How should you embrace AI and machine learning in an engineering company?
Arne Matthyssen, RHEA Group Chief Technology and Innovation Officer and Vice-President Benelux, believes that companies cannot and should not ignore AI, because it allows them to serve their customers better, as well as driving innovation. Here he explains how we have done this successfully at RHEA.
Contrary to what you might think from recent media coverage, artificial intelligence (AI) and machine learning (ML) have been around for decades. I remember two decades ago, a friend of mine created an application using genetic algorithms to guide us when buying and selling shares on the stock market, without the need for any historical knowledge of the companies we were trading in.
Over the last 20 years, AI and ML have evolved and taken many shapes and forms. Together, they are applied in many tools and systems that we use on a daily basis: some are more apparent, e.g. ChatGPT; others are less obvious, such as the AI in your washing machine.
Within my responsibility as CTO of a system and security engineering company, we have identified AI and ML as among the most transversal competences that we have in the company, as they touch on most of our engineering skills, they fit in most of our offerings and together they are a key capability that enables us to respond to the high-level needs of our customers. (If you want to know how our markets’ needs have changed over time, have a look at my previous article.)
How did we get buy-in from our engineers?
For some, we had to break through the perception that ‘AI and ML’ was a new and separate offering. With others, we had to convince them about the benefits they would provide to them and their teams. The escalating and very concrete interest from our markets, and AI’s inclusion in the R&D programmes of organizations such as the European Space Agency (ESA), NATO and the European Commission (EC), provided external proof of the potential benefits.
Our Earth observation (EO) data processing teams have been using AI for many years specifically for EO data processing applications. For a few years now, we have been conveying the message of what AI and ML can also bring to our other engineering capabilities and offerings.
Mainly the story was, AI and ML can:
- Automate and increase autonomy, decision-making and decision intelligence
- Recognize patterns and enhance data quality
- Widen the solution space and generate solutions.
Together with our AI and ML Competence Area Lead, Andrea Cavallini, we mapped these three areas of AI and ML capabilities on to the key functions used in RHEA’s different application domain offerings, mainly data analysis, digital engineering, ground segment systems and cybersecurity.
Our AI and ML competence community works closely with universities and knowledge centres, through which they get insights into and access to state-of-the-art algorithms. These are then customized to fulfil the specific needs of the various domains internally. Our AI and ML experts collaborate closely with our application domain experts to understand the context and the exact needs and behaviours required when introducing AI and ML.
AI and ML – major enablers for progress and innovation
When it became clear that AI and ML are enablers and can have significant benefits, both for end-users/clients and for our development and system engineering teams, then embracing them was never in any doubt.
In less than a year, this has resulted in portfolio development projects in which AI and ML are used for:
- Enhancing automation and autonomy in our space ground segment and operations solutions
- Automation, pattern recognition and decision-making in our cybersecurity offerings
- Pattern recognition in, and quality enhancement of, EO images
- Wider design solution spaces being explored and generated in the frame of our model-based system engineering activities.
Another field of expertise that has our interest is where engineering is heavily influenced by regulation; for example, for our Cybersecurity division we are looking into the use of AI and ML to automatically assess if a specific software or hardware design/development fulfils the requirements of the new European Union NIS2 (Network and Information Security) directive. We will use it to do a dynamic risk assessment and then advise on how to make the development compliant with the rules and regulations.
In addition, we have placed the following AI and ML applications on our radar: prognostics and forecasting systems and supportive diagnostics. And we are devoting a lot of our attention to trustworthy and regulated AI.
We align our AI and ML roadmaps with our markets’ and engineering needs. As such, we ensure it is an added value enabler for our solutions to match the requirements of our customers to have increased autonomy and decision intelligence in a world with more data and more data sources to operate, manage and understand.
Andrea Cavallini, RHEA Group’s Competence Area Lead for AI and ML, Data Analysis and EO.
The task of internally selling AI and ML to our engineers is done. And it was not a difficult one, once we had identified the specific aspects of AI and ML from which they and their solutions and applications could benefit!
Find out more
Find out more about our activities around AI and ML.
Or contact Arne Matthyssen via our Contact Us page.