John Rachlin

Associate Teaching Professor, Northeastern University

E-mail	j.rachlin@northeastern.edu
Web	https://www.khoury.northeastern.edu/home/rachlin/
Office Hours	TBD on Zoom. By appointment only. Please email me with your availability to arrange other times.

Course Information

Course Description

This course offers an introduction to artificial intelligence (AI) for non-computer science majors, providing the opportunity to explore key AI concepts, applications, and ethical considerations such as fairness, transparency, accountability, and privacy. Students will engage with real-world case studies, discussions, and hands-on exercises to develop a deeper understanding of AI’s strengths, limitations, and societal impact. Topics include AI history, generative AI, and machine learning, with applications in business, science, and the arts. The course will focus on critically assessing AI output and identifying key evaluation criteria for AI-generated content. Students will have the opportunity to use AI to analyze data, explore intelligent decision-support, assess AI risks and benefits, and employ AI as a creative collaborator. No prior programming background is required.
4.000 credit hours

Prerequisites: None

Textbooks and Readings

Required: Melanie Mitchell (2020) Artificial Intelligence: A Guide for Thinking Humans, Picador.
A very nice overview of the history and concepts of AI.

Required: Provost and Fawcett (2013) Data Science for Business:
What you need to Know About Data Mining and Data-Analytic Thinking, O'Reilly Media.
This book goes into much more detail about the key concepts and algorithms of machine learning while remaining accessible to non-CS majors. A useful reference.

Required: Brian Christian (2021)The Alignment Problem: Machine Learning and Human Values, W. W. Norton & Company
Addresses many of the ethical conundrums currently facing society due to the rise of Machine Learning.

Required: Terrence Sejnowski (2018)The Deep Learning Revolution, The MIT Press
Deep learning is the foundation for generative AI. This book present the people, and technological milestones that made it all possible.

Recommended (One short story possibly required): Ted Chiang (2019) Exhalation, Vintage Books.
A highly thought-provoking collection of AI-themed science fiction short stories. I might have you read one short story: The Lifecycle of Software Objects which delves into the societal implications of sentient programs and their legal rights.

Additional readings may be assigned from books freely available to Northeastern students through O'Reilly E-Books or distributed as PDFs.

Class Recordings and Advice for taking a synchronous (live) online class.

Classes will be recorded. Recordings are available through Canvas....Zoom Meetings....Cloud Recordings. Recordings should not be used as a substitute for coming to class. In fact, I do take attendance, and attendance counts towards your final participation grade.

I understand that taking a class online can be a challenge for some students. I will do everything in my power to make the class as personalized and engaging as possible. This course was not designed for 100,000 anonymous strangers. I will tailor the material to your questions and feedback. Group discussions will be an important part of the experience. You can make this class more interesting and fun by asking questions, engaging in discussions and debates, expressing opinions, and having a voice.
Please turn your cameras on as you would in any professional meeting.

Evaluation

The final grade for this course will be weighted as follows:

Homework: 50%
Project: 30%
Participation (attendance, discussions, etc.): 20%

Final grades will be assigned based on the following scale. Computed grades are NOT rounded.

Letter	Range
A	94 - 100
A-	90 - 94
B+	87 - 89
B	83 - 86
B-	80 - 82
C+	77 - 79
C	73 - 76
C-	70 - 72
D+	67 - 69
D	63 - 66
D-	60 - 62
F	<60

Homework Late Policy

My homework late policy is:

Up to 48 hours late: 10% penalty
After 48 hours: Not accepted.

Please email me for special accommodations.

Academic Misconduct

Homework is a creative process. Individuals or pair groups (when allowed) must reach their own understanding of problems and discover paths to their unique solutions. During this time, discussions with friends and colleagues are encouraged—you will do much better in the course, and at Northeastern, if you find people with whom you regularly discuss problems. But those discussions should take place verbally. If you simply copy solutions or large blocks of text from another student you are breaking the rules. Each solution must be largely the product of your own mind. However, each assignment will clearly specify whether using AIs such as ChatGPT is permitted and how they may be used. For most assignments, the appropriate use of AI will be both permitted and encouraged!

