Jun 28, 2021

9 Awesome Examples of Value Created Through Deep Learning & AI

Shrikant Damani, Growth Marketer
Shrikant Damani
9 Awesome Examples of Value Created Through Deep Learning & AI
Shrikant Damani, Growth Marketer
Shrikant Damani
Jun 28, 2021

9 Awesome Examples of Value Created Through Deep Learning & AI

Are you curious to learn about how deep learning can be used in various applications? In this article, we will look at 9 amazing examples of how deep learning AI is used in practice. For each example, we will discuss the application and the advantages of this technology.
9 Awesome Examples of Value Created Through Deep Learning & AI

Table of contents

We live in the age of remarkable technological advancements. With every passing day, the human race ticks off another box on its checklist. One that for eons would have seemed unimaginable. The pioneer of this quest being the dawn of Artificial Intelligence.

Over time, humans have invested extraordinary amounts of resources in evolving and perfecting AI. The goal being transformation that yields optimal efficiency for multiple industries and applications.

In this article, we go over one such subset of AI- Deep Learning. Let's decode what deep learning is, how it works, and real-life examples of deep learning in action.

  • Deep Learning is a sub-branch of Machine Learning, a form of Artificial Intelligence. Its innovation has helped overcome limitations of Machine Learning and made AI applicable to a broader set of use cases. Let's start at the very top and first understand Artificial Intelligence.

What is Artificial Intelligence or AI?

Artificial Intelligence bases itself on the idea that human decisions are mathematical computations. This implies that it is possible to train machines with algorithms and get the same conclusions a human would.

Evolution of AI

The concept of a humanoid or machine capable of human-like thinking has been around for centuries. It has made appearances in mythology, legends, and fictional creations over and over. From Talos in Greek Mythology to Golem in Jewish Folklore, humans remain intrigued by AI since time immemorial.

AI, as an academic discipline, was founded in 1956. It was a quest by scientists from a variety of fields to create an artificial brain.

Over the years, AI has garnered itself cheerleaders as well as skeptics. Continual efforts by those committed to the idea have gradually led to a consensus about the utility of AI.\

The first such monumental achievement being Deep Blue. It was the first computer chess-playing system produced by IBM. On 11 May 1997, Deep Blue beat the reigning world champion, Garry Kasparov. It did so through its capacity of processing 200,000,000 moves per second.

AI in the 21st Century

The 21st Century came equipped with evolved computer systems, access to big data, and optimism towards AI. This fueled the massive acceptance and application of various AI tools across industries.

AI mania took over the markets and made its place in the fields of ecology, economics, and even consumer products. Since then, rapid advancements are being made through research and development.

The global market for AI hardware, software, services, and technology is forecasted to grow from USD 58.3 billion in 2021 to USD 309.6 billion by 2026. There has been significant progress in text analysis, image and video processing, and even speech recognition.

AI-based tools have made their way into our day-to-day lives today. While we recognize some as AI, some are more cloaked. Banking software, data mining, and even Google's Search engine are among the noteworthy achievements of AI. However, they often get reduced to just products of evolving computer science.

What is Machine Learning or ML?

Machine Learning is a type of Artificial Intelligence. Algorithms of Machine Learning analyze large amounts of data to detect patterns. This analysis empowers the algorithms to predict outcomes based on historical behaviour.

Evolution of Machine Learning

For years, Machine Learning was a part of the AI training program. However, towards the end of the 1970s, AI focused on knowledge-based approaches and abandoned algorithms. This caused a rupture between both disciplines.

Technicians and researchers from the field reorganized themselves into a separate field. The focus now was on solving day-to-day, real-life problems.

Machine Learning in the 21st Century

The dawn of the internet brought with it more straightforward access to data. In the 1990s, ML was able to demonstrate its utility to a wider audience and flourish.

Since then, applications of Machine Learning have provided solutions to common industry problems. Sales data analysis, product recommendations, dynamic pricing have helped businesses get more robust. Speech recognition, face recognition, and fraud detection has made our systems safer.

Machine Learning algorithms are around us more often than ever. Our Facebook feeds, Netflix recommendations and even stock predictions are powered by Machine Learning through Deep Learning.

What is Deep Learning?

Deep Learning is a subset of Machine Learning. An effort to replicate the neural network of the human brain forms the basis of DL algorithms. The evolution of deep learning has enabled machines to make far more complex predictions than before. It has also allowed for greater accuracy than ever.

As the name suggests, Deep Learning is far deeper and more multi-layered than Machine Learning. It overthrows linear learning and adapts to a more elaborate process. As Deep Learning evolves, the goal remains to achieve high-level, accurate output through raw input data.

What Are Neural Networks?

Neural Networks are the foundation that Deep Learning algorithms work on. Designed to replicate the workings of the human brain, these form a multi-layered web.

Neurons make up the many layers of a neural network. These interconnected neurons facilitate the transfer of information. The layers of a neural network can be roughly divided into three types.

