Joshua Tenenbaum
Joshua Brett Tenenbaum (Josh Tenenbaum) is
Professor of Computational Cognitive Science at the Josh Tenenbaum
Massachusetts Institute of Technology.[2] He is known Born 21 August 1972
for contributions to mathematical psychology and Citizenship United States
Bayesian cognitive science. According to the Alma mater Yale University
MacArthur Foundation, which named him a MIT
MacArthur Fellow in 2019, "Tenenbaum is one of the
Known for Bayesian cognitive science
first to develop and apply probabilistic and statistical
modeling to the study of human learning, reasoning, Awards Fellow, Society of Experimental
and perception, and to show how these models can Psychologists (2007)
explain a fundamental challenge of cognition: how our Fellow, Cognitive Science
minds understand so much from so little, so Society (2013)
quickly."[3] Fellow, MacArthur (2019)[1]
Scientific career
Biography Fields Artificial intelligence
Cognitive science
Tenenbaum grew up in California. His mother was a Institutions Stanford University
teacher[4] and his father is Internet commerce pioneer MIT
Jay Martin Tenenbaum.[5] Thesis A Bayesian Framework for
Concept Learning (https://dspac
His research direction was strongly influenced by his
e.mit.edu/handle/1721.1/1671
parents' interest in teaching and learning, and later by
4) (1999)
interactions with cognitive psychologist Roger
Shepard, during his years at Yale.[4] Doctoral Whitman Richards
advisor
Tenenbaum received his undergraduate degree in Doctoral Thomas L. Griffiths, Rebecca
physics from Yale University in 1993, and his Ph.D. students Saxe
from MIT in 1999.[2] 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."[3]
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.[6][7]
In 2018, R & D Magazine named Tenenbaum their "Innovator of the Year."[4]
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."[3]
Tenenbaum's recent research includes teaching AI systems to imitate human face-recognition methods[7]
and programming AI to understand cause and effect.[8]
Publications
Tenenbaum has a list of his publications on his MIT web page (http://web.mit.edu/cocosci/josh.html) and
on Google Scholar (https://scholar.google.com/citations?hl=en&user=rRJ9wTJMUB8C&view_op=list_w
orks&sortby=pubdate).
References
1. "Joshua Tenenbaum" (https://www.macfound.org/fellows/1050/). Retrieved September 26,
2019.
2. "JOSHUA BRETT TENENBAUM Curriculum Vitae" (http://web.mit.edu/cocosci/archive/Pape
rs/JBT-CV-June2020.pdf) (PDF). MIT. June 2020.
3. "Joshua Tenenbaum - MacArthur Foundation" (https://www.macfound.org/fellows/1050/).
www.macfound.org. Retrieved October 16, 2019.
4. Panjwani, Laura (December 18, 2018). "AI, Cognitive Science Researcher Josh Tenenbaum
Named R&D Magazine's 2018 Innovator of the Year" (https://www.rdmag.com/article/2018/1
2/ai-cognitive-science-researcher-josh-tenenbaum-phd-named-r-d-magazine-2018-innovato
r-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." " "Eulogy" (http
s://ed.stanford.edu/news/alumna-bonnie-tenenbaum-educator-and-philanthropist-remember
ed-her-generosity-students). December 8, 2020.
5. Luttrell, Sharon Kahn (May 7, 2007). "Marty Tenenbaum '64, SM '66" (https://www.technolog
yreview.com/s/407860/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."
6. Knight, Will (September 12, 2018). "A plan to advance AI by exploring the minds of children"
(https://www.technologyreview.com/s/612002/a-plan-to-advance-ai-by-exploring-the-minds-o
f-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."
7. Trafton, Anne (March 4, 2020). "A new model of vision" (https://news.mit.edu/2020/computer
-model-brain-vision-0304). 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)."
8. Knight, Will (March 9, 2020). "If AI's So Smart, Why Can't It Grasp Cause and Effect?" (http
s://www.wired.com/story/ai-smart-cant-grasp-cause-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."
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