Top AI trends for engineers in 2023

The progression of AI from futuristic curiosity to critical enterprise tool is a testament to its value for engineers. Gartner recently predicted that enterprises that adopt AI engineering practices would outperform their peers in operationalising AI models by at least 25%, which adds pressure for organisations to continue pushing their adoption of AI.

Below are four major AI trends engineers can expect to adopt or plan around this year.

Teaching machines about the real world: physics-informed AI

In addition to data-centric AI approaches, model-centric AI approaches are also gaining traction. Most data-centric AI models are trying to optimise to the highest accuracy based on the data received, allowing models to make any conclusions without regard to real-world rules and principles. As AI continues to expand into more and more research areas, such as complex engineered systems, models need to consider physical constraints to be relevant worldwide.

Similarly, Reduced Order Modelling (ROM), using physics-based reduction models, is a new trend gaining traction and providing a lower computational barrier of entry to high-fidelity models that are too computationally intensive to be used for system-level design. Using AI can speed up simulations by replacing a first-principles model of a system, all while preserving the expected fidelity of the system.

Demand for collaboration across AI: Open access to AI will continue to expand

A growing trend is for researchers, engineers and data scientists to leverage the work of each other in the name of innovation. We see the need for more collaboration based on a few trends in engineers’ workflows and responsibilities.

The primary trend driving cross-collaboration is that research is increasingly being done in AI, which creates more urgency for the latest models to be available on demand. A high quantity of high-quality models allows all practitioners to build on the latest research in less time than ever before.

The second trend is a growing reliance on open-source solutions. Models may come from several different frameworks, so engineering teams will want solutions that bridge the gap between their preferred system of choice and the end solution. Enter interoperability between frameworks, allowing AI to be incorporated into more diverse fields of study.

Finally, companies are increasingly working with academia to take advantage of the accelerating pace of research focused on AI for their specific application.

Companies will focus on smaller, more explainable AI models

When engineers and scientists first explore models, accuracy is the primary motivating factor, and other model trade-offs may not be a focus. However, AI practitioners are learning that for models to be relevant, they must be deployed, fit onto hardware, and provide easily explainable decisions.

A rising trend is using traditional machine learning models to meet the requirements of low-cost, low-power devices with explainable output. Parametric models are also an example of ‘old is new again’, as we see a growing number of companies wanting guaranteed results fitting specific formulas and parameters. Traditional machine learning techniques aren’t cutting-edge, but they get the job done in an understandable and repeatable manner.

AI becomes essential in the design, development and operation of state-of-the-art engineered systems

It is unlikely that a breakthrough engineering innovation doesn’t contain AI. AI will continue to impact established fields, including those working with time-series and sensor data. As AI pushes toward the mainstream in all industries and applications, complex engineering systems that don’t contain AI become outliers.

The growing trend of electrification is an example of AI opening doors to even more applications like battery management, virtual sensing and reduced-order modelling. However, engineers working in more established fields that have recently incorporated AI may need a background in the technology. This creates demand for specific reference examples that engineers can use to identify how to incorporate AI into their work with minimal disruption.

The question is no longer if AI will impact businesses, but rather when this will happen and what this will look like for individual organisations. Continued AI adoption has implications across an organisation — from cross-disciplinary collaboration to unique component design — so it’s critical for engineers to identify the use cases that align with their short- and long-term goals and implement them accordingly.

Stephane Marouani is Country Manager ANZ at MathWorks.

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