Introduction to Applied AI for Professionals
Overview
The Foundations of Applied AI course is an introductory exploration into the world of artificial intelligence (AI), designed for undergraduates with no prior experience in AI. This online, 3-credit course offers a deep dive into AI's core concepts, applications, and the ethical implications of deploying AI technologies across various industries. Through multimedia lectures, case studies, and hands-on projects, students will gain a comprehensive understanding of how AI can transform business, healthcare, and more, while also navigating the ethical considerations vital for responsible AI use.
By the end of the course, students will be equipped to identify AI opportunities, understand AI's potential impacts, and discuss the importance of ethical frameworks in AI development. This course lays the groundwork for a future in AI, preparing students for further specialization or to apply AI insights in their fields.
Course Overview and Weekly Breakdown
Course Overview
Welcome! This course and the resources found here aim to equip professionals with the knowledge and skills to critically analyze, apply, and innovate with the essential principles of artificial intelligence (AI) across various industrial contexts. These authored and curated resources emphasize the design and implementation of ethical AI solutions, ethical management of AI projects, and the advocacy of responsible AI use. This resource is broken down into modules that mirror the course AI 210: Applied AI Foundations offered at St. Thomas University.
Those modules are broken down below:
Module Breakdown
Module 1: Introduction to AI and Its Core Principles
- Module Learning Objective (MLO): Identify fundamental AI concepts and their applications.
- Types of Resources for this Module:
- Videos and resources on AI history, key technologies, and terminologies.
- Introduction to major AI applications in business, healthcare, and other industries.
Module 2: AI Applications in Professional Settings
- Module Learning Objective (MLO): Describe opportunities for AI integration in professional settings.
- Types of Resources for this Module:
- Case studies highlighting AI success stories across different sectors.
- Interactive discussion on potential AI integration in students' industries.
Module 3: Analyzing Real-World AI Implementations
- Module Learning Objective (MLO): Analyze real-world AI implementations across industries.
- Types of Resources for this Module:
- Deep dives into case studies of AI use, focusing on strategy, outcomes, and challenges.
- Group discussions on lessons learned and applicability.
Module 4: Ethical AI Use and Its Importance
- Module Learning Objective (MLO): Outline strategies for ethical AI use.
- Types of Resources for this Module:
- Workshops on ethical considerations in AI, including privacy, fairness, and transparency.
- Exploration of ethical frameworks and guidelines.
Module 5: Designing Ethical AI Solutions
- Module Learning Objective (MLO): Design and implement innovative solutions using generative AI technologies.
- Types of Resources for this Module:
- Project-based learning on designing AI solutions with ethical considerations at the forefront.
- Use of AI tools and platforms for prototyping.
Module 6: Managing and Leading AI Projects
- Module Learning Objective (MLO): Manage AI projects from initiation to completion, focusing on ethical considerations.
- Types of Resources for this Module: Seminars on project management methodologies adapted for AI projects. Leadership and ethical decision-making exercises.
Module 7: AI and the Future of Everything
- Module Learning Objective (MLO): Articulate a plan to promote the ethical use of AI and responsible innovation.
- Types of Resources for this Module: Futuring industries with a focus on the impact of AI and discussions on regulatory requirements and societal impacts.
Module 1: Introduction to AI and Its Core Principles
Articles to read:
Bellini, V., Cascella, M., Cutugno, F., Russo, M., Lanza, R., Compagnone, C., & Bignami, E. G. (2022). Understanding basic principles of Artificial Intelligence: a practical guide for intensivists. Acta bio-medica : Atenei Parmensis, 93(5), e2022297.
Dobrin, S. (2021, September 30). The four keys to trustworthy AI. IBM Blog.
Chojnowska, M. (2023, May 8). The basics of artificial intelligence - Understanding the key concepts and terminology. Sunscrapers.
Videos to Watch:
Introduction to Artificial Intelligence
Core Concepts of AI and Their Technologies
Machine Learning: Understanding the basics of algorithms and models that learn from data.
Deep Learning: Exploring neural networks and their capabilities in processing complex data inputs.
