Robert Roe interviews Loren Dean, on the use of AI in modelling and simulation

Artificial Intelligence (AI) is surging in popularity and considered a transformative technology that can enhance systems in almost every industry vertical and application. However, AI as a technology is still in its infancy, and so careful steps must be taken to ensure that AI is combined appropriately with the modelling and simulation tools used to build these systems. At this Years Matlab Expo, held in the UK in October, Loren Dean, senior director of engineering for Matlab products at Mathworks, highlighted the importance of creating ‘AI-driven systems’ that require more than just intelligent algorithms. In his keynote presentation, Dean noted the importance of providing insight from domain experts coupled with implementation details, including data preparation, compute-platform selection, modelling and simulation, and automatic code generation to support the integration of the AI component into the final engineered system. If these points are considered carefully, then modelling and simulation users can adopt AI and deploy the technology across embedded systems, edge devices, on-premise IT/OT systems, and cloud platforms.

Can you start by defining AI and what you mean by the phrase ‘AI-driven systems’? Artificial intelligence is the capability of a computer or machine to match or exceed

intelligent human behavior. AI relies on learning algorithms, such as machine learning and deep learning, that are trained to perceive the environment, make decisions, or take actions. AI-driven systems are complex integrations with AI algorithms that enable full or partial automation within complex environments. We see more and more

companies exploring and integrating AI in systems they create. This is especially true in industrial applications in industries like automotive, aeronautics, industrial machinery, oil and gas, and electric utilities. In these cases, AI is being used to automate a process (e.g. defect detection with visual inspection on an assembly line) or to improve a system (e.g. lane detection in an automated driving application. We come across these a lot at MathWorks since we focus on engineering and science in industry.

What is the potential for AI to drive changes in system development with modelling and simulation? What we see going on with AI is that it’s really transforming engineering. It is taking applications and solutions that engineers have traditionally built and it is enabling them to provide more capable systems as well as new offerings that were previously unavailable. We see this transformation occurring across all industries and applications including robotics, industrial automation, medical devices, electrification, automated driving and autonomous systems. It is occurring both in systems that are being

28 Scientific Computing World October/November 2019

deployed and also in services and capabilities that augment these systems, for instance with predictive maintenance applications. There is a lot of interest and

activity going on with AI and it is poised to have a dramatic impact on us, but it is still in its infancy - people are still trying to understand it and how to apply it.

What is required to implement AI? Whether you are talking about deep learning, machine learning, or reinforcement learning - all of these are types of AI modelling algorithms - AI is being applied in interesting ways that were previously unachievable and are now practical to do. If you just focus on AI

”There is a lot of interest and activity with AI – it is poised to have a dramatic impact on us – but it is still in its infancy”

algorithms, you generally don’t succeed. It is more than just developing your intelligent algorithms, and it’s more than just adding AI - you really need to look at it in the context of the broader system being built and how to intelligently improve it. For this you need the knowledge of the domain experts, the people who have designed the systems, know how they are used, and are

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