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.

There is a great imbalance in the vast internet marketplace that has yet to be addressed and is quite ripe for the picking. In fact, this imbalance is probably at the root of the astronomical stock market valuations of existing and new companies like Google, facebook, Twitter and their ilk.

It turns out that your data is valuable.  Very valuable.  And it also turns out that you are basically giving it away.  You are giving it away – not quite for free but pretty close.  What you are getting in return is personalization. You get advertisements targeted at you providing you with products you don’t need but are likely to find quite iresistable.  You get recommendations for other sites that ensure that you need never venture outside the bounds of your existing likes and dislikes. You get matched up with companies that provide services that you might or might not need but definitely will think are valuable.

Ultimately, you are giving up your data so businesses can more efficiently extract more money from you.

If you are going to get exploited in this manner, it’s time to make that exploitation a two way street. Newspapers, for instance, are rapidly arriving at the conclusion that there is actual monetary value in the information that they provide.  They are seeing that the provision of vetted, verified, thougful and well-written information is intrinsicly worth more than nothing.  They have decided that simply giving this valuable commodity away for free is giving up the keys to the kingdom.  The Wall Street Journal, the New York Times, The Economist and others are seeing that people are willing to pay and do actually subscribe.

There is a lesson in this for you – as a person. There is value in your data.  Your mobile movements, your surf trail, your shopping preferences  It  should not be the case that you implicitly surrender this information for better personalization or even a $5 Starbucks gift card.  This constant flow of data from you, your actions, movements and keystrokes ought to result in a constant flow of money to you.  When you think about it, why isn’t the ultimate personal data collection engine, Google Glass, given away for free? Because people don’t realize that personal data collection is its primary function.  Clearly, the time has come for the realization of a personal paywall.

The idea is simple, if an entity wants your information they pay you for it.  Directly.  They don’t go to Google or facebook and buy it – they open up an account with you and pay you directly.  At a rate that you set.  Then that business can decide if you are worth what you think you are or not.  You can adjust your fee up or down anytime and you can be dropped or picked up by followers. You could provide discount tokens or free passes for friends.  You could charge per click, hour, day, month or year.  You might charge more for your mobile movements and less for your internet browsing trail.  The data you share comes with an audit trail that ensures that if the information is passed on to others without your consent you will be able to take action – maybe even delete it – wherever it is.  Maybe your data lives for only a few days or months or years – like a contract or a note – and then disappears.

Of course, you will have to do the due diligence to ensure you are selling your information to a legitimate organization and not a Nigerian prince.  This, in turn, may result in the creation of a new class of service providers who vet these information buyers.

This data reselling capability would also provide additional income to individuals.  It would not a living wage to compensate for having lost a job but it would be some compensation for participating in facebook or LinkedIn or a sort of kickback for buying something at Amazon and then allowing them to target you as a consumer more effectively. It would effectively reward you for contributing the information that drives the profits of these organizations and recognize the value that you add to the system.

The implementation is challenging and would require encapsulating data in packets over which you exert some control.  An architectural model similar to bitcoin with a central table indicating where every bit of your data is at any time would be valuable and necessary. Use of the personal paywall would likely require that you include an application on your phone or use a customized browser that releases your information only to your paid-up clients. In addition, some sort of easy, frictionless mechanism through which companies or organizations could automatically decide to buy your information and perhaps negotiate (again automatically) with your paywall for a rate that suits both of you would make use of the personal paywall invisible and easy. Again this technology would have to screen out fraudulent entities and not even bother negotiating with them.

There is much more to this approach to consider and many more challenges to overcome.  I think, though, that this is an idea that could change the internet landscape and make it more equitable and ensure the true value of the internet is realized and shared by all its participants and users.

Everyone who is anyone loves bandying about the name of Clayton Christensen, the famed Professor of Business Administration at the Harvard Business School, who is regarded as one of the world’s top experts on innovation and growth and who is most famous for coining the term “disruptive innovation“. Briefly, the classical meaning of the term is as follows. A company, usually a large one, focuses on serving the high end, high margin part of their business and in doing so they provide an opening at the low end, low margin market segment.  This allows for small nimble, hungry innovators to get a foothold in the market by providing cheap but good enough products to the low end who are otherwise forsaken by the large company who is only willing to provide high priced, over-featured products.  These small innovators use their foothold to innovate further upmarket providing products of increasingly better functionality at lower cost that the Big Boys at the high end.  The Big Boys are happy with this because those lower margin products are a lot of effort for little payback and “The Market” rewards them handsomely for doing incremental innovation at the high end and maintaining high margins.  In the fullness of time, the little scrappy innovators disrupt the market with cheaper, better and more innovative solutions and products that catch up to and eclipse the offerings of the Big Boys, catching them off guard and the once large corporations, with their fat margins, become small meaningless boutique firms.  Thus the market is disrupted and the once regal and large companies, even though they followed all the appropriate rules dictated by “The Market”, falter and die.

