Tedious tasks of artificial intelligence can be outsourced thanks to a new blockchain-based framework

The world of work of the future will be driven by a staggering amount of data. To achieve this, businesses, developers, and individuals will need better artificial intelligence (AI) systems, better trained workers, and more efficient data processing servers.

While large tech companies have the resources and expertise to meet these demands, they remain out of reach for most small and medium-sized businesses and individuals. To address this need, an international team of researchers led by Concordia has developed a new framework to make complex AI tasks more accessible and transparent to users.

The framework, described in an article published in the journal Information sciencespecializes in providing solutions to deep reinforcement learning (DRL) demands. DRL is a subset of machine learning that combines deep learning, which uses layered neural networks to find patterns in huge datasets, and reinforcement learning, in which an agent learns to make decisions by interacting with its environment based on a reward/penalty system.

DRL is used in industries as diverse as gaming, robotics, healthcare, and finance.

The framework pairs developers, businesses, and individuals with specific but out-of-reach AI needs with service providers who have the resources, expertise, and models they need. The service is crowdsourced, built on a blockchain, and uses a smart contract (a contract with a predefined set of conditions embedded in the code) to match users with the appropriate service provider.

“Crowdsourcing the DRL training and design process makes the process more transparent and accessible,” says Ahmed Alagha, a doctoral student in the Gina Cody School of Engineering and Computer Science and lead author of the paper.

“With this framework, anyone can sign up and create a history and profile. Based on their expertise, training and grades, they can be assigned tasks requested by users.”

Democratizing the DRL

According to his co-author and thesis supervisor Jamal Bentahar, a professor at Concordia’s Institute for Information Systems Engineering, this service opens up the potential offered by DRL to a much broader population than was previously available.

“To train a DRL model, you need computing resources that are not available to everyone. You also need expertise. This framework offers both,” he explains.

The researchers believe that their system design will reduce costs and risks by distributing computational effort via the blockchain. The potentially catastrophic consequences of a server failure or malicious attack are mitigated by having dozens or hundreds of other machines working on the same problem.

“If one centralized server goes down, the whole platform goes down,” Alagha says. “Blockchain gives you distribution and transparency. Everything is recorded there, so it’s very difficult to tamper with.”

The difficult and expensive process of training a model to work well can be shortened by having an existing model that requires only relatively minor adjustments to fit a user’s particular needs.

“For example, suppose a large city develops a model that can automate traffic light sequences to optimize traffic flow and minimize accidents. Smaller cities may not have the resources to develop one themselves, but they can use the one developed by the large city and adapt it to their own situation.”