Zuckerberg and Musk back software startup that mimics human learning

San Francisco startup Vicarious aims to create 'a computer that thinks like a person except it doesn't need to eat or sleep'

Vicarious is developing 'machine learning software based on the computational principles of the human brain'. Photograph: Sebastian Kaulitzki / Alamy/Alamy

Some of Silicon Valley’s biggest names are backing a hitherto low-profile tech startup that aims to recreate the human neocortex as computer code.

Vicarious, a four-year-old San Francisco-based startup, claims to be “building software that thinks and learns like a human”. According to the Wall Street Journal Facebook's Mark Zuckerberg and Tesla's Elon Musk have just invested $40m in the company.

They join Peter Thiel, a PayPal billionaire, whose Founders Fund targets cutting edge technology. Ashton Kutcher, actor and tech investor, is also investing, as is Facebook co-founder Dustin Moskovitz.

The neocortex is the outer layer of the cerebral hemispheres and in humans is crucial to the use of the senses as well as activities such as language, motor commands and spatial reasoning.

According to the company’s website, Vicarious is developing “machine learning software based on the computational principles of the human brain. Our first technology is a visual perception system that interprets the contents of photographs and videos in a manner similar to humans. Powering this technology is a new computational paradigm we call the Recursive Cortical Network.”

The company has already managed to create software that will solve Captcha, the online tests used by many websites to supposedly identify humans from computers. Company founder Scott Phoenix told the WSJ that if they are successful, Vicarious will have created "a computer that thinks like a person except it doesn't need to eat or sleep".

Phoenix said his aim was to create a computer that can understand not just shapes and objects but the textures associated with them. He said he hopes Vicarious’s computers will learn to how to cure diseases and create cheap, renewable energy, as well as performing the jobs that employ most human beings. “We tell investors that right now, human beings are doing a lot of things that computers should be able to do,” he said.

The investment comes amid a boom in funding for artificial intelligence ventures, In January IBM announced it was investing more than $1bn to create the Watson Group, a 2,000-employee division dedicated to developing its self-learning super-computer. The money includes $100m to fund startups that find creative uses for Watson.

Earlier this week IBM announced a partnership with the New York Genome Center that will attempt to use Watson to identify the genetic components of brain cancer.

ORIGINAL: The Guardian

Dominic Rushe in New York
21 March 2014

Facebook Creates Software That Matches Faces Almost as Well as You Do

Facebook’s new AI research group reports a major improvement in face-processing software.

Why It Matters

Advances in the relatively new artificial-intelligence field known as deep learning could fundamentally reshape what computers can do.

Asked whether two unfamiliar photos of faces show the same person, a human being will get it right 97.53 percent of the time. New software developed by researchers at Facebook can score 97.25 percent on the same challenge, regardless of variations in lighting or whether the person in the picture is directly facing the camera.

That’s a significant advance over previous face-matching software, and it demonstrates the power of a new approach to artificial intelligence known as deep learning, which Facebook and its competitors have bet heavily on in the past year (see “Deep Learning”). This area of AI involves software that uses networks of simulated neurons to learn to recognize patterns in large amounts of data.

“You normally don’t see that sort of improvement,” says Yaniv Taigman, a member of Facebook’s AI team, a research group created last year to explore how deep learning might help the company (see “Facebook Launches Advanced AI Effort”). “We closely approach human performance,” says Taigman of the new software. He notes that the error rate has been reduced by more than a quarter relative to earlier software that can take on the same task.

Head turn: DeepFace uses a 3-D model to rotate faces, virtually, so that they face the camera. Image (a) shows the original image, and (g) shows the final, corrected version.

Facebook’s new software, known as DeepFace, performs what researchers call facial verification (it recognizes that two images show the same face), not facial recognition (putting a name to a face). But some of the underlying techniques could be applied to that problem, says Taigman, and might therefore improve Facebook’s accuracy at suggesting whom users should tag in a newly uploaded photo.

However, DeepFace remains purely a research project for now. Facebook released a research paper on the project last week, and the researchers will present the work at the IEEE Conference on Computer Vision and Pattern Recognition in June. “We are publishing our results to get feedback from the research community,” says Taigman, who developed DeepFace along with Facebook colleagues Ming Yang and Marc’Aurelio Ranzato and Tel Aviv University professor Lior Wolf.

DeepFace processes images of faces in two steps. First it corrects the angle of a face so that the person in the picture faces forward, using a 3-D model of an “average” forward-looking face. Then the deep learning comes in as a simulated neural network works out a numerical description of the reoriented face. If DeepFace comes up with similar enough descriptions from two different images, it decides they must show the same face.

The performance of the final software was tested against a standard data set that researchers use to benchmark face-processing software, which has also been used to measure how humans fare at matching faces.

Neeraj Kumar, a researcher at the University of Washington who has worked on face verification and recognition, says that Facebook’s results show how finding enough data to feed into a large neural network can allow for significant improvements in machine-learning software. “I’d bet that a lot of the gain here comes from what deep learning generally provides: being able to leverage huge amounts of outside data in a much higher-capacity learning model,” he says.

The deep-learning part of DeepFace consists of nine layers of simple simulated neurons, with more than 120 million connections between them. To train that network, Facebook’s researchers tapped a tiny slice of data from their company’s hoard of user images—four million photos of faces belonging to almost 4,000 people. “Since they have access to lots of data of this form, they can successfully train a high-capacity model,” says Kumar.

ORIGINAL: Technology Review
By Tom Simonite
March 17, 2014