Chapter 3. AI and Creating the First Multidisciplinary AI Lab

Bohyun Kim


Chapter 3 of Library Technology Reports (vol. 55, no. 1), "AI and Creating the First Multidisciplinary AI Lab"

In this chapter, contributing author Bohyun Kim discuss artificial intelligence (AI), machine learning, and deep learning and why they are important for libraries. Kim shares how the University of Rhode Island created the first multidisciplinary AI lab, which launches in the fall of 2018. She discusses how the AI lab will be used to further research, discussion, and exploration of AI, and shares how such an environment can help facilitate multidisciplinary collaboration and foster interdisciplinary thinking. Kim shares the future hopes of the AI lab and AI.

Full Text:



Alan M. Turing, “Computing Machinery and Intelligence,” Mind 59, no. 236 (October 1, 1950): 433–460.

John McCarthy, Marvin L. Minsky, Nathaniel Rochester, and Claude E. Shannon, “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence: August 31, 1955,” AI Magazine 27, no. 4 (2006): 12–14.

For an excellent history of AI research, see Margaret A. Boden, “Chapter 1. What is Artificial Intelligence,” in AI: Its Nature and Future (Oxford University Press, 2016), 1-20.

Christof Koch, “How the Computer Beat the Go Master,” Scientific American, March 19, 2016,

David Silver et al., “Mastering the Game of Go with Deep Neural Networks and Tree Search,” Nature 529, no. 7587 (January 2016): 484–89,

Chris Welch, “Google Just Gave a Stunning Demo of Assistant Making an Actual Phone Call,” The Verge (blog), May 8, 2018,

Tony Peng, “Global Survey of Autonomous Vehicle Regulations,” Synced, March 15, 2018,

Alison DeNisco Rayome, “Dossier: The Leaders in Self-Driving Cars,” ZDNet, February 1, 2018,

Geri Piazza, “Artificial Intelligence Enhances MRI Scans,” National Institutes of Health (NIH) Research Matters (blog), April 10, 2018,

Devin Coldewey, “NYU and Facebook Team up to Supercharge MRI Scans with AI,” TechCrunch, August 20, 2018,

For many excellent examples of algorithmic biases, see Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, 1 edition (New York: Crown, 2016) and Safiya Umoja Noble, Algorithms of Oppression: How Search Engines Reinforce Racism (NYU Press, 2018).

Elizabeth Gibney, “Self-Taught AI Is Best yet at Strategy Game Go,” Nature News, October 18, 2017,

Bohyun Kim, “AI Lab at a Library? Why Artificial Intelligence Matters & What Libraries Can Do,” American Library Association 2018 Annual Conference, June 25, 2018, The presentation slides for this talk are available at

“Big Data Collaborative at the University of Rhode Island,” University of Rhode Island, accessed September 19, 2018,

“URI Is New Home to DataSpark,” URI Today, April 11, 2017,

“URI to Launch Artificial Intelligence Lab,” URI Today, December 20, 2017,

“‘People of Color in AI’—A Discussion on Ethical Implications and Impacts,” University of Rhode Island Libraries, April 2018,

“The SMILE Program (Science and Math Investigative Learning Experience),” accessed September 20, 2018,

“The Rhode Island STEAM Center,” accessed September 20, 2018,

For some of the media coverage of the AI Lab at URI, see “Press,” AI Lab at University of Rhode Island, accessed September 20, 2018,

“NVIDIA DGX-1: Essential Instrument of AI Research,” NVIDIA, accessed September 20, 2018,

Alan Stevens, “GPU Computing: Accelerating the Deep Learning Curve,” ZDNet, July 2, 2018,

“TensorBook: Deep Learning Laptop,” Lambda Labs, accessed September 20, 2018,

Bohyun Kim and Brian Zelip, “Growing Makers in Medicine, Life Sciences, and Healthcare,” Conference Presentation, ACRL 2017 Conference, Baltimore, MD, March 24, 2017. The presentation slides for this talk are available at


  • There are currently no refbacks.

Published by ALA TechSource, an imprint of the American Library Association.
Copyright Statement | ALA Privacy Policy