Machine learning represents a sub-segment of artificial intelligence (AI). It is considered one of the innovative drivers of Industry 4.0 and is indispensable for handling big data.
Even an average Internet user is confronted with machine learning results on a daily basis when they receive customized product suggestions on one of the large eCommerce sites. The results provided by major search engines are also based on machine learning. Other applications include:
• Voice and mood analyses for sales and after sales
• Chatbots as well as digital assistants
• Minimizing risks as part of financial transactions
• Image recognition and image analyses for diagnosing illnesses
• Preventing credit card fraud
The triumph of Machine Learning has only just begun.
New options are added almost daily. Machine learning can be used as part of analysis, in data, system, and infrastructure monitoring, as well as in marketing and sales. It supports and accelerates development and roll-out processes. Major recruiters are now also relying on algorithms to, for instance, cut the costs for pre-selecting applicants. Consequently, they can verify the actual suitability of potential candidates for the job vacancy even more accurately and on a more personal basis. Perfectly maintained databases represent an elementary requirement for this purpose.
Machine learning is increasingly also used for operative tasks. Machine learning and artificial intelligence are taking over more and more areas. Systems themselves are making decisions that had previously been made by people.
How Does Machine Learning Work?
Most conventional software programs are programmed once, tested and then they are immediately applicable. Algorithms are being developed as part of machine learning and deep learning (a sub-segment of machine learning) that must initially learn to ultimately deliver increasingly more perfect results. As a result, it is important to identify in customer service which areas require quick reactions, for instance because a request demands for a quick reaction. Adaptive software scans all incoming emails and pre-filters them. In this process, adaptive IT systems independently identify regularities. However, this can only work if systems can actually fall back on all relevant data and they have been provided with the matching learning algorithms. This requires software engineers, engineering specialists and programmers to adapt to a new approach.
For instance, it is necessary to decide how the IT system is intended to learn. Specialists distinguish between supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and active learning.
A Host of Application Options Evokes a Growing Demand for Machine Learning Specialists.
Many software specialists are keen on discovering new ground they can develop using AI and machine learning. Their commitment and courage is currently handsomely remunerated and we expect this will continue in the future. AI experts and machine learning specialists are in demand and they are (on average) even better paid than IT experts in other sectors. How much significance companies attach to digitalizing even complex business processes also becomes evident from the fact that IT managers are at the helm of the majority of these projects.