Emerging technologies such as Big Data and Artificial Intelligence are advancing at an astonishing pace and made in-part possible by the incredible advancements in deep learning.
Deep learning is part of a larger family of artificial machine learning that aims to mimic human-like learning and logic through human-made artificial neural networks. The benefit of deep learning is its ability to survey massive data sets and make sophisticated decisions based on these massive data sets that aren’t achievable by humans. Deep learning models learn complicated concepts that power sophisticated decision-making iteratively. These systems compare new data with baseline data allowing these systems to learn effectively. And to improve the accuracy of these systems, they have to be fed more data to build more sophisticated decision-making criteria.
Understandably, this technology has the potential to disrupt every silo of business once this type of technology is commercially viable. To this point, according to the latest report by Market Research Future (MRFR), the deep learning market is set to reach a value of USD 17.4 billion by 2023. The application of deep learning paired with new and upcoming technologies such as machine learning, big data, and cybersecurity is set to reimagine today’s modern business environment.
From big data to AI, nearly every evolving technology branch has benefited from the profound value of deep learning. In the following sections, we’ll dig into how exactly this artificial machine learning branch has helped advance emerging technologies.
Big Data to Expand Avenues of Deep Learning
Deep learning models traditionally rely on structured and unstructured data for building decision-making processes. In speech recognition and text translation, big data paired with this technology allows applications to build more sophisticated speech recognition and text translation applications resembling near human-like qualities. Further, computer vision applications have also evolved through the pairing of big data and deep learning. Here, computer vision applications can make more human-like decisions providing benefits to a wide variety of silos from military to medicine.
Lastly, Labeling and graphic processing have increased in their capacity to handle large volumes of data and play a key role in training deep learning models. These evolutions will likely provide value in shipping, pharmaceuticals, and other industries dependent on labeling and graphic design.
Cybersecurity Enhancements Through Deep Learning
One of the major developments in cybersecurity is the deep learning-enabled application known as Deep Instinct. Deep Instinct has developed a mobile and endpoint cybersecurity solution for leveraging deep learning and detecting real-time threats across servers, endpoints, and mobile phones. This deep learning-enabled technology can prevent zero-day attacks and predict unknown attacks through deep learning algorithms. It can differentiate between harmful and harmless attacks and can instantly extend its protection across networks. It can witness high adoption rates owing to its capability for identifying ransomware across sectors of education, financial services, and healthcare.
Another branch of artificial machine learning known as Artificial Intelligence (AI) is the design pursuit to design self-aware technological systems that mimic human intelligence, rationality, and personality. AI has evolved from a rudimentary talking chatbot to a sophisticated full-time assistant bot. Today, the most advanced AI systems can rapidly translate languages and identify the net’s images due to its labeling. Following this incredible evolution, organizations are now employing AI to solve some of their most difficult challenges.
Deep learning can be thought of as the learning component of an autonomous AI machine. By enabling AI systems with the back-end learning capabilities of deep learning, researchers hope to develop advanced artificially intelligent systems that can tackle some of society’s biggest challenges, such as curing cancer, developing safe autonomous driving networks, and advancing all aspects of medicine.
Deep learning models can also play a role in edge computing. Researchers are finding that these systems can help machines identify between various products and spur industrial automation. These systems can address surface defects, identify products through their brightness and shape, and run complex inspections at sites without human intervention. In doing so, deep learning-backed edge computing can effectively build more resilient computing systems while minimizing human intervention.
The use of computing and networking devices has shifted the burden of allocating and storing data to edge computing. The rise of smart factories, biometric recognition, and a shift to the cloud has created a large opportunity for deep learning models in edge computing. These systems within edge computing provide a level of automation in how these IoT-enabled devices are trained with various simulations and work in tandem with artificial intelligence to glean edge intelligence. This process requires the virtualization of either the network or a combination of virtual machines and containers for maximizing the allocation of resources and isolate services for faster computing. Issues of privacy, risk control, and response latency need to be addressed for improving the speeds in edge computing.
This fascinating technology is evolving slowly but will surely continue to provide incredible value for emerging technologies as it progresses. Whether it’s AI, Cybersecurity, or Big Data, we’ll be sure to see fascinating advancements as deep learning continues to push the boundaries of emerging technologies.