The main development trends of AI that engineers cannot miss in 2024
Time:2024-03-18
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With the increasingly widespread application of AI in various industries, it will continue to profoundly influence the development and progress of human society, and completely change all aspects of technology and human interaction. According to Forrester‘s prediction, by 2024, enterprise AI programs will help increase work efficiency and creative problem-solving abilities by 50%. AI will have an impact on the work of engineers and educators, helping them save time and giving them more energy to focus on advancing other projects in science and engineering.
The success of complex AI systems depends on whether simulation is integrated into the design and development of engineering systems.
The three major trends driving the sustained development of AI in 2024:
AI and simulation are crucial for designing and developing engineering systems
As AI becomes mainstream in various industries and applications, complex engineering systems that do not use AI will become incompatible. Engineering systems integrate components and subsystems from multiple fields, creating intelligent systems that can perceive and respond to the surrounding world. For example, wind turbines combine mechanical components (turbine blades and gearbox), electrical components (generator), and control components (blade pitch). The reason why complex AI systems are popular is mainly because the design and development of these systems incorporate more simulation.
Simulation is a widely validated method used to perform multi domain modeling and simulation required for developing complex systems. AI can process data from sensors to assist in developing perceptual and autonomous systems. However, as system complexity increases, the computational complexity of some simulations may become too high for both system level and embedded design, especially in tests that require real-time model operation. In this case, AI can also enhance simulation by using reduced order models.
The Reduced Order Model (ROM) can provide acceptable accuracy for system level testing of control algorithms while accelerating simulation. The ROM model can complement the first principles model to create variant implementations, allowing for trade-off analysis between accuracy, performance, and complexity.
More and more engineers are exploring how to integrate AI based ROM models into systems. This helps accelerate desktop simulation influenced by third-party high fidelity models, enabling hardware in the loop testing by reducing model complexity, or accelerating finite element analysis (FEA) simulation.
AI practitioners must consider the performance of models when deploying them to edge devices where speed and memory are crucial.
For embedded AI, small models are preferred; For computer vision and language models, large-scale models are still preferred
AI models may have millions of parameters and require a lot of memory to run. In research, accuracy is the primary consideration, but when deploying AI models to hardware, a trade-off needs to be made between memory and accuracy. AI practitioners must consider how their performance will differ when deploying models to devices that are crucial for speed and memory. AI can be added as a smaller component to existing control systems without relying on end-to-end AI models, such as those commonly used for detecting objects in computer vision.
A particularly important topic when discussing smaller AI models is incremental learning. Incremental learning is a machine learning method that enables models to continuously learn by updating their own knowledge in real-time as new data becomes available; This is an efficient edge deployment method.
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 provide assistance to students. Much like the Internet or mobile phones, GenAI is initiating a revolution that will improve the status quo of the entire engineering education field.
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. In this way, the professor no longer needs to spend time teaching low-level concepts like before, but can now focus on teaching advanced topics such as the design and implementation of complex engineering systems. By using technologies such as ChatGPT to run simulations and creating interactive exercises and experiments, professors can save time and enable students to better participate.
Professors can teach students essential skills for effectively mastering GenAI, such as prompt engineering. This helps students cultivate critical thinking skills that apply what they have learned, rather than relying solely on computers to solve problems. Therefore, it is best for students to achieve independent learning in various engineering disciplines, and engineering educators can share professional knowledge in more advanced concepts while further expanding the curriculum.
Conclusion
As AI matures, it will play an increasingly significant role in improving the efficiency and potential of engineers and educators. It is wise for engineers to use AI assisted simulation and smaller AI models when building complex engineering systems. 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 wiser decisions, obtain actionable suggestions, and improve efficiency.
Author: Johanna Pingel, MathWorks AI Product Marketing Manager
About MathWorks
MathWorks is a world leading developer in the field of mathematical computing software. MATLAB from the company is known as the "language of scientists and engineers", which is a programming environment that integrates algorithm development, data analysis, visualization, and numerical calculation. Simulink is a modular modeling environment for simulation and model-based design of multi domain and embedded engineering systems. These products serve engineers and scientists worldwide, helping them accelerate their pace of invention, innovation, and development in various industries such as automotive, aerospace, communications, electronics, industrial automation, and more. MATLAB and Simulink products are fundamental teaching and research tools for many top universities and academic institutions worldwide. MathWorks was founded in 1984 and is headquartered in Natick, Massachusetts, USA. It has 34 branches worldwide and over 6000 employees.
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