The main AI development trends that engineers can’t miss in 2024
By Johanna Pingel, Product Marketing Manager, MathWorks AI
As AI is increasingly used in various industries, it will continue to profoundly affect the development and progress of human society and completely change all aspects of technology and human interaction. Forrester predicts that by 2024, enterprise AI initiatives will help increase work efficiency and creative problem-solving capabilities by 50%. AI will have an impact on the work of engineers and educators alike, helping them save time so they can focus on other projects that advance science and engineering.
Three major trends that will drive the continued development of AI in 2024:
AI and simulation are critical to designing and developing engineering systems
As AI becomes mainstream across industries and applications, complex engineering systems that do not use AI will look out of place. Engineered systems bring together components and subsystems from multiple domains to create intelligent systems that can sense and respond to the world around them. For example, wind turbines use a combination of mechanical components (turbine blades and gearbox), electrical components (generator), and control components (blade pitch). The popularity of complex AI systems is mainly due to the greater integration of simulation into the design and development of these systems.
Simulation is a widely proven method for performing the multi-domain modeling and simulation required to develop complex systems. AI can process data from sensors to help develop sensing and autonomous systems. However, as system complexity increases, some simulations can become too computationally expensive for system-level and embedded designs, especially in tests that require running the model in real time. In this case, AI can also enhance the simulation by using reduced-order models.
Reduced order models (ROMs) can speed up simulation while still providing acceptable accuracy for system-level testing of control algorithms. ROM models can complement first-principles models to create variant implementations so that trade-off analysis between accuracy, performance, and complexity can be performed.
More and more engineers are exploring how to integrate AI-based ROM models into systems. This helps accelerate desktop simulations influenced by third-party high-fidelity models, enable hardware-in-the-loop testing by reducing model complexity, or accelerate finite element analysis (FEA) simulations.
AI practitioners must consider the performance of their models when deploying them to edge devices where speed and memory are critical.
For embedded AI, small models are preferred; for computer vision and language models, large models are still preferred
AI models can have millions of parameters and require large amounts of memory to run. In research, accuracy is the primary consideration, but when deploying AI models to hardware, there is a trade-off between memory and accuracy. AI practitioners must consider how model performance will differ when deployed to devices where speed and memory are critical. AI can be added to existing control systems as smaller components without relying on end-to-end AI models, such as those commonly used to detect objects in computer vision.
When discussing smaller AI models, a particularly important topic is incremental learning. Incremental learning is a machine learning method that enables a model to continuously learn by updating its own knowledge in real time as new data becomes available; this is an efficient method for edge deployment.
The success of complex AI systems depends on integrating simulation into the design and development of engineering systems.
GenAI helps engineering professors teach more advanced topics
Generative AI (GenAI) is a disruptive technology. In 2024 and beyond, engineering professors will use this technology on a large scale in the classroom to assist students. Much like the internet or mobile phones, GenAI is creating a revolution that will improve the entire field of engineering education.
The main advantage of using GenAI in the classroom is that it can help save time when teaching basic skills such as computer programming to engineering students. This way, professors no longer have to spend as much time teaching low-level concepts as before and can now focus on teaching advanced topics such as the design and implementation of complex engineering systems. By using technology like ChatGPT to run simulations and create interactive exercises and experiments, professors can save time and better engage students.
Professors can teach students the skills necessary to effectively master GenAI, such as prompt engineering. This helps students develop critical thinking skills that apply what they learn, rather than relying solely on computers to solve problems. As a result, students are best served as independent learners in a variety of engineering disciplines, and engineering educators can further expand the curriculum while sharing expertise in more advanced concepts.
As AI matures, it will play an increasingly clear role in improving the productivity and potential of engineers and educators. When building complex engineering systems, engineers would be wise to use AI-assisted simulation and smaller AI models. In the academic field, generative AI helps educators save energy and make students more independent. With the help of AI, many industries and educational institutions can make smarter decisions, obtain actionable recommendations, and improve efficiency.
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