3 Questions You Always Wanted to Ask about Machine Learning

The cognitive and automation technologies behind artificial intelligence (AI) are quietly reshaping the world. Machine learning, deep learning, neural networks, biometrics, natural language processing, Big Data, and predictive analytics have unlocked our imagination, and things that were a dream a few decades ago are now a reality.

According to the Gartner’s 2016 Hype Cycle, machine learning is now at its peak of inflated expectations, emerging as one of the most innovative and diverse application technologies. Everybody in the tech world is talking about it day and night, though few have actually managed to practice it so far, and the following question certainly comes to mind.

Isn’t it just a marketing buzzword?

Machine learning is a burning subject right now, widely discussed in many articles and at nearly all tech events, but interestingly, it is not new. In 1959, computer scientist Arthur Samuel defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed.” Since then, it has evolved from a foggy concept to a popular technology used by both major tech giants and innovative startups around the globe.

The machine learning market is growing at an accelerating pace. IDC forecasts that machine learning and AI spending will surge toward $47 billion by 2020. Machine learning is not science fiction or a marketing gimmick; it is a real and promising instrument that is already forcing massive and fundamental changes in the way businesses operate. Some famous examples of machine learning in use include Apple’s Siri, Amazon Echo, the self-driving Google Car, and online recommendations from Netflix.

Over the next 10 years, machine learning is expected to be among the most disruptive technologies due to increases in computational power, near-endless amounts of data, and unprecedented advances in deep neural networks. It will become part of the human experience and the digital business ecosystem, and companies wishing to thrive in the digital economy have to explore it proactively. Here, however, the next and more difficult question arises.

Can I apply machine learning to my business?

Machine learning is a topic of high practical relevance in various domains—from government, healthcare, and transportation to sales, marketing, financial services, and insurance. It has a tremendous range of business applications, such as predictive maintenance, supply chain optimization, fraud detection, healthcare personalization, traffic reduction, aircraft scheduling, and many more.

Government agencies use machine learning for data mining to increase efficiency and save money. Banks apply machine learning to spot investment opportunities, identify clients with high-risk profiles, and pinpoint cyber-threat warning signs. In healthcare, the technology helps professionals use data from wearable devices and sensors to assess patients’ health in real time.

Oleg Dyrdin, Auriga’s expert, comments:

Working on a high-load cardiac monitoring system for one of Auriga’s clients, we trained a three-layer neural network to recognize emergency cardiac conditions using data provided by the open resource called PhysioBank. An in-depth statistical analysis of a massive amount of cardiac data coming from the sensors of portable ECG Holter devices, combined with weather data, enables not only continuous monitoring of hundreds of thousands of patients simultaneously but also prevention of cardiac disorders and makes the system comparable to real-time treatment.

In sales and marketing, machine learning personalizes your online shopping experience by analyzing your buying history and promoting items you might like based on your previous purchases. Machine learning is also a great tool for the transportation industry, which relies on making routes more efficient. It even helps to find new oil and gas sources and streamline oil distribution.

The number of machine learning use cases for different fields is vast and still expanding. The technology is now available to everyone via multiple open source projects and affordable machine learning-based solutions and APIs. Nevertheless, there is one more important question to answer.

What are the limits of machine learning?

Machine learning is a fascinating technology filled with enormous potential, but it is necessary to understand that, as with any other technological tool, it has its limits—and there are many of them. To begin with, non-experts may consider machine learning unbiased, but human bias can easily seep into the data machine learning uses and, thus, influence the predictions.

Moreover, though the very definition of machine learning seems to eliminate programmer intervention from the machine learning processes, it is impossible. The human element is still very important, as all the algorithms machine learning solutions are based on are created by humans, and even when machines teach themselves, it is a human who chooses the preferred patterns.

Finally, machine learning is not a perfect fit for all problems. If you think that a machine learning system will produce results from any data, in any situation, forget it. Machine learning is mostly applied to Big Data, and to properly train a model, you need to have access to the most representative and high-quality data from real-world scenarios.

Summing up, we have already come a long way in the first fifty years of AI, and machine learning is the main reason for its recent successes. However, the technology is still developing, and we have a much longer way to go. Nobody knows what the future holds for machine learning, but with deep understanding and the right expertise, you can use it to create a competitive advantage today.