Artificial intelligence

As an “AI first” company, Lynker Analytics believe machine learning technology, when informed by temporal location data, is an enormously powerful tool which enables us to provide far deeper insights into our footprint and the impact, performance and resilience of systems within our world.

We think our unique utilisation of artificial intelligence and deep learning technologies will help our customers better predict and quantify the impact their business is having on the environment and help them understand the costs the environment is having on their business.

We believe the next evolution of geospatial technology has Artificial Intelligence at its centre because of the far greater inference capability and scalability.

Where machine learning can help your organisation

Machine learning is the subclass of AI that learns from data without explicit programming. Machine learning is very effective at problem solving when the pattern or signal in your data is too vast or complex to comprehend or the process of analysis is too time consuming due to the number of inputs and samples. 

Machine learning allows the business to cast its net wide for data related to a problem. In some cases, removing the need for subject matter experts and allowing a purely empirical approach to producing analytic models, making predictions and feeding insight and visualisation.

“Machine Learning enables reasoning over vast amounts of data.”

For example, say you need to understand the degradation of a gas network. You may have historic data of condition and materials. Match this, over time and location, with environmental data such as weather, soil, slope, land use and network usage.

Machine Learning algorithms can model using all these inputs and your result will reflect the contributions of all these factors, individually and with complex interactions. You can then make predictions of condition and gain insights to the importance of each input to the condition of the network.

Where you have lots of data and non-obvious relationships between data and outcomes, or where you have pattern-based decision making that requires human time to analyze and make decisions – Machine Learning can be part of the solution to offer you insights and efficiency


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