May 17, 2017

Programming AI | Control & decision support | Industrial Systems

This is the second installment of a six part series from our recently published whitepaper; A fundamentally different approach for building intelligent industrial systems. You can download the complete paper here.

Programming AI to improve control and enhance real time decision support for multidimensional, industrial systems quickly outstrips the capabilities of generic AI solutions. At the core of the issue is the lack of talent and/or tools that can combine an organization's subject matter expertise with complex machine learning technologies to build application-specific AI models.

Subject matter expertise, in the form of data, models, and simulations, is critical to understanding the different variables, behaviors, and constraints that drive the efficient operation of industrial systems. Paired with powerful machine learning libraries and techniques, like TensorFlow and reinforcement learning, specific domain expertise can significantly improve the prediction accuracy of produced intelligence models, as well as the automation and operational efficiency of targeted systems.

Alternative solutions force unnecessary tradeoffs

Up until now there has not been a platform available for enterprises to efficiently fuse together subject matter expertise and AI without requiring an advanced degree in machine learning. Consequently, enterprises have been forced to compete for the rare talent that can work with low-level AI toolkits, limit use cases to those with established APIs, or be unnecessarily constrained by black box solutions.

To learn more about Bonsai’s fundamentally different approach for programming AI, visit our How it works page.

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