Microsoft Introduces Icebreaker to Address the Famous Ice-Start Challenges in Machine Learning

The acquisition and labeling of training data remains one of the major challenges for the mainstream adoption of machine learning solutions. Within the machine learning research community, several efforts such as weakly supervised learning or one-shot learning have been created in order to address this issue. Microsoft Research recently incubated a group called Minimum Data AI to work on different solutions for machine learning models that can operate without the need of large training datasets. Recently, that group published a paper unveiling Icebreaker, a framework for "wise training data acquisition" which allow the deployment of machine learning models that can operate with little or no-training data. The current evolution of machine learning research and technologies have prioritized supervised models that need to know quite a bit about the world before they can produce any relevant knowledge.