AI is the ‘new industrial revolution’ for offshore survey

Artificial intelligence has the capacity to revolutionise the marine energy industry, but part of the challenge is deciding which data to conserve and how.

AI simulation tools are considered the “new industrial revolution”, according to Andrijana Horvat, research and development lead at Europe-based hydrographic survey provider Hidrocibalae.

AI will have the “greater impact on the global economy than any other industrial revolution, even more than the first industrial revolution”, she said while speaking at the Ocean Business event in Southampton on Wednesday.

Nearly three quarters of the world’s organisations have already “intubated” AI into at least one business function, according to Horvat.

“The question isn’t any more will AI change the world,” she said. “It is how it will change the world.”

AI’s use in its simplest terms in the offshore industry involves the application of computer science that uses methods used by naval industries to analyse data, learn from it, and from experience, and make decisions.

The biggest benefit is that it doesn’t need an existing programme for each and every scenario, as it has the power to analyse data and create a better way to make a decision based on a dataset, Horvat said.

One challenge of using data in the offshore surveying industry is the ability to distinguish between what is a real object in data and what is noise, she warned.

© Jessica Davies/DCT Media

‘Too important to limit’

One application for AI in ocean surveying is to manage workflows yet datasets in the offshore industry are “too important to limit to AI”, Horvat said.

She said the industry should “carefully choose the battles we take on” in AI.

The challenges facing the industry now include expanding the scope of projects and the size of data sets, more specific land requirements, and shorter project deadlines.

She said the industry is under “constant pressure to deliver the data as quickly as possible”.

Advances in AI technology have “drastically reduced the time and man power required for data collection”, she said.

“Great improvements have been made,” in particular around targeting and automating data and conversion of those numbers, according to Horvat.

“We just scratched the surface,” she said. “There [are] still quite a lot of manual and repetitive tasks.”

The gap between processing speed and industry demand is actually currently at capacity, which she warned has implications for the sustainability of the industry.

“If you relieve processors from the manual repetitive tasks, they could actually more focus on the quality of the interpretation on the areas where we actually need the human interaction,” she added.