Demystifying the Future Of Self-Supervised Deep Learning

Self-supervised Learning is the solution for the limitations of Deep learning like requirement of large amount of data for huge computation process.

Over the years the integration of AI technology in day to day life has rendered humans to function feasibly. Applications such as Chatbots, Virtual Assistance, online translators are heavily influenced by the concept of Deep Learning. Amazon's Alexa, Apple's Siri, Google's assistant are some of the examples heavily governed by deep learning.

However, despite its everyday use, Deep Learning tend to have shortcomings, which have been discussed by experts over the years.

What is Deep Learning?

Deep Learning is a branch of machine learning where artificial neural networks in the form of algorithms and inspired by human brain learn from large amount of data that requires high computation process.

The Neural Network algorithm , is a network of functions that understands and translates the input data of one form into that of desired output. This works in the form of neuron of the brain.

Though, Deep Learning does not require human assistance, but it does require strong computing processes that requires large amount of data, that has been viewed as one of the limitations by experts.

Deep Learning can be classified into- Supervised learning, Reinforcement learning and Self-supervised learning.

• Supervised Learning – Supervised learning is the learning that requires huge amount of annonated training data. Infact, expert view limitations of deep learning as Supervised learning that needs supervision to compute large amount of data.

• Reinforcement Learning – In reinforcement learning the system is provided with an empty slate with limited amount of data and the results are generated with trial and error method. It requires high computation power.

• Self-supervised Learning – Self-supervised learning is to develop a deep learning system that can fill in the blanks on its own. It is unsupervised and learns from unlabelled data.

Limitations Of Deep Learning

There are two main shortcomings of deep-learning which have been agreed on by critics and experts.

• Deep Learning systems does not provide reasoning for a solution. In order to create a system that would reasons, a large amount of data is required.

• Though Deep Learning is good at providing a solution to a problem, it is incapable of diving a complex task into subtasks.

• Unlike Supervised learning, a self-supervised learning, the labels are automatically generated from data.

Future of Self-Supervised Learning

In AAAI conference, Yann LeCun, one of the founder of deep learning, stated that Self-Supervised learning as the future of deep learning. He said,"I think self-supervised learning is the future. This is what's going to allow to our AI systems, deep learning system to go to the next level, perhaps learn enough background knowledge about the world by observation, so that some sort of common sense may emerge. "

The only example of Self-supervised learning that the world use today is Transformers, which doesn't requires a labeled data. It is trained in a plethora of unstructured data and are proven to be better in generating structured texts, engaging in conversation and answering questions. The transformers are being used in Google's BERT, and Facebook's RoBERTa.

Apart from this they are being used to perform differentiation and integration and thus solving mathematical equations.

Thus, Self-Supervised learning has vast benefits-

• Increase in the amount of information output by AI

• The improvement in the output of an image or a set of image, as compared to supervised Deep learning and Reinforcement Deep Learning.

• It applies reasoning, and composes a task into subtasks.