Engineering Ethics: Rise of the Machines - The Emergence of Artificial Intelligence

In Engineering Ethics: Rise of the Machines - The Emergence of Artificial Intelligence, you'll learn ...

• The historical evolution of artificial intelligence from early computation theory to modern generative models, including key milestones and paradigm shifts
• The conceptual foundations of artificial intelligence, including machine learning, neural networks, and large language models, and how they differ from human cognition
• The significance of the Turing Test, Moore’s Law, and other landmark ideas in shaping how intelligence, capability growth, and technological limits are understood
• The societal, ethical, and existential implications of advanced artificial intelligence, including risks, governance concerns, and future trajectories

Overview

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Credit: 2 PDH

Length: 22 pages

Artificial intelligence is rapidly becoming embedded in the systems, tools, and decision-making processes that engineers design, specify, and oversee. This course provides engineers with a practical, non-programming-focused examination of how artificial intelligence has evolved, how modern AI systems such as machine-learning models and generative tools function, and why their capabilities raise important professional, ethical, and safety considerations for licensed engineers.

Beginning with the historical foundations of computing and AI, the course traces key technological milestones that led to today’s data-driven and autonomous systems. Engineers will gain a clear, conceptual understanding of topics such as neural networks, large language models, and exponential growth in computing power, without requiring advanced mathematics or software expertise.

The course then shifts to the implications of AI in engineering practice, including risk management, system reliability, bias, misuse, and the limits of automated decision-making. Special emphasis is placed on the ethical responsibilities of engineers when deploying or relying on AI-enabled systems that may affect public safety, infrastructure, and society at large.

By framing AI through the lens of professional engineering judgment and responsibility, this course equips engineers to better evaluate emerging technologies, recognize potential risks, and make informed decisions consistent with their obligations to protect the public.

Learning Objectives

Upon completion of this course, participants will be able to:

• Explain the early theoretical foundations of computing and artificial intelligence established by Alan Turing and their relevance to modern AI systems.
• Describe the progression from symbolic, rule-based AI to data-driven machine learning and deep learning approaches.
• Analyze the causes and consequences of AI boom-and-bust cycles, including historical AI winters and their impact on research direction.
• Explain the role of increased computational power, data availability, and algorithms in enabling the resurgence of neural networks and deep learning.
• Identify practical applications of artificial intelligence across industries such as healthcare, transportation, finance, and consumer technology.
• Describe the structure and purpose of the Turing test and its use as a behavioral measure of machine intelligence.
• Evaluate the limitations and criticisms of the Turing test as an indicator of true understanding or consciousness in machines.
• Explain the operating principles of large language models, including the generative, pretrained, and transformer-based components of systems such as ChatGPT.
• Distinguish human reasoning from pattern-based text generation in modern conversational AI systems.
• Explain the implications of Moore’s Law for exponential growth in computing capability and its influence on AI advancement.
• Describe the relevance of Asimov’s Three Laws of Robotics as a conceptual framework for discussing AI ethics and control.
• Identify potential risks associated with advanced AI systems, including misuse, loss of control, and unintended emergent behavior.
• Explain the concept of technological singularity and how accelerating AI development could challenge human oversight and decision-making.
• Analyze how speculative scenarios such as the paperclip maximizer illustrate alignment and value-misalignment problems in AI design.
• Summarize the open questions surrounding AI sentience, self-improvement, and long-term coexistence between humans and intelligent machines.

Certificate of Completion

You will be able to immediately print a certificate of completion after passing a multiple-choice quiz consisting of 20 questions. PDH credits are not awarded until the course is completed and quiz is passed.