2024-02-09 10:30:58
As AI becomes increasingly adopted in many fields, it continues to drive major advancements and revolutionize various aspects of technology and human interactions.
According to the forecasts of Forresterenterprise AI initiatives are expected to increase productivity and creative problem solving by 50% in 2024. AI will impact the work of engineers and educators, saving them time and enabling them to devote himself to other projects that advance science and engineering.
Here are the three major trends that will allow AI to continue its growth in 2024.
AI prediction #1: AI and simulation become essential in the design and development of technical systems
As AI becomes more prevalent in all fields and applications, the few complex technical systems that do not have it will become marginal. Technical systems combine components and subsystems from multiple domains to create intelligent systems that can perceive the world around them and respond accordingly. A wind turbine, for example, combines mechanical (turbine blades and transmission system), electrical (generator) and control (blade pitch control) components. The growing success of complex AI systems is mainly due to the increasingly common integration of simulation into the design and development of these systems.
Simulation is a proven way to perform the multi-domain modeling necessary for the development of complex systems. AI can process data from sensors to contribute to the development of perception systems and autonomous systems. However, as systems become more complex, some simulations may become too computationally intensive for system-level and embedded design, especially for tests that require running a model in real time. In this scenario, AI can also improve simulations using reduced order models.
Reduced Order Models (ROMs) can speed up simulations while providing acceptable accuracy for testing system-level control algorithms. They can complement first-principles-based models, creating variant implementations that enable trade-off analysis between accuracy, performance, and complexity.
More and more engineers are considering integrating AI-based ROM models into their systems. This can help speed up computer simulation driven by a third-party high-fidelity model, enable Hardware-in-the-Loop testing by reducing model complexity, or accelerate Finite Element Analysis (FEA) simulations. ).
AI Prediction #2: Small Models Favor for Embedded AI, While Large Models Will Persist for Computer Vision and Language Models
AI models can have millions of parameters that require a large amount of memory to execute. In research, accuracy is everything, but when it comes to deploying AI models on hardware, the tradeoffs between memory and accuracy come into conflict. AI professionals should consider how the performance of their models will differ when deployed on devices where speed and memory are crucial. It is possible to add AI as a smaller component into existing control systems rather than relying on end-to-end AI models, such as those typically used in Computer Vision to detect objects.
Incremental learning is a particularly relevant topic when dealing with small AI models. Incremental learning is a machine learning approach that allows the model to learn continuously by updating its own knowledge in real time as new data becomes available. This method is considered effective for deployment on edge devices.
AI Prediction #3: Generative AI Helps Engineering Educators Teach More Advanced Topics
Generative AI is a revolutionary technology that engineering professors will use in classrooms to help students at scale in 2024 and beyond. Like the Internet or cell phones, generative AI is launching a revolution that will improve the entire engineering education landscape.
The main benefit of generative AI in the classroom is that it saves time when teaching fundamental skills to engineering students, such as computer programming. By freeing up the time that instructors previously spent introducing basic concepts, they can now focus on teaching advanced topics such as the design and implementation of complex engineering systems. Instructors can save time and better engage students by using technologies like ChatGPT to run simulations and create interactive exercises and labs.
Professors can teach students the skills needed to use generative AI effectively, such as how to design prompts. This will help students develop critical thinking skills that they can leverage instead of relying exclusively on machines to find solutions. As a result, students will be encouraged to practice independent learning in various engineering disciplines, while teachers will be able to further expand their curriculum by imparting their expertise on more advanced concepts.
So, as AI becomes more assertive, its role in improving the productivity and potential of engineers and teachers becomes more and more pronounced. When building complex technical systems, engineers would be wise to use AI-assisted simulation and smaller AI models. Within the academic community, generative AI offers teachers the opportunity to reduce their efforts, while allowing students to increase their autonomy. AI enables smarter decisions, actionable insights, and improved efficiencies in many areas of industry and education.
Johanna Pingel, AI Product Marketing Manager, MathWorks
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