Week 2: Demystifying AI – Past, Present, and Generative AI

Foundations – The Digital Revolution and AI's Context

Learning Objectives:
  • Gain a foundational understanding of what AI is and its historical evolution.
  • Differentiate between various types of AI and their applications.
  • Understand the growing significance and implications of generative AI.

Chapter 4: Welcome to the world of AI

This chapter defines AI as the capability of a machine to imitate intelligent human behavior. It explains that the recent resurgence of AI is due to a massive increase in data, a dramatic rise in computing power, and significant advancements in machine learning algorithms. It distinguishes between two major forms of AI: predictive and generative. Predictive AI analyzes historical data to forecast future outcomes, while generative AI creates new, original content like images and text by learning from existing data patterns. It highlights that the hype around AI stems from the ease with which these concepts can be applied.

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Chapter 5: The past, present, and future of AI

This chapter provides a historical context for AI, noting its periods of enthusiasm and disillusionment, often called "AI winters". It explains that early AI efforts relied on symbolic logic and expert systems, which struggled with the complexity of the real world. The modern era is defined by machine learning, where systems learn from data, and deep learning, which uses multi-layered neural networks. The chapter cautions against overhyped expectations, noting that AI is often overused as a term and that current AI systems are still far from mimicking human qualities like empathy and morality. It also emphasizes that AI projects, like traditional software projects, require careful management and that failures are possible.

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Chapter 6: The importance of generative AI

This chapter focuses on generative AI, highlighting tools like ChatGPT, Gemini, and Claude. These tools can generate human-like responses and perform knowledge-generating tasks. The chapter notes that generative AI is a disruptive development because it creates new and original content, moving AI from a tool that automates repetitive tasks to a partner in creative work. This has made AI relevant to a wider audience and provides a "wake-up call" about its capabilities. However, the chapter also raises concerns, such as a generative AI's inability to determine what is true or false, and the ethical challenges related to intellectual property and data privacy. It emphasizes the need for leaders to not only understand the capabilities of generative AI but also to establish clear guidelines for its responsible use.

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Quiz
What are some common misconceptions about AI that the course aims to demystify?

Quizzes are short tests or games designed to assess knowledge on a particular topic. They can be educational, entertaining, or both—helping people learn new facts, challenge themselves, and have fun along the way.

Briefly describe the difference between narrow AI (ANI) and general AI (AGI).

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What are two key historical milestones in the development of AI mentioned?

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How has the availability of data and computing power influenced the recent "rise of AI"?

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Define "Generative AI" and provide an example of its application.

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What makes generative AI particularly disruptive compared to previous forms of AI?

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Name two popular generative AI tools mentioned in the introduction.

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What are some potential benefits of AI in service delivery mentioned?

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What are some key concerns that arise with the increasing capability and adoption of AI, beyond just job displacement?

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How doenthe readings suggest we can make progress in understanding the implications of AI?

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Activities for Consideration
  • AI Tool Exploration: Research a generative AI tool (e.g., ChatGPT, Gemini, Midjourney) and experiment with its capabilities relevant to your work. Share your findings with a colleague.
  • Ethical Brainstorm: In a small group, discuss a potential application of generative AI within your industry. Identify and debate at least three ethical implications or risks associated with that application.
  • Current AI Landscape Mapping: Identify where AI is currently being used (or could be used) within your department or organization. Consider both established applications and potential new uses.
Further Reading
  1. "What is AI? Everything You Need to Know" by IBM
  2. "A Brief History of AI: From Thinking Machines to Deep Learning" by Towards Data Science
  3. "What is Generative AI? An Explainer" by Google Cloud
  4. "The Promise and Peril of Generative AI" by Stanford University Human-Centered AI