In the preliminary report from the MIT Task Force on the Future of Libraries, we make several references to the importance of optimizing library content, data, and metadata for machine learning applications.
We imagine a repository of knowledge and data that can be exploited and analyzed by humans, machines, and algorithms. This transformation will accelerate the accumulation and validation of knowledge, and will enable the creation of new knowledge and of solutions to the world’s great challenges. Libraries will no longer be geared primarily to direct readers but instead to content contributors, community curators, text-mining programs, machine-learning algorithms, and visualization tools.
I am convinced that machine learning is going to have a major impact on the advancement of knowledge in lots of ways we can’t anticipate, and I want to understand it better. I am also convinced that without the intervention of folks who understand the biases built into our collections in terms of content, organization, and description; machine learning applications will re-inscribe and reify existing inequalities.
To that end, I’m trying to put together a reading list to get smarter about what machine learning is, what it can do for libraries, and what libraries can do to support and inspire creative, productive, just and inclusive applications of machine learning. Here’s my very incomplete initial list. Additional suggestions welcome in the comments.
- Machine Learning: The new AI by Ethem Alpaydin (part of the Essential Knowledge Series from MIT Press)
- Always Already Computational: Library Collections as Data, an IMLS-funded project, Thomas Padilla.
- “New AI-Based Search Engines are a “Game Changer” for Science Research”, by Nicola Jones, Nature magazine on November 12, 2016
- Artificial Intelligence Is Lost in the Woods, by David Gelernter, in MIT Technology Review
- Searching for Lost Knowledge in the Age of Intelligent Machines, by Adrienne Lafrance, The Atlantic.
- AI Songsmith Cranks Out Surprisingly Catchy Tunes, by Will Smith, MIT Technology Review
- And this list of resources: Hitchhiker’s guide to data science, machine learning, R, Python, from Vincent Granville