The university's academic integrity policy discusses actions regarded as violations and consequences for students:
http://www.northeastern.edu/osccr/academic-integrity

Schedule

Note: This schedule is subject to change and will be adjusted as needed throughout the semester.

Week	Topic
1	Can Machines Think? What constitutes intelligence, and can machines truly think? We examine the philosophical origins of artificial intelligence, tracing its roots from ancient automata through the pivotal 1956 Dartmouth Conference where the field was formally born and the term “Artificial Intelligence” was coined. We introduce several paradigms that have shaped AI's evolution from models of cognitive psychology, rule-based expert systems, and brute-force algorithmic search.
2	The History of AI: From Logic to Learning From George Boole's Laws of thought to perceptrons, neural networks, and generative AI. Key milestones including the Logic Theorist, Expert Systems, IBM Deep Blue, and early chatbots. Moravec's Paradox—tasks trivial for humans (recognizing faces, understanding speech, navigating a crowded room) prove extraordinarily difficult for machines, while activities requiring high-level reasoning (chess, mathematical proofs) can be solved through computational brute force. The evolving definition of AI.
3	How Machines Learn: Early Models Supervised and Unsupervised learning for classifying objects, predicting outcomes, and recognizing patterns in data. We'll cover some of the early (but still widely used) approaches to machine learning including k-Nearest Neighbors (kNN), decision trees, perceptrons, and shallow neural networks. The module emphasizes real-world business applications, exploring how companies use machine learning for customer analytics—from predicting which customers are likely to churn to segmenting markets based on purchasing behavior. Students will investigate how recommendation systems like those powering Netflix, Amazon, and Spotify learn individual preferences to maximize user engagement. The importance of data quality, representation, and ethical considerations in machine learning systems.
4	How Machines Learn: Deep Learning Advanced multi-layer neural networks and their applications including image understanding, speech recognition, stock trading bots, advances in chess and go, and self-driving cars. Issue of explainability and hidden biases will be explored. From optical character readers to facial recognition and self-driving cars. The module raises essential questions about privacy, consent, and the balance between technological capability and civil liberties in an increasingly surveilled society.
5	How Machines Learn: Nature-Inspired Computing Well cover learning techniques inspired by Charles Darwin's theory of evolution by natural selection and discuss how these approaches provide a basis for intelligent decision-support tools used in domains from manufacturing to medicine. We'll cover genetic algorithms which represent solutions as bit strings (sequences of 1's and 0's) and which are subject to biological-like transformations that mimic the way our DNA has evolved over the history of life on Earth. Evolutionary computing techniques are also being used to refine and optimize traditional machine learning models.
6	Talking to Machines: From Eliza to Claude From simple chatbots like ELIZA to Large Language Models (LLMs). How modern systems learn patterns from vast amounts of text data (essentially most of the readable internet) and use transformer architecture with attention mechanisms to predict what word should come next in any given context—a deceptively simple process that somehow gives rise to sophisticated conversational abilities, creative writing, and apparent reasoning skills.
7	Critical Thinking with AI Critical thinking in the age of AI. Type I errors (false positives) and Type II errors (false negatives), and measures of accuracy. Students will examine real-world failures in legal AI systems that have produced discriminatory sentencing recommendations and hiring algorithms. Issues of accountability and transparency. Methods for assessing the quality, accuracy, and reliability of AI-generated content. Students will develop systematic approaches to verification, learning when and how to cross-reference AI outputs with authoritative sources, and understanding the difference between AI that can help with research versus AI that should never be trusted for factual claims.
8	The Alignment Problem How to ensure that algorithmic systems make decisions that align with human values and societal expectations. Through real-world case studies, we'll investigate how well-intentioned AI systems can produce outcomes that violate our sense of fairness and justice, examining controversial applications in hiring algorithms that systematically exclude qualified candidates from underrepresented groups, risk assessment tools used in parole decisions that perpetuate racial disparities in the criminal justice system, and lending algorithms that deny loans based on zip codes or other proxies for protected characteristics. Building fairness into AI systems,
