By Dr. Piyush Kumar, Professor and Head-AIML and IPR Cell, Nitte Meenakshi Institute of Technology Bengaluru, India
Possessing the knowledge of Artificial Intelligence (AI) and Machine Learning (ML) has become important in engineering education today. There are multiple justifications for incorporating AI and ML as important concepts in the engineering curriculum.
AI and ML are being increasingly used across industries such as manufacturing, finance, healthcare, and security among others. A high demand now exists for engineers who have the knowledge and understanding of AI and ML. These two fields are needed to create and implement new-age intelligent systems. The integration of AI and ML into engineering education can prepare students for the changing job market.
AI and ML lead to the automation and complete optimisation of engineering procedures. Professionals can design and create algorithms and models that can enhance productivity, efficiency and reduce expenses. When students become conversant with AI and ML, they begin to equip themselves with the necessary skills that are required to deal with real-life engineering challenges.
The reliance and importance of data are only increasing across the world. The International Data Corporation (IDC) predicts that by 2030, global data will grow to 175 zettabytes. And to handle this huge database, there is a demand for tools such as AI and ML. Not just that, it is becoming very important for professionals to make data-driven decisions and therefore, the demand for engineers with data analysis skills is constantly rising. By learning AI and ML, students gain the essential data-centric perspective that lends them an edge in making proficient decisions.
AI and ML are not only important tools in themselves, but they also constitute the fundamental components of intelligent systems and robotics. It is, therefore, very necessary for students to understand the underlying principles and algorithms that govern the functioning of these technologies. Integrating AI and ML with the engineering curriculum can open newer opportunities for students who wish to explore various fields such as robotics, control systems and computer vision.
Not only technologies but AI and ML also encompass a wide range of disciples. The field of AI and ML intersects with various other academic disciplines, including computer science, mathematics, statistics, and other branches of engineering. The integration of AI and ML with engineering education provides students an opportunity to engage in cross-disciplinary collaboration and promotes a more holistic view of the engineering field. The interrelated approach also leads to innovative ideas and approaches to solving problems.
The implementation of AI and ML presents some ethical and social implications that need careful consideration. Incorporating these subject matters into engineering curricula guarantees that students understand the potential ramifications and obligations linked to artificial intelligence technologies. Engineering courses can play a significant role in fostering a workforce that values ethical considerations in technological advancements by advocating for responsible AI development.
AI and ML will have a substantial impact on the evolution of engineering and technology. The incorporation of these concepts into the academic curriculum can serve as a means to safeguard the engineering programs of educational institutions against future uncertainties and prepare students with the necessary proficiencies to excel in a world that is increasingly influenced by artificial intelligence.
But it is also important to acknowledge that this integration calls for checks and balances. There should be a balance between the core subjects and the emerging technologies. There should also be an equilibrium between theory, practicals, and ethics.