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.

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 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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It’s safe to say we’ve been distracted for the last few years. While a tumultuous political, social and economic landscape has seized Britain, in the background a technological revolution has been taking place. No longer a futuristic concept, artificial intelligence (AI) is making its way into our lives right now. 

Self-learning machines are already found in devices and cloud services used by three in four global consumers. They’re also dictating which media we consume, how we communicate with each other and what our jobs entail. Could human intelligence soon be replaced?

Perhaps not. There are a huge number of misconceptions around AI, not to mention fear about its capabilities. The best way to define its true meaning, abilities and potential impact upon the world is to speak to those using it today. I took part in a series of focus groups with data scientists, business leaders, academics and students, all of whom work closely with this technology. As those who are shaping how AI impacts our society, their views show whether we should embrace AI, or fight back.

A change is coming to jobs, but unemployment won’t rise

One topic was pervasive: job losses due to AI-driven automation. While it’s positive that most participants believed AI would create more jobs than it replaced, there was little agreement on the duration, severity or consequences of job losses resulting from AI in the short term. In particular, younger participants tended to be more pessimistic about their future prospects, anticipating a significant rise in AI-enabled inequality and a breakdown of social cohesion. Some feared the powerful technology being placed in the hands of a few could drive a much greater divide between those with power, wealth and influence and those without.

Yet, while there was some trepidation from this current and future workforce, the message from the boardroom was loud and clear: workers have little to fear from AI. The reason for this was simple – even in an AI-driven future, humans would remain a valuable commodity worth investing in. They would continue to deliver value that machines do not.

Autonomous robot assistants aren’t on the cards… yet

As several of the professors and data scientists informed us, we are still a long way from the ‘general intelligence’ so often portrayed in science fiction. Despite the hype, most AIs are designed to be very good at solving a specific problem and under very particular parameters. Introduce a variable and the system breaks down or a new model needs to be created.

Time and time again, the respondents reminded us that human creativity, insight and contextual awareness were key to making AI work. Technical executives in the C-suite told us how they ensured any autonomous processes were closely monitored and supervised by human employees. AI solutions with hidden internal workings weren’t worth the risk, due to a lack of transparency and explainability.

These sorts of validation roles have started to emerge only recently. With time, however, more transparent processes where employees review, understand and resolve the decisions made by AI systems will be a massive source of employment. Like any piece of software, the quality of AI insight depends on the quality of the data you feed into it, and it takes a human to know and judge what is good for it.

The world as we know it is changing for good

Technological revolutions are nothing new. Each generation is faced with a new set of technologies which upend stability in favour of progress. How many Uber drivers, YouTubers and app developers did you know at the start of the millennium? Just as the internet revolutionized life as we knew it, AI is powerful enough to cause seismic change across all industries. But, as one respondent on our focus groups put it, “AI will replace us just like computers did. That’s to say, it won’t.”

Today’s genuine AI users argue that public perceptions of AI often contain elements of sci-fi. In reality, the future belongs to the cyborg, rather than the android. This is a key distinction: rather than imitating humans and challenging us at our own game, our economy will be defined by humans able to work hand in glove with AI. In a team, humans and AI can develop simultaneously to make better decisions, improve productivity, and ultimately boost humanity to new heights – and that revolution has already started.

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