Nicole Kidman’s Prosthesis Fools Better-Than-Human Face Recognition Algorithm

Last month in his keynote talk at the NVidia GPU Technology Conference, Andrew Ng (Baidu Chief Scientist, Stanford Professor, Google Brain team founder) described face comparison technology he and his team at Baidu developed. If you haven’t taken his Coursera machine learning course, that’s ok, this post doesn’t assume technical knowledge about machine learning.

The challenge is to compare two pictures containing faces and decide whether they are pictures of the same person or two different people. In the experiment, 6000 pairs of images were examined, both by humans and by algorithms from various teams around the world. Three teams, including Baidu, Google and the Chinese University of Hong Kong achieved better-than-human recognition performance. The Baidu team made just 9 errors out of the 6000 examples.

Here is a slide from Professor Ng’s talk with images of the ones they got wrong:

Face Recognition

You may notice that the top-left image pair is of movie actress Nicole Kidman, which the Baidu system incorrectly classified as two different people. What may be less obvious, is that the second image of Ms. Kidman was taken from her film, “The Hours” in which she is wearing a prosthetic nose.

Nicole Kidman in "The Hours"
Nicole Kidman in “The Hours”

Here are the overall results from implementations done by teams throughout the world. The dashed line indicates human performance at the same task. People typically think of recognizing faces as a very innate human task, but once again, we can be surprised that machine learning algorithms are now capable of equalling or outperforming humans.

More Human than Human

In his talk, Professor Ng credits two major factors as contributing to the vast improvements in machine learning technology over the past five years. As an analogy, think of launching a rocket into orbit: we need 1) both giant rocket engines and 2) lots of rocket fuel.

The rocket engine in his scenario is the incredible computational performance improvements brought to us by GPU technology. The fuel then, is access to huge amounts of data, coming to us from the prevalence of internet connected sensors, online services, and our society’s march toward digitization.

To perform this astounding face comparison judgment, algorithms are trained on facial data. The flow chart in the image above shows that if the algorithm is not performing well on the training data, we need a more powerful rocket engine.

High Performance Computing
The Talon 2.0 high performance computing system

 

The second part of the flowchart above illustrates that if the algorithm is learning the training data quite well, but performing poorly when presented with new examples, then perhaps more “rocket fuel” is required, so gathering more training data would be the logical approach to improve the system.

Professor Ng compares the benefits of high performance computing with cloud computing and states that better training performance may be achievable say with 32 GPUs in a co-located rack than by a larger number of servers running in the cloud. His reasoning is that communication latency, server downtime and other failures are more prevalent in cloud-based systems because they are spread out across more machines with more network connections.

Machine learning has been demonstrated to be good at many things. The recent improvements are not limited to face comparisons, in fact in this same talk improvements to Baidu’s speech recognition system were shown to perform well in noisy environments.

 

 

 

The Internet of Things: The Connected World Is Here and Now

Look around you and you will see thousands of “things” all within your immediate vicinity. Your keychain. Your desk chair. Your favorite coffee mug filled with Italian Roast coffee. Your dying ficus plant. With today’s technology, there is no reason why these things cannot communicate with you, in-real time.

  • Your plant should tell you, not only that it needs water, but how much and what position to place it during the day
  • Your coffee mug should know what kind of roast you want to drink today
  • You should be able to find your keys at a moment’s notice because you have a bad habit of misplacing them the moment you are about to go somewhere
  • Your desk chair should automatically adjust itself when it detects you are sitting with poor posture (reminder: stop slouching)

As luck would have it, there are technologies for each one of these things, being built. Right. Now. (See for yourself: Plant | Coffee | Keys | Chair).

The Hardware (R)evolution.

While  cliche, the world we are living in is becoming increasingly more connected, more now than ever before. While in research in development only a few years ago, technologies like RFID, NFC, and Zigbee are enabling the next generation of connected devices in a cost and energy efficient way. In fact, consumer goods that weren’t previously connected 18 months ago are now online. Recent examples, include:

As enabling technologies become cheaper and smaller, companies will be forced to innovate and think about how their offline products can get online.

Personalization.

Getting offline products into the 21st century is only the tip of the iceberg. Enhancing these products with connected technologies has to transform the product experience, be personal, and have utility. The bar for product experiences is so high, not executing against these objectives will result in a gimmicky, failure of an experience.

For example: A shoe company may want to create a running shoe with a GPS Dot. These “online shoes” should not only track where (and how long) the user was running, but it should provide actionable insights based on what the shoe company already knows about you: recommend running trails based on your running style and preferences, alert you when your friends are close by, give you a discount if you walk by a their store, and let you know how hard to run based on your body fat and weight goals.

Utility vs. Privacy

Privacy generally is a topic of concern when more devices become online and “all knowing.” As we’ve seen from the internet and media today, and in light of recent NSA privacy concerns, users are willing to give up certain liberties to connect with friends (Facebook), share their thoughts (Twitter), utilize free email (Google), or make free international calls over the internet (Microsoft/Skype). We believe its important for companies who are contemplating an online product strategy understand these implications and balance the utility an online product with the user’s privacy and the company’s ethics/values.