Episode 20: Let's Do the Time Warp! (to the "Founding" of "Artificial Intelligence"), November 6 2023
Nov 21, 2023
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The hosts time travel back to the founding of artificial intelligence at Dartmouth College in 1956. They explore the grant proposal and debunk AI hype. They discuss machine learning, imagination, and the funding of self-driving cars. They also talk about understanding complex systems and biases in machine translation. Additionally, they touch on hate speech and the closure of an AI smoothie shop. Finally, they mention a failed AI-driven restaurant and a strange AI-developed Coke.
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Quick takeaways
The podcast explores the hype surrounding the origins of artificial intelligence, highlighting the belief that machines can simulate every aspect of learning and intelligence.
The researchers' proposals for the Dartmouth Summer Research Project on AI in 1956 ranged from applying information theory and developing programming languages to investigating abstraction and randomness in AI.
The episode emphasizes the importance of studying the synthesis of brain models and environmental models, suggesting a parallel development approach to understand the relationship between the brain and the environment.
Deep dives
AI hype is as old as the field itself
The podcast episode discusses the origins of artificial intelligence and highlights how AI hype has been present since the field's early days.
The Dartmouth Summer Research Project on AI
The episode explores the grant proposal that funded the Dartmouth Summer Research Project on AI in 1956, emphasizing the hype surrounding the study of thinking machines and the conjecture that every aspect of learning and intelligence can be simulated by a machine.
Proposals for AI research
The podcast delves into the proposals made by researchers for the Dartmouth Summer Research Project, ranging from applying information theory to computing machines, developing programming languages, engaging in machine learning, and investigating abstraction and randomness in AI.
The Importance of Environmental Models and Brain Synthesis
The podcast explores the significance of studying the synthesis of brain models and environmental models. It emphasizes the need to start with simple aspects of the environment and gradually progress to more complex activities. The speaker discusses the idea of mechanized intelligence and the assumption that machines can perform advanced human thought activities such as music composition and playing chess. The proposal suggests parallel development of theoretical environments and corresponding brain models to better understand the relationship between the brain and the environment.
Machine Learning and Training for Desired Behavior
The podcast episode delves into research proposals regarding machine learning and training. One proposal, put forth by Emil Minsky, discusses the design of a machine that can be trained through trial and error to exhibit a range of input-output functions and gold-seeking behavior. The episode emphasizes the importance of allowing machines to abstract sensory material and establish motor abstractions related to changes in the environment. It also explores the idea of machines building abstract models of their environment, leading to external experiments that appear imaginative.
Emily and Alex time travel back to a conference of men who gathered at Dartmouth College in the summer of 1956 to examine problems relating to computation and "thinking machines," an event commonly mythologized as the founding of the field of artificial intelligence. But our crack team of AI hype detectives is on the case with a close reading of the grant proposal that started it all.
This episode was recorder on November 6, 2023. Watch the video version on PeerTube.