Artificial Intelligence – Past, Present, and Future

Moor, J. H. (2006). The Dartmouth College Artificial Intelligence Conference: The Next Fifty Years. AI Magazine, 27, 87-91.


This three-unit micro course is one of a series in the Carnegie Mellon University Africa online certificate program in Artificial Intelligence and Machine Learning.

Course Description  |  Learning Objectives  |  Outcomes  |  Lecture Notes  |  Recommended Reading  | 

Course Description

This micro course provides a foundation for other micro courses, presenting a comprehensive introduction to field of artificial intelligence (AI) and the role that machine learning plays in AI. It exposes the technical foundations and the impact that AI and machine learning have had on people's lives as the twin engines that power the fourth industrial revolution. It explains the scope of AI: how it has developed over the past sixty-five years and how it is likely to develop in the future, the way it has been applied in several application sectors, its impact on society, and its relevance to Africa. The micro course requires approximately forty-two hours of study. It comprises fourteen hours of content, i.e., the amount that would normally take fourteen hours to deliver in a classroom setting. Each hour of content is provided in course notes and summarized in a 10-12 minute video presentation. Students will spend the remaining time studying the material covered in the video lecture, encapsulated in the video lecture slides, course notes, and auxiliary reading material, and periodically taking self-check quizzes comprising three to five multiple choice questions. Student progress is assessed by a series of multiple-choice tests.

Learning Objectives

Students will learn about the different components that make up the disciplines of artificial intelligence and machine learning, how they have evolved, where they stand today, and the challenges they face. They will learn about the roots of AI and machine learning in cybernetics, connectionism, and symbolic knowledge-based computationalism and how these approaches have shaped the field today. Students will learn how recent developments in deep learning have revolutionized the field. They will be introduced to modern probabilistic machine learning and inference, and to contemporary approaches to knowledge representation and reasoning. They will explore the many ways that AI and machine learning are being applied in several sectors of the economy, powering the fourth industrial revolution. Students will be exposed to the ethical and social impact of AI and machine learning and the need to democratize these fields. Finally, they will be exposed to the grand challenges facing AI and machine learning and the imperative to make AI and machine learning explainable and trustworthy.


After completing this course, students should be able to:

  1. Explain how AI evolved from both cybernetics and computationalism.

  2. Distinguish between connectionist machine learning and symbolic artificial intelligence..

  3. Identify the limitations of perceptrons and explain how the back-propagation revitalized interest in them.

  4. Identify the breakthroughs that were the basis of the power of modern deep neural networks.

  5. Compare and contrast knowledge-based reasoning and probabilistic machine learning & inference.

  6. Explain what is meant by structural inference and distinguish it from other forms of inference..

  7. Explain the difference between supervised, unsupervised, self-supervised, and reinforcement learning.

  8. Identify example applications of AI and machine learning in medicine, robotics, social media, the web, and sports.

  9. Identify the technical, societal, and ethical challenges that must be overcome if AI and machine learning is to achieve its full potential to improve the lives of people across the world.

Lecture Notes

Module 1: What is AI, where did it come from, and where is it taking us?
Lecture 1: AI and The Fourth Industrial Revolution and commentary.
Lecture 2: The Early Years of AI and commentary.
Lecture 3: The End of the AI Winter and commentary.

Module 2:The Nature of AI
Lecture 1: Symbolic AI and GOFAI and commentary.
Lecture 2: Connectionist AI: From Perceptrons to Deep Neural Networks and commentary.
Lecture 3: Statistical Machine Learning and commentary.

Module 3: Example Applications
Lecture 1: AI applications in Medicine and commentary.
Lecture 2: AI applications in Robotics and commentary.
Lecture 3: AI applications for the Web and Social Media and commentary.
Lecture 4: AI applications in Sports and commentary.

Module 4: Future Challenges
Lecture 1: Collaborating with Machines and Robots and commentary.
Lecture 2: Self-learning and Self-programming Machines and commentary.
Lecture 3: Social and Ethical Aspects of AI and commentary.
Lecture 4: Intelligence, Brains, and Consciousness and commentary.

Recommended Reading

Cangelosi, A. and Vernon, D. (2022). "Artificial Intelligence: Powering the Fourth Industrial Revolution", in EPS Grand Challenges: Physics for Society at the Horizon 2050, coordinated by the European Physical Society.

Timeline of the major developments in connectionism & artificial neural networks (from Module 2, Lecture 2: AIML01-02-02).

David Vernon's Personal Website