Machine Learning, Cyber Security, Women Empowerment and More
Image credit: pixabay.com

 

Isn’t the world and life fast and amazing?! The world is moving extremely fast, and I have been through a number of events recently, including meeting inspiring and dynamic managing women just before the International Women’s Day that happened last Sunday, March 8.

To devote a short piece of this article to women in tech, it could be great to mention great women who use machine learning algorithms in order to create women community in the technology world and promote equality. Glassbreakers have developed machine learning algorithm that is using LinkedIn profile information in order to bring together women with similar career goals and experiences for mentoring and networking purposes. I believe the technology should be using natural language understanding and semantic web tools as well in order to classify key-words and build patterns. Another fabulous case of innovative and creative technology use!

Other prominent uses of machine learning touch upon the subject that has received the hype recently – cyber security. The importance of the domain is not overestimated – with people’s lives becoming more and more public and all the activity that happens over the Internet it is crucial to protect personal and corporate data. Machine learning and artificial intelligence have been found useful in identifying patterns of suspicious behavior over the Internet in order to identify and even predict the possibility of ever-changing cyber-attacks. Rachel King describes how software can analyze and learn the patterns of any user’s behavior in order to detect any anomaly, comparing it to the technology used by banks and credit card companies “to learn when and where customers typically use their cards to identify suspicious purchases”[i]. Dtex Systems and Brighterion Inc. uses microagents pushed to the user devices, especially in the high-risk cases like the ones of employees leaving the company, in order to collect metadata – locations where emails or company’s servers are accessed, time patterns, etc. The algorithm is further transferring this data into knowledge and learns to detect irregularities and predict risk.

A similar use case enhanced by the use of deep learning is presented by PayPal for fraud detection. Deep learning, i.e. algorithms that are inspired by the functioning of human brain allow “non-linear” approach to the data analysis and have helped PayPal to set up a “detective-like methodology”[ii].

More amazing use cases for machine learning are currently spreading over healthcare, travel and communication industries. We have already mentioned machine translation becoming immediate and benefitting from image recognition and natural language understanding techniques. Learning algorithms will make automatic instant translation helping companies, intelligent cars and occasional travelers to cope with road signs, international meetings and other globalization challenges[iii].

While we are dealing with the big amounts of data to make machine learn and predict patterns, this data needs to be protected and secured. All the modern technologies make sense when supporting one another being used in creative solutions. Big data, machine learning and cyber security trends should be used together in the digital and connected economy for a better result.

 

 



[i] The Security Download: Anticipating Cyberattacks with Machine Learning by Rachel King for The CIO Report, The Wall Street Journal, March 9, 2015, available online at http://blogs.wsj.com/cio/2015/03/09/the-security-download-anticipating-cyberattacks-with-machine-learning/ accessed on March 10, 2015

[ii] How PayPal uses deep learning and detective work to fight fraud by Derrick Harris for Gigaom, March 6, 2015, available online at https://gigaom.com/2015/03/06/how-paypal-uses-deep-learning-and-detective-work-to-fight-fraud/, accessed on March 9, 2015

[iii] How machine learning will feed innovation, Impact LAB, March 9, 2015, available online at http://www.impactlab.net/2015/03/09/how-machine-learning-will-feed-innovation/, accessed on March 10, 2015