Natural Language Processing: How machines understand and generate human language.
Computer Vision: Enabling computers to see and interpret visual information from the world.
Methodologies of AI
Supervised, Unsupervised, and Reinforcement Learning: Differentiating the learning techniques and their use cases.
Generative Adversarial Networks: Introduction to AI's ability to create.
Transfer Learning: Leveraging pre-existing models for new problems.
Supplemental Resources (Optional)
Module 2: AI Applications in Professional Settings
Articles to Read
Cardona, M. A., Rodriguez, R. J., & Ishmael, K. (2023, May). Artificial Intelligence and the future of teaching and learning: Insights and recommendations. Office of Educational Technology | U.S. Department of Education.
EDUCAUSE. (2023). 2023 Educause horizon report: Teaching and learning eduction.
Elahi, M., Afolaranmi, S.O., & Martinez Lastra, J.L. (2023). A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment. Discov Artif Intell 3(43).
McKinsey & Company. (2023, August 1). The state of AI in 2023: Generative AI’s breakout year.
Takyar, A. (2023, October 3). AI use cases & applications across major industries. LeewayHertz - AI Development Company.
Videos to Watch: Applications of AI Across Industries
Real-world examples of AI Integration - Business:
Real-world examples of AI Integration - Education:
Real-world examples of AI Integration - Healthcare:
Real-world examples of AI Integration - Finance:
Real-world examples of AI Integration - Retail:
Real-world examples of AI Integration - Manufacturing:
Supplemental Resources (Optional)
Aspen Institute. (2024). Following Dirty Money Around the World. Aspen Ideas [Audio Podcast].
Module 3: Analyzing Real-World AI Implementations
Articles to read:
Spielkamp, M. (June 12, 2017). “Inspecting Algorithms for Bias.” MIT Technology Review.
Hao, K. (January 21, 2019). “AI Is Sending People to Jail—and Getting It Wrong.” MIT Technology Review.
Emerging Technology from the arXiv (October 22, 2015). “Why Self-Driving Cars Must Be Programmed to Kill.” MIT Technology Review.
Videos to Watch:
Supplemental Resources (Optional):
Module 4: Ethical AI Use and Its Importance
Articles to Read:
Boothman, B. (2020, December 4). Ethical concerns mount as AI takes bigger decision-making role. Harvard Gazette.
Marr, B. (2021, September 10). How do we use artificial intelligence ethically? Forbes.
Videos to Watch:
Supplemental Resources (Optional)
Aspen Institute. (2024). Hacked medical devices at risk. Aspen Ideas [Audio Podcast].
Module 5: Designing Ethical AI Solutions
Podcasts to Listen to:
Aspen Institute. (2024). How your data powers artificial intelligence. Aspen Ideas [Audio Podcast].
Videos to Watch:
Supplemental Resources (Optional):
Module 6: Managing and Leading AI Projects
Articles to Read:
Atera Team. (2024, March 1). A CIO’s guide to crafting a winning AI strategy in 2024. Atera.
Srivastava, S. (2023, August 24). How to effectively manage AI projects. Appinventiv.
Tang, T. (2021, July). How to manage AI projects effectively. DataCamp.
Videos to Watch:
Supplemental Resources (Optional)
Module 7: AI and the Future of Everything
Articles to Read:
Dutt, D., Ammanath, B., Perricos, C., & Sniderman, B. (2024, January). State of generative AI in the enterprise 2024. Deloitte United States.
Zimmerman, M. (2021). Hands-on AI projects for the classroom: A guide on ethics and AI. ISTE.
Podcast Episodes to Listen To:
Aspen Institute. (2024). Artificial intelligence and the future of everything. Aspen Ideas [Audio Podcast].
Videos to Watch:
Supplemental Resources (Optional):
Snowflake. (2024). Data + AI Predictions 2024.
Aspen Institute. (2024). Rebuilding trust in science. Aspen Ideas [Audio Podcast].
Aspen Institute. (2024). Can robots curb loneliness? Aspen Ideas [Audio Podcast].