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To do so, they developed a “Bayesian Program Learning” (BPL) framework, where concepts are represented as simple computer programs. For instance, the letter ‘A’ is represented by computer code —resembling the work of a computer programmer— that generates examples of that letter when the code is run. Yet no programmer is required during the learning process: the algorithm programs itself by constructing code to produce the letter it sees. Also, unlike standard computer programs that produce the same output every time they run, these probabilistic programs produce different outputs at each execution. This allows them to capture the way instances of a concept vary, such as the differences between how two people draw the letter ‘A.’
While standard pattern recognition algorithms represent concepts as configurations of pixels or collections of features, the BPL approach learns “generative models” of processes in the world, making learning a matter of “model building” or “explaining” the data provided to the algorithm. In the case of writing and recognizing letters, BPL is designed to capture both the causal and compositional properties of real-world processes, allowing the algorithm to use data more efficiently. The model also “learns to learn” by using knowledge from previous concepts to speed learning on new concepts—e.g., using knowledge of the Latin alphabet to learn letters in the Greek alphabet. The authors applied their model to over 1,600 types of handwritten characters in 50 of the world’s writing systems, including Sanskrit, Tibetan, Gujarati, Glagolitic—and even invented characters such as those from the television series Futurama.
In addition to testing the algorithm’s ability to recognize new instances of a concept, the authors asked both humans and computers to reproduce a series of handwritten characters after being shown a single example of each character, or in some cases, to create new characters in the style of those it had been shown. The scientists then compared the outputs from both humans and machines through “visual Turing tests.” Here, human judges were given paired examples of both the human and machine output, along with the original prompt, and asked to identify which of the symbols were produced by the computer.
While judges’ correct responses varied across characters, for each visual Turing test, fewer than 25 percent of judges performed significantly better than chance in assessing whether a machine or a human produced a given set of symbols.
“Before they get to kindergarten, children learn to recognize new concepts from just a single example, and can even imagine new examples they haven’t seen,” notes Tenenbaum. “I’ve wanted to build models of these remarkable abilities since my own doctoral work in the late nineties. We are still far from building machines as smart as a human child, but this is the first time we have had a machine able to learn and use a large class of real-world concepts—even simple visual concepts such as handwritten characters—in ways that are hard to tell apart from humans.”
The work was supported by grants from the National Science Foundation to MIT’s Center for Brains, Minds and Machines (CCF-1231216), the Army Research Office (W911NF-08-1-0242, W911NF-13-1-2012), the Office of Naval Research (N000141310333), and the Moore-Sloan Data Science Environment at New York University.
