However, with the increased sizes of datasets, it becomes more and more complex or too timely for humans to solve the relevant problems. Machine Learning can accomplish this task and extract simplified patterns that can be used to predict data or recognize new and unknown inputs.
Even though machine learning is often perceived as a futuristic technology, it has widespread real-life applications today. For example, looking at the automotive industry, one often just thinks about self-driving robot cars. However, machine learning is already used for predictive maintenance and adaptive fuel efficiency, as well as sales prediction, part design, and material optimization. This universality allows machine learning to be utilized in almost any kind and size of a company, from freelancers to large cooperations, supporting individuals to streamline their work and taking over repetitive, complex or dangerous tasks.
This increased work efficiency yields a radical shift in the way we will work, which is often seen as at least as significant as the industrial revolution. Similar to steam engines that replaced muscle power, machine learning will support, enhance, or could even fully replace brainpower. Neither substitute seemed or seem appealing, but history shows that each revolution resulted in an economic leap increasing the average living standard whilst freeing up work capacity for other innovative undertakings – and we can already see the onset of these new occupations today. Data curators prepare, tag and classify training data; auditors evaluate the algorithms to eliminate bias, and privacy controllers ensure that all procured and generated data and algorithms follow current privacy laws and ISO standards. Since machine learning algorithms consist of relatively simple functional units, most companies do not develop new ones, and many of their software developers perform one or several of these new occupations.