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What is Machine Learning?

Written by Dr. Christine Izuakor for Veriato, a cybersecurity company

Over the last century, our technology devices have gone from being clunky systems that require tons of human interaction, to modern machines that seem to have a mind of their own. Our phones can do things like autocomplete sentences before we finish typing, suggest purchases based on sites we’ve visited in the past, and even predict our schedules on any given day based on our prior habits. This is all possible due to the growth of artificial intelligence and machine learning.

Generally, artificial intelligence is the concept of making systems “smart” and empowering them to complete tasks humans may typically complete. Machine learning, one of many methods used to build artificial intelligence, leverages data and patterns to replicate human behavior with limited human direction. Furthermore, the machine is able to learn from those transactions and dynamically adapt responses. These technological advancements are making life easier for the everyday consumer and changing the game for businesses. Here are 5 things that can help you better understand machine learning.

Machine learning has been evolving for almost a century.

The earliest records of machine learning date back to the mid-1900s when Alan Turing formed the Turing Test. The test was created to determine if a computer could operate intelligently, like a human being. To pass the test, the computer would need to, through conversation, trick a human being into believing that it was also a human being. The machine is said to have passed the test, and since then, machine learning has evolved drastically. In 2010, Microsoft developed Kinect, a machine learning technology that allowed people to interact with computers using movement and gestures. IBM’s Watson, a machine learning based question and answer technology, stunned the industry when it beat its human competitors at jeopardy soon after. Within the same decade, Google Brain and Google X Labmade advancements in this space including the creation of a machine learning algorithm that could autonomously browse YouTube and identify videos containing cats. Microsoft created the Distributed Machine Learning Toolkit which enabled the efficient distribution of machine learning problems across multiple computers. These pioneering technologies began to pave the way for diverse machine learning applications in businesses.

It’s making a difference across almost every industry.

Machine learning has become a key element in solving complex problems in various industries. In healthcare, it’s used for tumor detection, drug discovery and DNA sequencing. In transportation, it’s used for predictive maintenance on air, water, and ground vehicles. In finance, it’s used for credit scoring and algorithmic trading. It’s making industries all over, better, faster, and more efficient – the security industry is no exception.

In physical security, machine learning is utilized in face recognition, motion detection, and object detection solutions. In cybersecurity, it’s changing the game when it comes to areas like threat detection, network defense, penetration testing, and more.

Machine learning can solve some of our greatest cybersecurity challenges.

The same way that machine learning can be used to predict your schedule on your phone is the same way that it can be used to understand normal user behavior in corporate networks. When something seems “off,” or there are significant anomalies, machine learning based solutions can not only alert an analyst of the suspicious behavior but dynamically fix the problem if trained to do so. The technology can detect these patterns of malicious activity from thousands of logs and data points, at a much faster rate than a human being can. This is not to say that technology can replace the need for analysts, but that the two can work more efficiently together to protect systems and data. Whether trying to detect insider threats, network security issues, zero-day vulnerabilities and more, machine learning can be applied to challenges in cybersecurity and other industries in ways that change the game forever.

The concept can be intimidating for users, but awareness helps.

Businesses are embracing machine learning mainly due to the efficiency and optimization of resources that it enables. For the everyday user, the benefits may not be so clear. When your phone begins predicting your schedule based on your prior movement or your social media feed includes targeted ads with topics you only discussed with a person via email, users get concerned. How much are consumers willing to sacrifice for convenience? Many people, especially those who don’t understand the technology well, find this concept to be somewhat intimidating. Such reservations regarding machine learning capabilities can be reduced with more education and awareness about the benefits, trade-offs, and how the technology works to improve the users’ overall experience.

The machines must keep learning too.

Conceptually, machine learning enables computers to function like high performing human beings. Yet, the reality is that no matter how polished and prepared a human being is, we are still prone to mistakes and hiccups. Machine learning based solutions are no exception. Essentially, the Machine Learning Algorithms are only as advanced as the data they are being trained with. So, what happens when there are outliers or scenarios the machine learning algorithm is not programmed to handle? When faced with this kind of uncertainty, human beings often think on their toes and try to make the best decision with the information they have. In Cybersecurity, Machine learning allows companies to sift through petabytes of data autonomously within seconds, with the intent of finding anomalous behavior. This is a feat a human being simply cannot achieve. As Machine Learning advances, progress has been made towards alleviating some of the challenges associated with the technology. Also, due to the autonomous and independent nature of the technology, errors can be problematic if not addressed within a reasonable timeframe. The good news is that, like a seasoned human, machine learning gets better with time, the right data and practice.

Machine learning has proven to be a positive disruptor for many industries with a wealth of successful real-world applications. It’s been one of the most rapidly evolving areas of technology and is predicted to continue impacting most sectors and the jobs within them. From predictive analytics engines that generate shopping recommendations to the technology used in many security and antivirus applications, we’ll continue to see machine learning applications mature and change technology as we know it.

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