The one area of computing that is improving by how we use our mobile devices and the Internet is machine learning. At the moment, machine learning is a buzzword in the digital technology world and for good reason, if you ask me: We have more data than ever before and it represents a greater step forward in how computers can learn. The goal is to create a machine that could mimic the human mind. In order to do that it needs learning capabilities, reasoning and abstract thinking.


Can we make machines learn to do things?

Well, machine learning is not a new phenomenon. In fact, I was curiously waiting how this new technology technique would be adopted to our daily activities. Machine learning is related to statistics and data mining, because of the attraction of knowledge by data. You might provide a computer with a teaching set of photographs, some of which say, “this is a mountain” and some of which say, “this is not a mountain.” Then you could show the computer a series of new photos and it would begin to identify which photos were of mountains. Every photo that it identifies correctly or incorrectly gets added to the teaching set.

The program effectively becomes smarter and better at completing its task over time. This is a very common example for recognition in images, where the algorithm for machine learning is given a set of data, then asked to use that data to answer a certain question.


Who is using it?

Almost every large online retailer will recommend items you may want to purchase. These recommendations are based on key data points; for example, previous shopping history, your recent searches, or even based on who your friends are and what posts you liked on social media. The list of companies is growing by the day in addition to the various applications of machine learning. Common applications of machine learning in today’s technology include search recommendations, voice recognition, video analysis, fraud detection, location data and so on. These current technologies are being improved daily by greater data analytics and advancements in the state of the art of machine learning research.


Which techniques are being used?

Also, the financial industry and governments use machine learning techniques, since they have access to multiple sources of data that can be mined for insights. Machine learning can help to identify warning signs of fraud and detect cyber attacks.

When I look at the information security industry, I see trends that lead me to the conclusion that machine learning approaches are a must for the industry. As we know from previous cyber attacks, cyber security holds a present danger to companies in every industry and it is unlikely to be resolved anytime soon. This threat is constantly changing, as hackers find new ways to infect systems. In order to deal with this, organisations must be extremely quick to adapt their security countermeasures and techniques of machine learning are the only technology currently available with this potential.

Another trend is the collection and storage of large amounts of useful data, which is already being performed in cyber security.

It would be difficult to find an IT specialist, like myself, who is not currently overwhelmed by the amount of raw data that is collected every day. Machine learning techniques are favourable to improve the security development of an organisation.

These approaches are probably implemented at some level within organisations. Future augmentation will show an increase in the number of areas where machine learning techniques are prevalent.

Without machine learning, a change to survive in this complex security landscape will be impossible. It is a fact that we are surrounded by adaptive smart things that can systematise some of our most common daily demands in a split of a second.

“If you lend your consciousness to someone else, you are a robot.” – Prince

Written by Manisha Ghisai, IT Run Specialist at ABN AMRO Bank.
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