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.




Health IT 2015 Summary

Secure Texting

Pager Explosion

Now that the 2015 HIMSS conference in Chicago has wrapped up, I will try to summarize the trends I observed and how Concrete Interactive fits in. It is clear that secure text messaging is a much-needed feature in healthcare. There are at least 2 established companies vehemently pursuing it: TigerText  Imprivata (via their Coretext feature), and several startups presenting at HIMSS: DiagnotesMyCareText, Cotap.

As we know TigerText just closed a $21M VC round. They claim to have 300 enterprise customers mostly in healthcare, including 4 of the largest for-profit hospital chains.

What isn’t clear to me is whether secure messaging is a separate app, or really a feature to be used with EHR (Electronic Health Record) apps that health companies already have. So for example, Imprivata’s CoreText is really positioned more as a feature of their larger system.

However, as a separate app, secure texting following the BYOD (bring your own device) (yes, this is literally the way they talk about it), is a very attractive feature that many people want, and could provide a solid scenario for deeper involvement or integration at a custom development level.

IoT Health

Medical Devices

Another clear area of expansion is in medical device connectivity. For example, Qualcomm Life bought Healthy Circles, a deal supposedly in the $375M range. It is an iPhone app for continuous care. The patient goes home and plugs in a local bluetooth/3g router into a wall outlet. All the continuous care devices (Class 2 FDA approved medical devices) such as blood pressure monitoring, step counting, pulse, temp, glucose monitoring, even home ventilators and other Class 3 (high risk) devices.

The physician gets a portal. The patient can view and augment the data on the iPhone, though the app isn’t even required. This pattern is repeated over and over by other companies: device, connectivity, app, cloud-based portal.

Machine Learning

Eye Code

The industry is only just awakening to the fact that data science will play a big role. Channels of information such as medical devices and apps are beginning to provide the big data they will use. I did make a nice connection at Wolters Kluwer. They are already doing rule-based processing to de-dupe health data. So if a doctor writes COD, they expand that to codeine. But they want to improve their systems via natural language processing (NLP).

I also met with Piers Nash from the University of Chicago at a Genomics SIG. He’s working with NCI and already has 6 Petabytes (PB) of genomic data from >10,000 patients online and available to the public (after a straightforward application process). He’s looking to host algorithms next and run compute cycles from virtual machines (PAAS type like AWS). One basic problem they are trying to improve is referred to as Single Nucleotide Variation calling (SNV calling). The problem is that each person’s DNA is slightly different, because we are different people. The trick is to identify which nucleotide (DNA letters) are different because of normal genetic variation between people, vs. mutations that cause cancer. One interesting aspect of this problem is that as algorithms improve, past recommendations may become invalid. And there may be a liability aspect at work. Samsung genomics was also in attendance at this meeting. They are launching an initiative to sequence tumors and make recommendations, but it’s similar to others already out there, such as Paradigm.

Also at the genomics meeting was Michael Hultner, the Chief Scientist for Lockheed Martin’s health and life sciences division. They are bidding as are many others for the UK’s 100,000 genomes project. He says their expertise lies in the integration of many technologies and thinks they are well positioned in the health space (not just outer space). So it’s fascinating to see the kinds of companies entering or expanding in this market.

Big Picture Strategy

The healthcare IT space is rapidly expanding as healthcare laws such as Meaningful Use Stage II come into effect increase incentives to leverage advances in the technology. There are many land grabs playing out. Any space worth entering will have competition, but based on my assessment of the overall quality in the space, I believe Concrete Interactive is well positioned to innovate, and stand up great apps against much larger players than ourselves.


Bed babes tout their wares
Bed, not booth babes

This year’s Healthcare Information and Management Systems Society conference in Chicago is a veritable candy store of high-tech healthcare. Yes the smart hospital beds and baby monitoring bracelets are fascinating. But perhaps the highest impact, most impressive technology on offer is what you can’t see—the software. Though it has about as much shazam as a bed pan, the coming health communication infrastructure known as HL7 FHIR (pronounced like “fire”) will allow access to the coveted Electronic Health Record (EHR) via many new applications and devices.

