Melanie Mitchell

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Melanie Mitchell
Born
Los Angeles, California, US
Alma mater
Genetic algorithms
InstitutionsUniversity of Michigan
Santa Fe Institute
Los Alamos National Laboratory
OGI School of Science and Engineering
Portland State University
Thesis Copycat: A Computer Model of High-Level Perception and Conceptual Slippage in Analogy-Making  (1990)
Doctoral advisorDouglas Hofstadter and
John Holland
RelativesJonathan Mitchell (brother)[1]

Melanie Mitchell is an American scientist. She is the Davis Professor of Complexity at the

cellular automata, and her publications in those fields are frequently cited.[2]

She received her PhD in 1990 from the

(Farrar, Straus, and Giroux).

Life

Melanie Mitchell was born and raised in Los Angeles, California.[4] She attended Brown University in Providence, Rhode Island, where she studied physics, astronomy and mathematics. Her interest in artificial intelligence was spurred in college when she read Douglas Hofstadter's Gödel, Escher, Bach.

After graduating, she worked as a high school math teacher in New York City. Deciding she "needed to be" in artificial intelligence, Mitchell tracked down Douglas Hofstadter, repeatedly asking to become one of his graduate students. After finding Hofstadter's phone number at MIT, a determined Mitchell made several calls, all of which went unanswered. She was ultimately successful in reaching Hofstadter after calling at 11 p.m., and secured an internship working on the development of Copycat.[5]

In the fall of 1984, Mitchell followed Hofstadter to the University of Michigan, submitting a "last minute" application to the university's doctoral program.[6] She earned her Ph.D. in 1990 with the dissertation Copycat: A Computer Model of High-Level Perception and Conceptual Slippage in Analogy-Making.

Career

Mitchell is a Professor at the Santa Fe Institute and Portland State University. Mitchell developed the Complexity Explorer platform for the Santa Fe Institute, which offers online courses. More than 25.000 students took Mitchell's course "Introduction to Complexity".[7] In 2018, Barbara Grosz, Dawn Song and Melanie Mitchell organised the workshop "On Crashing the Barrier of Meaning in AI".[8] She features regularly as guest expert in the Learning Salon, an online interdisciplinary meeting about biological and artificial intelligence.[9]

Awards

In 2020, Mitchell received the Herbert A. Simon Award.[10]

Views

While expressing strong support for AI research, Mitchell has expressed concern about AI's vulnerability to hacking as well as its ability to inherit social biases. On

superintelligent machines, but that current technology was not close to being able to solve this current problem.[11] Mitchell believes that humanlike visual intelligence would require "general knowledge, abstraction, and language", and hypothesizes that visual understanding may have to be learned as an embodied agent rather than merely viewing pictures.[12]

Selected publications

Books

Articles

References

  1. . Retrieved November 6, 2018.
  2. ^ Google Scholar search for Melanie Mitchell
  3. ^ Mitchell, Melanie (October 4, 2002). "IS the Universe a Universal Computer?" (PDF). Science (www.sciencemag.org). pp. 65–68. Retrieved March 23, 2013.
  4. ^ "Catalyzing Computing Podcast Episode 15 - Interview with Melanie Mitchell Part 1" (PDF). Computing Community Consortium.
  5. ^ Mills, Kevin. "Melanie Mitchell Introduction" (PDF).
  6. ^ Magazine, John Pavlus, Quanta. "The Computer Scientist Training AI to Think with Analogies". Scientific American. Retrieved December 2, 2021.{{cite web}}: CS1 maint: multiple names: authors list (link)
  7. ^ "Santa Fe Institute profile: Melanie Mitchell". Retrieved May 2, 2023.
  8. ^ "On Crashing the Barrier of Meaning in AI; AI Magazine, 41(2), 2020, 86–92" (PDF). Retrieved May 2, 2023.
  9. ^ "The Learning Salon". Retrieved May 2, 2023.
  10. ^ "Portland State University, Department of Computer Science: Professor Melanie Mitchell receives Herbert A. Simon Award". Retrieved May 2, 2023.
  11. ^ "Fears about robot overlords are (perhaps) premature". Christian Science Monitor. October 25, 2019. Retrieved May 10, 2020.
  12. ^ "What Is Computer Vision?". PCMAG. February 9, 2020. Retrieved May 10, 2020.

External links