Examples of this sort of evolution are many.  The Japanese automobile manufacturers used this sort of approach to disrupt the large American manufacturers in the 70s and 80s; the same with Minicomputers versus Mainframes and then PCs versus Minicomputers; to name but a few.  But when you think about it, sometimes disruption comes “from above”.  Consider the iPod.  Remember when Apple introduced their first music player?  They weren’t the first-to-market as there were literally tens of MP3 players available.  They certainly weren’t the cheapest as about 80% of the portable players had a price point well-below Apple’s $499 MSRP.  The iPod did have more features than most other players available and was in many ways more sophisticated – but $499?   This iPod was more expensive, more featured, higher priced, had more space on it for storage than anyone could ever imagine needing and had bigger margins than any other similar device on the market. And it was a huge hit.  (I personally think that the disruptive part was iTunes that made downloading music safe, legal and cheap at a time when the RIAA was making headlines by suing ordinary folks for thousands of dollars for illegal music downloads – but enough about me.)  From the iPod, Apple went on to innovate a few iPod variants, the iPhone and the iPad as well as incorporating some of the acquired knowledge into the Mac.

And now, I think, another similarly modeled innovation is upon us.  Consider Tesla Motors.  Starting with the now-discontinued Roadster – a super high end luxury 2 seater sport vehicle that was wholly impractical and basically a plaything for the 1%.  But it was a great platform to collect data and learn about batteries, charging, performance, efficiency, design, use and utility.  Then the Model S that, while still quite expensive, brought that price within reach of perhaps the 2% or even the 3%.   In Northern California, for instance, Tesla S cars populate the roadways seemingly with the regularity of VW Beetles.  Of course, part of what makes them seem so common is that their generic luxury car styling makes them nearly indistinguishable, at first glace, from a Lexus, Jaguar, Infiniti, Maserati, Mercedes Benz, BMW and the like. The choice of styling is perhaps yet another avenue of innovation.  Unlike, the Toyota Prius whose iconic design became a “vector” sending a message to even the casual observer about the driver and perhaps the driver’s social and environmental concerns.  The message of the Tesla’s generic luxury car design to the casual observer merely seems to be “I’m rich – but if you want to learn more about me – you better take a closer look”. Yet even attracting this small market segment, Tesla was able to announce profitability for the first time.

With their third generation vehicle, Tesla promises to reduce their selling price by 40% over the current Model S .  This would bring the base price to about $30,000 which is within the average selling price of new cars in the United States.  Even without the lower priced vehicle available, Tesla is being richly rewarded by The Market thanks to a good product (some might say great), some profitability, excellent and savvy PR and lots and lots of promise of a bright future.

But it is the iPod model all over again. Tesla is serving the high end and selling top-of-the-line technology.  They are developing their technology within a framework that is bound mostly by innovation and their ability to innovate and not by cost or selling price.  They are also acting in a segment of the market that is not really well-contested (high-end luxury electric cars).  This gives them freedom from the pressures of competition and schedules – which gives them an opportunity to get things right rather than rushing out ‘something’ to appease the market.  And with their success in that market, they are turning around and using what they have learned to figure out how to build the same thing (or a similar thing) cheaper and more efficiently to bring the experience to the masses (think: iPod to Nano to Shuffle).  They will also be able thusly to ease their way into competing at the lower end with the Nissan Leaf, Chevy Volt, the Fiat 500e and the like.

Maybe the pathway to innovation really is from the high-end down to mass production?

I’ve had occasion to be interviewed for positions at a variety of technology companies. Sometimes the position actually exists, other times it might exist and even other times, the folks are just fishing for solutions to their problems and hope to save a little from their consulting budget. In all cases, the goal of the interview is primarily to find out what you know and how well you know it in a 30 to 45 minute conversation. It is interesting to see how some go about doing it. My experience has been that an interview really tells you nothing but does give you a sense of whether the person is nice enough to “work well with others“.

But now, finally folks at Google used big data to figure out something that has been patently obvious to anyone who has either interviewed for a job or was interviewing someone for a job. The article published in the New York Time details a talk with Mr. Laszlo Bock, senior vice president of people operations at Google.  In it, he shared that puzzle questions don’t tell you anything about anyone.  I maintain that they tell you if someone has heard that particular puzzle question before. In the published interview Mr. Bock, less charitably, suggests that it merely serves to puff up the ego of the interviewer.

