The Bonseyes AI Marketplace provides the reuse of data, meta data, and models among separate legal entities, to significantly reduce design and development time in building systems of artificial intelligence. It supports many actors in the AI value chain such as Challenge Providers, Data Providers, Data Scientists, Application Developers, Infrastructure Providers, and Solution Integrators. Other available platforms are either vendor-specific or do not provide entire value chain from challenges to deployment.
Bonseyes targets high impact societal applications such as healthcare, automotive, and manufacturing that demand low latency, real-time, low power AI systems of systems while preserving user privacy. It’s potential impacts are significant to:
- Boost competitiveness through top European intelligent product/service research while reducing CAPEX, experts, and time to adoption.
- Boost technology transfer through the mobilization of talent, innovation hubs, pilots with integrated and reusable assets, tools, and European industry user-driven challenges.
- Boost tech convergence through fertilizing technology leadership, AI coalitions and interworking sharing current market best practices in deployment of AI with distributed and decentralized technologies.
Overall, the project has progressed beyond the start-of-the-art in distributed and decentralized AI systems-of-systems development through:
- Reduction in development time by 50% compared to monolithic system design methods, through the reuse of data, meta data, and models among separate legal entities made possible by the Bonseyes AI Marketplace, a data marketplace that modularizes the AI systems’ development value-chain.
- Reduction in cost of ownership by a factor of 5 related to training of deep learning models compared to current training approaches designed for the cloud. Deep learning training methods for resource constrained devices that enable models with near state-of-the-art accuracy that are tailored for embedded, constrained, distributed systems operating in real environments with noisy, sometimes missing data.
- Enabling distributed deep learning, where part of the training can be achieved in embedded devices themselves, partially alleviating the need to transmit vast amounts of labelled data back to the cloud. This step is a key enabler for eventual unsupervised learning on mobile devices.
- Predictive tools that automate optimal on-device deployment of a model on the target embedded system given a specific power/space/time constraint. Enabling low power “always-on” intelligence on edge devices. Tools optimized for various low power universal reference developer platforms supporting “always-on” intelligence paradigms.
- Privacy and data isolation for deep learning methods that can be selected and adapted by the data providers or model owners. Robust data collection tools for IoT devices scalable to a very large number of devices and maintain privacy and data isolation, while enabling real-time data processing on the “edge” device to identify when data is “abnormal”.
