Cognitive computing has an aim to solve complex problems characterised by uncertainty and ambiguity. Which in other words means problems that are only solved by human cognitive thought.

While machine learning and artificial intelligence can be seen as being a traditional process mapped approach, cognitive computing utilises broader “mental” processes, including self-learning.

A good example provided by Cognizant is around the drinking water production. Making sure that treated water is sourced, treated and delivered in the in real time. The real time nature of water treatment and delivery has the potential to vastly increase risks and costs.

Traditional management has relied heavily on SCADA based control systems and the usage of real time information. Including, customer usage, storage in networks and energy tariffs to drive the treatment operation of pumping, treating and distribution.

Efficient production systems depend heavily on data collection, modelling, visualisation, and situational intelligence from cognitive computing to overcome these issues. Cognitive computing in water production planning uses real-time data to gather, sort, and analyse in a comprehensive and holistic way.

Within the water sector this is still in its infancy. But it’s expected to expand at a significant rate, driven by the opportunities to decrease workforce sizes and decrease risk. Expect to see a series of pilots in coming years with general take up in the next decade.

Cognitive Computing - Nixon Clarity Blogs

David Nixon has worked the water industry for over 30 years across a variety of utilities, engineering and business consultancies. David currently acts as director and advisor to a variety of organisations across Australia.

Credit: Techopedia & Cognizant