I think it’s only a matter of time before big data is used again to figure out another obvious fact – that even asking simple or complex programming questions serves as no indicator of on-the-job success.  Especially now in the age of Google and open-source software.  Let’s say you want to write some code to sort a string of arbitrary letters and determine the computational complexity, a few quick Google searches and presto – you have the solution.  You need to understand the question and the nature of the problem but the solution itself has merely become a matter of copying from your betters and equals who shared their ideas on the Internet.  Of course, such questions are always made more useless when the caveat is added – “without using the built-in sort function” – which is, of course, the way you actually solve it in real life.

Another issue I see is the concern about experience with a specific programming language. I recall that the good people at Apple are particularly fond of Objective C to the point where they believe that unless you have had years of direct experience with it, you could never use it to program effectively.  Of course, this position is insulting to both any competent programmer and the Objective C language. The variations between these algorithmic control flow languages are sometimes subtle, usually stylistic but always easily understood. This is true of any programming language.  In reality, if you are competent at any one, you should easily be able to master any another. For instance,  Python uses indentation but C uses curly braces to delineate code blocks.  Certainly there are other differences but give any competent developer a few days and they can figure it out leveraging their existing knowledge.

But that still leaves the hard question.  How do you determine competency?  I don’t think you can figure it out in a 45 minute interview – or a 45 hour one for that matter – if the problems and work conditions are artificial.  I think the first interview should be primarily behavioral and focus on fit and then, if that looks good, the hiring entity should then pay you to come in and work for a week solving an actual problem working with the team that would be yours. This makes sense in today’s world of limited, at-will employment where everyone is really just a contractor waiting to be let go. So, in this approach, everyone gets to see how you fit in with the team, how productive you can be, how quickly you can come up to speed on a basic issue and how you actually work a problem to a solution in the true environment. This is very different from establishing that you can minimize the number of trips a farmer takes across a river with five foxes, three hens, six bag of lentils, a sewing machine and a trapeze.

I encourage you to share some of your ideas for improving the interview process.

I think it’s high time I authored a completely opinion-based article full of observations and my own prejudices that might result in a littany of ad hominem attacks and insults.  Or at least, I hope it does.  This little bit of prose will outline my view of the world of programmable logic as I see it today.  Again, it is as I see it.  You might see it differently.  But you would be wrong.

First let’s look at the players.  The two headed Cerberus of the programmable logic world is Altera and Xilinx.  They battle it out for the bulk of the end-user market share.  After that, there are a series of niche players (Lattice Semiconductor, Microsemi (who recently purchased Actel) and Quicklogic), lesser lights (Atmel and Cypress) and wishful upstarts (Tabula, Achronix and SiliconBlue).

Atmel and Cypress are broadline suppliers of specialty semiconductors.  They each sell a small portfolio of basic programmable logic devices (Atmel CPLDs, Atmel FPGAs and Cypress CPLDs).  As best I can tell, they do this for two reasons.  First, they entered the marketplace and have been in it for about 15 years and at this point have just enough key customers using the devices such that the cost of exiting the market would be greater than the cost of keeping these big customers happy.  The technology is not, by any stretch of the imagination, state of the art so the relative cost of supporting and manufacturing these parts is small.  Second, as a broadline supplier of a wide variety of specialty semiconductors, it’s nice for their sales team to have a PLD to toss into a customer’s solution to stitch together all that other stuff they bought from them.  All told, you’re not going to see any profound innovations from these folks in the programmable logic space.  ‘Nuff said about these players, then.

At the top of the programmable logic food chain are Altera and Xilinx.  These two titans battle head-to-head and every few years exchange the lead.  Currently, Altera has leapt or will leap ahead of Xilinx in technology, market share and market capitalization.  But when it comes to innovation and new ideas, both companies typically offer incremental innovations rather than risky quantum leaps ahead.  They are both clearly pursuing a policy that chases the high end, fat margin devices, focusing more and more on the big, sophisticated end-user who is most happy with greater complexity, capacity and speed.  Those margin leaders are Xilinx’s Virtex families and Altera’s Stratix series. The sweet spot for these devices are low volume, high cost equipment like network equipment, storage systemcontroller and cell phone base stations. Oddly though, Altera’s recent leap to the lead can be traced to their mid-price Arria and low-price Cyclone families that offered lower power and lower price point with the right level of functionality for a wider swath of customers.  Xilinx had no response having not produced a similarly featured device from the release of the Spartan3 (and its variants) until the arrival of the Spartan6 some 4 years later.  This gap provided just the opportunity that Altera needed to gobble up a huge portion of a growing market.  And then, when Xilinx’s Spartan6 finally arrived, its entry to production was marked by bumpiness and a certain amount of “So what?” from end-users who were about to or already did already migrate to Altera.

