RESEARCH ARTICLES
◥
COGNITIVE SCIENCE
Human-level concept learning
through probabilistic
program induction
Brenden M. Lake,
1
* Ruslan Salakhutdinov,
2
Joshua B. Tenenbaum
3
People learning new concepts can often generalize successfully from just a single example,
yet machine learning algorithms typically require tens or hundreds of examples to
perform with similar accuracy. People can also use learned concepts in richer ways than
conventional algorithms—for action, imagination, and explanation. We present a
computational model that captures these human learning abilities for a large class of
simple visual concepts: handwritten characters from the world’salphabets.Themodel
represents concepts as simple programs that best explain observed examples under a
Bayesian criterion. On a challenging one-shot classification task, the model achieves
human-level performance while outperforming recent deep learning approaches. We also
present several “visual Turing tests” probing the model’screativegeneralizationabilities,
which in many cases are indistinguishable from human behavior.
D
espite remarkable advances in artificial
intelligence and machine learning, two
aspects of human conceptual knowledge
have eluded machine systems. First, for
most interesting kinds of natural and man-
made categories, people can learn a new concept
from just one or a handfu l of examples, w he r ea s
standard algorithms in machine learning require
tens or hundreds of examples to perform simi-
larly. For instance, people may only need to see
one example of a novel two-wheeled vehicle
(Fig. 1A) in order to grasp the boundaries of the
new concept, and even children can make mean-
ingful generalizations via “one-shot learning”
(1–3). In contrast, many of the leading approaches
in machine learning are also the most data-hungry,
especially “deep learning” models that have
achieved new levels of performance on object
and speech recognition benchmarks (4–9). Sec-
ond, people learn richer representations than
machines do, even for simple concepts (Fig. 1B),
using them for a wider range of functions, in-
cluding (Fig. 1, ii) creating new exemplars (10),
(Fig. 1, iii) parsing objects into parts and rela-
tions (11), and (Fig. 1, iv) creating new abstract
categories of objects based on existing categ ories
(12, 13). In contrast, the best machine classifiers
do not perform these additional functions, which
are rarely studied and usually require special-
ized algorithms. A central challenge is to ex-
plain these two aspects of human-level concept
learning: How do people learn new concepts
from just one or a few examples? And how do
people learn su ch abstrac t, ric h, a nd fle xible rep-
resentations? An even greater challenge arises
when putting them together: How can learning
succeed from such sparse data yet also produce
such rich representations? For any theory of
RESEARCH
1332 11 DECEMBER 2015 • VOL 350 ISSUE 6266 sciencemag.org SCIENCE
1
Center for Data Science, New York University, 726
Broadway, New York, NY 10003, USA.
2
Department of
Computer Science and Department of Statistics, University
of Toronto, 6 King’s College Road, Toronto, ON M5S 3G4,
Canada.
3
Department of Brain and Cognitive Sciences,
Massachusetts Institute of Technology, 77 Massachusetts
Avenue, Cambridge, MA 02139, USA.
*Corresponding author. E-mail: brenden@nyu.edu
Fig. 1. People can learn rich concepts from limited data. (A and B)Asingleexampleofanewconcept(redboxes)canbeenoughinformationtosupport
the (i) classification of new e xampl es, (ii) gener ation of new e xamples, (iii) pa r si ng an object into pa rt s and r elations (part s se gmented b y color ), and (iv)
generation of new concepts from related concepts. [Image credit for (A), iv , bottom: With permiss ion fr om Glenn R oberts and Mo tor cycle Mo jo Mag azine ]
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