All around us we see that as technology advances, the sort of society we live in undergoes transformations as well. Changes are often slow and subtle, and it is only on reflection that you realize that a substantial evolution has taken place. You may have noticed it already and only subconsciously come to realize it. Our society is becoming more and more service oriented.

And at that same time, these services are being provided with the expectation of the disbursement of some sort of gratuity. This happens with increasing regularity in more situations where it was previously unexpected. Certainly, the social contract was that restaurants and coffee shops are fair game for tips, but bookstores, clothing shops and corner groceries once seemed off limits for that. That is no longer the case. The only certainty is change and keeping ahead of those changes or at least riding the crest of change is the only way forward. And that’s where technology can come to your aid.

Now Available in the Google Play store!

Tips Please! is your personal tip solicitation app conveniently loaded on your mobile phone allowing you to participate in the growing gratuity community.

How Do I Use It?

Consider these classic social use cases:

FAQ

1. This app is a godsend! How much does it cost?
   Tips Please! 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 Tips Please! collect?
   Tips Please! collects no personal information and does not communicate with any external servers. It does not review or collect your messages or store them anywhere. It should be noted though that by downloading the application you have likely identified yourself as someone who is looking to make a quick buck for essentially doing nothing. So, you’re pretty much a regular person.
4. It sort of seems like I don’t actually get any tips. What’s up with that?
   That’s weird. We’ll surely look into that and get back to you after a thorough investigation. We assure you that the app has be exhaustively tested by at least 3 or 4 passively interested Gen Xers while primarily focused on TikTok videos but intent on not pursuing any valuable job leads.
5. 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 users can recoup some of the app store subscription fees they forgot they are still paying.
6. 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.

Oh, you’ve been there! You’ve had that experience! When that person said or did that thing! That time that customer service rep was so polite but so unhelpful! When your friend made that face or responded in that way that she knows you hate! And you had to respond – right then and in that moment – to really let them know what you thought.

Now – you can! With 100% certainty that your every seething word will be captured by this custom messaging app including every CAPS LOCK obscenity, every misspelled swear word and every misused adjectival phrase. It will all be there. Ready to be fired off into the ether.

Then when you’re ready to send your screed, this custom messaging app takes over after you hit “Send”

Now Available in the Google Play store!

You need this custom messaging app and you need it now and you can get it here and you should.

How Do I Use It?

The interface is identical to all other messaging apps. If you want to import your contacts to ease your path to rage, you simply push the Contacts button and choose the recipient(s) from you contact list. When the recipient is an anonymous bot-like corporate surrogate and not in your contacts then just type their contact information (phone number, email or name) and the app will route it automatically.

After selecting the recipient, have at in the Message box, freely typing your thoughts and feelings, unencumbered by spelling correctors, sentence completion suggestions or AI assistants. It’s all you!

When it’s all written down and ready, just it the Send button.

FAQ

1. This app is a godsend! How much does it cost?
   And Another Thing! 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 And Another Thing! collect?
   And Another Thing! collects no personal information and does not communicate with any external servers. It does not review or collect your messages or store them anywhere. It should be noted though that by downloading the application you have likely identified yourself as someone with vitriol to spare. Not that that make you different or special, really.
4. It sort of seems like I can’t send any of the messages I author. I always have to hit Cancel. What’s up with that?
   That’s weird. We’ll surely look into that and get back to you after a thorough investigation. We assure you that the app has be exhaustively tested by throngs of angry, rage-filled and fully vitriolic individuals while waiting in line at the local DMV.
5. 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 freely express the vitriol afforded by this new technology. At the moment, they are quite busy sending vitriolic messages to the Apple Developer Program about all sorts of perceived slights.
6. 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.

Since its first arrival on the scene, Llama Detector has stood at the vanguard of new technology aimed at bettering humanity and enriching our life experience. Much has changed since those halcyon days of 2014 when Llama Detector entered the technology mainstream. Image processing and machine learning were in their infancies, and one could immediately understand how powerful Llama Detector could be if it actually leveraged any of those approaches.