The battle between Altera and Xilinx is based on ever-shrinking technology nodes, ever-increasing logic capacity, faster speeds and a widening variety of IP cores (hard and soft) and, of course, competitive pricing.  There has been little effort on the part of either company to provide any sort of quantum leap of innovation since there is substantial risk involved.  The overall programmable logic market is behaving more like a commodity market.  The true differentiation is price since the feature sets are basically identical.  If you try to do some risky innovation, you will likely have to divert efforts from your base technology.  And it is that base technology that delivers those fat margins.  If that risky innovation falls flat, you miss a generation and lose those fat margins and market share.

Xilinx’s recent announcement of the unfortunately named Zynq device might be such a quantum innovative leap but it’s hard to tell from the promotional material since it is long on fluff and short on facts.  Is it really substantially different from the Virtex4FX from 2004?  Maybe it isn’t because its announcement does not seem to have instilled any sort of fear over at Altera.  Or maybe Altera is just too frightened to respond?

Lattice Semiconductor has worked hard to find little market niches to serve.  They have done this by focusing mostly on price and acquisitions.  Historically the leader in in-system programmable devices, Lattice saw this lead erode as Xilinx and Altera entered that market using an open standard (rather than a proprietary one, as Lattice did).  In response, Lattice moved to the open standard, acquired FPGA technology and tried to develop other programmable niche markets (e.g., switches, analog).   Lattice has continued to move opportunistically; shifting quickly at the margins of the market to find unserved or underserved programmable logic end-users, with a strong emphasis on price competitiveness.  They have had erratic results and limited success with this strategy and have seen their market share continue to erode.

Microsemi owns the antifuse programmable technology market.  This technology is strongly favored by end-users who want high reliability in their programmable logic.  Unlike the static RAM-based programmable technologies used by most every other manufacturer, antifuse is not susceptible to single event upsets making it ideal for space, defense and similar applications. The downside of this technology is that unlike static RAM, antifuse is not reprogrammable.  You can only program it once and if you need to fix your downloaded design, you need to get a new part, program it with the new pattern and replace the old part with the new part.  Microsemi has attempted to broaden their product offering into more traditional markets by offering more conventional FPGAs.  However, rather than basing their FPGA’s programmability on static RAM, the Microsemi product, ProASIC, uses flash technology.  A nice incremental innovation offering its own benefits (non-volatile pattern storage) and costs (flash does not scale well with shrinking technology nodes). In addition, Microtec is already shipping a Zynq-like device known as the SmartFusion family.  The SmartFusion device has hard analog IP included.  As best I can tell, Zync does not include that analog functionality.  SmartFusion is relatively new, I do not know how popular it is and what additional functionality its end-users are requesting.  I believe the acceptance of the SmartFusion device will serve as a early bellwether indicator for the acceptance of Zynq.

Quicklogic started out as a more general purpose programmable logic supplier based on a programming technology similar to antifuse with a low power profile.  Over the years, Quicklogic has chosen to focus their offering as more of a programmable application specific standard product (ASSP).  The devices they offer include specific hard IP tailored to the mobile market along with a programmable fabric.  As a company, their laser focus on mobile applications leaves them as very much a niche player.

In recent years, a number of startups have entered the marketplace.  While one might have thought that they would target the low end and seek to provide “good enough” functionality at a low price in an effort to truly disrupt the market from the bottom, gain a solid foothold and sell products to those overserved by what Altera and Xilinx offer; that turns out not to be the case.  In fact, two of the new entrants (Tabula and Achronix) are specifically after the high end, high margin sector that Altera and Xilinx so jealously guard.

