Deep Learning: Challenges and Prospects

The challenges and prospects of deep learning explained with the recent advancements and applications like ChatGPT and DALL-E 2

Photo by Tanner Boriack on Unsplash

Before you begin, if you would like to go through a quick intro to deep learning, do check this article out:

A Not-So-Deep Introduction to Deep Learning

In this post, we will discuss about the challenges and prospects of deep learning. In addition to those, we will also talk briefly about the recent achievements made possible by deep learning.

Challenges in Deep Learning

Deep learning is a field that is evolving continuously. In this process of evolution, there are many challenges researchers need to tackle with to take deep learning to new heights. Some of those challenges are discussed below:

• Deep learning requires huge amount of data: To date, the deep learning algorithms we know require large datasets to train and test models to enable them to give out well- informed predictions in a fast and efficient manner. But it is not necessary that large datasets will always be available for all purposes. So, the challenge for researchers is to develop such deep learning algorithms that can work more efficiently to deliver more accurate informed predictions or decisions than the present-day deep learning algorithms without needed large datasets for training and testing.
• Deep learning requires heavy computational resources: Since deep learning algorithms require huge datasets to be able to work, powerful hardware elements like GPU are inevitable. These powerful hardware elements are expensive, and they consume a lot of power. So, it is an important task to develop more powerful and energy-efficient hardware for the purposes of deep learning.
• Deep learning models are domain-specific: The deep learning systems developed in the current world are good at solving a particular problem. If the architecture of a deep learning system is developed for speech recognition, it…