Deep Learning: Pushing the Boundaries of Emerging Technologies | 7wData

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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.

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. 

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.