‘Machine learning’ is a term that has grown in prominence in recent years and is only likely to be used more in the coming decade.
More and more businesses around the world are focusing on finding new ways to increase efficiencies, minimise waste and grow profits, and the ability to do this is largely reliant upon whether technological advancements can be integrated and operated successfully.
Many corporations – especially those with the capacity to harness large amounts of data – are utilising the power of machine learning to help achieve these benefits. Machine learning is, quite simply, a means of teaching technologies – generally in the form of an intelligent computer system – how to come up with more accurate predictions, make better decisions, and ultimately save the company money.
Making its own decisions
The tasks that can be carried out via machine learning-enabled technologies are vast and diverse; from being able to identify what objects and elements are contained within a photograph to being able to spot obstacles in the road that will inform self-driving cars that they need to brake, machine learning – when used adequately – can be remarkably powerful.
The primary difference between machine learning and traditional coding is that, while coded software can only work within the parameters set by the designers, machine learning has been given the ability to make decisions on its own – based on data source analysis and information it garners over time – that can either directly alter how machines, equipment or pieces of software operate or can offer up suggestions that can either be entrenched or ignored by employees.
One company that is using machine learning in an intelligent and interesting way is Greensteam. This organisation is helping shipping vessels of all shapes and sizes to make better decisions when it comes to route planning and fuel optimisation.
This clever use of machine learning means that shipping companies are able to reduce fuel costs, make more journeys per year and minimise their environmental impact.
Of course, all of these improvements and enhancements can only be made if data can be captured and subsequently understood.
For any companies seeking to make marginal gains by figuring out how to make overall operations smoother, methodical, streamlined and coherent, machine learning tools can make a huge impact, but only if a method – or methods – of collecting data has already been put in place.
Image by Jae Rue from Pixabay