The vast majority of machine learning models are trained using a static dataset; that is, the dataset is fixed and does not change over time. This can be a problem when dealing with time-sensitive data, as the model may not be able to adapt to changes in the underlying data distribution. Active learning is a…
Tag: active-learning
Data annotation quality
The process of data annotation is a fundamental challenge for artificial intelligence, as it is well-known that neural networks require a large number of accurately labeled training examples to learn effectively. This bottleneck limits the applicability of deep learning to many domains. In order to address this challenge, a number of new approaches have been…
Lack of labeled data
The lack of labeled data limits the current state of AI. This is a problem because most data is unstructured and not labeled. We need to find new and creative ways to label data to solve this problem. One way to label data is through active learning. Active learning is a process where the user…