Semisupervised machine learning, the next frontier in AI

Description

Current deep learning approaches require large amounts of labeled data. The creation of labeled data is expensive, error prone, and time consuming. Despite these challenges, in the last decades tremendous successes in machine learning have been achieved in the area of supervised learning that requires the compilation of large datasets with labels (for example, grouping pictures based on the person in the image). In contrast, unsupervised learning algorithms do not require labels and require minimal human participation. However, due to significant technical difficulties, they haven’t been as successful as supervised learning algorithms.

Vinay Rao and Santi Adavani walk you through a new method to overcome the technical difficulties of self-supervised learning to extract value from very large unlabeled datasets using machine learning with minimal human intervention in cybersecurity, precision medicine, and predictive maintenance applications. The new method circumvents these difficulties and clears the way to scaling unsupervised learning algorithms to large and complex datasets. They demonstrate the method on a complex dataset through the choice of the correct comparison function between the objects of the dataset and the fully automatic algorithm and algorithm parameter selection.