Show Brenden Lake's machine learning program a randomly chosen letter from a library of 30-plus world alphabets, one the machine has never seen before, and the program can produce new "handwritten" versions that are indistinguishable from human-generated letters. Even more fascinating: When asked to invent and draw new alphabetic letters in the same style as a handful of examples (for example, the Hebrew letters זַ ,א, or ה), the program created new letters that a panel of 117 judges could not distinguish from what humans made when given the same tasks.

Compared to several of top machine learning programs (even those based on artificial neural networks) the program by Lake and two other computer and cognitive scientists at New York University was far and away the best at both tasks. But what's more amazing is the basics of this breakthrough: The program could grasp a new concept after just a single example, which Lake says is a first in computer science. The program is outlined today in the journal Science.

Show us a sentence in any font from Impact to Comic Sans and we have no trouble reading it

"We're interested in closing the gap between machine learning and the way people learn new concepts," Lake says. "People often only require one or just a handful of examples to understand something new, while even the best machine learning programs often require tens or hundreds of examples."

The Spirit of the Letter

Why focus on the designs of alphabets and letters? It turns out that parsing the shapes of letters is an excellent arena to test machine learning programs. That's because letters of any alphabet can be written in a vast variety of styles and fonts. Yet for humans, this complexity is not a concern. Show us a sentence in any font from Impact to Comic Sans and we typically have no trouble reading it, even if the letters themselves are stylized in far different ways.

Can you tell the difference between humans and machines? Humans and machines were given an image of a novel character (top) and asked to produce new exemplars. The nine-character grids in each pair that were generated by a machine are (by row) 1, 2; 2, 1; Brendan Lake

Douglas Hofstadter, the famed Pulitzer Prize-winning cognitive scientist ("who was certainly an inspiration for our work," Lake says) addressed the reason why in an essay back in 1982. According to Hofstadter, when we humans see letters, we somehow look behind the curves and serifs, tapping into the "spirit of the letter." This is not some airy philosophical theory. Right now may be your first time seeing a Cyrillic д, but you already understand the basis of the symbol well enough that, if you created new fonts for it, a native Russian would likely have little trouble reading them.

For a computer today, that seemingly simple process—extracting the nebulous ideas and concepts behind words, pictures, or symbols—seems like impossible task. Computers don't think in terms of concepts. It's the same reason why Google Translate and other computer language translations still can fail so spectacularly. Language translation is about translating the meanings behind words, not just the words themselves.

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The Virtual Stroke of the Pen

Lake's team was inspired by collecting reams of data on how people draw unfamiliar letters. "We found that if you asked 20 people online to draw a version of [some] unfamiliar letter, there would be a surprising amount of consistency in the way they approached their choice of strokes, and the direction and order of those strokes," he says. That similarity in approach led Lake to imagine that maybe people were tapping into some "internal programming, rather than just copying a visual pattern," Lake says. Then he wondered, what would that program look like, and could a computer learn it?

Here's a simplified version of how Lake's program works. "When the program receives the raw image of a new letter," Lake says, it tries to recreate that letter, using a memory of virtual penstrokes it already knows. For example, when presented with the letter P, the program might consider copying it by drawing a line stoke and a curve, "but it also might consider drawing a complete circle with a line hanging off of it," says Lake.

In fact, the machine will consider a wide variety of ways to create this letter. "The core of the program is probabilistic—every time you run it it produces a different [interpretation]," Lake says. The key to the program's success was the team's tireless effort in slowly refining the virtual penstrokes in the program's memory, and carefully weighing the order and direction in which these hypothetical strokes should be drawn.

So what's next? "We think this 'one-shot' learning approach could be fit into a variety of machine learning paradigms," says Lake, who images that other human symbols like visual gestures and spoken words could also be conceptually conveyed to computers. That is, as long as researchers can cleverly parse and combine the components—like the virtual penstrokes—that make compose the larger symbols.

"There's still a big gap between what people are capable of and what we can do with machine learning today, but our program has captured a range of new learning capabilities," which are human-like in "their ability to generalize from a single example in much more powerful ways," says Lake.

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