Artificial Intelligence: An AI-Generated Reading List

We asked ChatGPT to write an AI reading list, which seems really lazy, but we wanted to see how well it follows directions. We used this query: “Can you please put together an annotated bibliography of seminal journal articles, open access books, and research papers about AI that are in the JSTOR database?” Here’s what it gave us. As you’ll notice, it interpreted “part of the JSTOR database” rather loosely, which took us hours to sort out. We bet you can do better. Let us know. We’d like to hire a human to do a Reading List that helps those of us with humanities degrees understand how we got here.
“A Logical Calculus of the Ideas Immanent in Nervous Activity,” by Warren S. McCulloch and Walter Pitts (1943)
This paper is often considered the founding document of artificial neural networks. McCulloch and Pitts proposed a mathematical model of the neuron and demonstrated how it could be used to perform logical operations. [Editor’s note: This particular article is not on JSTOR, but here are a few related papers that might be helpful to read. “The First Computational Theory of Mind and Brain: A Close Look at McCulloch and Pitts’s ‘Logical Calculus of Ideas Immanent in Nervous Activity’.” More on Walter Pitts here.]
“Computing Machinery and Intelligence,” by Alan Turing (1950)
This paper is often considered one of the earliest and most important works in the field of AI. Turing proposed the concept of a “universal machine” that could perform any computation that could be done by a human being, and argued that this machine could be used to simulate human intelligence. Turing’s paper proposed what is now known as the “Turing test” for determining whether a machine can exhibit intelligent behavior. [On Turing’s obituary.]
“A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence,” by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon (1956)
This paper is the original proposal for the Dartmouth Conference, which is often considered the birthplace of AI as a field of study. The authors proposed a two-month summer research project that would bring together researchers from a variety of disciplines to study the problem of “making machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”
“The Unreasonable Effectiveness of Mathematics in the Natural Sciences,” by Eugene Wigner (1960)
While not specifically about AI, Wigner’s paper has been influential in shaping thinking about the role of mathematics in scientific discovery. Many AI algorithms are based on mathematical principles, and this paper provides insight into why these principles are so effective.

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