by Charles Q. Choi

One of the greatest challenges the field of artificial intelligence faces is to simulate the workings of a human brain. Now an AI company reveals its software can solve the world’s most widely used test of a machine’s ability to act human, Google’s reCAPTCHA, by copying how human vision works.

The founder of modern computing, Alan Turing, developed the Turing test, which asks if it is possible to devise machines capable of acting human, and in doing so helped spawn the field of artificial intelligence. The most famous version of his test asks whether a machine can mimic a person well enough in a conversation over text to be indistinguishable from human — if so, a computer could be argued to be at least as intelligent as us.

The most commonly used Turing test is the CAPTCHA, which stands for “Completely Automated Public Turing test to tell Computers and Humans Apart.” CAPTCHAs are designed to see whether users are human, often to prevent Internet-crawling bots from accessing computing services or collecting sensitive data.

CAPTCHAs usually challenge website visitors to transcribe a string of distorted letters and digits. This problem is designed to be hard for computer programs and easy for humans. Although computers are very good at recognizing single letters or digits, they have always had trouble breaking up or segmenting a string of letters or digits into single characters they can then recognize, especially if the letters or digits overlap in unpredictable ways.

These days, a CAPTCHA scheme is considered broken if an algorithm can successfully decode it at least 1 percent of the time. Which makes the following news startling if it holds true: San Francisco startup Vicarious says its prototype AI software can reliably achieve success rates of up to 90 percent against modern CAPTCHAs from Google, Yahoo, PayPal and others.

“We should be able to solve all text-based CAPTCHAs,” Vicarious co-founder Dileep George tells Txchnologist. The company announced its findings in a statement Oct. 28.

Training an artificial brain

The system they have developed is an artificial neural network, a computing design that mimics how the brain works. Artificial neurons are components of computer programs that mimic neurons in brains. Each artificial neuron can send, receive and process information. The program feeds data into artificial neurons to train the neural network. The neuron then lets the network know when it has solved a given problem, such as correctly identifying a letter or digit. The network then alters the way data is relayed between these neurons, and the problems are tested again. Over time, the network learns which arrangements between neurons are best at computing desired answers.

“What we have done is reexamined the architecture of neural networks, which has largely remained the same since the ‘70s and '80s,” George says. “If you look at a standard neural network, there are connections between neurons that form one level, and this one level of neurons gets replicated again and again and again. In our system, there are much richer interconnections between neurons than in standard neural networks. By changing the architecture, our network can train much more efficiently."

For instance, while conventional neural networks might need more than 10,000 handwriting examples to recognize a single letter or digit, "our system can recognize digits with on the order of 10 or 20 examples, or fewer,” he says.

While past CAPTCHA-beating algorithms strove to break strings of characters into segments of single letters or numbers they could then identify, with Vicarious’s system, “segmentation and recognition are part of the same process, just as it is with people,” he says. “Humans understand CAPTCHAs not because they are cleaning up the clutter before recognizing letters, but because the letters stand out for us. We can segment the letters out because we recognize the letters — we don’t segment and then recognize."

Others applauded the company’s development as a significant moment for artificial intelligence.

AI researcher Nils Nilsson, a Stanford University emeritus professor who did not take part in this research, says, "They’ve been able to do something with their technology that was thought to be a unique ability of humans, and they are able to do it with just a few training samples. I’d like to know what other applications there might be of their technology.”

Facebook cofounder and Vicarious board member Dustin Moskovitz said in a statement, “We should be careful not to underestimate the significance of Vicarious crossing this milestone. This is an exciting time for artificial intelligence research, and they are at the forefront of building the first truly intelligent machines."

Big deal or another stepping-stone?

However, not all outside experts thought Vicarious achieved a major milestone.

"This is maybe the tenth or twentieth time someone’s claimed to have broken CAPTCHA,” says Carnegie Mellon University computer scientist Luis von Ahn, who did not participate in the work.

Since Vicarious has not disclosed how their system operates in an academic paper, von Ahn says, outside experts could not easily judge how well it actually worked. Still, he thinks it is inevitable that someone will design a CAPTCHA capable of circumventing Vicarious’s work.

“For instance, if one were to just show you a picture and ask you what’s in that picture, this program of Vicarious’s couldn’t do that,” von Ahn says.

However, the company notes its software could solve many other challenges linked with computer perception, including picture, face and speech recognition. “There’s lots of ground to cover in visual perception, such as labeling all the objects in a scene, or labeling what actions were performed in a video,” says Vicarious cofounder D. Scott Phoenix. “There are many other Turing tests we have planned to work on."

In terms of applications of this advance, "our goal is to build intelligent machines — there may be some possibilities for commercialization along the way,” Phoenix says. “For instance, we could take our current system and apply it to valuable and interesting problems, such as recognizing street signs and house numbers, or read arbitrary text in documents and books.”

Vicarious notes that it is not releasing its software publicly. “Security companies won’t have to replace text-based CAPTCHAs with something different,” Phoenix says.

Top Image: CAPTCHA login protection via Shutterstock.



Charles Q. Choi has written for Scientific American, The New York Times, Wired, Science and Nature, among others. In his spare time, he has traveled to all seven continents, including scaling the side of an iceberg in Antarctica, investigating mummies from Siberia, snorkeling in the Galapagos, climbing Mt. Kilimanjaro, camping in the Outback, avoiding thieves near Shaolin Temple and hunting for mammoth DNA in Yukon.