Input Layer

Data for input is first broken down into pixels. Each pixel is then assigned to a neuron on the input layer. Channels then carry this information onto the next layer. They also determine which neurons in the following layer are to be activated.

Hidden Layers

The process of analysis and transfer through channels continues through multiple hidden layers. Select neurons are activated at each step to provide the correct output. The bias (the number assigned to neurons) and the weight of the channels are constantly adjusted. They also differ among layers and algorithms to determine the output received.

Output Layer

At the other end of the web is the output layer. Data transferred and analyzed between the input and hidden layers manifest through the output layer.

Types of Deep Learning

Deep Learning or Machine Learning can be carried out in various methods. The route chosen determines how the algorithm analyzes the data, the amount of human intervention required, and the final output. There are two major types in which learning can take place.

Supervised Learning (SL)

In this method, the variables are well-labelled. This means input is already tagged with the correct output. The machine is in training to map the two together.

As the name suggests, supervised learning is like a student learning under the supervision of a teacher to reach the correct answers.

This method works great for more straightforward tasks. You start by creating a well-labelled training dataset. After the training process, you can conduct a data test. A subset of the training data is the basis of the test to judge if the output predicted is correct.

[Infographic: Process- shapes labelled with names, learning, test, output]

SL models work in real-life applications like fraud detection, spam filtering, risk assessment, or even social media algorithms. It’s the most commonly used of the three types.

Unsupervised Learning

As opposed to Supervised Learning, input data in Unsupervised Learning is not labelled. Instead, training occurs with unlabeled datasets. Patterns are identified without specified prompts. Unsupervised learning is similar in comparison to how the human brain processes information.

Analysis of raw input to find underlying similarities and group the data based on these is the goal of Unsupervised Learning.

The absence of labels allows for complex and intricate processing. It opens up the scope of what type of data can be analyzed.

9 Applications and Examples of Deep Learning with Examples

1. Entertainment

Deep learning has benefited the process of creating, publishing, and delivering entertainment media. Analysis of human body language through cameras has made modelling virtual characters easier. Deep video analysis has made the process of editing, audio-video sync, and transcriptions faster. Filmmaking is being revolutionized thanks to Deep Learning.

Streaming services and social media platforms make use of Deep Learning. It helps them offer a more personalized experience to the end-user. From recommendations to advertisements, Deep Learning facilitates the best targeting. Netflix, Amazon, YouTube, Facebook incorporate Deep Learning in their algorithms.

Sports entertainment, too, reaps the benefits of DL. Analysis of player emotions, audience response, etc., help pick the best highlights from hours’ worth of footage. A great example of this was IBM Watson at Wimbledon 2018.

2. Virtual Assistants

Virtual assistants today are as empowered as a human at your beck-and-call. They can take notes, perform actions, and even offer suggestions at your voice's command.

Our virtual assistants make use of deep learning to extract data from us. Right from our voice, accent, places we visit, to songs we love, they know it all. This helps them get better and more personalized to your needs over time.

Deep learning forms the foundation of Siri, Alexa, Google Assistant, and most other virtual assistants.

3. Visual Recognition

Visual recognition systems range from basic to multi-layered ones. Deep learning models can identify and sort images based on location, items, and even people.

Image analysis for obscenity on social media platforms helps create a safer environment for all. Visual recognition helps access the right images from the vast libraries of search engines. It also sorts images in your gallery so you can find what you need quickly.

Face recognition has been used in security applications for years. Smartphones, too, now unlock recognizing your face. These revolutions are all thanks to Deep Learning.

4. Healthcare

The healthcare industry is the prime example of the contribution of Deep Learning towards making human lives better. Over the years, GPU-based systems have made the job of healthcare workers easier. They have also contributed towards efficient diagnosis, standardized treatment, and overall better performance.

Deep learning has empowered healthcare systems to,

  • Address the shortage of quality workers
  • Conduct accurate early-stage diagnosis
  • Offer better pathology reports
  • Predict outbreaks or epidemics
  • Standardize route of treatment
  • Develop new drugs and vaccines

While often met with skepticism, Deep Learning is increasingly used for research purposes. Many healthcare giants, too, are adopting Deep Learning models to offer faster, better treatment and reduce costs.

5. Natural Language Processing (NLP)

The analysis of text or speech and its comprehension to offer the right output is Natural Language Processing or NLP.

The complexities and nuances of human language are endless. Hence, systems like Deep Learning that learn and improve as they go have the upper hand.

NLP is being accepted in summarizing long-form reading material, like legal documents. They also help in classifying text, analyzing sentiment, and answering questions.

Customer care and experience chat tools have also found an excellent use for NLP. The ability to understand complexities and even build phrases independently allows bots to perform with excellent efficiency.

6. Fraud Detection

The banking and finance sector is no stranger to fraudulent transactions and scammers. The adoption of Deep Learning-based security systems has helped add an extra layer of protection.

