Machine teaching with Microsoft’s Project Bonsai

With machine learning (ML) at the heart of much of modern computing, the interesting question is: How do machines learn? There’s a lot of deep computer science in machine learning, producing models that use feedback techniques to improve and training on massive data sets to construct models that can use statistical techniques to infer results. But what happens when you don’t have the data to build a model using these techniques? Or when you don’t have the data science skills available?

Not everything that we want to manage with machine learning generates vast amounts of big data or has the labeling necessary to make that data useful. In many cases, we might not have the needed historic data sets. Perhaps we’re automating a business process that’s never been instrumented or working in an area where human intervention is critical. In other cases we might be trying to defend a machine learning system from adversarial attacks, finding ways to work around poisoned data. This is where machine teaching comes in, guiding machine learning algorithms towards a target and working with experts.

Introducing Project Bonsai

Microsoft has been at the forefront of AI research for some time, and the resulting Cognitive Service APIs are built into Azure’s platform. It now offers tools for developing and training your own models using big data stored in Azure. However, those traditional machine learning platforms and tools aren’t Microsoft’s only offering, as its Project Bonsai low-code development tool offers a simple way of using machine teaching to drive ML development for industrial AI.

Delivered as part of Microsoft’s Autonomous Systems suite, Project Bonsai is a tool for building and training machine learning models, using a simulator with human input to allow experts to build models without needing programming or machine learning experience. It doubles as a tool for delivering explainable AI, as the machine teaching phase of the process shows how the underlying ML system came to a decision.

Building machine teaching with simulators

At the heart of Project Bonsai is the concept of the training simulation. These implement a real-world system that you want to control with your machine learning application, and so you need to build using familiar engineering simulation software, such as MATLAB’s Simulink or custom code running in a container. If you’re already using simulators as part of a control system development environment or as a training tool, these can be repurposed for use with Project Bonsai.

Training simulators that have a user interface are a useful tool here, as they can capture user input as part of the training process. Simulators need to make it very clear when an operation has failed, why it has failed, and how the failure happened. This information can be used as inputs to the training tool, helping teach the model where errors may occur and enabling it to find signs of the error occurring. For example, a simulator being used to train a Project Bonsai model to control an airport luggage system could indicate how running conveyors too fast will cause luggage to fall off, and running too slow can cause bottlenecks. The system then learns to find an optimum speed for maximum throughput of bags.

Copyright © 2021 IDG Communications, Inc.

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