|Alma mater||Yale University|
|Known for||Bayesian cognitive science|
|Thesis||A Bayesian Framework for Concept Learning (1999)|
|Doctoral advisor||Whitman Richards|
|Doctoral students||Rebecca Saxe|
Joshua Brett Tenenbaum is Professor of Cognitive Science and Computation at the Massachusetts Institute of Technology. He is known for contributions to mathematical psychology and Bayesian cognitive science. Tenenbaum previously taught at Stanford University, where he was the Wasow Visiting Fellow from October 2010 to January 2011.
Tenenbaum received his undergraduate degree in physics from Yale University in 1993, and his Ph.D. from MIT in 1999. His work focuses on analyzing probabilistic inference as the engine of human cognition and as a means to develop machine learning.
At MIT, where he leads the Computational Cognitive Science lab at MIT, he is also head of an AI project called the MIT Quest for Intelligence.
In 2018, R & D Magazine named Tenebaum their "Innovator of the Year."
- "Curriculum Vitae" (PDF). MIT. Aug 2010.
- "Thomas A. Wasow Visiting Scholars in Symbolic Systems".
- Panjwani, Laura (December 18, 2018). "AI, Cognitive Science Researcher Josh Tenenbaum Named R&D Magazine's 2018 Innovator of the Year". R & D Magazine. Retrieved February 10, 2019.
Tenenbaum’s scientific work currently focuses on two areas: describing the structure, content and development of people’s common sense theories, especially intuitive physics and intuitive psychology, and understanding how people are able to learn and generalize new concepts, models, theories and tasks from very few examples, a concept known as “one-shot learning.”
- Luttrell, Sharon Kahn (May 7, 2007). "Marty Tenenbaum '64, SM '66". MIT Technology Review. Retrieved February 10, 2019.
Meanwhile, his son Josh Tenenbaum, PhD ‘98, has followed his father’s footsteps to MIT. He’s an assistant professor in the Department of Brain and Cognitive Science.
- "Curriculum Vitae" (PDF). MIT. Jan 2011.
- Knight, Will (September 12, 2018). "A plan to advance AI by exploring the minds of children". MIT Technology Review. Retrieved February 10, 2019.
For instance, in 2015 he and two other researchers created computer programs capable of learning to recognize new handwritten characters, as well as certain objects in images, after seeing just a few examples. This is important because the best machine-learning programs typically require huge quantities of training data.
- Falcon, William (November 30, 2018). "This Is The Future Of AI According To 23 World-Leading AI Experts". Forbes. Retrieved March 20, 2019.
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