How Has Deep Learning Revolutionized The Tech Industry?

by Freya Scammells

A Turnover Started 78 Years Ago

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20 OCTOBER, 2021

Have you ever wondered how Google Translate and Alexa work? How do self-driving cars perceive their environment and detect objects? How do Facebook recommend pages or products? 

Well, they all work because of deep learning. It is a subset of machine learning that has revolutionized the world with its advancements in technology and in every business sector.
It employs algorithms to process data, imitate thinking processes, understand human speech, and visually recognize objects. But it is not a new concept in the world of technology.

The history of deep learning dates back to 1943, when Walter Pitts and Warren McCulloch designed a computer model by imitating the neural nervous system of the human brain. They used threshold logic i-e, a combination of algorithms and mathematics, to mimic the thought process of a human. Since then, deep learning has evolved and played a significant role in automating human life.

Before we delve deeper into the applications of deep learning in various fields, we must know what deep learning is. So, deep learning, a buzz in the artificial intelligence world, is a subfield of machine learning that deals with the algorithms inspired by the structure and function of the human brain.

It teaches computers to learn from examples so that they can perform tasks intuitive to humans. In deep learning, neural networks play an essential role. A neural network is a set of algorithms or a system that we implement to identify relevant relationships in a dataset.

A neural network modeled on the human brain consists of an input layer, multiple hidden layers, and an output layer. The data is fed as an input to the neurons. Then, the information is transferred to the next layer using appropriate weights and biases. The output is the final value predicted by the output layer.

Deep Learning Revolution

So, deep learning imitates the way the human brain processes data to solve complex problems. Its applications are powering a wide range of industries as it has become more widespread over the years. Let's have a look at the revolutions brought by deep learning across various industries.

🤖 Image Detection

We know that the human brain quickly encounters various entities of the visual world and distinguishes objects. Imitating the human brain, deep learning models can view visuals as an array of numerical values and find patterns in the digital image. This technology is highly applicable to the things related to computer vision, which trains computers to achieve high-level understanding from digital images and videos. For instance, Facebook has enough data on images to work with, making deep learning for image detection much accessible.

Another example is the iPhone's Facial Recognition that uses deep learning to identify data points from our face to create a deep map for unlocking our phone or spotting us in images. This way, deep learning helps us protect our phone from unwanted unlocks and make our experience hassle-free even when we are in poor lighting, have changed our hairstyle, or lost weight. 

🤖 Automated Translations

We can also find the application of deep learning in automated translations of image language. For instance, it has become possible with the Google Translate app to automatically translate photographic images with text into a real-time language of our choice.

Likewise, virtual assistants such as Amazon Echo, Alexa, Siri, and Google Assistant use deep learning capabilities to develop a customized user experience for us. They learn to recognize our accent and voice and present us with a secondary human experience by using deep neural networks.
These virtual assistants also help us shop, navigate, make salon appointments, take notes and translate them to text, and so on.

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🤖 Agriculture

We know that agriculture is everywhere, and it's an industry that works with nature, but that does not mean it's not getting the latest advancements. There is a reduction in certain crop yields due to global warming. Due to this fluctuation, the workers have to think of ways to maintain supply and demand necessities. Here, deep learning comes into play.

The workers can easily use data from sensors and satellites for better understanding the coming weather patterns. Then, they can get a jump start on production after ensuring that temperatures don't hinder their yields as much.

🤖 Customer Relationship Management

It is essential to communicate with clients because it is a vital data source that can evaluate their behaviour, deal with various issues, change process approach and operations, and eventually transform the customer journey.

We can enable the companies to analyze, manage and streamline customer interactions by integrating deep learning models for customer relationship management (CRM). For instance, Burberry, a luxury fashion retailer, used big data and deep learning to reinvent its entire business model. Using the deep learning models, the company established more profound connections with its customers and delivered personalized recommendations, both online and in-store.

🤖 Hospitality

The hospitality industry is a vast sector that includes event planning, lodging, and customer-oriented jobs in various amusement parks. Deep learning AI technology has progressed so far on the customer side of things. For instance, hotels can now use robots to clean, greet, and deliver room service. Further, hospitality businesses use tech with deep learning to predict demands and occupancy throughout the year and personalize guests' experiences.

🤖 Fraud Detection

We know that almost every industry is susceptible to anomaly and criminal activities in today's digital world. There isn't any constant pattern involved in frauds, so it is complex to detect and predict them in advance. However, deep learning models have proven effective in spotting system vulnerabilities and suspicious behaviour in customer accounts.

They have offered companies a more comprehensive and adaptable system than traditional fraud detection systems that were often limited. These deep learning-powered systems are typically used to recognize patterns in customer transactions and credit scores that help classify suspicious behaviours and outliers.

🔍 Conclusion

The massive deployment of deep neural network architecture, computational power, and big data has improved the conventional statistical models for predicting optimized knowledge. Apart from many applications of deep learning in our daily lives, many people do not recognize its significance.

Currently, many organizations are adopting the breakthroughs of advanced technologies like machine learning, the Internet of Things, artificial intelligence to remain competitive in the industry.
So, deep learning outshines other techniques when it comes to solving complex problems such as natural language processing, speech recognition, and image classification.

It's because we have to worry less about feature engineering.

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About the Author

Freya supports the tech talent toolbox arm of Reflection X where she is coaching technologists to achieve their full potential. With over 10 years of experience within the Tech and AI recruitment space, she has been from working closely with thousands individuals and leaders during hiring processes while partnering with very early stage start-ups all the way to some of the top AI Labs in the world.

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