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Artificial intelligence (AI) and machine learning (ML) are positioned to disrupt the way we live and work, even the way we interact and think. Machine learning is a core sub-area of AI. It makes computers get into a self-learning mode without explicit programming.

At this point, most organizations are still approaching ML as a technology in the realm of research and exploration. In this first article of a series, we delve deeper into the world of machine learning and its applications. The following articles will focus on building an ML implementation plan. In doing so we not only understand the concepts behind the technology, but also why it can make the difference between keeping up with competition or falling further behind.

Gartner defines machine learning as:Advanced learning algorithms composed of many technologies (such as deep learning, neural networks and natural language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information.

Machine learning is the process of teaching computers to develop intuitive knowledge and understanding through the use of repetitive algorithms and patterns. Machine learning in lay-man's terms is the process of schooling a repetitive activity to a dumb system that needs to develop some innate intelligence. The goal is to feed the system large amounts of data so it learns from each pattern and its variations, so it can eventually be able to identify the pattern and its variants on its own. The advantage a machine has over the human mind here is its ability to ingest and process large amounts of data. The human brain, although limitless in its capacity to ingest data, may not be able to process it at the same time and can only recall a limited set at one time.

There are three key types of machine learning: supervised, unsupervised and reinforced.

Other aspects of machine learning include neural networks and deep learning.

Neural networks have been studies for a long time. These algorithms endeavor to recognize the underlying relationships in data, just the way the human brain operates.

Deep learning is a class of machine learning algorithms that involves multiple layers of neural networks where the output of one network becomes the input to another.

The key to understanding machine learning is to understand the power of data. These algorithms work by finding patterns in massive amounts of data. This data, encompasses a lot of thingsnumbers, words, images, videos, sound files etc. Any data or meta data that can be digitally stored, can be fed into a machine-learning algorithm.

Related Article: Machine Learning Fragmentation Is Slowing Us Down: There Is a Solution

Machine learning, in conjunction with deep learning, have a wide variety of applications in our home and businesses today. It is currently used in everyday services such as recommendation systems like those on Netflix and Amazon; voice assistants like Siri and Alexa; car technology in parking assist and preventing accidents. Deep learning is already heavily used in autonomous vehicles and facial recognition systems. As the technology matures and receives widespread acceptance, we expect to see its applicability grow in these areas:

And many more .

Related Article: Why Artificial Intelligence May Not Offer the Business Value You Think

The availability of widespread computing power though the use of cloud technologies along with an increasing volume of readily available data has driven a number of advancements in the field of AI and ML. Organizations need to first build an understanding of the technology itself, collaborate on building a vision for using the technology internally and then build an implementation plan collaboratively between business and IT. In part two of this ML series we will focus on building a vision and implementation plan.

Geetika Tandon is a senior director at Booz Allen Hamilton, a management and technology consulting firm. She was born in Delhi, India, holds a Bachelors in architecture from Delhi University, a Masters in architecture from the University of Southern California and a Masters in computer science from the University of California Santa Barbara.

The views and opinions expressed in these articles are those of the author and do not necessarily reflect the official policy or position of her employer.

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Getting Started With Machine Learning: Definition and Applications - CMSWire