Models identify patterns in customer transactions, keep track of credit scores and raise the alarm at the sight of anomalous activity. Such implementations have helped credit card frauds and saved money in recovery and insurance.

7. Language Translation

As the world gets smaller, the need to be able to translate information increases. Deep Learning has enabled software to identify letters and translate them into the intended language.

Automatic Machine Translations can currently be done in two manners- automatic translation of text and image translation.

Such tools come in handy for not just global business purposes but also day-to-day life. Whether you're a tourist or wish to perform a special gesture for your friend abroad, language is no longer a barrier. This is all thanks to apps like Google Translate, Google Lens, etc., that incorporate Deep Learning to bridge gaps.

8. Pixel Restoration

For years, camera quality on smartphones as well as security systems remained problematic. In many cases, it still does. Zooming into videos to identify people is often handicapped through limited resolution.

Pixel Recursive Super Resolution, a DL network trained by Google Brain researchers in 2017, found a solution. The network was able to take low-resolution images of faces and enhance them. The enhancement was significant enough to highlight prominent features and enable identification.

The applications of image-enhancing through deep learning are plenty. But most prominently, they can be used by police departments and law enforcement to supercharge justice systems.

9. Self-Driving Cars

An idea that once only made appearances in fantastic dreams, self-driving cars are now more a reality than ever. What fuels this spectacular feat humans so close to perfecting? You guessed it, Deep Learning.

A multilayered web of Deep Learning algorithms brings driverless cars to life. Self-driving cars can identify signage and paths, maneuver through traffic, and even accommodate real-time elements like road blockages.

This is made possible through data from cameras, sensors, and geo-mapping. Research continues to enhance our Deep Learning models to perfect self-driving vehicles.

Driverless cars are set to solve many daily human challenges. They can be used for everyday transport as well as commercial deliveries, among many use cases.

Market Response to Deep Learning

At its conception, Deep Learning set out to solve real-life problems through its solutions. It would be an understatement to say it has managed to meet its goal.

With each passing day, the acceptance towards and adoption of Deep Learning reaches new industries. Let's look at some critical factors of the market that give an insight into the promising future of DL.

  • For the forecasted period of 2020 to 2025, the Deep Learning market is predicted to register a CAGR of 42.56%
  • As of 2019, North America held the highest share in the DL market.
  • The Oceanian sub-region and Indo-Pacific showcase the highest growth rate for the Deep Learning market
  • Significant players include Facebook Inc., Google LLC, Microsoft Corporation, IBM Corporation, and Amazon Web Services Inc.

Limitations of Deep Learning

Given the revolutionary force that Deep Learning is, it may sound unfair to point out its limitations. However, it's essential to look at limitations as just that- limitations. They mean nothing more than scope for improvement and growth.

Let's look at some of the current limitations of Deep Learning as we know it.

1. Need for enormous amounts of data

The efficiency of any Deep Learning model depends on the quantity and quality of training data. To no surprise, such large amounts of data are not accessible to all.

Such a high dependency of deep learning systems on the abundance of data poses a limitation. It also leads to unfortunate events, like when this British Police software couldn't tell sand dunes from nudes.

2. Inability to understand the context

Another shortcoming of Deep Learning is its inability to adapt to changing contexts.

For instance, a Deep Learning model trained to play one game can beat the reigning human champion at it. However, offer it another game and the same set of instructions in the model do not ensure victory.

The need for Deep Learning models to be retrained with every change in context could be seen as a limitation in times of fast growth.

Researchers and scientists have put their efforts into pointing out other limitations of Deep Learning. While some stem from straight skepticism, some are genuine constructive criticism. Steady advancements are being made towards improving Deep Learning and its efficacy.


Deep Learning already makes an integral part of our day-to-day lives and the services we use. The future, too, seems to hold wider acceptance and adoption of Deep Learning. Its utility in multiple areas and industries showcases the potential of DL models.

Forecasts show phenomenal growth for the Deep Learning market in the coming years. It's a good time for businesses to dive into the AI & DL world. It is also a hopeful picture for businesses and industries that will benefit from Deep Learning.

Shrikant Damani
Growth Marketer
ABout the AUTHOR
Shrikant Damani
Growth Marketer

Shrikant is a growth marketer at Scalenut, where he focuses on developing strategies to nurture the Scalenut community and improve user experience through content marketing and SEO. In addition, he works to enhance the quality of AI outputs through prompt engineering. A MICA graduate and a Chartered Accountant, he utilizes both his creative and analytical skills to create effective solutions.

View all articles by this Author -->
Thank you!
Our Product Specialist will connect with you shortly. In the meanwhile, please explore Scalenut
Oops! Something went wrong while submitting the form.
Free content strategy call with expert
Free content strategy call with expert
Strategy with an all-in-one SEO solution
Get a personalized demo of Scalenut’s features
Showcase practical business use cases your whole team can use.
Learn about pricing options for your use case
Schedule Demo
Create SEO-Ready Blog with Scalenut
Try Scalenut for Free
Boost Your SEO Game