An easy to read diagram
An easy to read diagram

Also very impressive and a bit more visible were the beautiful mobile workflow apps like Nextgen’s “Go for iPad.” What I like about this electronic health record and dictation recording tool is that it does not do everything. The heavy lifting of setting up records is done on the desktop (templating in healthcare parlance), and on-the-go actions such as dictation and prescription refills, can be executed in short order on the iPad.

NextGen Go
NextGen Go

I also learned that Greenway, a software provider of Practice Management (PM) and EHR tools, has an app marketplace (think iTunes). Topping their offering is Phreesia, a check-in app for iPad can replace all that form filling in the doctor’s office with a few taps of a touchscreen.

The Internet of Things (IoT) was also present, from Tyco’s tracking bracelets, for babies and elders, to decibel logging sensors that monitor noise levels. Quietyme, a HealthBox and Gener8tor accelerator graduate, establishes a mesh network of small volume monitors in each hospital room, the corridor, nurses station, etc. They perform some fancy data analytics (in partnership with Miosoft and Zero Locus). CEO John Bialk says that by comparing noise levels in patient rooms with patient surveys, they can document and predict which noisy areas are having a negative impact on healing. And from Ascom, voice over internet protocol (VoIP) portable devices are like little cordless phones that nurses can use on the local area network (LAN). Their Android device even supports internet instant messaging.

Mesh networking decibel monitor
Mesh networking decibel monitor

Thank you to all those who visited with Concrete Interactive, and those who described their wonderful products, software, services and innovation.

Chris Isham from Sidus BioData, Chris Andreski from Ascom, Suzy Fulton from Greenway, Bernard Echiverri from Corepoint, Piers Nash from University of Chicago, Ben Bush from Orchard Software, Mark Lynch from Tyco Security Products, Huey Zoroufy from Quietyme, Matt Ward from Imprivata, Stevie Bahu from Modis Health IT, Michael Hultner from Lockheed Martin, Sungsoo Kang from Samsung.


Amazon AWS at HIMSS 2015

Concrete Interactive is available for meetings at HIMSS 2015, the healthcare IT conference in Chicago this April 12-16.

And I know you’ll be almost as excited to learn that for the first time this year Amazon will be making a full-fledged appearance at HIMSS. What’s even more remarkable is that some of the leaders of the AWS HIPAA compliance team, such as Chris Crosbie HIPAA Solutions Architect, Jessie Beegle their Business Development Manager for the Healthcare Industry, and Kenzie Kepper member of the AWS Healthcare Marketing Team will be present and accepting meetings.

You can request a meeting if interested in learning more about hosting HIPAA applications on AWS. Here’s the signup link:

In my experience with the Amazon Popup Loft in San Francisco, the AWS team is very giving of their time and expertise. These aren’t your typical Apple “Genius” types who fall into a prescribed script about fixing your iPhone. The solution architects and technical team members who are available at the Popup Loft are the actual people with inside technical knowledge of the AWS service, and they have been happy to dive into our application details.

So, how does one implement a HIPAA compliant software application on Amazon Web Service? Back when Concrete Interactive built our first HIPAA app in 2012, assigning responsibility across the network infrastructure was quite a challenge. Nowadays, Amazon has drawn a bright line at the hypervisor, the piece of network virtualization software that manages the particular application’s server. Their shared responsibility model ensures from the hypervisor outward, throughout the rest of the AWS network, it is Amazon’s responsibility to secure PHI.

AWS shares responsibility for PHI with Concrete Interactive
AWS shares responsibility for PHI with BAA signatories like Concrete Interactive


AWS specifically supports HIPAA compliant infrastructure through six of their services today: Amazon EC2, Amazon EBS, Amazon S3, Amazon Redshift, Amazon Glacier, and Amazon Elastic Load Balancer.

Specifically on EC2, you must use a dedicated instance. This comes with a higher monthly fee, but it’s peanuts compared with building your own compliant datacenter.

According to Amazon’s HIPAA compliance video, over 600 companies have signed their Business Associates Agreement (including us!) This agreement allows our HIPAA compliant apps to be validated, and shows where PHI responsibility lies, depending on which side of the hypervisor line it is used, stored, or transferred.

If you are interested in meeting with Concrete Interactive at HIMSS 2015, please drop us a line. In partnership with Amazon AWS, and FDA Compliance Advisor David Nettleton, we hope to shed light on any of your HIPAA, healthcare, web or mobile app development questions.