Harnessing AI for engineering development

Knowledge is a curious thing. As an individual you can never have too much, but as an organisation, it’s possible to have such vast quantities of data that it becomes difficult to extract useful information out of it using conventional methods. This is the world of Big Data – a familiar buzzword from computing, but also a practical tool for engineering development if it’s harnessed in the correct way.

It can almost be thought of as machine intuition. Conventional models perform well when presented with clearly-defined problems governed by well-understood physics, but they can struggle with complex, multi-dimensional data. Conversely, artificial intelligence thrives on large, complex datasets. Self-learning algorithms can churn through historical data, spotting patterns that can be used to predict future outcomes. Unlike traditional models, this doesn’t need to be anchored to a set of pre-existing equations, although it can be used in conjunction with conventional techniques.

“It’s the equivalent of going up to an experienced chief engineer and asking, ‘what’s your gut feeling, will this work?’” said Dr Richard Ahlfeld, CEO of Monolith AI. “You can literally have an AI version of that expertise, which comes back and says, ‘we’ve looked at those parameters, they’re very similar to 10 different tests we’ve done in the past, and eight of those were a disaster’.”