The Dartmouth Summer Research Project on Artificial Intelligence was an academic workshop sometimes considered to be the founding moment for artificial intelligence as a field of research. Held for eight weeks in Hanover, New Hampshire in 1956, the conference gathered twenty or so of the brightest minds in computer- and cognitive science for a workshop committed to the idea that:
Thank you for sharing and summarizing this remarkable little episode. Here are two things that struck me:
(1) Once upon a time, it seems, the idea was to *simulate* human intelligence, not to *replicate* or *re-create* it. It seems, then, that theorists used to understand that models are illuminating because they are partial, not because they are exhaustive. I wonder whether theorists today are as keen not to confuse the map with the territory.
(2) By contrast to the judiciousness mentioned above, it's jarring to see the application of some astonishingly unilluminating dichotomies: deduction and induction, rule-guidedness and randomness, reason and intuition. The most paradigmatically human reasoning we humans do is abductive reasoning — broadly and roughly, reasoning to make sense of things. And that kind of reasoning undermines each of these dichotomies by sublating each pole. Erik Larson, who writes over at Colligo here on Substack, has written about this in a wonderful book, The Myth of Artificial Intelligence (Belknap Press, 2022).
John McCarthy was hired at Dartmouth in 1955 by John Kemeny after Kemeny became the chair of the Mathematics department. 29-year-old Kemeny was just a year older than McCarthy. They overlapped at Princeton. Kemeny participated in the 1956 summer project and helped McCarthy obtain a Sloan Foundation Fellowship, which took the latter to MIT. Later in his career, Kemeny co-created the BASIC programming language and served as the president of Dartmouth. Remarkable people!
This remarkable text offers a detailed historical account of early contributions to the field of artificial intelligence (AI). It highlights the significant roles played by pioneers like John McCarthy, Claude Shannon, and Marvin Minsky. The text underscores the collaborative and interdisciplinary nature of AI research during its formative years.
Thank you for sharing and summarizing this remarkable little episode. Here are two things that struck me:
(1) Once upon a time, it seems, the idea was to *simulate* human intelligence, not to *replicate* or *re-create* it. It seems, then, that theorists used to understand that models are illuminating because they are partial, not because they are exhaustive. I wonder whether theorists today are as keen not to confuse the map with the territory.
(2) By contrast to the judiciousness mentioned above, it's jarring to see the application of some astonishingly unilluminating dichotomies: deduction and induction, rule-guidedness and randomness, reason and intuition. The most paradigmatically human reasoning we humans do is abductive reasoning — broadly and roughly, reasoning to make sense of things. And that kind of reasoning undermines each of these dichotomies by sublating each pole. Erik Larson, who writes over at Colligo here on Substack, has written about this in a wonderful book, The Myth of Artificial Intelligence (Belknap Press, 2022).
John McCarthy was hired at Dartmouth in 1955 by John Kemeny after Kemeny became the chair of the Mathematics department. 29-year-old Kemeny was just a year older than McCarthy. They overlapped at Princeton. Kemeny participated in the 1956 summer project and helped McCarthy obtain a Sloan Foundation Fellowship, which took the latter to MIT. Later in his career, Kemeny co-created the BASIC programming language and served as the president of Dartmouth. Remarkable people!
Wonderful writeup, thanks JØRGEN :)
This remarkable text offers a detailed historical account of early contributions to the field of artificial intelligence (AI). It highlights the significant roles played by pioneers like John McCarthy, Claude Shannon, and Marvin Minsky. The text underscores the collaborative and interdisciplinary nature of AI research during its formative years.