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Introduction
Deep learning has taken artificial intelligence by storm and has infiltrated
almost every business application. Because almost all content and
transactions are now being recorded in a digital format, a vast amount of
data is available for exploration by machine learning algorithms. However,
traditional machine learning techniques struggle to explore the intricate
relationships presented in this so-called Big Data. This is particularly acute
for unstructured data such as images, voice, and text.
Deep learning algorithms can cope with the challenges in analyzing this
immense data flow because they have a very high learning capacity. Also,
deep neural networks require little, if any, feature engineering and can be
trained from end to end. Another advantage of the deep learning approach is
that it relies on architectures that require minimal supervision (in other words,
these architectures learn automatically from data and need little human
intervention). These architectures are the so-called “unsupervised” of weakly
supervised learning. Last, but not least, they can be trained as generative
processes. Instead of mapping inputs to outputs, the algorithms learn how to
generate both inputs and outputs from pure noise (i.e., generative adversarial
networks). Imagine generating Van Gogh paintings, cars, or even human faces
from a combination of a few hundred random numbers.
Google language translation services, Alexa voice recognition, and
self-driving cars all run on deep learning algorithms. Other emergent
areas are heavily dependent on deep learning, such as voice synthesis,
drug discovery, and facial identification and recognition. Even creative
areas, such as music, painting, and writing, are beginning to be disrupted
by this technology. In fact, deep learning has the potential to create such a
profound transformation in the economy that it will probably trigger one
of the biggest revolutions that humanity has ever seen.
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