In this module, students learn several components embedded within the broad AI domain; they will also understand (a) several types of machine learning (supervised, unsupervised, reinforcement and deep learning); (b) types of Artificial Neural Networks; (c) System1/System 2 thinking, legal issues in AI/ML and problems in aligning machine and human goals in AI/ML applications.

In this module, students will learn about contributions to AI progress from (a) fully-evolved and midstream (and still evolving) technologies; (b) midstream and still-evolving technologies, as well as emergent technologies, and (c) insights from Kurzweil’s Law of Accelerating Returns to learn how the creative integration of multiple technologies over time accelerates AI progress.

The focus of this module is on the abilities of AI that are assessed in the context of what we know about human abilities; students will learn about human-AI collaboration, understand key advantages and disadvantages associated with AI. Additionally, students will be exposed to a variety of AI/ML use cases (or application examples in the business context); this will help increase their familiarity with AI/ML deployment across several industries, and companies within an industry.

In this module, we assess AI’s impact from two opposing perspectives: first, students will learn the very impressive productivity gains expected from AI for the foreseeable future along with the corresponding rise in AI investments/infrastructure and GDP growth; second, predictions of dramatic job losses from AI/ML adoption that unfortunately presents a sobering view. Finally, students will assess the challenges associated with modeling human judgment with machine learning, explore the implications of automation and the AI Chasm.

This module focuses on comparisons and contrasts at multiple levels; for example, at the company level, focusing on company-specific AI strategies may generate insights on successful approaches to leverage the company’s strengths. Similarly, focusing on nations sensitizes students to regional/cultural/political forces shaping the adoption and deployment of AI; an industry specific focus may generate many use cases that students can learn from; and finally, focusing on specific business functions within a company may be an thoughtful exercise to tightly integrate AI deployment within a company across its business functions. The discussion in this module emphasizes many AI use cases.

This module focuses on areas within the AI industry that are growing fast because of their very promising potential for aiding new discoveries and new use cases. Students will learn about the history of Generative AI, market size and growth rate, exciting avenues for potential innovations in Generative AI applications. In addition, students will explore the concept of Explainable AI as a potential tool to overcome inherent limitations underlying AI/ML predictions and recommendations i.e., the lack of explanations or rationales underlying those predictions and recommendations.

Students will understand key elements of two important concepts in AI practice: AI Ethics and Responsible AI. Students will be able to describe the basics of AI Ethics and Anthropomorphism; they will learn about moral/ethical dilemmas or bias issues that may confront AI systems or devices; within the broad realm of Responsible AI, students will develop an understanding of fairness, transparency, accountability and safety concepts. Finally, given the emergent and current regulatory framework for AI at the global level, students will learn about responsible AI practices in the context of managing Data, Privacy and Compliance issues.

This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course.