Previously on Vitamin D Blog, I mentioned the Pennsylvania school that allegedly turned on webcams of laptops given to students, recording them in their homes without their knowledge. The school claimed that the cameras only were activated 42 times to recover lost laptops. Later it turned out that “activated” meant ongoing surveillance.  In all, 56,000 images, as well as websites visited and chat threads, were captured.  The suit alleges email from a staffer saying the surveillance was like “a little…soap opera,” and a response from the administrator of the program saying, “I know, I love it.”  (Security technology advocates to administrator: YOU’RE NOT HELPING.)

Afterward, the developer of the technology, Absolute Software, announced they were removing the feature. The company claimed “Theft Track” was a legacy feature of a product meant for “lifecycle management.” The company blog stated, “[W]ebcam pictures are not a useful tool in tracking down the location of a stolen computer.” (Apparently not, if you need 56,000.)

Distancing themselves from this feature sounds credible, however, since the company has another product that is designed for theft recovery. You have to file a police report first; then Absolute locates the machine using its IP address (no pictures), and informs the local police directly.  This seems like a much smarter approach.   As their Marketing VP said to Computerworld, “Even if you are able to locate the laptop on your own, what do you do then?”

Subsequently, in response to this event Senator Arlen Specter introduced the Surreptitious Video Surveillance Act of 2010. This bill would make it illegal to video anyone secretly in a residence who has “a reasonable expectation of privacy.”

Will this make nanny-cam recording illegal? Hidden ones might be illegal. The bill’s press release outlines important exceptions, however, for residential surveillance with consent, cameras in the workplace, undercover operations and “residential security systems which use video cameras.”

I suppose  highlighting abuses of security technology isn’t the best way to increase our sales, but it’s important to acknowledge privacy implications and educate people about them.  For more information on digital privacy rights, check out the Electronic Frontier Foundation.

Tip #1: If you see a lit LED like the green light above, your webcam is on. Tip #2: No one has developed software that can see through tape placed over the lens.

Have you ever used a product that is so difficult you asked yourself, “How did they ship this steaming pile?” Doesn’t anyone evaluate usability?

Many companies make mistakes conducting usability testing, but I want to discuss people who do it right–and still get it wrong.  In other words, when can results mislead you?

Usability testing typically involves showing people products and asking them to try several tasks. This gives you a good assessment of how easy your product is to learn, but could miss (or create) problems in other areas of usability.

For example, a designer once told me testing showed people couldn’t find a given feature. So they decided to display a dialog offering the feature every time you used the app. Problem solved. Unfortunately, other  problem created, as in, I can’t believe you show me this stupid screen every time. And follow-up testing testing might not detect such problems since people are more patient in “tests” than in real life (e.g., they may actually read wizard text rather than impatiently clicking “Next”). To cite the Heisenberg principle (albeit inaccurately), the act of measuring usability can affect the results.

Also, usability testing captures the first experience, not learning curves.  As novelty wears off, swearing may ensue.  Or conversely, some features are difficult to discover but delight customers anyway (classic example: ejecting Mac floppy disks by dragging to the trash).   A test subject once explained this paradox to me by saying, “it’s intuitive once you figure it out.” (Tip: when testing, repeat troublesome tasks to test retention.)

Most important, over time the value of intuitiveness declines, but the value of efficiency increases. You stop learning new features, but extra steps for the ones you use often become annoying. (For the theoretical extreme of “the most intuitive product ever,” see the Onion spoof of a Mac with no keyboard but a giant iPod wheel.  “Everything is just a few hundred clicks away.”)

In other words, myopic reliance on process can create a false sense of success.  Understand the limitations of usability testing, and augment it with real-world evaluation over time.

If HTM theory describes how brains work, could you build a conscious machine? Well, first you’d need a theory of consciousness. David Chalmers divides consciousness theory into “easy” and “hard” problems.  “Easy” problems involve cognitive capabilities like attention, autonomy, being awake. To explain these, you “need only specify a mechanism that can perform the function.” Easy peasy.

The “hard” problem is explaining why we have subjective experience (feelings like pain, emotions, and wonder).  Chalmers argues theories ignoring the hard problem are cop-outs.

But if I wrote a product brief for a conscious machine, first I’d ask, who needs consciousness? That’s because I don’t see HTM leading to sentient machines, but I do think  HTM could solve problems people think require consciousness.

For example, conscious machines might excel at autonomous problem solving. But you could also imagine autonomous programming without sentience. Army ants aren’t conscious, but they’re autonomous enough to ruin your safari.

Maybe sentient robots would be creative. But you can talk about creative HTM networks without introducing consciousness.

Or maybe conscious robots would seem more “human.” But if HTM leads us to creative, autonomous robots, our instinct to anthropomorphize could make clever UI tricks eerily effective. For starters, replace the vacant stare of sci-fi robots with eyes that track speakers and have simulated saccades.

In other words, if C3PO brings you vodka tonics and shoots Imperial Stormtroopers, why upgrade to the sentient model? Could you even tell the difference? (Philosophers call these “zombies”—not the flesh eating undead, but indistinguishable imitations of sentient beings.)

On the other hand, if consciousness could provide benefits beyond advanced intelligence, what are they? If you had a “conscious” Roomba with the intelligence of a pigeon, it might just be annoying.

Don’t get me wrong.  Sentient robots would be fascinating. And maybe they would be better (say some recursive observational algorithms required for self-awareness enable sentient machines to solve harder problems). I’m just suggesting HTM may not be a path to “hard-problem” consciousness, but an “easy-problem” level of intelligence would enable a pretty kick-ass robot.

P.S. Check out Jeff Hawkins’ thoughts on consciousness.

Descartes' dualist theory of consciousness: how will this lead to Cylons?

If Vitamin D has state-of-the-art human recognition, why not try recognizing faces?

We considered this, first by evaluating existing technologies.  NYU reports that in “laboratory conditions” facial recognition can generate 99% accuracy. So is this a “solved problem?”  What about the real world?  Well, mug shots and passport photos control camera angles, distances and lighting.  This lets you generate consistent metrics on geometric relationships between eyes, nose, cheekbones, and jaw. Altering your facial topology is hard, though just in case, Canadian passport photos require a “neutral facial expression.”  (Apparently, the Canadian government frowns on smiling.)

If the images you try to recognize are also consistent, like in lab tests, rules-based “template matching” works well.  Unfortunately, in the real world, criminals avoid walking up to cameras and posing. They wear sunglasses, face various angles, change appearance with age.  (Or they smile maniacally when shooting you.)  As a result, the NYU study found real-world accuracy ranging from 60% to systems that “were in fact not able to recognise any of the subjects.” Bummer. You tend to want zero tolerance when errors lead to detainment and cavity searches.

Facial recognition in photo applications is a different story. Your friends cooperate to show their faces, simplifying things significantly.  Polar Rose claims about 76%  accuracy, which it says compares favorably with Picasa and iPhoto.  76% could be distressing in the cavity-search case, but elegant photo-application interfaces make you happy with what works rather than upset with what doesn’t. (The technology does create privacy concerns, however.)

So what about HTM?  Its inherent robustness suggests HTM could recognize faces better in poor conditions. The problem, though, is that HTM is a classification algorithm. Detecting humans is a two-category problem (people or non-people). But using classification to find Osama-bin Laden is a 6,692,030,277-category problem (or however many people are in your watch list).

A more attainable short-term problem might be to tell members of your family apart.  Or we could try isolating faces in videos and licensing an existing facial recognition system. Otherwise, for now we’ll invest more in areas we feel we can offer stronger differentiation.