I have noticed that as people age, they become finer and finer versions of themselves. Their eccentricities become sharper and more pronounced; their opinions and ideas more pointed and immutable; their thoughts more focussed. In short, I like to say that they become more perfect versions of themselves. We see it in our friends and acquaintances and in our parents and grandparents. It seems a part of natural human development.

Back in 2006, Netflix initiated the Netflix Prize with the intent of encouraging development of improvements in the accuracy of predictions about how much someone is going to enjoy a movie based on their movie preferences and rewarding the winner with $1,000,000. Contestants were given access to a set of Netflix’s end-users’ movie ratings and were challenged to provide recommendations of other movies to watch that bested Netflix’s own recommendation engine. BellKor’s Pragmatic Chaos team was announced as the winner in 2009 having manage to improve Netflix’s recommendations by 10% and walked off with the prize money.

What did they do? Basically, they algorithmically determined and identified movies that were exceptionally similar to the ones that were already liked by a specific user and offered those movies as recommended viewing. And they did it really well.

In essence what the Bellkor team did was build a better echo chamber. Every viewer is analyzed, their taste detailed and then the algorithm perfects that taste and hones it to a razor sharp edge. You become, say, an expert in light romantic comedies with a strong female lead, who lives in a spacious apartment in Manhattan, includes many dog owners, no visible children and often features panoramic views of Central Park.

Of course, therein lies the rub. A multifaceted rub at that. As recommendation engines become more accurate and discerning of individual tastes they remove any element of chance, randomness or error that might serve to introduce new experiences, genres or even products into you life. You become a more perfect version of you. But in that perfection you are also stunted. You are shielded from experimentation and breadth of experience. You pick a single pond and overfish it.

There are many reasons why this is bad and we see it reflected, most obviously, in our political discourse where our interactions with opposing viewpoints are limited to exchanges of taunts (as opposed to conversations) followed by a quick retreat to the comfort of our well-constructed echo chambers of choice where our already perfected views are nurtured and reinforced.

But it also has other ramifications. If we come to know what people like to such a degree then innovation outside safe and well-known boundaries might be discouraged. If Netflix knows that 90% of its subscribers like action/adventure films with a male hero and lots of explosions why would they bother investing in a story about a broken family being held together by a sullen beekeeper. If retail recommendations hew toward what you are most likely to buy – how can markets of unrelated products be expanded? How can individual tastes be extended and deepened?

Extending that – why would anyone risk investment in or development of something new and radically different if the recommendation engine models cannot justify it. How can the leap be made from Zero to One – as Peter Theil described – in a society, market or investment environment in which the recommendation data is not present and does not justify it?

There are a number of possible answers. One might be that “gut instincts” need to continue to play a role in innovation and development and investment and that risk aversion has no place in making the giant leaps that technology builds upon and needs in order to thrive.

A more geeky answer is that big data isn’t yet big enough and that recommendation engines aren’t yet smart enough. A good recommendation engine will not just reinforce your prejudicial tastes, it will also often challenge and extend them and that we don’t yet have the modelling right to do that effectively.  The data are there but we don’t yet know how to mine it correctly to broaden rather than narrow our horizons. This broadening – when properly implemented – will widen markets and opportunities and increase revenue.

On June 2, 2014, we released our first Android application to the Google Play store.  “Llama Detector” is a lifestyle app that gives end-users ability to detect the presence of llamas in social situations.  It affords the end-user greater comfort in their daily interactions by allowing them to quietly and quickly detect hidden llamas wherever they may be. It does this using the your platform’s on-device camera hardware and peripherals. This amazing and technologically advanced application is guaranteed to provide end-users with seconds or even minutes of amusement. This posting serves as the end-user documentation and FAQ listing.

Usage

Using the Llama Detector is simple and straight forward.  The application prioritizes use of the rear-facing camera on your device.   If the device has no rear-facing camera then the front-facing camera is used. If the device has no camera then you will need to ask the supposed llama directly if it is a llama or spend a few minutes carefully examining the suspected area for llamas.

Upon launching the application, point the camera at the item or region that you suspect to be llama-infested.  Depress the button labelled ‘Scan’ when you have successfully framed the area that needs to be analyzed. The image will be captured and the red scan line will traverse the screen and the detection process will begin.

If you decide against analysis after beginning the scan, for any reason, you may cancel the operation by depressing the ‘Cancel’ button. Otherwise, scanning will continue for approximately 10 seconds.  After scanning, the Llama Detector will indicate if any llamas have been detected. Sometimes other items are detected and Llama Detector is able to indicate what it has identified.

If you would like to alter the detection sensitivity of the application, you may do so through the application preferences.  Choose the preferences either through the soft button or menu bar.  Then, display the Llama Sensitivity Filter.  Enter an integer value between 1 and 1000 where 1000 is the highest sensitivity value (and 1 is lowest).  This will alter the detection algorithm characteristics. A higher value will make the results more accurate with fewer false positives.  The default value is 800.

FAQ

1. How much does this amazing application cost?

Llama Detector is an absolutely free download from the Google Play store.

2. Free? That’s crazy! How do you do that?

How do we do it? Volume.

3. What sort of personal information does Llama Detector collect?

Llama Detector collects no personal information and does not communicate with any external servers. It should be noted though that by downloading the application you have identified yourself as either a llama or a llama enthusiast.

4. I went to the zoo and used Llama Detector at the llama exhibit but it detected no llamas.  Why is that?

Llamas are very difficult to hold in captivity.  They tend to sneak out of their pens and hang out at the concession stands eating hot dogs and trying to pick-up women. For this reason, most zoos use camels or, in some cases, baby giraffes dressed up as llamas in the llama pens.  The Llama Detector application can be used to indicate if a zoo is engaged in this sort of duplicity.  For this reason, many zoos nationwide ban the use of Llama Detector within the confines of their property.

5. I used Llama Detector in my house and it detected a llama in my bathroom. Now I am afraid to use the bathroom.  What do I do?

Llamas are quite agile and fleet of foot.  It is important to note that detection of llamas should be run multiple times for surety.  If the presence of a llama is verified, start making lettuce noises and slowly move to an open space.  The llama will follow you to that space.  Then stop making the lettuce noises.  The llama will wonder where the lettuce went and start looking around the open space.  Then quickly and silently proceed into the now llama-free bathroom.

6. When will the iOS version be available?

Our team of expert programmers are hard at work developing a native iOS version of this application so that iPhone user can enjoy the comfort and protection afforded by this new technology. The team is currently considering whether to wait for the release of iOS 8 to ensure a richer user experience.

7. I have another question but I don’t know what it is.

Feel free to post your questions to android at formidableengineeringconsultants dot com.  If it’s a really good question, we’ll even answer it.