Toward artificial intelligence that learns to write code
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.
Program synthesis, or teaching computers to code, has long been a goal of AI researchers. A computer that can program itself is more likely to learn language faster, converse fluently, and even model human cognition. All of this drew Solar-Lezama to the field as a graduate student, where he laid the foundation for SketchAdapt.
Solar-Lezama’s early work, Sketch, is based on the idea that a program’s low-level details could be found mechanically if a high-level structure is provided. Among other applications, Sketch inspired spinoffs to automatically grade programming homework and convert hand-drawn diagrams into code. Later, as neural networks grew in popularity, students from Tenenbaum’s computational cognitive science lab suggested a collaboration, out of which SketchAdapt formed.
Rather than rely on experts to define program structure, SketchAdapt figures it out using deep learning. The researchers also added a twist: When the neural networks are unsure of what code to place where, SketchAdapt is programmed to leave the spot blank for search algorithms to fill.
“The system decides for itself what it knows and doesn’t know,” says the study’s lead author, Maxwell Nye, a graduate student in MIT’s Department of Brain and Cognitive Sciences. “When it gets stuck, and has no familiar patterns to draw on, it leaves placeholders in the code.
