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Active Learning: Why Some Data Are More Equal Than Others and How You Can Use it to Your Advantage
Artificial Intelligence is a technology that thrives on two kinds of fuel – computing power and data. Their increasing affordability is the driving force behind the recent AI boom. In fact, many of the key ideas behind artificial neural networks have been around for decades. However, it took democratization... Read more
Guided Labeling: Human-in-the-Loop Label Generation with Active Learning and Weak Supervision
Paolo is a speaker for ODSC East 2020 this April 13-17. Be sure to check out his talk, “Guided Labeling: Human-in-the-Loop Label Generation with Active Learning and Weak Supervision,” there! One of the key challenges of utilizing supervised machine learning for real-world use cases is that most algorithms and... Read more
An Introduction to Active Learning
The current utility and accessibility of machine learning is in part due to the exponential increase in the availability of data over time. While data is abundant, labels that are required for specific supervised machine learning tasks can be difficult to obtain. At ODSC West in 2018, Dr. Jennifer... Read more
Crash Course: Pool-Based Sampling in Active Learning
Active learning is a class of machine learning problems where labeled data isn’t available for supervised algorithms. Let’s take the classic setup as an example. Say we have pictures of birds and want to classify them by type, but the images don’t have labels for what kind of bird... Read more