Machine Learning Simplified: Building an Understanding
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.
What Is Machine Learning?
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.
- Supervised Learning: Is the most prevalent form of machine learning today. In this kind of learning the data is labeled to tell the machine exactly what patterns it should look for. This is the kind of learning used by Netflix or Amazon when they look for similar shows to watch or similar products to shop for.
- Unsupervised Learning: Requires no labels for any of the input data. The machine just looks for whatever patterns it can find. The goal here is to introduce the algorithm to multiple groups/types of information and then establish labels based on what is “learned” by the algorithm. Unsupervised learning algorithms aren't designed to single out specific types of data, they simply look for data that can be grouped by similarities, or for anomalies that stand out. It is akin to letting a child look at different objects and then classify them according to color, function, entertainment value, etc. Unsupervised algorithms are not as popular as supervised ones, however with the increasing use of ML in cybersecurity, operational improvement and automation etc. their applicability has increased. Unsupervised learning can in fact also be used to create and label data for supervised learning.
- Reinforced Learning: Is the latest frontier of machine learning and the least explored in terms of applicability as well as usage. Expectations are we'll see a tremendous increase in reinforced learning as computing power increases and data volumes to feed into existing algorithms also increase. A reinforcement algorithm learns through measuring various aspects of data provided to it and then starts replicating these behaviors. It is similar to rewarding or punishing a child for its behavior. It is this kind of learning used for gaming such as Google’s AlphaGo, the program that famously beat the best human players in the complex game of Go.
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.
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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 things—numbers, words, images, videos, sound files etc. Any data or meta data that can be digitally stored, can be fed into a machine-learning algorithm.
Applications of Machine Learning
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:
- Medical diagnosis and personalized medicine.
- Education and training, especially in the use of educational software for people with disabilities.
- Weather and storm prediction systems.
- Sensor technology.
- Building efficiencies into our agricultural, supply chain and maintenance systems.
- Fraud detection and market predictions.
- Speech and image recognition.
And many more ….
Machine Learning Is Here to Stay
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.
About the Author
Geetika Tandon is Managing Director with Deloitte consulting LLP with over 20 years of industry experience with technology consulting. She started her career in IBM as a developer working on voice and RFID solutions, moving to middleware implementation and then acquired deep expertise in IT modernization, helping multiple government agencies move to a cloud and DevOps environment.