Supervised Learning (SL) is an aspect of Artificial Intelligence (AI) that involves human oversight of the training and learning phases of a machine. There are many variations of supervised learning. This allows scientists to fine-tune their learning algorithms to make more efficient models.

However, the presence of unsupervised learning in the field of Deep Learning (DL) might be a misleading term that overemphasizes the progress we’ve made in Machine Learning (ML) and Explainable AI (XAI). The true definition of unsupervised learning would suggest that machines have become sentient, evoking similarities to Hollywood Sci-Fi.

What is supervised learning?

Supervised Learning algorithms are AI models that apply user-labeled training data to new data, and assentient learning by example similar to humans. The supervision comes from programmers and developers who observe the model to determine how well it is performing, judge the accuracy of its outputs, and make manual adjustments to certain weights or biases.

Semi-Supervised learning is reserved for models that require less oversight from humans. This gives the machine more space to learn on its own, only needing to interact with humans at key junctions for evaluation so that they can measure metrics and output accuracy. 

In both methods, data labeling plays an important role. This is the process of attaching meaningful information to a dataset almost like context clues. The model’s goal is to use these labels to help generate more complex outputs on unseen data that the machine has not been trained on. 

Applications of supervised learning

Supervised Learning can be applied to both Recurrent and Convolutional Neural Networks, helping to train models. Recurrent networks can especially complement semi-supervised algorithms because they cycle processed data through the hidden layers, giving the machine more information to analyze. 

There are many ways that supervised learning is incorporated into modern technology including image recognition and fraud detection. Supervised learning can help machines with the early stages of recognizing the most basic features within an image pixel by pixel or spot unusual patterns in a set of financial transactions. 

Why unsupervised learning is unique

Unsupervised Learning, on the other hand, presents a wildly drastic change in development for an AI model. In an unsupervised model, the machine is encouraged to learn as much as it can based on the given training data on its own, learning by experience with only minor supervision. 

However, it is that minor supervision that skews the true meaning of non-supervised machines. In its purest form, an unsupervised machine would be entirely capable of learning on its own with its own evaluation criteria without the intervention of humans at any stage. 

While rudimentary examples may exist today, a machine of this caliber would fundamentally shift the landscape of artificial technology, pushing machines closer to sentience and Artificial General Intelligence (AGI). 

Future of machine learning

AGI, the next stage of AI, represents highly autonomous systems that are capable of performing all the same intellectual tasks that humans can do. This includes learning, adapting, showing curiosity, etc., and would signal one of science’s most significant breakthroughs.

Without a doubt, unsupervised learning will play a significant role in the development of AGI as it requires machines to learn from unlabeled data the same way humans do to understand the world around them, developing abstract thought and comprehending complex, unstructured data.