This is the third 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.
Bringing together expertise in neuroscience and developer platforms, Bonsai was founded in 2014 by Mark Hammond and Keen Browne with the vision of making intelligence a core component of every hardware and software application. Recognizing the shortage of data science talent capable of building sophisticated AI models, they created an AI development platform that abstracts away the complexity of libraries like TensorFlow, making the programming and management of AI models more accessible to developers and enterprises. Bonsai achieves this vision by applying a proven approach to a new problem, providing an abstraction layer above the low-level AI mechanics.
Before databases were commonplace, it was very difficult to work with data in sophisticated ways. Databases solved this problem nicely, but they didn’t do it by providing a massive toolkit to tweak and tune all the low-level database mechanics. Instead, databases shifted up the level of abstraction, allowing developers to focus on the problem they were trying to solve.
AI suffers from a very similar problem today. The low level machine learning libraries and algorithms are very difficult to work with. To make AI more accessible, the answer is not to expose these vast, complex toolkits to developers. Just like databases did for data, Bonsai has shifted up the level of abstraction. As shown in the graphic above, Bonsai provides a developer with a special purpose programming language to codify the concepts unique to their problem domain, a runtime that generates and manages all the low level mechanics for them, and the libraries to connect the resulting AI models into hardware and software applications.
For more information about the Bonsai AI Development Platform visit our Product page.