|Alma mater||Yale University|
|Known for||Bayesian cognitive science|
|Thesis||A Bayesian Framework for Concept Learning (1999)|
|Doctoral advisor||Whitman Richards|
|Doctoral students||Thomas L. Griffiths, Rebecca Saxe|
Joshua Brett Tenenbaum (Josh Tenenbaum) is Professor of Computational Cognitive Science at the Massachusetts Institute of Technology. He is known for contributions to mathematical psychology and Bayesian cognitive science. According to the MacArthur Foundation, which named him a MacArthur Fellow in 2019, "Tenenbaum is one of the first to develop and apply probabilistic and statistical modeling to the study of human learning, reasoning, and perception, and to show how these models can explain a fundamental challenge of cognition: how our minds understand so much from so little, so quickly."
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. According to the MacArthur Foundation, "Tenenbaum is one of the first to develop and apply probabilistic and statistical modeling to the study of human learning, reasoning, and perception, and to show how these models can explain a fundamental challenge of cognition: how our minds understand so much from so little, so quickly."
At MIT, Tenebaum is a professor of computational cognitive science and a member of CSAIL, MIT’s Computer Science and Artificial Intelligence Laboratory. He leads MIT's Computational Cognitive Science lab and is also head of an AI project called the MIT Quest for Intelligence.
In 2018, R & D Magazine named Tenenbaum their "Innovator of the Year."
In 2019, Tenenbaum was named a MacArthur Fellow. The MacArthur webpage describes his work as follows: "Combining computational models with behavioral experiments to shed light on human learning, reasoning, and perception, and exploring how to bring artificial intelligence closer to the capabilities of human thinking."
- "Joshua Tenenbaum". Retrieved 2019-09-26.
- "JOSHUA BRETT TENENBAUM Curriculum Vitae" (PDF). MIT. June 2020.
- "Joshua Tenenbaum - MacArthur Foundation". www.macfound.org. Retrieved 2019-10-16.
- 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.
- 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.
- Trafton, Anne (March 4, 2020). "A new model of vision". MIT News. Retrieved March 20, 2021.
'What we were trying to do in this work is to explain how perception can be so much richer than just attaching semantic labels on parts of an image, and to explore the question of how do we see all of the physical world,' says Josh Tenenbaum, a professor of computational cognitive science and a member of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Center for Brains, Minds, and Machines (CBMM).
- Knight, Will (March 9, 2020). "If AI's So Smart, Why Can't It Grasp Cause and Effect?". Wired. Retrieved March 20, 2021.
The test devised by Tenenbaum is important, says Kun Zhang, an assistant professor who works on causal inference and machine learning at Carnegie Mellon University, because it provides a good way to measure causal understanding, albeit in a very limited setting.