Lecture Series: Philosophy of AI

How should we think about Artificial Intelligence? The rapid evolution and wide availability of text/image generators has sparked a cultural discussion dealing with the realities and possibilities of intelligent machines. Above any policy or even current ethical issues, we have a crisis of meaning: AI raises existential questions about humanity and its place in the world. 

We are philosophically adrift; how do we understand this moment, and where should we go next? 

This series takes a broad, interdisciplinary stance to understand the basics of what AI systems are today, the fundamentals of computing, contemporary issues, and a survey of philosophical concepts closely tied to these questions. From there, we can examine several questions in more depth, such as: 

• What is the nature of the scientific project? 

• How can we make ethical AI? 

• What could sentient machines teach us about consciousness? 

• Can science and technology reach ultimate truths about the universe? 

Objectives

1.     Understand the basic functionality and theory behind Artificial Intelligence systems.

2.     Acquire grounding in fundamental philosophical issues raised by AI

3.     Examine AI’s current / potential impacts on society / the scientific project

Sessions

Myth and Facts About Artificial Intelligence

• What is an intelligent machine?

• What is a Large Language Model?

• The Turing Test

Key Literature: A.M. Turing, “Computing Machinery and Intelligence”

Computation, Logic and Mind

• How do computers “think”?

• What are the limits of reason and mind?

• The Chinese Room

Key Literature: J. Searle, “Minds Brains and Programs,” T. Nagel, “What is it like to be a bat?”

AI and the Economics of Computing

• Economic thinking: what is the value proposition for LLMs?

• Web 2.0 and the “Race to Intimacy”

• The Singularity, job automation and eschatological thinking

Key Literature: C. Doctorow, “What kind of bubble is AI?” T. Chiang, “Why computers won’t make themselves smarter”

The Genetics of Algorithm

• What is “information”?

• Natural Selection and Teleology

• Genetic variation and LLM design

Key Literature: M. Mitchell, “Life and Evolution in Computers”

"But What If We Get It Right?": Ethical Dilemma and Possibility

• Can we make “ethical AI,” or “AI ethically”?

• What are Artificial Intelligence tools good for?

• Who gets to anticipate?

Post-Truth, Post-Theory: Knowledge and Science After AI

• How can we know what’s real?

• Does science need people in a world of learning machines?

• Can we ever find ultimate truths?

Key Literature: H. Jonas, "Towards a Philosophy of Technology"