2016 left several significant footprints in the technology landscape and one shining star in this picture, however, remained the Machine Learning. As the world is gradually being reshaped by machines possessing intelligence and making our lives easier, Machine Learning is slowly becoming an integral part of our daily life. It encompasses every significant aspect of our lives and poses to emerge as the ruler in 2017 and the times to come. So how is machine learning gradually changing the world around? Here are a few ways in which it is doing so.
Machine Learning in Search Engine
That search engines rely on machine learning to improve their services is no secret today. The most powerful form of machine learning being used today is “deep learning” which builds a complicated mathematical structure called the neural network built on a huge volume of data, much in a manner analogous to that of a human brain. Implementing these Google has introduced some amazing services like voice recognition, image search and many more. How they come up with more interesting features is what time will tell us.
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Machine Learning in Education
According to Alfred Essa, Vice President Analytics and R&D at McGraw-Hill Education, data is driving remarkable amount of innovation in learning and education. With the growing use of digital books and adaptive learning, a more tailored and customizable learning experience is on the path of evolution. An increasing trend of moving away from traditional learning is taking over, as people learn with different speeds and possess different adaptive capacity based on their background and exposure.
Using machine learning, teachers can accurately gauge how much of the lessons, the students are able to consume, how they are coping with the lessons taught and whether or not they are finding it too much to consume. Of course, this allows the teachers to help their students grasp the lessons and prevent the at-risk students from falling behind or even worst, dropping out.
Not just this, machine learning algorithms can create predictive learning paths for the student, as well. As students proceed through a course with an adaptive learning software, these algorithms serve up additional content for the student, if reinforcement is needed, or allow the student to move ahead to the next lesson if the subject matter has already been mastered. McGraw-Hill Education’s product ALEKS – a web-based artificial intelligence and learning system, breaks up the domain into concepts using graph theory. A student learning Algebra will be tested when he or she begins learning, pinpointing with exact details what a student knows or doesn’t know, and then creating the appropriate path through the domain that is selected based upon the initial start point of the student.
Machine Learning in Digital Marketing
Most organizations rely on its marketing and sales team for expansion, count on the carefully designed customer surveys and organizational initiative to create a differential offering. All of these approaches being human and manual are prone to errors making marketing decisions and measurement of results difficult. This is where machine learning can help significantly. Machine learning allows a more relevant personalization so that the companies can interact and engage with the customer at every stage of the customer’s lifecycle. Using sophisticated segmentation in machine learning companies can focus on the appropriate customer at the right time with the right message. Companies mostly possess information which can be leveraged to learn their behavior, product usage pattern, order frequency. Machine learning finds a pattern in these data and helps find what leads to a good customer outcome. It takes a multidimensional approach which identifies these various patterns to predict the behavior of a customer in a similar situation, enabling marketers to scale and enhance those inherently personalized customer interaction decisions. E-commerce giants including Amazon and eBay are using this multidimensional approach to building a rich customer experience.
Machine Learning in Health Care
Machine learning health care applications seem to remain a hot topic for last three years. Several promising start-ups of machine learning industry are gearing up their effort with a focus toward healthcare. These includes Nervanasys (acquired by Intel), Ayasdi, Sentient, Digital Reasoning System among others.
Computer vision is one of the most significant contributors in the field of machine learning which uses deep learning. It’s active healthcare application for ML Microsoft’s InnerEye initiative which started in 2010, is currently working on image diagnostic tool.
Memorial Sloan Kettering (MSK)’s Oncology department is aiming for a partnership with IBM Watson. MSK possess a huge amount of data which can be leveraged to drive cancer treatment using machine learning. Another interesting application in healthcare is through ResearchKit introduced by Apple. It is aiming to do a treatment of Parkinson’s disease and Asperger’s syndrome and allows the users to access interactive apps (one of which applies machine learning for facial recognition) which help assess their conditions over time; their use of the app feeds into the ongoing progress data into an anonymous pool for future study.
Personalized medicine is soon speculated to become the new buzz in the world of medicine and healthcare as everyone’s treatments and health recommendations are tailored based on their medical history, past conditions, diet, genetic lineage and more. All these makes for an optimistic start for the cure of several incurable diseases.
Machine Learning in Financial Trading
Finance and trading have remained another key sector which remarkably benefited from machine learning. More so as the high volume of historical records and quantitative nature of the data in finance world makes artificial intelligence well suited for finance and trading. Currently, machine learning plays an integral role in several phases of it, like approval of loans, managing assets, assessing risks and many more. A very commonly used algorithm in financial landscape is Robo-advisor which helps calibrate a financial portfolio to the goals and the user’s risk tolerance. Robo-advisors have gained significant popularity with the consumer base who invests without a physical advisor. Deep learning has enabled calibrating real-time trading decisions. Machine learning is also playing an integral role in fraud detection in finance and trading where systems can detect unique activities or behaviors and flag them for security teams.
All these and many more examples show how machine learning will be our guide to revolution for the next century making our lives easier, happier and more connected. And, how much of this revolution do we see in 2017? Well, we’ll have to wait for a little and find that out.
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