An AI development platform for industrial systems

Kyle Wiggers, Microsoft launches Project Bonsai, an AI development platform for industrial systems, Venture Beat, May 19, 2020.

Microsoft announced the public preview of Project Bonsai, a platform for building autonomous industrial control systems, during its Build 2020 online conference. The company also debuted an experimental platform called Project Moab that’s designed to familiarize engineers and developers with Bonsai’s functionality.

Project Bonsai is a “machine teaching” service that combines machine learning, calibration, and optimization to bring autonomy to the control systems at the heart of robotic arms, bulldozer blades, forklifts, underground drills, rescue vehicles, wind and solar farms, and more. Control systems form a core component of machinery across sectors like manufacturing, chemical processing, construction, energy, and mining, helping manage everything from electrical substations and HVAC installations to fleets of factory floor robots. But developing AI and machine learning algorithms atop them — algorithms that could tackle processes previously too challenging to automate — requires expertise....

In September 2017, Bonsai established a new benchmark for autonomous industrial control systems, successfully training a robot arm to grasp and stack blocks in simulation. It performed a claimed 45 times faster than a comparable approach from Alphabet’s DeepMind.

Microsoft refers to the abstraction process as machine teaching. Its central tenant is problem-solving by breaking down workloads into simpler concepts (or subconcepts) and then individually training them before combining them. This technique is also known as hierarchical deep reinforcement learning, when AI learns by executing decisions and receiving rewards for actions that bring it closer to a goal. The company claims this technique can decrease training time while allowing developers to reuse concepts. ...

As Pall explained, rewards in reinforcement learning describe every correct step that an AI tries. Crafting these rewards — which must be expressed mathematically — is difficult because they have to capture every nuance of multistep tasks. And improperly crafted rewards can result in catastrophic forgetting, where a model completely and abruptly forgets the information it previously learned.

“What machine teaching does is that it takes a lot of these hard problems and really puts the problem on rails. It constrains how you specify the problem,” added Pall. “The [Bonsai platform] automatically selects the algorithm and [parameters] … from a whole suite of options, and it has abstraction goals, which rather than requiring a user to specify a reward, instead has them specify the outcome they want to achieve. Given a state space and this outcome, we automatically figure out a reward function against which we train the reinforcement learning algorithm.”...

SCG is among the companies that tapped Project Bonsai to imbue their industrial control systems with machine learning. SCG’s chemical division created a simulation within the Bonsai platform to speed up the process of optimizing petrochemical sequences, to the tune of 100,000 simulations per day, each modeling millions of scenarios. Microsoft claims the fully trained model is able to develop a sequence in a week, whereas it previously required several months for a group of experienced engineers.

“Polymers are designed with a particular application in mind. In order to figure out the stages of manufacturing, you need to know the mixing, temperature, and other factors,” said Pall. “The process of coming up with a plan of how a polymer can be manufactured takes six months, traditionally, because it’s done inside a simulator with a human expert guiding the simulator and trying a step, eventually getting it right, and then moving on to the next step. Bonsai found a BRAIN that surfaces solutions to the manufacturability of a given polymer and then controls machines to produce it.”