Nowadays, digital innovation seems to be a concept embedded in everyone of us. We seamlessly flow through apps and platforms in a natural way, adopting new innovative solutions in our everyday life at an extraordinary rate. This revolution in the digital era of innovation is sustained by three main pillars: platforms, crowd and intelligent machines. In this context, it can be argued that platforms are replacing products, and crowds redefine core activities within organizations. Will Artificial Intelligence re-shape society as we know it by gradually replacing activities currently performed by humans?
Machine Learning, a subfield of Artificial Intelligence, “gives computers the ability to learn without being explicitly programmed” (Samuel, 1959). In some cases, certain tasks present enormous challenges when trying to manually program them. To overcome this, a machine is “trained” using algorithms and data that enable it to learn how to do a given task. Some of the most popular fields of research and applications in machine learning are:
- Speech recognition: the ability to recognize spoken words and translate them into text for voice user interfaces, data entry, etc.
- Image recognition: one of the most commonly used machine learning applications. It can be used either for object recognition (i.e.: for self-driving cars), face recognition (i.e.: China’s security network) and many other uses.
- Medical diagnosis: used to asses and help solve diagnostic problems across different medical disciplines by analysing data and predicting outcomes. Healthcare data driven systems are being widely discussed for implementation with the help of machine learning. It can bring great benefits in terms of quality of health service, overall health improvement, better information for potential research, and considerable economic savings for the governments that adopt it.
- Natural language processing: with the advancement of automated digital assistants and more efficient customer service, machine learning has been used to develop revolutionary technologies such as Google Assistant.
- Self-driving cars: Google’s self driving car employs machine learning to automatically calculate large amounts of data.
Considering what machine learning brings in terms of market opportunities, organizations that are up to date with current practices can benefit from the exponential growth of structured and unstructured data that has been generated in the recent years. Our SCOUT search engine uses big data analytics and machine learning to identify relevant information, offering companies up-to-date access to what’s going on in their area of interest.
Benefits and stakeholders
By developing more effective ways of processing and understanding data, businesses can develop a competitive advantage within their market. Governments, on the other hand, can tackle growing problems like the increasing strain on healthcare systems by aging populations by standardizing health care practices and predicting flu cycles. Some of the relevant stakeholders that can benefit from machine learning are governments, insurance companies, financial services, transportation, energy companies, etc. However, the potential applications are much broader and a wide range of different players can benefit from implementing machine learning in their processes.
- A. L. Samuel, “Some studies in machine learning using the game of checkers,” in IBM Journal of Research and Development, vol. 44, no. 1.2, pp. 206-226, Jan. 2000.