Now Available in the Google Play Store!

With this newly released generation of Llama Detector, there are many more advanced computational techniques for us to avoid using. This includes, of course, artificial intelligence, quantum computing, molecular computing, large scale meta-heuristics and conflict‑free replicated data types. We ensured the underlying llama detection technology was improved to be both fully aware and completely devoid of these methodologies in order to bring you the cutting-edge results you had grown to expect – even from that first-generation application – within the framework of this second-generation reimagining.

As before this lifestyle app gives end-users the ability to detect the presence of llamas in any real-world situation.  By leveraging your platform’s high resolution on-device camera hardware and peripherals, images are deeply analyzed and scrutinized to reveal hidden contexts, meanings, contradictions, ambiguities and tensions that would otherwise remain invisible to the naked eye. This app is 100% 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 high-resolution 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 suspected llama directly whether it is a llama or not or you will need to spend a few minutes carefully examining the suspected area for llamas with a magnifying lens and a feather duster.

Upon launching the application, point the camera at the item or region that you suspect to be llama tainted.  Depress the button labelled ‘scan for llamas’ 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 analysis and detection process will begin.

The scanning and analysis process duration will vary according to the sensitivity set. After scanning and analysis, the Llama Detector will indicate if any llamas have been detected. Sometimes when other non-llama items of significance are detected, 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 settings (gear icon). Then, adjust the Llama Detection Sensitivity.  Use the slider to choose a value between 1% and 100%.  This will alter the detection algorithm characteristics. A higher value will make the results more accurate with fewer false positives.  The default value is 50%. Note, though, that the higher sensitivity values will draw more power and will likely cause short brownouts in the vicinity. But – it may be worth it!

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. As llama detection technology advances, will this app remain on the cutting edge?

You can rest assured that this app will serve as the gold standard for llama detection. Until it doesn’t.

7. 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 however somewhat concerned as they have received packages containing llama spit from an address in Cupertino, CA.

8. 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.

After a long and mysterious absence, the veritable essential mobile application force that is Formidable Engineering Consultants has stormed back to reclaim its crown. The developer of the ingenious and invaluable Llama Detector has returned. A soon-to-be-released bevy of applications will disrupt your life and, potentially, all life. While the specifics remain fully confidential, one of the applications will be a newly, redeveloped and fully enhanced Llama Detector incorporating new algorithms, techniques and strategies to turn the entire llama detection community on its head.

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.

This is the second installment in my irregular series of book reviews for O’Reilly Media. In the interests of full disclosure, I received this ebook for free in exchange for this review. I get to keep it even if I hate it and they will publish this review on their web site even if I trash the volume completely.

The book under the microscope this time is “The R Cookbook” by Paul Teetor. For those of you unfamiliar, R is a powerful, free, open source programming language and environment used for statistical programming and analysis. It features a rich graphical display language to assist in data visualization. You can think of it as a scripting language akin to Excel Spreadsheets or a variant of MATLAB focused on statistics. The language includes a full suite of community-developed, sector-specific libraries that provide re-usable functions typical of industry needs. These libraries indicate the areas in which R has found popularity.This includes the worlds of finance, genomics, statistics and data science.

The R Cookbook describes itself as a book for the user who is somewhat familiar with R but needs easy access to useful techniques and common R program building blocks. The book is arranged as a series of recipes. Each recipe describes a problem that you might trying to solve and then a solution or possible solutions to resolve the issue. For instance “You want a basic statistical summary of your data” is described as the problem to solve and then the text provides you with at least one approach to providing a solution to that problem.

The structure of the book is such that it begins with simpler recipes and builds its way up to more complex ones. In fact, because of this structure, I would recommend this book as a great tool to learning R for the novice despite the book’s self-identification of that being its incorrect use. The rational behind this recommendation is that the beginning recipes are tasks like “How do I install R?” and similar novice tasks. It then builds slowly from there into a use cases and scenarios of increasing complexity and utility.

