As all self-respecting geeks will know, it’s important to bow to your sensei. But how do we train for Artificial Intelligence (AI) competency?
Arboreally named AI company Bonsai from Berkeley, CA wants to take a good chunk of the training and learning curve out of the way. The company’s core product allows developers to build intelligent systems by ‘completely automating’ the management of complex machine learning libraries and algorithms.
Developers can use Bonsai to program AI models that improve system control and real-time decision support.
The software is used to increase automation and improve operational efficiency of industrial systems including robotics, manufacturing, supply chain, logistics, energy and utilities.
Bonsai Siemens research
Bonsai has now worked with Siemens to deploy AI on a real-world machine in a test environment.
Using Bonsai’s AI Platform, Siemens subject matter experts trained an AI model to auto-calibrate a Computer Numerical Control (CNC) machine more than 30x faster than an expert human operator.
For a fun ‘what is’ video to learn more about Computer Numerical Control click here.
CNC machines, or computer-controlled machine tools, have revolutionised manufacturing since their inception in the 1940s. However, the value that CNC machines provide global manufacturers is constrained by high maintenance costs. To achieve highest possible quality of production, CNC machines need to be recalibrated frequently, as even minor friction leads to errors that result in costly manufacturing imperfections.
Mark Hammond, CEO and co-founder of Bonsai has explained that the Bonsai platform’s core is a ‘machine teaching’ technique, which enables subject matter experts such as specialist engineers to train machines to perform complex tasks.
Lessons & rewards
Using a simple scripting language, experts can design the ‘lessons’ and ‘rewards’ required to train each task similar to a resource from somewhere like Salesforce. Bonsai’s AI Engine supports a range of deep reinforcement learning algorithms, along with the logic for choosing the best-fit algorithms and guiding the training.
In this way, the experts are able to leverage AI without themselves having to gain a deep understanding of machine learning.