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.
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