How Useful is Self-Supervised Learning?

                                  How Useful is Self-Supervised Learning?

Self-supervised learning is a way of  training computers to do tasks without humans providing labelled data. It is a main subset of unsupervised learning where outputs or are derived by machines that label, categorize, and analyze information on their own then draws conclusions based on correlations and connections. Self-supervised learning can also be an autonomous form of supervised learning because it does not require human input in the form of data labelling. In contrast to unsupervised learning, self-supervised learning does not focus on clustering and grouping that is commonly associated with unsupervised learning. 

The concept of self-supervised learning aims to address challenges in supervised learning when it comes to collecting, handling, cleaning, labeling, and analyzing data. Developers who want to create an image classification algorithm, therefore, create supervised learning-capable systems to collect comprehensive data to get a representative sample. Apart from feeding the computer image datasets, developers need to classify the images before they can be used for training. The process is arduous and time-consuming compared with how humans approach learning.

The human learning process is multifaceted. It involves both supervised and unsupervised learning processes. While we learn via experiments and curiosity, we also acquire knowledge better using fewer and simplified data. Even now, this remains a challenge for deep learning systems. While we have seen advances in learning-based AI systems that can break down speech, images, and text, performing complex tasks remains a challenge for these. That is what self-supervised learning is trying to address.In short, self-supervised learning allows AI systems to break down complex tasks into simple ones to arrive at a desired output despite the lack of labeled datasets.