Demand Grows For Deep Learning Talent
The lack of skilled IT workers is hurting the deployment of emerging technology, according to a new survey from Gartner. In areas from cloud to cybersecurity, this crisis is expected to last for years to come.
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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