The book includes great examples that illustrate the power of R in doing data transformation, probability and statistical analysis. It also shows how you can use R to provide meaningful graphical representations of your results. The chapter on ‘Useful Tricks’ is what seals the deal for me providing 19 great pointers to allow you to improve your R analyses.

The hoi polloi are running fast towards the banner marked “Internet of Things“.   They are running at full speed chanting “I-o-T, I-o-T, I-o-T” all along the way. But for the most part, they are each running towards something different.  For some, it is a network of sensors; for others, it is a network of processors; for still others, it is a previously unconnected and unnetworked  embedded system but now connected and attached to a network;  some say it is any of those things connected to the cloud; and there are those who say it is simply renaming whatever they already have and including the descriptive marketing label “IoT” or “Internet of Things” on the box.

So what is it?  Why the excitement? And what can it do?

At its simplest, the Internet of Things is a collections of endpoints of some sort each of which has a sensor or a number of sensors, a processor, some memory and some sort of wireless connectivity.  The endpoints are then connected to a server – where “server” is defined in the broadest possible sense.  It could be a phone, a tablet, a laptop or desktop, a remote server farm or some combination of all of those (say, a phone that then talks to a server farm).  Along the transmission path, data collected from the sensors goes through increasingly higher levels of analysis and processing.  For instance, at the endpoint itself raw data may be displayed or averaged or corrected and then delivered to the server and then stored in the cloud.  Once in the cloud, data can be analyzed historically, compared with other similarly collected data, correlated to other related data or even unrelated data in an attempt to search for unexpected or heretofore unseen correlations.  Fully processed data can then be delivered back to the user in some meaningful way. Perhaps the processed data could be displayed as trend display or as a prescriptive suite of actions or recommendations.  And, of course, the fully analyzed data and its correlations could also be sold or otherwise used to target advertising or product or service recommendations.

There is a further enhancement to this collection of endpoints and associated data analysis processes described in my basic IoT system.  The ‘things’ on this Internet of Things could also use to the data it collects to improve itself.  This could include identifying missing data elements or sensor readings, bad timing assumptions or other ways to improve the capabilities of the overall system.  If the endpoints are reconfigurable either through programmable logic (like Field Programmable Gate Arrays) or through software updates then new hardware or software images could be distributed with enhancements (or, dare I say, bug fixes) throughout the system to provide it with new functionality.  This makes the IoT system both evolutionary and field upgradeable.  It extends the deployment lifetime of the device and could potentially extend the time in market at both the beginning and the end of the product life cycle. You could get to market earlier with limited functionality, introduce new features and enhancement post deployment and continue to add innovations when the product might ordinarily have been obsoleted.

Having defined an ideal IoT system, the question becomes how does one turn it into a business? The value of these IoT applications are based on the collection of data over time and the processing and interpretation (mining) of said data.  As more data are collected over time the value of the analysis increases (but likely asymptotically approaching some maximal value).  The data analysis could include information like:

• Your triathlon training plan is on track, you ought to taper the swim a bit and increase the running volume to 18 miles per week.
• The drive shaft on your car will fail in the next 1 to 6 weeks – how about I order one for you and set up an appointment at the dealership?
• If you keep eating the kind of food you have for the past 4 days, you will gain 15 pounds by Friday.

The above sample analysis is obviously from a variety of different products or systems but the idea is that by mining collected and historical data from you, and maybe even people ‘like’ you, certain conclusions may be drawn.

Since the analysis is continuous and the feedback unsynchronized to any specific event or time, the fees for these services would have to be subscription-based.  A small charge every month would deliver the analysis and prescriptive suggestions as and when needed.

This would suggest that when you a buy a car instead of an extended service contract that you pay for as a lump sum upfront, you pay, say, $5 per month and the IoT system is enabled on your car and your car will schedule service with a complete list of required parts and tasks exactly when and as needed.

Similarly in the health services sector, your IoT system collects all of your biometric data automatically, loads your activity data to Strava, alerts you to suspicious bodily and vital sign changes and perhaps even calls the doctor to set up your appointment.