9	From Data to Discovery: Science with AI How artificial intelligence is transforming scientific research by serving as a powerful collaborator in the process of discovery. Creating data-driven dashboards with Claude. Applications in drug discovery, astronomy, and climate change. We'll also consider how AI-driven research tools are changing the nature of scientific collaboration and the skills needed for modern researchers, raising questions about reproducibility, peer review, and the role of human intuition in an increasingly automated research landscape.
10	Privacy in the Age of AI How AI has transformed privacy in the digital age, creating unprecedented capabilities for surveillance, data collection, and behavioral prediction. Students will investigate the vast ecosystem of data collection that surrounds modern life—from smartphones that track our location every few seconds to smart home devices that listen to our conversations, web browsers that record our interests and searches, and social media platforms that analyze our relationships, emotions, and political leanings. We'll examine current privacy regulations like GDPR and CCPA, while questioning whether traditional concepts of privacy and consent are adequate for an age of algorithmic inference.
11	AI and the Changing Nature of Work How artificial intelligence is fundamentally reshaping work across industries, from entry-level positions to senior professional roles. We'll explore changing human resource management practices, from AI-powered resume screening and video interview analysis to algorithmic performance monitoring and predictive employee retention models. Will AI eliminate jobs or create new opportunities?
12	AI and the Law This module explores the complex legal and regulatory landscape surrounding artificial intelligence, examining how lawmakers and courts struggle to govern technologies that evolve faster than legislation can be written. The module delves into pressing copyright battles, where content creators sue AI companies for training models on copyrighted materials without permission, raising fundamental questions about fair use, transformative work, and the economics of creative industries in an AI era.
13	AI as Creative Partner This module explores artificial intelligence's expanding role in creative industries, examining how generative AI is transforming journalism, marketing, entertainment, and artistic expression. We'll examine the collaborative potential of AI as a creative partner rather than replacement, exploring how artists, writers, and designers use AI systems to overcome creative blocks, explore new aesthetic possibilities, and rapidly prototype ideas while maintaining their unique artistic voice.
14	Teaching and Learning with AI This module explores how artificial intelligence is transforming education, from personalized tutoring systems that adapt to individual learning styles to AI-powered educational tools that can provide instant feedback and support. We'll investigate practical strategies that learners can employ to maximize AI's educational value—from crafting effective prompts that elicit clear explanations to using AI as a study partner for generating practice problems, creating study guides, and facilitating self-assessment.
15	Future Minds: Humanity and AI in Partnership This capstone module invites students to explore competing visions of humanity's future alongside artificial intelligence, examining scenarios that range from optimistic collaboration to existential transformation. We'll investigate the concept of technological singularity, where AI potentially surpasses human intelligence and accelerates its own development beyond human comprehension, alongside transhumanist visions of human-AI merger that blur the boundaries between biological and artificial minds. The course concludes by empowering students to envision their own preferred futures and consider their role in shaping the trajectory of human-AI coevolution.

Inclusive Class

Northeastern University values the diversity of our students, staff, and faculty; recognizing the important contribution each makes to our unique community.

Respect is demanded at all times throughout this course. In the classroom, not only is participation required, it is expected that everyone is treated with dignity and respect. We realize everyone comes from a different background with different experiences and abilities. Our knowledge will always be used to better everyone in the class.

We strive to create a learning environment that is welcoming to students of all backgrounds. If you feel unwelcome for any reason, please let me know or reach out to your academic advisor so we can work to make things better.

Northeastern is committed to providing equal access and support to all qualified students through the provision of reasonable accommodations so that each student may fully participate in the learning experience. If you have a disability that requires accommodations, please contact the Disability Resource Center http://www.northeastern.edu/drc/, DRC@northeastern.edu, 617-353-2675. Accommodations cannot be made retroactively and to receive an accommodation, a letter from the DRC or LDP is required.

Society of Minds: AI for Everyone

Time: TBD

Location: TBD