In previous topics we had discuss the term of Self-supervised learning. In general Self-supervised learning is for training computers to do tasks without humans providing labeled data. Is Self-supervised learning is better than supervised learning?
Supervised learning requires labelled data. That data is typically labelled by a domain expert, i.e. someone who is expert at identifying what labels go with what data. In the legal context, this will be a lawyer or legally trained individual. In the consumer space, this is often you! For instance, Facebook is great at automatically tagging your friends in photos.
In supervised learning, models need to find the mapping function to map the input variable (X) with the output variable (Y). Supervised learning needs supervision to train the model, which is similar to as a student learns things in the presence of a teacher. Supervised learning can be used for two types of problems which is Classification and Regression.
Unlike supervised learning, unsupervised learning does not require labelled data. This is because unsupervised learning techniques serve a different process. they are designed to identify patterns inherent in the structure of the data. A typical non-legal use case is to use a technique called clustering.
This is used to segment customers into groups by distinct characteristics (e.g. age group) to better assign marketing campaigns, product recommendations or prevent churn. Unsupervised learning does not need any supervision. Instead, it finds patterns from the data by its own. Unsupervised learning can be used for two types of problems which is Clustering and Association.
So, which is better supervised or unsupervised? The answer is neither. They serve similar but different purposes, albeit they are often combined to achieve an end result. We are now living in the world where artificial intelligence (AI) x machine learning (ML) x deep learning (DL) and all the sub emerging technologies working toward a transforming future, it worth to know what is the coming and emerging technologies and what it impacts in the world, scope, speed and scale to be.
E-SPIN as being a value integrator to assist enterprise customers to implement various digital transformation technology, including self-supervised learning machine to accelerate their speed, scale and scope objective in related to digital transformation (DT). E-SPIN since 2005, already in the business of supply, consultancy, integration, training and maintenance of enterprise technology and systems for enterprise customers and government agencies. Feel free to contact E-SPIN for your project and operation requirements.
