Deep learning is one of the most important parts of AI and has significantly contributed to the field. However, it requires huge amounts of data to extract useful inputs. Today, there is a strong focus from the researcher to reduce its data-dependency as it has become one of the leading AI techniques. It has useful applications in fields such as computer vision, natural language processing, and other sensitive applications. This makes it more important for researchers to move beyond the limitations of deep learning. This requirement has led to the development of self-supervised learning.
This new type of learning has the potential to become one of the most crucial parts in boosting the AI system towards data-efficient. It is really hard to predict the success of a particular technique that might revolutionize AI but there is hope from this technology. In order to understand self-supervised learning, we need to first understand a little about deep learning.
The limitation of deep learning is basically a limitation of supervised learning. However, deep learning is not just supervised learning and it can be applied to different paradigms including supervised learning, semi-supervised learning or unsupervised, and reinforcement learning. However, most of the algorithms for AI applications are based on supervised learning models such as facial and speech recognition systems, image classifiers, and many other applications. Other learning algorithms in deep learning have very limited applications.
Nevertheless, only supervised learning has enough quality data that have the potential to capture all possible scenarios. Despite all the data, if a model is presented with an example that differs from training examples, it starts behaving in unpredictable ways. This brings us to the three major challenges of deep learning:
- First, we need to develop an AI system that can learn from a small amount of data.
- Second, we need to create a deep learning system that is capable of reasoning.
- Third, we need to create systems that can learn complex action sequences and can divide tasks into subtasks.
Self-supervised learning will help in developing a system in deep learning that is capable of learning to fill the blanks. Some of the close examples of self-supervised learning systems are transformers that are successful in natural language processing. They train on corpora of unstructured text and are good at engaging in conversation, generating text, and answering questions. Recently a researcher found that the evolution of transformers might be able to researchers move beyond tasks such as statistical approximation and pattern recognition.
Self-supervised learning could become the future of deep learning because it allows the system to learn about the world through observation. Help them to reach next level to develop a kind of common sense. The most important benefit of self-supervised learning is gain from the outputted information. This output will improve the whole image by learning a lot more information about the world with lesser data.
Transformer’s has succeeded in dealing with discrete data such as words and mathematical symbols but have failed to transfer to the domain of visual data. This becomes it is difficult to represent prediction and uncertainty in images as well as videos. For example, for every video segment, there is an infinite number of future possibilities. Hence, it becomes difficult for a system to predict a single outcome for a video’s next frame. This is the problem researcher are currently dealing with in order to apply self-supervised learning to a wide variety of modalities.
There is a world of possibilities; however, if researcher can discover the working of uncertainty problem they will be able to unlock a key component of AI. Afterward, the next revolution of AI in future will neither be supervised, nor purely reinforced.