Machine Learning is a set of computer programs that, in contrast to classical ones, can learn from experience without requiring detailed instructions. Such programs can thus predict future events and even solve problems of which properties are not known yet. It means that Machine Learning does not try to provide specific solutions directly but rather automates the entire solution finding process based on examples. Hence, it relies on predicting solutions using limited data, called training sets, which translate into features vectors that result in test sets.
In other words,
The main tasks of computers is to automate human tasks. Some of these tasks are simple and repetitive, such as “move X from A to B.” It gets far more interesting when the computer has to make decisions about problems that are far more difficult to formalize. That is where we start to encounter basic machine learning problems..
Machine Learning Use Cases
A good illustration of the aforementioned is Google Reverse Image Search which represents a solution to a machine learning problem using scale-invariant feature transform to extract millions of properties from every single image. When uploading an image, the platform builds a mathematical model around it, compares its features with trained data sets and then returns matching results. Facebook’s image tagging feature uses similar convolutional neural networks. Another excellent example of machine learning is Google Translate which utilizes a compelling, deep neural networks system that continually learns how to decipher most complex communicational situations. More than that, machine learning is now being widely used for video analysis with an increasing number of competitions that provide professionals opportunities to work with quality datasets and solve a real-life problem as thoroughly explained in Michael Karchevsky’s article.
With accrual of significant amounts of data together amidst advances in computational power, machine learning has developed tremendously over the last few years hence yielding unprecedented precision and margin errors with leading platforms such as Google Cloud, FB Learner, Amazon Machine Learning, Azure Machine Learning, and Uber Michelangelo ML.
Machine Learning in Business
In the midst of all advances mentioned above impacting the way people live, how can organizations benefit from machine learning and use it to develop innovative solutions for business-related problems and also transform the way people work? The very first element is management executive’s awareness that artificial intelligence is a powerful instrument to craft and most importantly execute a strategic plan. In addition to that, it should be at the core of digital strategic initiatives with a clear understanding of machine learning from both data and business perspectives. However, all of the above is possible only when responsible departments refrain from being reluctant to information and data sharing, thus overcoming the often-felt fear from artificial intelligence being a threat that will unveil weaknesses and break comfort zones.
Last but not least, machine learning and its applications will undoubtedly continue to make life easier but also transform industries and organizations through computing, sorting and connecting data sets with high speed and accuracy superior to that of humans.