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Learning with Big Data

Learning is a human nature. The ability to make decisions comes from knowledge and there are two sources that the brain uses to build the knowledge – data and information. The process of building the knowledge is data mining. Data mining involves processing of data and identifying patterns and trends inside the information.

Data mining principles have been around for many years, but, with the advent of Big Data era, it is even more prevalent. 90% of data in the world today has been created in the last two years alone, IBM estimates. And this trend in data volumes will only continue since we stepped in into era of Internet of Things (IoT) and very close to Worldwide Internet coverage. So, we can say that humanity is ready to gather huge amounts of data which will be able to change our map of the world (knowledge) by the use of different or more precise insights. Data are to this century what oil was to the last one: a driver of growth and change. Flows of data have created new infrastructure, new businesses, new monopolies, new politics and—crucially—new economics.

The growth of Big Data has created a number of emerging roles in data mining and analytics. Positions such as Data Analyst and Data Scientist are in demand and use several data mining techniques and principles. Data mining techniques help professionals provide insights into available data sets.

In addition to Data Analyst and Data Scientist roles the need of data mining dramatically increase importance of Machine Learning (ML) and Artificial Intelligence (AI). AI is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. In other words AI is a mechanical brains able to mimicking human decision making processes and carrying out tasks in ever more human ways. ML is a subset of AI and can be defined as a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.

The evolution have gone through data mining techniques like Association, Classification, Clustering, Decision trees, Self-organizing maps, Markov models (CMM), Genetic algorithms, etc. and comes to the most promising AI technique - Neural Networks. The development of Neural Networks has been key to teaching computers to think and understand the world in the way we do, while retaining the innate advantages they hold over us such as speed, accuracy and lack of bias. A Neural Network is a computer system designed to work by classifying information in the same way a human brain does. It can be taught to recognize, for example, images, and classify them according to elements they contain. ML takes some of the core ideas of AI and focuses them on solving real-world problems with Neural Networks designed to mimic our own decision-making. Deep Learning focuses even more narrowly on a subset of ML tools and techniques, and applies them to solving just about any problem which requires “thought” – human or artificial. We can say that at the current moment evolution of AI is at the state of Deep Learning.

Impressive examples of how Deep Learning is being used today:

        Navigation of self-driving cars – Using sensors and onboard analytics, cars are learning to recognize obstacles and react to them appropriately using Deep Learning.        

Recoloring black and white images – by teaching computers to recognize objects and learn what they should look like to humans, color can be returned to black and white pictures and video.

        Predicting the outcome of legal proceedings – A system developed a team of British and American researchers was recently shown to be able to correctly predict a court’s decision, when fed the basic facts of the case.

        Precision medicine – Deep Learning techniques are being used to develop medicines genetically tailored to an individual’s genome.

        Automated analysis and reporting – Systems can analyze data and report insights from it in natural sounding, human language accompanied with infographics which we can easily digest.

        Game playing – Deep Learning systems have been taught to play (and win) games such as the board game Go.