In recent years, artificial intelligence solutions have proven to be an asset to all types of businesses. Computers are becoming powerful to the point they can perform complex tasks accurately and quickly, sometimes without supervision.
In this article, we will learn how several different industries all use deep learning applications to their advantage.
What Is Deep Learning?
Deep learning is an artificial intelligence (AI) function that utilizes human-like learning capabilities to perform tasks. Also known as deep neural network or deep neural learning, this learning technique can achieve state-of-the-art accuracy without human assistance.
Artificial Neural Network vs. Deep Learning
What is the difference between deep learning and artificial neural networks? Deep learning and neural networks are often used interchangeably, however, there are many differences between the two.
Artificial Neural Network
Artificial neural networks (ANNs) mimic the connected networks of neurons that make up the human brain. ANNs act like these interconnected brain cells to learn and perform in a humanlike manner.
The human brain is comprised of many different parts that all process different kinds of information, flowing from one level of neurons to the next, gathering more insight along the way. This cycle is imitated in ANNs, but instead, a machine sends information from layer to layer until a decision is made. Examples of different layers of an ANN are data input, thought, decision making, memory, reasoning, and action.
Without ANNs, deep learning wouldn't be possible. Rather than teaching a computer how to process and learn from data, deep learning works by the computer training itself to process and learn from data. This self-teaching system is made possible by filtering information through ANNs different layers, like how the human brain functions.
In short, neural networks and deep learning are closely connected, as deep learning is only made possible through ANNs.
How Does Deep Learning Work?
Deep learning is a type of machine learning that uses artificial neural networks (ANN), algorithms inspired by the structure and functions of the human brain.
During this process, a machine gathers information from images, text, and sounds to achieve the same results that come naturally to humans. Like the human brain, deep learning models process data and identify patterns to effectively make decisions.
This advanced technology is responsible for voice control in devices like smartphones and Bluetooth speakers, as well as driverless cars that detect stop signs and pedestrians.
4 Deep Learning Applications
Practical applications of deep learning can be found in countless industries today as the technology has become more affordable to implement. The following sectors have recently benefited from application areas of deep learning.
- Banking Industry
- Manufacturing Industry
- Pharmaceutical Industry
- Oil and Gas industry
Consider the corresponding examples of deep learning applications to understand the upside of implementing this technology in your business.
1. Deep Learning in Finance and Banking
Deep learning technology plays many roles in the finance and banking industries, from detecting high-level fraud to improving customer experience. Here are a few popular deep learning use cases in banking and finance.
Of all the reasons for financial institutions to implement machine learning technology, fraud protection is one of the biggest.
In 2019, 3.2-million cases of fraud were reported to the Federal Trade Commission (FTC).
Fortunately, deep learning in investment banking can be used to combat fraudulent financial transactions. With deep learning, systems can quickly scan through vast amounts of electronic data, detecting unusual activities and flagging them instantly.
Exchanges that can be frustrating for humans are a breeze for machines. Deep learning in finance is responsible for improved chatbot solutions, resulting in better customer service.
Deep learning allows chatbots to quickly learn from previous interactions and therefore efficiently resolve customer inquiries. This advanced technology is designed to adapt to every customer based on behavioral changes and patterns.
Advancements in deep learning have improved image recognition accuracy far beyond human capabilities. These systems scan and analyze legal documents at phenomenal speeds, a process known as document understanding, allowing banks to significantly increase accuracy and productivity.
Major companies across financial and banking industries are using deep learning applications to their advantage. JP Morgan Chase & Co. has heavily invested in AI, with a technology budget of $9.6 billion. In 2017, the company implemented a new machine learning program that managed to complete 360,000 hours of finance work in a matter of seconds.
2. Deep Learning Applications in Manufacturing
The use of various technologies and solutions in a manufacturing ecosystem, also called smart manufacturing, has been growing in popularity over the years. Deep learning applications can learn and resolve manufacturing challenges without guidance or supervision, making them a valuable addition to any team. Consider the most common applications.
Maintenance issues and the associated problems can be expensive and time-consuming, which is why it is a common goal for manufacturers to make predictions with deep learning technology.
In predictive maintenance, algorithms are used to predict upcoming failures of a machine, component, or system. Workers are then alerted of the problem, allowing them to perform focused maintenance to prevent the failure in a timely fashion.
Deep learning processes consider a machine's intricate behavioral patterns, as well as the complex data relating to the overall manufacturing operations.
Predictive Quality and Yield
Reducing production losses and inefficiencies is a challenge for manufacturers, making industrial AI extremely beneficial.
With predictive quality and yield technology, deep learning algorithms are used to understand individual production processes and identify causes of production losses using multivariate analysis. With deep learning, computers consider various factors such as yield, quality, throughput, emission, waste, and energy efficiency.
If the computer identifies a problem, automatic alerts are generated, informing the production team of the issue. This technology provides workers with knowledge and recommendations on how to prevent losses before they even happen.
3. Deep Learning in Pharmaceuticals
Deep learning technology is increasingly finding its way into the healthcare sector. In the US health-care system, AI is used to optimize innovation, improve research and clinical trials, and build new tools for physicians, consumers, and regulators. Here are a few examples of pharmaceutical companies using deep learning to their advantage.
Disease identification is a top priority for machine learning in medicine. Data is plentiful in the pharmaceutical industry but finding resources to work with said data presents a challenge. Analyzing data is time-consuming, costly, and requires a lot of brainpower. So, when computers are advanced to the point where they can complete these tasks on their own, results are achieved much faster.
BERG, an AI-powered biopharma company, uses modern technology to research and develop diagnostics and treatments in multiple areas, including oncology, neurology, and rare disease.
Epidemic Outbreak Prediction
Machine learning and AI are also used to monitor and predict epidemic outbreaks around the globe in real-time. Deep learning utilizes satellite data, social media content, historical information, and other resources.
Predicting outbreak severity is very useful in third-world countries, where medical infrastructure and access to treatment are often limited. This technology has been used to predict malaria outbreaks by analyzing data such as positive case numbers, historical statistics, temperature, rainfall, and more.
The use of AI during the COVID-19 pandemic helped target the origin of the virus and predict its spread to help slow transmission. Artificial intelligence was also used to monitor misleading information that began to spread in the early months of the pandemic.
4. Deep Learning in Oil and Gas
The Oil and Gas Industry is becoming increasingly technology-driven. AI applications help to streamline the workforce, deliver accurate models, optimize extraction. The following applications are commonly used in the oil and gas industry today.
Deep learning in oil and gas extraction processes is beneficial for improving subsurface characterization. Computer systems are used to predict a formation's reaction to certain drilling techniques, as well as determining a formation's pore size distribution. This allows engineers to pinpoint optimal routes through rock formations.
The oil and gas industry also uses machine learning for predictive maintenance in the field. When equipment isn't well maintained or working to the best of its ability, work will be delayed, and money is lost. From exploration to delivery, deep learning technology ensures that machines are working and delivering.
O&G processes have several opportunities for technological improvement. Learn how Intelligent Automation in Oil & Gas can help your organization evolve today.
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