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Martin Luther King and the Ghost in the Machine

Martin Luther King and the Ghost in the Machine

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Martin Luther King and the Ghost in the Machine

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  1. Martin Luther King and the Ghost in the Machine Kalamazoo College 2003 MLK Week Teach-in

  2. The birth of the modern US Civil Rights Movement • Dec. 1 1955: Rosa Parks refuses to move to the back of the bus. • Bus boycott organized; King elected president of Montgomery Improvement Association. • Nov. 13, 1956: US Supreme Court declares race segregation illegal; boycott ends, and full, integrated service restored. • King achieves national prominence as civil rights leader.

  3. The birth of artificial intelligence research • Summer, 1956: First Darthmouth Conference on AI:“We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire.” • First common use of the term “artificial intelligence • http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html

  4. 1956 Dartmouth AI Conference Attendees

  5. Lost opportunties • The Civil Rights movement and AI research started at the same time, • They never talked to each other. • What opportunities were lost as a result?

  6. King on Technology • Warns against technology as “Moloch” • Worried about “automation” leading to people being thrown out of work. • Worried about destructive power of violence.

  7. Opportunities to be of (peaceful) service to the community • Majority of AI research has always been funded by the military. • But AI problems are everywhere (although not always fundable). • The opportunity to found an non-racist, inclusive science.

  8. King on technology Automation can be used to generate an abundance of wealth for people…. Our society, with its ability to perform miracles with machinery has the capacity to make miracles for men--if it values men as highly as it values machines (From “If the Negro wins, Labor wins.”) Through our scientific and technological genius, we have made of this world a neighborhood and yet we have not had the ethical commitment to make of it a brotherhood. (From “Remaining awake through a great revolution”)

  9. “The Ghost in the Machine” • Gilbert Ryle description of Cartesian dualism • Body is different from Mind/Soul, etc. (see your philosophy teacher…)

  10. Teaching Whiteness • Teaching Whiteness, The End of Innocence, Gail B. Griffin, to be published… • Stories and reflections and critical essays on of being White, teaching writing at Kalamazoo College. • You did go to chapel today, didn’t you?

  11. A quote from Teaching Whiteness The irony (or paradox, or both) of whiteness is that its failure to name itself, while it arrogates one kind of godlike power (the power of universality and ubiquity), denies another. For to be universal and ubiquitous--to be Everything, Everywhere--is in fact to be Nothing, and Nowhere, in particular….As the absent agent in a passive construction, whiteness erases itself. White language says, in short, “I am not here; I do not exist.” It does so, of course, to avoid implicating itself in the relations, past and present, of racism. But the price for such exoneration is eternal absence, non-being--ghostliness.

  12. To reflect on, model, build demos about AI’s own Whiteness. Another missed opportunity

  13. What is AI? • Dartmouth goals • “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” • Automatic computers, use language, neuron nets, theory of computation, self-improvement, abstractions, randomness and creativity…

  14. What is AI?

  15. AI is hard • Language is AI-Hard • Vision is AI-Hard • Planning is AI-Hard • …. Is AI-Hard • Schank’s “medieval” view of AI

  16. What if there are only so many ideas to discover? Erdös’s “God’s book of Proofs” Kepler’s “Thinking God’s Thoughts After Him”

  17. N good ideas … M researchers Idea discovery A researcher is a kind of an experiment; the probability that a given researcher will discover an idea is P(n,m). Let’s assume: ideas are independent; researchers are independent; P(n,m) is constant, call it p.

  18. N good ideas … M researchers Idea discovery (2) The probability that an idea is discovered by at least one researcher: 1-(1-p)m

  19. N good ideas … M researchers (up to a point). Idea discovery (3) How to improve success? 1-(1-p)m Increase the exponential. 1-(1-p)m

  20. You’re handpicking the invite list to Dartmouth…up to 100! p=.01 White, non-Hispanic Males in US White, non-Hispanic US All US 1940 43 .351 88 .587 100 .643 2000 34 .289 69 .500 100 .634

  21. Idea discovery (model 2) N good ideas M researchers Ideas and people are not “colorless.” Let’s assume: ideas are independent; researchers are independent; but P(n,m) is greater if the color of n and m is the same than if they are different.

  22. Idea discovery (model 2) N good ideas M researchers nb = number of blue ideas; ng = number of green ideasmb = no. of blue researchers; mg = green researchersp= : probability if colors match; p : prob. If not match Let I(p,m)=1-(1-p)m; with colors, average discoveries are: I(nb,p=,mb)+(1-I(nb,p=,mb))* I(nb,p,mg) for blue ideas I(ng,p=,mg)+(1-I(nb,p=,mb)) *I(ng,p,mb) for green ideas

  23. Back to Dartmouth… p= .01p .001 White, non-Hispanic Males in US only White, non-Hispanic Males Green; WNH Females Blue White, non-Hispanic Males Green; All others Blue 1940 43,0 .197 43,45 .385 43,57 .423 2000 34,0 .161 34,35 .317 34,66 .419

  24. And if “color of ideas” reflects idealized “color of researcher” p= .01p .001 White, non-Hispanic Males in US only White, non-Hispanic Males Green; WNH Females Blue White, non-Hispanic Males Green; All others Blue 1940 43,0 .175 43,45 .386 43,57 .428 2000 34,0 .121 34,35 .318 34,66 .445

  25. What are some of these ideas? • Themes of “being human” which are not captured by viewing the field (artificial intelligence) as building agents that engage in “rational action.”

  26. Dr. King is my research advisor… • Justice, mercy, conversion, forgiveness, violence, revenge, race, politics, resistance, persuasion, honor, dignity, sacrifice, love, evil … • (To be fair, some researchers have done AI research, especially when doing “story understanding”)

  27. “Analogical” reasoning • “It is always difficult to get out of Egypt, for the Red Sea always stands before you with discouraging dimensions. And even after you’ve crossed the Red Sea, you have to move through a wilderness with prodigious hilltops of evil and gigantic mountains of opposition.” (King’s sermon during boycott)

  28. Missed Opportunities • To be of peaceful, just service. • To found an anti-racist science. • whose practitioners reflected the makeup of society, • and came to a better understanding of race and “Whiteness” in our cognitive models. • To model and demonstrate the fuller strands of what it means to be human.

  29. What could AI be? Thinking like a human. Thinking rationally. Acting like a human. Acting rationally. Being like a human. (computational humanism) Acting humanely. (humane computing; “cognitive technology”)