What Is Deep Learning (And Why Enterprises Should Care)?
Deep learning, a subset of Artificial intelligence, is poised to become incredibly influential in the near future.
In this data-centric world, the potential to recognize and use date insights is vital to strategize business solutions and use experience. Today’s leading organizations have started adopting deep learning to analyze their extensive database related to documents, images, and gestures. It is also being used to translate voice to texts and predicting the future with past information.
What is Deep Learning?
Deep learning is a much broader aspect of artificial intelligence. It involves teaching computers to think like a human brain. It encompasses different types of applications, including natural language processing, machine learning, computer vision, and many others. Enterprises are already using deep learning to power fraud detection technology, enhance cybersecurity, demand forecasting, recommendation engines, streaming analytics, and many other applications. These tools improve as they ingest more and more data and find their correlations and patterns with the data.
Deep learning processes data through many layers.
Let’s take an example of a deep learning system which is trained for computer vision. The system might first learn to recognize the edges of the objects which appear in the images. This information gets fed to the next layer, which might learn to recognize corners or other features of the pictures. The system goes through the same process again and again until it eventually develops the ability to identify the objects and can even recognize the human faces. Most of the deep learning systems rely on different types of computer architecture called deep neural network.
Why Should Enterprises Care about Deep Learning?
According to Garter, deep learning is expected to generate more than $13 billion in business value for enterprises at the end of 2020. Due to advanced computational powers, volume, velocity, and variety of data, it has become the most disruptive class of technologies for the next decade.
A survey was conducted in 2018 for concluding the role of deep learning in enterprises. According to the results, 92% of respondents believed that deep learning plays a vital role in their future project, 54% described its purpose as necessary and only 28% of enterprises were already using deep learning.
While deep learning offers tremendous benefits to numerous business sectors, it is still at a nascent stage and has to overcome many hurdles to fulfill its promises. That said, top tech companies are already investing heavily in this new technology and are experiencing tremendous growth.
Revolutionary Cases of Deep Learning
Tech giants like Google, Apple have been practicing deep learning internally. They make use of this innovative technology to power their devices for speech and image recognition algorithms. Recently, online streaming companies like Netflix and Amazon started using deep learning for better user experience by powering their recommendation engines.
Let’s take a look at some of the revolutionary cases of deep learning that will help you prepare your business’s future deep learning strategy –
- Pattern Recognition
It is considered as the most vital breakthrough in the field of pattern recognition. It enables enterprises to analyze, monitor and process a multitude of things like information on how many times users contact customer care, how long they engage in an app, what are the latest market trends, shopping habits, and many other things.
- Advancement in Science and Healthcare
Deep learning is shaping the life sciences, health care, and medicine industry by blending data from various sources. The healthcare sector can entirely depend on deep learning as it can provide an accurate diagnosis, suggest appropriate remedies, analyze the symptoms, and also set up a live video call with doctors. Deep learning has dramatically improved the quality and accessibility of healthcare in areas where doctors and clinics are short in supply.
Cybersecurity is a matter of concern in this online world. Deep learning enables companies to reduce the risks of online fraud and malware by detecting threats. Deep learning algorithms can help in detecting malicious URLs and code, detect fraud, phishing, malware, and many other threats. As a self-taught algorithm, it recognizes user activities, including past data and quickly supports and protects against any suspicious activity which might put the valuable enterprise data at risk.
Primary Benefits with Deep Learning
- Time and Cost benefits
Most of the employees do the same repetitive jobs for the entire day. The neural networks have given artificial intelligence the brains to use the supervised and unsupervised data and learn from it. The deep learning algorithms use the data to perform such repetitive tasks. In terms of saving time, employees can use their free time in other creative and more value-added activities. On a different note, the management can keep their budget by hiring new employees to handle the massive data. The DL algorithms, thus, contribute to increased productivity saving time and money.
- Quality scores with accurate results
The human brain is associated with emotions and can provide biased output. Deep learning, on the other hand, offers solutions without any emotion. Any small error can result in a massive loss for the business. DL provides highly accurate results with the preset data, including multi-variable, time constraints, and across all the departments. Besides, it can improve the quality and efficiency of the process. Accurate results, high efficiency, speedy delivery, and better organization are the top outcomes of implementing deep learning.
Current Market Applications of Deep Learning
You can see that your life is surrounded by some beautiful, user-friendly, and innovative applications. As discussed above, the tech giants are heavily investing in deep learning; here are three significant market applications that are solidifying their place in our routines.
- Natural Language Processing Software
This tool allows the applications to interact with human language using a deep learning algorithm. It also provides voice recognition, which converts the data into understandable human language. The best examples of this software are Apple’s Siri, Amazon’s Alexa, Google’s Bixby, and many others.
- Image Recognition Software
This software identifies objects, people, places, and actions in the images. It uses machine vision techniques combined with deep learning to recognize the pictures through a camera system. It is widely used in smart devices and smart homes for enhanced security. Apart from providing improved security, it is also contributing to the health sector by analyzing images of skin to diagnose the early signs of skin cancer.
- Speech Recognition Software
This software analyzes the sound and tries to convert it into text. It can capture the words said by the user and type indirectly into the computer or smartphone. This software is ideal for physically disabled and for those who cannot work on their computers. Microsoft’s Cortana and Google’s Assistant are the best examples of speed recognition software.
Deep learning is transforming many industries including, fin-tech, healthcare, education, transportation, and many others by redefining the business process with digital intelligence.
Businesses can experience great potential by developing autonomous, self-taught deep learning applications which can be used to forecast results, detecting fraud, setting new customer base, and many others. The capabilities are enormous, and that is why it is the right time for enterprises to invest in deep learning to create new business solutions.