A new computer model can now mimic the human ability to learn new concepts from a single example instead of the hundreds or thousands of examples it takes other machine learning techniques, researchers say.
The new model learned how to write invented symbols from the animated show Futurama as well as dozens of alphabets from across the world. It also showed it could invent symbols of its own in the style of a given language.
The researchers suggest their model could also learn other kinds of concepts, such as speech and gestures.
Although scientists have made great advances in machine learning in recent years, people remain much better at learning new concepts than machines.
"People can learn new concepts extremely quickly, from very little data, often from only one or a few examples. You show even a young child a horse, a school bus, a skateboard, and they can get it from one example," says study co-author Joshua Tenenbaum at the Massachusetts Institute of Technology. In contrast, "standard algorithms in machine learning require tens, hundreds or even thousands of examples to perform similarly."
To shorten machine learning, researchers sought to develop a model that better mimicked human learning, which makes generalizations from very few examples of a concept. They focused on learning simple visual concepts — handwritten symbols from alphabets around the world.
"Our work has two goals: to better understand how people learn — to reverse engineer learning in the human mind — and to build machines that learn in more humanlike ways," Tenenbaum says.
Whereas standard pattern recognition algorithms represent symbols as collections of pixels or arrangements of features, the new model the researchers developed represented each symbol as a simple computer program. For instance, the letter "A" is represented by a program that generates examples of that letter stroke by stroke when the program is run. No programmer is needed during the learning process — the model generates these programs itself.
Moreover, each program is designed to generate variations of each symbol whenever the programs are run, helping it capture the way instances of such concepts might vary, such as the differences between how two people draw a letter.
"The idea for this algorithm came from a surprising finding we had while collecting a data set of handwritten characters from around the world. We found that if you ask a handful of people to draw a novel character, there is remarkable consistency in the way people draw," says study lead author Brenden Lake at New York University. "When people learn or use or interact with these novel concepts, they do not just see characters as static visual objects. Instead, people see richer structure — something like a causal model, or a sequence of pen strokes — that describe how to efficiently produce new examples of the concept."
The model also applies knowledge from previous concepts to speed learn new concepts. For instance, the model can use knowledge learned from the Latin alphabet to learn the Greek alphabet. They call their model the Bayesian program learning or BPL framework.
The researchers applied their model to more than 1,600 types of handwritten characters in 50 writing systems, including Sanskrit, Tibetan, Gujarati, Glagolitic, and even invented characters such as those from the animated series Futurama and the online game Dark Horizon. In a kind of Turing test, scientists found that volunteers recruited via Amazon's Mechanical Turk had difficulty distinguishing machine-written characters from human-written ones.
The scientists also had their model focus on creative tasks. They asked their system to create whole new concepts — for instance, creating a new Tibetan letter based on what it knew about letters in the Tibetan alphabet. The researchers found human volunteers rated machine-written characters on par with ones developed by humans recruited for the same task.
"We got human-level performance on this creative task," study co-author Ruslan Salakhutdinov at the University of Toronto.
Potential applications for this model could include handwriting recognition, speech recognition, gesture recognition and object recognition. "Ultimately we're trying to figure out how we can get systems that come closer to displaying human-like intelligence," Salakhutdinov says. "We're still very, very far from getting there, though."
The scientists detailed their findings in the December 11 issue of the journal Science.
Charles Q. Choi is a science reporter who contributes regularly to IEEE Spectrum. He has written for Scientific American, The New York Times, Wired, and Science, among others.