Artificial Intelligence: AI Generated Reading Lists

We asked ChatGPT to create an AI reading list. This seems very lazy, but I wanted to see how well the AI ​​follows instructions. We used the following query. “Could you compile the original journal articles, open access books, and annotated references of research papers on AI in the JSTOR database?” As you may have noticed, “part of the JSTOR database” is a pretty loose interpretation, and it took me hours to sort it out. I’m sure you can do better. tell us We want to hire people to create reading lists that help people with humanities degrees understand how they got here.

“The Logical Computation of Ideas Inherent in Neural Activity” by Warren S. McCulloch and Walter Pitts (1943)

This paper is considered the founding document of artificial neural networks. McCulloch and Pitts proposed a mathematical model of a 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.]

This paper is considered one of the earliest and most important studies in the AI ​​field. Turing proposed the concept of a “universal machine” that could perform any computation a human could do, and argued that this machine could be used to simulate human intelligence. Turing’s paper proposed what is now known as the “Turing test” to determine whether a machine can exhibit intelligent behavior. [On Turing’s obituary.]

This paper is the original draft of the Dartmouth Conference, considered the birthplace of AI as a research field. The authors gather researchers from various fields to solve the problem of “making machines use language, form abstractions and concepts, solve problems that only humans currently have, and improve machines themselves.” proposed a two-month summer research project to study

“The Irrational Effects of Mathematics in the Natural Sciences” by Eugene Wigner (1960)

Although not AI-specific, Wigner’s paper is 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. [This particular paper is not on JSTOR, but many mathematicians and scientists have engaged with it since it first appeared.]

perceptronby Marvin Minsky and Seymour Papert (1969)

This book is a seminal work in the field of neural networks, a key component of modern AI systems. The authors perceptron, a type of neural network that can be trained to recognize patterns in data, explored the limitations of this approach. Minsky and Papert argued that interest in neural networks has declined for decades because these networks are too restrictive to perform many useful tasks.

“Some useful things to know about machine learning” by Pedro Domingos (2012)

This paper provides a concise overview of key concepts in machine learning, including overfitting, bias-variance tradeoffs, and ensemble methods. It has been widely cited and is considered a useful reference for those working in this field.

In this paper, we introduced the concept of generative adversarial networks (GANs), a type of neural network architecture that can generate new data samples similar to a given dataset. The authors describe the theoretical foundations of his GANs and give some practical examples of their use. GANs are used in a wide range of applications, such as generating images and videos, creating deepfakes, and more.

What am I missing? please let me know. I need your version of this reading list.