In short, machine learning (ML) is based on algorithms that learn from and make predictions on data without the need for continued programmer input to achieve this. These systems have a concept (a pre-programmed goal or function) which provides the desired outcomes within certain parameters, and just like humans, machine learning systems will perform a task once, assess the outcome, and endeavour to achieve the most optimised route to achieve this again.
When machine learning has been applied to health, science and manufacturing, the results have been outstanding. ML has found cures to illnesses in a fraction of the time and budget of the previous human-led research. The automotive industry has seen its production lines augmented with robots that learn from and share these experiences with all its robots throughout the world to optimise and develop its manufacturing. Science has created digital environments based on real data that allow ML to explore likely outcomes before humans have yet to even discover this ‘physical’ environment in the real world.
The ability to learn from real-time data, be present in live environments and respond to manufacturing optimisation is proving instrumental in the reimagining of the workplace and our roles within it.