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

Sagun Raj Lage
6 min readJan 21, 2023
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:

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 cannot perform image recognition. Hence, to date, a single deep learning model can solve the problems related to that specific domain only. So, it is a challenge to develop models that can solve problems related to a wide variety of domains. One of the efforts made to overcome this challenge is, MultiModel, that can solve problems related to various domains like speech recognition, language translation and image captioning.
  • Deep learning is a black box: Deep learning algorithms contain large amounts of hidden layers to make predictions. Those hidden layers cannot be accessed by human experts and hence, there is no human control possible in those layers. So, there are chances of wrong or unexpected predictions being delivered by the algorithm. This becomes a risk when the model is used in sensitive fields like medical science, astronomy, defense etc.

Prospects of Deep Learning

There are many research and implementation work taking place in the field of deep learning. So, it can be said that deep learning still has a long way to go. Some prospects of deep learning are discussed below:

  • Development of deep learning models useful in more than one domain: Currently, deep learning models are trained in such a way that they can solve the problems of a particular domain only. But research work like MultiModel are trying to overcome this limitation of deep learning models being domain-specific by developing a deep learning model that solves problems related to multiple domains like speech recognition, language translation and image captioning.
  • Increment in the implementation of deep learning in various application areas: Deep learning can change the whole landscape of fields like medical science, industry, robotics, transportation, agriculture, finance, fraud detection etc. For instance, it can facilitate the applications like computer vision and natural language processing to implement automation and decrease the need of human intervention in the various fields like agriculture, medical science, transportation etc. Also, it can be used to make predictions and detect patterns and anomalies in a large amount of data in the fields like finance and research. Hence, there is a very high probability of the use of deep learning in various fields.
  • Integration of deep learning tools into Software Development Kits: With the increment in the implementation of deep learning in various sectors, there are possibilities of deep learning tools and libraries being integrated into various popular software development kits (SDKs). This will allow software developers to design, configure, train, and even integrate new models into software in an easy and quick manner.
  • Misuse of deep learning technologies: As deep learning technologies become powerful and popular, there are chances of those technologies being misused for unethical purposes if proper rules and policies are not implemented. For example, by using powerful deep learning models, speech synthesis can be misused to impersonate some person’s voice.

Recent Applications of Deep Learning

The advancement in the field of deep learning has enabled humans to use it for various purposes. From automatically generating the highlights of a match for broadcasts to analyzing the preference of the users to provide tailored content recommendations; from powering various virtual assistants to detecting frauds and analyzing X-rays and other scans of body parts; deep learning has advanced to a great extent and there is a massive growth in the number of industries adopting deep learning technologies. Some recent incidents about how deep learning technologies are being used to make various applications possible are discussed below:

  • ChatGPT: Released in 2022, ChatGPT is a variant of GPT (Generative Pre-trained Transformer) which is a large-scale neural network-based language model developed by OpenAI. It uses deep learning techniques to generate human-like text based on a given input.
    The main component of the model is a transformer architecture, which is a type of deep neural network that was introduced in the 2017 paper “Attention Is All You Need”. The transformer architecture is based on a self-attention mechanism, which allows the model to attend to different parts of the input text when generating the output. The transformer architecture is composed of multiple layers, each of which is made up of multiple sub-layers such as multi-head attention, position-wise feed-forward network. These layers are designed to capture the underlying patterns in the input data and generate the output.
    The model is pre-trained on a large corpus of text data, which allows it to learn patterns and relationships between words and phrases. This pre-training step is an important aspect of deep learning, as it allows the model to learn from a large amount of data, rather than just the limited amount of data that is available for fine-tuning. During the fine-tuning process, the model is further trained on a specific task, such as natural language understanding, response generation or dialogue systems. The fine-tuning process also makes use of deep learning, as it allows the model to learn task-specific features and relationships that are relevant to the task at hand.
    In summary, ChatGPT makes use of deep learning by using transformer architecture, which is a neural network-based approach that allows the model to learn patterns and relationships in the input data and generate human-like text. The model also makes use of pre-training and fine-tuning techniques, which are important aspects of deep learning.
  • DALL-E 2: Developed by OpenAI and released in 2022, DALL-E 2, is a system that takes a description in natural language as its input and delivers a realistic image or art as output. Built upon CLIP and diffusion models, two powerful deep learning algorithms, DALL-E 2 is giving a new perspective towards the development of creative applications.
  • Driverless cars that deliver food: Deep learning has made applications like self-driving cars possible. Now, as a next step towards advancement, the Uber Artificial Intelligence Labs at Pittsburg is actively working to develop driverless cars with the smart features like food delivery. Uber Eats, Uber’s online food ordering and delivery platform, launched its two autonomous food delivery pilots in Los Angeles in May 2022.
  • On-Device Deep Neural Network: In 2017, as an effort by Apple to make artificial intelligence based features like face detection, speech recognition, speech synthesis, language translation, image manipulation and virtual assistant features available and more efficient in iPhone, it released its A11 chip containing an in-built neural engine that helps to leverage the power of neural networks and machine learning in an energy-efficient manner. Hence, the device no longer has to rely upon the CPU or GPU for AI related purposes. And since Apple has made the APIs to leverage the neural engine public, even third-party app developers can use the power of neural engine in their apps.



Sagun Raj Lage

Author of Getting Started with WidgetKit (2021) | Research Associate at UGA Savannah River Ecology Laboratory | iOS Engineer | Full Stack Engineer