DEEP LEARNING: AN OVERVIEW IN SCIENTIFIC APPLICATIONS

15May - by aiuniverse - 0 - In Deep Learning

Source: analyticsinsight.net

Over the last few years, deep learning has seen a huge uptake in popularity in businesses and scientific applications as well. It is defined as a subset of artificial intelligence that leverages computer algorithms to generate autonomous learning from data and information. Deep learning is prevalent across many scientific disciplines, from high-energy particle physics and weather and climate modeling to precision medicine and more. The technology has come a long way, when scientists developed a computer model in the 1940s that was organized in interconnected layers, like neurons in the human brain.

Deep learning signifies substantial progress in the ability of neural networks to automatically create problem‐solving features and capture highly complex data distributions. Deep neural networks are now the state-of-the-art machine learning models across diverse areas, including image analysis and natural language processing, among others, and extensively deployed in academia and industry.

Developments in this technology have a vast potential for scientific applications and medical imaging, medical data analysis, and diagnostics. In scientific settings, data analysis is understanding as recognizing the underlying mechanisms that give rise to patterns in the data. When this is the goal, dimensionality reduction, and clustering are simple and unsupervised but highly effective techniques to divulge concealed properties in the data.

In a report, titled A Survey of Deep Learning for Scientific Discovery, where former Google CEO Eric Schmidt and Google AI researcher Maithra Raghu have put together a comprehensive overview on deep learning techniques and their application to scientific research. According to their guide, deep learning algorithms have been very effective in the processing of visual data. They also describe convolutional neural networks (CNNs) as the most eminent family of neural networks and very constructive in working with any kind of image data.

In scientific contexts, one of the best applications of CNNs is medical imaging analysis. Human experts such as radiologists and physicians have mostly performed the medical image interpretation. However, owing to large variations in pathology and potential fatigue of human experts, researchers now have started capitalizing on computer-assisted interventions. Already, many deep learning algorithms are in use to analyze CT scans and x-rays and assist in the diagnosis of diseases. Recently, in the time of crisis caused by COVID-19, scientists have started using CNNs to find out symptoms of the virus in chest x-rays.

Deep learning algorithms are also effective is natural language processing. It deals with building computational algorithms to automatically assess and represent human language. Today, NLP-based systems have enabled a various number of applications, and are useful to train machines to perform complex natural language-related tasks like machine translation and dialogue generation.

Moreover, deep learning models originally inspired by biological neural networks, which encompasses artificial neurons, or nodes, connected to a web of other nodes through edges, allowing these artificial neurons to collect and send information to each other.

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