Software specialist INFORM has been named as one of 2018’s most promising providers of artificial intelligence (AI) solutions.
The company, which has been working with AI technology for more than 20 years, started developing knowledge-based systems by testing fuzzy logic and fuzzy reasoning.
Dr. Ulrich Dorndorf, CTO of INFORM, said: “Over the years, we've added Machine Learning as a second area in our AI activities, and the two are now working in parallel together.”
Dr. Eva Savelsberg and Matthew Wittemeier discuss the relationship between humans and technology in a recent Port Technology technical paper
According to INFORM, its “Hybrid AI” approach uses knowledge-driven algorithms, based on mathematical optimization, and data-driven algorithms based on advanced analytics, machine learning and deep learning.
Hybrid AI is designed to give customers the widest range of benefits, as leveraging computer algorithms with human expertise yields superior results.
In addition to this, data-driven AI can harvest large amounts of data to detect hidden patterns and reveal new insights, as well as possessing the ability to learn and improve algorithmic decision-making.
Adrian Weiler, CEO of INFORM, also commented: “We do not believe in AI running wild, we believe that human control on a meta-basis is essential for AI to function properly.
“It is like auto-pilot in an aircraft; it performs its role very well 90 percent of the time but depending on the situation, one can take control back from the autonomous system.
Dr. Eva Savelsberg, Senior Vice President of INFORM’s Logistics Division, added: “We’ve been implementing Cyber-Physical Systems incorporating AI, data analytics, robotics, and human machine interfaces at container terminals around the world for several years now.
“We’re always on the lookout for ways to implement further substantial improvements that better our customer’s bottom lines.”
— Port Technology (PTI) (@PortTechnology) October 16, 2018
A Machine Learning assessment project carried out by INFORM reviewed data from different terminals, finding several areas where improvements could be made.
Among those areas included were dwell time, outbound mode of transport predictions, and predictions around integrated robotics systems.
Savelsberg concluded: “In our assessments, we found that these variables could be improved considerably leading to a noticeable overall improvement in resilience and cost reductions.”