The Impact of AI Technology on Various Engineering Disciplines
Language: English
Prof. Tolga Yüret
Course Description
One of definition of Artificial Intelligence consists of computer systems exhibiting behavior which otherwise would require human like intelligence. This kind of intelligent behavior calls upon a perception and understanding of the environment along with some general knowledge about what we call common sense which is distillation of past experiences about “how things work” in the most general sense. Aside from this more generalistic view of AI, we also have applications of it to narrower fields dealing with specific problems well defined within their own frameworks, just like the case of engineering.
Engineering uses natural science, mathematics, and a rational design process to solve technical problems pertaining to everyday life. Therefore, it is only natural to think about engineering as a human endeavor that requires human creativity, ingenuity, and intellect; which is in contrast to the mechanistic nature of AI. Furthermore, relatively recently there has been two periods of time (1974–1980 and 1987–2000) which we call AI winter where the progress on AI did stagnate considerably, disheartening the researchers about the future of AI. Luckily today we are in a period of time where the AI reached the highest levels of interest and funding in its history in terms of publications, patent applications, total investment and job openings. The successes of the current “AI spring” or “AI boom” are due to advances in language translation, image recognition, generative AI all fueled by deep neural networks. Indeed engineering also received its share from this AI boom in fields and applications like in design with generative design and intelligent CAD systems, in manufacturing by predictive maintenance and automated quality control, in optimization problems by genetic algorithms, and swarm intelligence techniques. Today we use AI for automatic code generation and testing in software engineering, for synthetic data generation and automatic schema design for data engineering, in design optimization and material use optimization for mechanical engineering, in infrastructure design and resource allocation for civil engineering, for automated circuit design and signal processing in electronics engineering, for material synthesis process optimization in chemical engineering, for automated drug discovery and medical imaging/diagnosis in biomedical engineering, for aircraft design, navigation and fuel efficiency in aerospace engineering, and for waste management optimization and climate modelling for environmental engineering. To summarize we see that today AI is used more than ever to help on engineering tasks in all disciplines on the following classes of roles and functions like for design and optimization, for automation of engineering tasks, for simulation and analysis, for predictive maintenance and fault detection, for autonomous robotics and automation in general, for energy optimization, for risk management.