The subscription fees should be low because they provide for efficiencies in the system that benefit both the subscriber and the service provider.  The car dealer orders the parts they need when they need them, reducing inventory, providing faster turnaround of cars, obviating the need for overnight storage of cars and payment for rentals.

Doctors see patients less often and then only when something is truly out of whack.

And on and on.

Certainly the possibility for tiered levels of subscription may make sense for some businesses.  There may be ‘free’ variants that provide limited but still useful information to the subscriber but at the cost of sharing their data for broader community analysis. Paid subscribers who share their data for use in broader community analysis may get reduced subscription rates. There are obvious many possible subscription models to investigate.

These described industry capabilities and direction facilitated by the Internet of Things are either pollyannaish or visionary.  It’s up to us to find out. But for now, what do you think?

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.

There is a huge focus on big data nowadays. Driven by ever decreasing prices and ever increasing capacity of data storage solutions, big data provides magical insights and new windows into the exploitation of the long tail and addressing micro markets and their needs.  Big data can be used to build, test and validate models and ideas Big data holds promise akin to a panacea.  It is being pushed as a universal solution to all ills.  But if you look carefully and analyze correctly what big data ultimately provides is what Marshall MacLuhan described as an accurate prediction of the present.  Big data helps us understand how we got to where we are today. It tells us what people want or need or do within a framework as it exists today.  It is bounded by today’s (and the past’s) possibilities and ideas.

But big data does not identify the next seismic innovation.  It does not necessarily even identify how to modify the current big thing to make it incrementally better

In the October 2013 issue of IEEE Spectrum, an article described the work of a company named Lex Machina. The company is a classic big data play.  They collect, scan and analyze all legal proceedings associated with patent litigation and draw up statistics identifying, for instance, the companies who are more likely to settle, law firms that are more likely to win, judges who are more favorable to defendants or the prosecution, duration and cost assessments of prosecutions in different areas.  So it is a useful tool.  But all it does is tell you about the state of things now.  It does not measure variables like outcomes of prosecution or settlements (for instance, if a company wins but goes out of business or wins and goes on to build a more dominant market share or wins and nothing happens).  It does not indicate if companies protect only specific patents that have, say, an estimated future value of, say, $X million or what metric companies might use in their internal decision making process because that is likely not visible in the data.

Marissa Meyer, the hyper-analyzed and hyper-reported-on CEO of Yahoo!, famously tests all decisions based on data.  Whether it is the shade of purple for the new Yahoo! logo, the purchase price of the next acquisition or value of any specific employee – it’s all about measurables.

But how can you measure the immeasurable?  If something truly revolutionary is developed, how can big data help you decide if it’s worth it? How even can little data help you?  How can people know what they like until they have it? If I told you that I would provide you with a service that lets you broadcast your thoughts to anyone who cares to subscribe to them, you’d probably say.  “Sounds stupid. Why would I do that and who would care what I think?”  If I then told you that I forgot one important aspect of the idea, that every shared thought is limited to 140 characters, you would have likely said, “Well, now I KNOW it’s stupid!”.  Alas, I just described Twitter.  An idea that turned into a company that is, as of this writing, trading on the NYSE for just over $42 per share with a market capitalization of about $25 billion.

Will a strong reliance on big data lead us incrementally into a big corner?  Will all this fishing about in massive data sets for patterns and correlations merely reveal the complete works of Shakespeare in big enough data sets? Is Big Data just another variant of the Infinite Monkey Theorem? Will we get the to point that with so much data to analyze we merely prove whatever it is we are looking for?

Already we are seeing that Google Flu Trends is looking for instances of the flu and finds them where they aren’t or in higher frequencies than they actually are.  In that manner, big data fails even to accurately predict the present.

It is only now that some of the issues with ‘big data’ are being considered.  For instance, even when you have a lot of data – if it is bad or incomplete, you still have garbage only just a lot more of it (that is where wearable devices, cell phones and other sophisticated but merely thinly veiled data accumulation appliances come into play – to help improve the data quality by making it more complete).  Then the data itself is only as good as the analysis you can execute on it.  The failings of Google Flu Trends are often attributed to bad search terms in the analysis but of course, there could be many other different reasons.

