Advanced Algorithms and AI in Engineering

Each professional engineering regulatory board across Canada has different CPD requirements, and some boards require strictly technical CPD courses related to your discipline. Courses in the area of Business Skills, Firm Management, or Personal Development may not qualify. Check your regulator's criteria to confirm the courses you need to fulfill your provincial requirements.

In Advanced Algorithms and AI in Engineering, you'll learn ...

• How AI and advanced algorithms differ from traditional programming – including the concepts of learning, randomness, and statistical reasoning behind AI systems
• The practical application of metaheuristic algorithms – such as simulated annealing and genetic algorithms – for efficiently solving complex engineering design problems.
• The structure and function of neural networks – how they are modeled after the human brain, how they are trained, and how they can be used for predictive design and decision-making.
• The benefits and limitations of AI in engineering – including computational advantages, the importance of human oversight, data security concerns, and ethical considerations in professional practice.

Overview

Many aspects of work are becoming increasingly automated. These advances are also finding their way into the engineering realm. The most recent iteration of these changes is often referred to as AI, but there are also other advanced algorithms that can be used to solve large problems.

This course discusses several advanced solution algorithms, what sets AI apart from previous generations of automation, a little about how they work, and opportunities and concerns in using these computational techniques in engineering. Unlike traditional software, which follows hard-coded logic and produces the same output for identical inputs, AI systems use statistical learning to generate adaptive, sometimes non-repetitive solutions. The course highlights the differences between AI and conventional automation, emphasizing AI's capacity to "learn" from data and improve over time.

Metaheuristic algorithms, such as simulated annealing and genetic algorithms, are introduced as effective techniques for navigating vast solution spaces where exhaustive searches are impractical. These algorithms find "good enough" solutions efficiently, making them suitable for tasks like optimizing structural designs or label placements in drawings.

The course also examines the use of cloud computing and multi-threading to accelerate complex computations. Neural networks are discussed in depth, illustrating how they mimic the human brain to perform tasks such as estimating design times based on component parameters.

Finally, the course addresses critical concerns with AI, including result accuracy, data security, and the need for engineering judgment. While AI can enhance productivity, professional engineers remain responsible for verifying results and ensuring public safety.

This course does not require deep programming knowledge or statistical skills. It is an introduction and overview of algorithms that are finding their way out of the labs and grad schools and into our offices.

Specific Knowledge or Skill Obtained

This course teaches the following specific knowledge and skills:

• The differences between traditional programming logic and AI-based computational methods
• The principles and applications of metaheuristic algorithms such as simulated annealing and genetic algorithms
• How AI models like neural networks function and how they are trained using input/output data sets
• How to apply objective functions to evaluate and guide the performance of optimization algorithms
• The role of randomness and statistical inference in AI and metaheuristic problem-solving
• How cloud computing and multi-threading enhance computational speed and efficiency in engineering tasks
• The limitations and potential errors (e.g., hallucinations) of AI-generated solutions.
• Ethical and professional considerations in using AI, including data privacy and engineer accountability
• When and how to use constraints effectively to reduce solution space in complex design problems
• The strengths and appropriate use cases for AI and advanced algorithms in engineering workflows

Certificate of Completion

You will be able to immediately print a certificate of completion after passing a multiple-choice quiz consisting of 15 questions. CPD credits are not awarded until the course is completed and quiz is passed.