Brian Dooley, tdwi & Cynthia Harvey, Datamation 26 Jun 2018 Viewed 134 Times Viewed by 70 people

Artificial intelligence and deep learning still emerging technologies, but they are poised to become incredibly influential in the near future.


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Artificial intelligence (AI) is in the midst of an undeniable surge in popularity, and enterprises are becoming particularly interested in a form of AI known as deep learning.

According to Gartner, AI will likely generate $1.2 trillion in business value for enterprises in 2018, 70 percent more than last year. "AI promises to be the most disruptive class of technologies during the next 10 years due to advances in computational power, volume, velocity and variety of data, as well as advances in deep neural networks (DNNs)," said John-David Lovelock, research vice president at Gartner.

Those deep neural networks are used for deep learning, which most enterprises believe will be important for their organizations. A 2018 O'Reilly report titled How Companies Are Putting AI to Work through Deep Learning found that only 28 percent of enterprises surveyed were already using deep learning. However, 92 percent of respondents believed that deep learning would play a role in their future projects, and 54 percent described that role as "large" or "essential."


What Is Deep Learning

Deep Learning is part of the much broader field of artificial intelligence. In a nutshell, artificial intelligence involves teaching computers to think the way that human beings think. That encompasses a wide variety of different applications, like computer vision, natural language processing and machine learning. Machine learning is the subset of AI that gives computers the ability to get better at a task without being explicitly programmed.

Deep learning is a particular kind of machine learning that became much more popular around 2012 when several computer scientists published papers on the topic. It's "deep" because it processes data through many different layers. For example, a deep learning system that is being trained for computer vision might first learn to recognize the edges of objects that appear in images. That information gets fed to the next layer, which might learn to recognize corners or other features. It goes through that same process over and over until the system eventually develops the ability to identify objects or even recognize faces.

Most deep learning systems rely on a type of computer architecture called a deep neural network (DNN). These are roughly patterned after biological brains and use interconnected nodes called "neurons" to do their processing work.


Talent Shortage

The increasing importance of deep learning has brought about another skills shortage as the desire for practitioners outpaces supply.

While deep learning has impressive capabilities, one big obstacle that is slowing widespread adoption is the skills shortage. When the O'Reilly survey asked people what was holding them back from adopting deep learning, the number one response was a lack of skilled staff. According to the Global AI Talent Report 2018, "There are roughly 22,000 PhD-educated researchers in the entire world who are capable of working in AI research and applications, with only 3,074 candidates currently looking for work." Enterprises are attempting to fill this gap by training their existing IT staff, but the process is slow.

This is creating another drain on recruitment processes and is developing into a global competition as deep learning becomes popular for every organization wishing to expand capabilities in areas specific to robotics and process automation.


What Skills Should You Have?

The skills required for deep learning are similar to those for analytics with some important differences. Machine learning is not primarily about databases or applying statistics to structured and unstructured data. Instead, it is an approach that attempts to navigate, interrogate, dissect, and derive results from any stream of data using a set of discrete and autonomous algorithms derived from neural networking.

The skills needed are heavily related to programming and to the existing body of work in the machine learning area. Popular programming languages include MATLAB, R, and Python; special skills include an understanding of core concepts in pattern recognition, and ability to understand and apply an ever-broadening mix of analytic strategies.

The applications for this technology are so critical for competitive advantage that the need for these skills is likely to explode during the next several years. In addition to a need for specialists in the general area of machine learning, there will be an increasing number of subspecialties as deep learning continues to develop new use cases and more powerful functions. It demands both new ways of thinking about data and new ways of modeling a solution.

Deep learning solutions do not exist in a vacuum and developing expertise in this area will entail issues of data access and streaming; sensors and robotics; and, above all, how to integrate autonomous and semi-autonomous deep learning systems with other technologies. Even within the general area of analytics, integrating these algorithms and architectures with other analytics approaches and processes will create demand for new skills.

As a rapidly evolving skill set, the greatest demand will be for experts with practical experience.


Practical Applications of Deep Learning

Deep learning is currently being used to power a lot of different kinds of applications. Some of the most common include the following:

  • Gaming: Many people first became aware of deep learning in 2015 when the AlphaGo deep learning system became the first AI to defeat a human player at the board game Go, a feat which it has since repeated multiple times.

  • Image Recognition: Deep learning is particularly useful for computer vision applications. Microsoft, Google, Facebook, IBM and others have successfully used deep learning to train computers to identify the contents of images and/or to recognize human faces.

  • Speech Processing: Deep learning is also good at recognizing human speech, translating text into speech and processing natural language. It can help identify the meaning of words from their context, and it enables chatbots and voice assistants like Siri and Cortana to carry on conversations with users.

  • Translation: The next logical step after training a deep learning system to understand one language is to teach it to understand multiple languages and translate among them. Several vendors have done just that and now offer APIs with deep learning-based translation capabilities.

  • Recommendation Engines: Users have become accustomed to websites like Amazon and services like Netflix offering them recommendations based on their previous activity. Many of these recommendation engines are powered by deep learning, which allows them to become better at making recommendation over time and enables them to find hidden correlations in preferences that human programmers might miss.

  • Text Mining: Text mining is the process of running analytics on text. It might, for example, enable people to determine the feelings and emotions of the person who wrote the text or it might extract the major ideas from a document or even compose a summary.

  • Analytics: Big data analytics has become an integral part of doing business for most enterprises. Machine learning, and specifically deep learning, promises to make predictive and proscriptive analytics even better than they already are.

  • Forecasting: One of the most common uses for analytics is for forecasting upcoming events. Enterprises are using deep learning to predict customer demand, supply chain problems, future earnings and much more.

  • Medicine: Deep learning also has a myriad of potential uses in the medical field. For example, it might be better than human radiologists at reading scans, and it could power diagnostic engines that could augment the capabilities of human physicians.

In the O'Reilly survey, respondents said that they were most interested in using deep learning for computer vision, text mining and analytics. Expect the list of potential use cases to grow as researchers find new ways to apply the technology.

 



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