4.1 - Computation
 Prof. Jack Reilly
 S2026

🧠 Think:
•	How do computers work? Can they ”simulate” minds? Or can they be minds? Practically, what goes into making a computer, and how do technological constraints structure the nature of AI itself?
📺 Watch:
•	Veritasium, “The World’s Most Important Machine”
•	CGP Grey, “How Machines Learn” (Note: this video has been retitled “How AI, like ChatGPT, learns”)
–	and “How Machines Really Learn” New Title: How AI, like ChatGPT, really learns
•	3Blue1Brown, “Large Language Models explained briefly”
–	recommended, but not required: “Transformers explained visually”
🎧 Listen:
•	Complexity Podcast Episode 3: What Kind of Intelligence is an LLM?
🌐 Browse:
•	Hicks et al, “ChatGPT is Bullshit” Ethics and Information Technology
•	Wolfram, S. “What is ChatGPT doing?”
•	A few items on evaluating AI Intelligence:
–	Humanity’s Last Exam: website and article: Phan et al, Humanity’s Last Exam
–	Ullman, “Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks”
–	Mitchell, “How do we know how smart AI systems are?”
📚 Additional Resources:
•	Complexity Podcast, Episode 2: The Relationship Between Language and Thought. We (regrettably) won’t get to this episode of the Complexity podcast in our class, but this is a good time to listen to it if you have time.
•	Only a few videos from 3Blue1Brown’s series on Neural Networks are assigned above, but the entire sequence is worth watching for a non-technical overview: https://www.3blue1brown.com/topics/neural-networks
•	Witt, Stephen. The Thinking Machine: Jensen Huang, Nvidia, and the World’s Most Coveted Microchip is worth reading in its entirety, but in particular, chapter 6, “Jellyfish” is an excellent non-technical overview of neural nets
•	Similarly, the Crash Course: Artificial Intelligence series provides a high level overview of many current AI principles
•	There is also a Crash Course: Computer Science for more about computers themselves
•	Finally, if you are already statistically inclined, the final episodes of Crash Course: Statistics also cover elements of machine learning and big data analysis building off of core statistical concepts
•	Rumelhart, “The Architecture of the Mind: A Connectionist Approach” Mind Design III
•	Churchland and Sejnowski, “The Computational Brain” Mind Design III
•	Cowie and Woodard, “The Mind is Not (Just) a System of Modules Shaped (Just) by Natural Selection” Mind Design III
•	Vaswani et al, “Attention Is All You Need” (Google paper)
Tip
•	“📖 Read”, “🎧 Listen”, and/or “📺 Watch” items are required content for the day, and should be read/heard/watched before class on that day.
•	“🌐 Browse” items should be briefly looked at but do not need to be read deeply unless you want to
•	“📚 Additional Resources” do not need to be looked at; they are there to serve, if useful, as further references for your debates, final projects, and general edification later.