5 Applications of Deep Learning in the Business World

Through the power of deep learning, a subset of machine learning that functions in accordance with algorithms and parameters that are based on the structure of the human brain, otherwise known as artificial neural networks (ANN), software engineers have been able to create new technology products and services that have completely transformed the consumer market as we currently know it today. This being said, some common applications of deep learning in the business world include the manufacturing of autonomous vehicles, financial fraud detection, facial recognition software programs, natural language processing (NLP), and fake news detection.

Autonomous vehicles

As seen in popular sci-fi movies in previous decades, as well as the international automotive and clean energy company Tesla, one of the most common applications of deep learning in the business world is autonomous vehicles. As driving a car is an extremely complex process even for human beings, creating vehicles that can function autonomously involves an even further level of nuance. To tackle the uncertainty involved in such an undertaking, software developers utilize ANNs, as the parameters that are used to create such algorithms are adaptive and can be gradually modified over time. Likewise, a major car manufacturer such as Tesla can leverage the massive amounts of training data they possess in order to train their autonomous vehicles in the numerous real-world situations that a human being would face when driving a car, such as inclement weather, sudden stops, and standard traffic patterns, among others.

Financial fraud detection

Another common application of deep learning in the business world is in financial fraud detection. As so many consumers around the world take advantage of online and digital services to access their financial information and accounts, thwarting cybercriminals who wish to pilfer such data can be extremely challenging. As a result, many financial institutions have looked to anomaly detection to identify and flag fraudulent financial transactions. Through the application of anomaly detection, financial institutions can identify rare or unusual patterns within their systems that could represent fraudulent behavior. Alternatively, many banks have also utilized logistic regression to aid in the detection and combating of credit card fraud.

Facial recognition software

Facial recognition software hinges on the ability of a machine learning algorithm to detect the occurrence of a person’s facial features within a video recording. When creating such software programs, a developer will use thousands of images depicting the faces of individuals to train a multi-layer neural network, with the goal of enabling this network to be able to identify the faces of other individuals in conjunction with the features of the images the algorithm was trained on. Through these training processes, software engineers can create products such as automatic video redaction software, as these technology solutions must be able to recognize a person’s face in an accurate and efficient manner on a consistent basis.

Natural language processing (NLP)

Many software programs and services that are designed to analyze and comprehend human communication, be it verbal or written, are also trained using deep learning algorithms. As human languages contain thousands of words, phrases, metaphors, etc, software engineers must ensure that the language models that are used to create NLP software programs contain as much training data and information as possible. To illustrate this point further, a customer looking to receive information concerning a recent purchase they made at a local clothing store from an online chatbot does not want to wait 5 minutes for the software to respond to a question that they have asked. As such, deep learning algorithms enable these chatbots to respond as effectively as possible.

Fake news detection

A final application of deep learning algorithms in our current business world is in detecting fake news articles. As the sharing of misinformation via popular online channels such as social media platforms has significantly increased in recent years, the components of ANNs are perfect for helping content creators and consumers alike identify news stories that have been obtained from unreliable sources or trustworthy sources. While a single person looking to compare fake news stories with factual information could spend weeks on such a task, an algorithm can easily compare this same data in a fraction of the time as it would take a person to do manually. In doing so, all parties involved can save much-needed time and resources.

While autonomous vehicles, financial fraud detection, facial recognition software, NLP, and fake news detection are just a few of the applications of deep learning algorithms within the world’s current business landscape, there are many other ways in which this technology has been put to work within a specific business context. As all machine learning algorithms are trained to identify some pattern or occurrence in relation to a dataset, any topic, problem, or issue that involves the collection of data could theoretically be addressed using such algorithms, be it directly or indirectly. With all this being said, we will surely see more applications of deep learning within the business world in the near future.