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Researchers have developed a
flexible way of combining deep learning and symbolic reasoning to teach
computers to write short computer programs. Here, Armando Solar-Lezama
(left), a professor at CSAIL, speaks with graduate student Maxwell Nye.
Photo: Kim Martineau
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Learning to code involves recognizing how to structure a program, and how to fill in every last detail correctly. No wonder it can be so frustrating.
A new program-writing AI, SketchAdapt, offers a way out. Trained on tens of thousands of program examples, SketchAdapt learns how to compose short, high-level programs, while letting a second set of algorithms find the right sub-programs to fill in the details. Unlike similar approaches for automated program-writing, SketchAdapt knows when to switch from statistical pattern-matching to a less efficient, but more versatile, symbolic reasoning mode to fill in the gaps.
“Neural nets are pretty good at getting the structure right, but not the details,” says Armando Solar-Lezama, a professor at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “By dividing up the labor — letting the neural nets handle the high-level structure, and using a search strategy to fill in the blanks — we can write efficient programs that give the right answer.”
SketchAdapt is a collaboration between Solar-Lezama and Josh Tenenbaum, a professor at CSAIL and MIT’s Center for Brains, Minds and Machines. The work will be presented at the International Conference on Machine Learning June 10-15...
SketchAdapt is limited to writing very short programs. Anything more requires too much computation. Nonetheless, it’s intended more to complement programmers rather than replace them, the researchers say. “Our focus is on giving programming tools to people who want them,” says Nye. “They can tell the computer what they want to do, and the computer can write the program.”
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Source: MIT News
