ECS 011: Artificial Intelligence for All

Summary of Course Content:

• Introduction to AI
  • What is AI?
  • The power of AI and how is AI is transforming our everyday life
  • AI and Creativity
  • Emerging applications of AI in Natural Language Processing, Healthcare, CyberSecurity, Business, Self-Driving Vehicles
• A Brief History of AI
  • Birth of AI (1940-1950)
  • Rule-Based Systems (1960)
  • Rise of Machine Learning
  • Deep Learning Revolution (2010 – now)
• AI Paradigms
  • Symbolic Systems
  • Connectionism
  • Embodied AI
  • Search and Heuristics
  • Task-based vs. Generalization-based AI
• Generative AI systems
  • ChatGPT
  • DALL-E (or another image model)
  • CodeLlama
  • Babble
  • Generative Adversarial Network
  • MuseNet
  • Demos of cutting-edge AI systems - what they are good at and some common failure modes
  • Intended to give students tools that will be useful now and in the near term
• AI and Data Science
  • AI needs data
  • Understanding Data and Knowledge Discovery in the context of AI and Data Science
  • Different types of data (Structured, Unstructured, Semi-structured)
  • Example of simple data analysis
• Public Policy Integration
  • Guest speakers from policy-making backgrounds can be invited to provide insights into how AI findings in natural sciences shape regulations and guidelines.
• AI Concepts and Terminology
  • AI’s Learning Process
  • Cognitive Computing, Terminology and Related Concepts
  • Traditional Machine Learning Algorithms
  • Evaluation Criteria
  • Reinforcement Learning
  • Neural Networks
  • Deep Learning
  • Transfer Learning
  • Generative Models
  • Adversarial Models
• AI in Practice
  • Using Black Box Models in Modern AI
  • Understanding Black-Box ML Models
  • Understanding Data to Train and Test Models
  • An Example of Training a Prediction Model
  • Supervised Learning
  • Unsupervised Learning
  • Evaluating  Models, Cost Functions and Metrics
• AI Benefits
  • Safer technologies
  • More desirable work
  • Boosted creativity and personal agency
  • Accelerated scientific development
• AI Risks and Ethical Considerations
  • The importance of data in AI.
  • Examples of biases and ethical issues in modern AI
  • AI and Data Privacy
  • Risks of strong artificial intelligence
  • AI Ethics and Regulations
• The Future with AI
  • The Evolution and Future of AI
  • What will our Society look like when AI is everywhere?
  • The AI Ladder - The Journey for adopting AI successfully
  • Artificial General Intelligence (AGI)

Illustrative Reading:

• Brian Christian, “The Alignment Problem: Machine Learning and Human Values,” W. W. Norton & Company; 1st edition (October 6, 2020), ISBN 978-0393635829
• Melanie Mitchell, “Artificial Intelligence: A Guide for Thinking Humans,” Farrar, Straus and Giroux; First Edition (October 15, 2019), ISBN 978-0374257835
• Stuart Russel, “Human Compatible: Artificial Intelligence and the Problem of Control,” Viking (October 8, 2019), ISBN 978-0525558613
• Max Tegmark, “Life 3.0: Being Human in the Age of Artificial Intelligence,” PENGUIN UK (July 18, 2018), ISBN 978-0141981802
• Tom Chivers, “The AI Does Not Hate You,” Orion Publishing Group (June 13, 2019), ISBN 978-1474608787
• Other articles and self-reading material

GE3
Science & Engineering
Scientific Literacy

Potential Course Overlap:
Course overlaps ECS 111 and ECS 170 in the coverage of AI basics; however, both ECS 111 and ECS 170 cover in much greater detail the application and/or implementation of AI. Course overlaps with ECS 171, EEC 174AY, EEC 174BY, EEC 179, and BIM 155 in the coverage of ML basics; however, the other courses cover in much greater detail the application and/or implementation of ML. Students completing this course will have a foundational understanding of AI/ML, but will not be yet equipped to apply its use or implement it as students that have completed any of the mentioned overlap courses.