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| Photo: Will Knight |
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| Fig. 1. People can learn rich concepts from limited data. (A and B) A
single example of a new concept (red boxes) can be enough information to
support the (i) classification of new examples, (ii) generation of new
examples, (iii) parsing an object into parts and relations (parts
segmented by color), and (iv) generation of new concepts from related
concepts. Photo: courtesy of Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum. |
Taking inspiration from the way humans seem to learn, scientists have created AI software capable of picking up new knowledge in a far more efficient and sophisticated way.
The new AI program can recognize a handwritten character about as accurately as a human can, after seeing just a single example. The best existing machine-learning algorithms, which employ a technique called deep learning, need to see many thousands of examples of a handwritten character in order to learn the difference between an A and a Z.
The software was developed by Brendan Lake, a researcher at New York University, together with Ruslan Salakhutdinov, an assistant professor of computer science at the University of Toronto, and Joshua Tenenbaum, a professor in the Department of Brain and Cognitive Sciences at MIT. Details of the program, and the ideas behind it, are published today in the journal Science.
Computers have become much cleverer over the past few years, learning to recognize faces, understand speech, and even drive cars safely, among many other things. And most of the progress has been made using large, or deep, neural networks. But there is a crucial drawback to these systems: they require oodles of data to learn how to do even the simplest task.
This limitation is largely due to the fact that the algorithms do not process information the way we do. Although deep learning is modeled on a virtual network of neurons—and the approach has produced very impressive results in perceptual tasks—it is a very rough imitation of the way the brain works. A deep-learning algorithm associates the pixels in an image with a particular character. The brain may process some visual stimuli in a similar way, but humans also use higher forms of cognitive function in order to interpret the contents of an image.
The researchers used a technique they call the Bayesian program learning framework, or BPL. Essentially, the software generates a unique program for every character using strokes of an imaginary pen. A probabilistic programming technique is then used to match a program to a particular character, or to generate a new program for an unfamiliar one. The software is not mimicking the way children acquire the ability to read and write but, rather, the way adults, who already know how, learn to recognize and re-create new characters.
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Additional resources
Journal Reference:
Human-level concept learning through probabilistic program induction
Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum.
Vol. 350 no. 6266 pp. 1332-1338
DOI: 10.1126/science.aab3050
Source: MIT Technology Review
