The Microsoft Research team leveraged some of the native capabilities of blockchain technologies to enable different levels of decentralization in machine-learning models.

Microsoft Decentralized & Collaborative AI

Decentralized & Collaborative AI on Blockchain(DCAI) is a framework to host and train machine-learning models on a blockchain infrastructure. The current version of DCAI is contrained to the Ethereum blockchain and leverages smart contracts as the main encapsulation mechanism for machine-learning programs. Conceptually, smart contracts are immutable programs that contain business logic that executes on a blockchain runtime. In the case of the DCAI framework, smart contracts are used to enable decentralized training mechanisms in machine-learning models.

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