Ethical Focus: How to Effectively Integrate AI in Engineering
AI is rapidly transforming engineering, offering new ways to optimize processes, boost productivity, and cut costs. Navigating this dynamic field can be challenging. The Institution of Mechanical Engineers (IMechE) is addressing this need with its ‘AI for Engineers’ course, designed to provide a practical understanding of AI.
The course, running in London from April 14-15, is led by consultant and AI specialist Ali Parandeh. He offers five key tips for integrating AI effectively into engineering workflows.
Overcoming Adoption Challenges
Many organizations are eager to adopt AI, driven by the successes of competitors. However, adoption can be hindered by a lack of quality data, skilled personnel, or a clear business case. Engineers should start by improving data collection and preparation strategies, developing fundamental AI skills, and identifying AI applications with measurable returns. Addressing these challenges is crucial for successfully implementing AI technologies in daily workflows.
Grasping AI Fundamentals
The field of AI evolves rapidly. Yet, the basic principles—data preprocessing, model training, and evaluation—remain constant. Engineers who understand these fundamentals will be better positioned to apply AI effectively across different industries. Different types of AI exist, each with its own applications. Understanding these different types gives engineers a framework for brainstorming ideas.
Utilizing Accessible Tools
Programming skills are not a prerequisite for using AI. Platforms such as Microsoft Azure Machine Learning Studio and Azure AI Foundry allow engineers to build AI models and chatbots without writing code. These tools come with guides that can be followed by those with a basic understanding of AI concepts such as what makes a good model, how to evaluate it, and understanding its limitations. These platforms allow engineers to implement AI rapidly.
Train on Python
While no-code solutions are useful, Python is the defining AI development language. Familiarity with languages such as MATLAB reduces the learning curve for Python. Python proficiency gives engineers access to a wide variety of AI tools and models, from TensorFlow and PyTorch to Scikit-learn, enabling customization and control of AI solutions.
Prioritizing Ethical Considerations
Ethical issues, including bias, fairness, and transparency, must be considered throughout the AI implementation process. Engineers should integrate ethical frameworks into their projects from the start.
IMechE’s AI for Engineers course next runs in London from 14-15 April.