Maybe, in the end, big data is just big hubris.  It lulls us into a false sense of security, promising knowledge and wisdom based on getting enough data but in the end all we learn is where we are right now and its predictive powers are, at best, based merely on what we want the future to be and, at worst, are non-existent.

Back in 1992, after the Berlin Wall fell and communist states were toppled one after another, Francis Fukuyama authored and published a book entitled The End of History and The Last Man.  It received much press at the time for its bold and seemingly definitive statement (specifically that whole ‘end of history’ thing with the thesis that capitalist liberal democracy is that endpoint). The result was much press, discussion, discourse and theorizing and presumably a higher sales volume for a book that likely still graces many a bookshelf, binding still uncracked.  Now it’s my turn to be bold.

Here it is:

With the advent and popularization of the smartphone, we are now at the end of custom personal consumer hardware.

That’s it.  THE END OF HARDWARE.  Sure there will be form factor changes and maybe a few additional new hardware features but all of these changes will be incorporated in smartphone handsets as that platform.

Maybe I’m exaggerating – but only a little.  Really, there’s not much more room for hardware innovation in the smartphone platform and as it is currently deployed, it contains the building blocks of any custom personal consumer device. Efforts are clearly being directed at gadgets to replace those cell phones.  That might be smart watches, wearable computers, tablets or even phablets. But these are really just changes in form not function.  Much like the evolution of the PC, it appears that mobile hardware has reached the point where the added value of hardware has become incremental and less valuable.  The true innovation is in the manner in which software can be used to connect resources and increase the actual or perceived power that platform.

In the PC world, faster and faster microprocessors were of marginal utility to the great majority of end-users who merely used their PCs for reading email or doing PowerPoint.  Bloated applications (of the sort that the folks at Microsoft seem so pleased to develop and distribute) didn’t even benefit from faster processors as much as they did from cheaper memory and faster internet connections.  And now, we may be approaching that same place for mobile applications.  The value of some of these applications is becoming limited more by the availability of on-device resources like memory and faster internet connections through the cell provider rather than the actual hardware features of the handset.  Newer applications are more and more dependent on big data and other cloud-based resources.  The handset is merely a window into those data sets.  A presentation layer, if you will.  Other applications use the information collected locally from the device’s sensors and hardware peripherals (geographical location, speed, direction, scanned images, sounds, etc.) in concert with cloud-based big data to provide services, entertainment and utilities.

In addition, and more significantly, we are seeing developing smartphone applications that use the phone’s peripherals to directly interface to other local hardware (like PCs, projectors, RC toys,  headsets, etc.) to extend the functionality of those products.  Why buy a presentation remote when you get an app? Why buy a remote for your TV when you can get an app? Why buy a camera when you already have one on your phone? A compass? A flashlight? A GPS? An exercise monitor?

Any consumer-targeted handheld device need no longer develop an independent hardware platform.  You just develop an app to use the features of the handset that you need and deploy the app.  Perhaps additional special purpose sensor packs might be needed to augment the capabilities of the smartphone for specialized uses but any mass-market application can be fully realized using the handset as the existing base and few hours of coding.

And if you doubt that handset hardware development has plateaued  then consider the evolution of the Samsung Galaxy S3 to the Samsung Galaxy S4.  The key difference between the two devices is the processor capabilities and the camera resolution.  The bulk of the innovations are pure software related and could have been implemented as part of the Samsung Galaxy S3 itself without really modifying the hardware.  The differences between the iPhone 4s and the iPhone 5s were a faster processor, a better camera and a fingerprint sensor.  Judging from a completely unscientific survey of end-users that I know, the fingerprint sensor remains unused by most owners. An innovation that has no perceived value.

The economics of this thesis is clear.  If a consumer has already spent $600 or so on a smartphone and lives most of their life on it anyway and carries it with them everywhere, are you going to have better luck selling them a new gadget for $50-$250 (that they have to order, wait for learn how to use, get comfortable with and then carry around) or an app that they can buy for $2 and download and use in seconds – when they need it?