The company with the most buzz is Tabula.  They are headed by former Xilinx executive, Dennis Segers, who is widely credited with making the decisions that resulted in Xilinx’s stellar growth in the late 1990s with the release of the original Virtex device. People are hoping for the same magic at Tabula.  Tabula’s product offers what they refer to as a SpaceTime Architecture and 3D Programmable Logic.  Basically what that means is that your design is sectioned and swapped in and out of the device much like a program is swapped in and out of a computer’s RAM space.  This provides a higher effective design density on a device having less “hard logic”.  An interesting idea.  It seems like it would likely utilize less power than the full design realized on a single chip.  The cost is complexity of the design software and the critical nature of the system setup (i.e., the memory interface and implementation) on the board to ensure the swapping functionality as promised.  Is it easy to use?  Is it worth the hassle?  It’s hard to tell right now.  There are some early adopters kicking the tires.  If Tabula is successful will they be able to expand their market beyond where they are now? It looks like their technology might scale up very easily to provide higher and higher effective densities.  But does their technology scale down to low cost markets easily?  It doesn’t look like it.  There is a lot of overhead associated with all that image swapping and its value for the low end is questionable.  But, I’ll be the first to say: I don’t know.

Achronix as best I can tell has staked out the high speed-high density market.  That is quite similar to what Tabula is addressing.  The key distinction between the two companies (besides Achronix’s lack of Star Trek-like marketing terminology) is that Achronix is using Intel as their foundry.  This might finally put an end to those persistent annual rumors that Intel is poised to purchase Altera or Xilinx (is it the same analyst every time who leaks this?).  That Intel relationship and a less complex (than Tabula) fabric technology means that Achronix might be best situated to offer their product for those defense applications that require a secure, on-shore foundry.  If that is the case, then Achronix is aiming at a select and very profitable sector that neither Altera nor Xilinx will let go without a big fight.  Even if successful, where does Achronix expand?  Does their technology scale down to low cost markets easily?  I don’t think so…but I don’t know.  Does it scale up to higher densities easily?  Maybe.

SiliconBlue is taking a different approach.  They are aiming at the low power, low cost segment.  That seems like more of a disruptive play.  Should they be able to squeeze in, they might be able to innovate their way up the market and cause some trouble for Xilinx and Altera.  The rumored issue with SiliconBlue is that their devices aren’t quite low power enough or quite cheap enough to fit their intended target market.  The other rumor is that they are constantly looking for a buyer.  That doesn’t instill a high level of confidence now, does it?

So what does all this mean?  The Microsemi SmartFusion device might be that quantum innovative leap that most likely extends the programmable logic market space.  It may be the one product that has the potential to serve an unserved market and bring more end-user and applications on board.  But the power and price point might not be right.

The ability of any programmable logic solution to expand beyond the typical sweet spots is based on its ability to displace other technologies at a lower cost and with sufficient useful functionality.  PLDs are competing not just against ASSPs but also against multi-core processors and GPUs.  Multi-core processors and GPUs offer a simpler programming model (using common programming languages), relatively low power and a wealth of application development tools with a large pool of able, skilled developers.  PLDs still require understanding hardware description languages (like VHDL or Verilog HDL) as well as common programming languages (like C) in addition to specific conceptual knowledge of hardware and software.  On top of all that programmable logic often delivers higher power consumption at a higher price point than competing solutions.

In the end, the real trick is not just providing a hardware solution that delivers the correct power and price point but a truly integrated tool set that leverages the expansive resource pool of C programmers rather than the much smaller resource puddle of HDL programmers. And no one, big or small, new or old, is investing in that development effort.

My social network appears to be wide, diverse and technologically savvy enough that I have a large number of friends and acquaintances with large Internet footprints. That includes people with a presence on a variety of social networking sites like facebook and LinkedIn, Twitter and Flickr feeds, multiple email accounts and even blogs.

Having a broad sample of such connections means that life cycle events are not unusual in this group either. That includes death. I have now – several times – had the oddly jarring event of having a message reminding me about a birthday of a friend who passed away or a suggestion to reconnect with a long-dead relative and similar communications from across the chasm – as it were.

There is both joy and sorrow associated with these episodes. The sorrow is obvious but the joy is in spending a few moments reviewing their blog thoughts or their facebook photos and, in essence, celebrating their life in quiet, solitary reflection. And it provides these people with their own little slice of immortality. It bolsters the line from the movie The Social Network saying that “The Internet isn’t written in pencil; it’s written in ink”.

This got me thinking.  In an odd way, this phenomena struck me as an opportunity.  An opportunity for a new Internet application.

I see this opportunity as having at least two possibilities. The first would be a service (or application) that seeks out the Internet footprint of the deceased and expunges and closes all the accounts. This might have to include a password cracking program and some clever manner to deduce or infer login names – for the cases where little is known about the person’s online activities.  It may be the case that after closing the account, the person may live on in the databases hidden behind the websites that are never purged, but they will be gone from public view.

The alternate would serve those who wish to be celebrated and truly immortalized. This would collect the entire presence of a person on the WWW and provide a comprehensive home page to celebrate their life, through their own words and images. This home page would include links to all surviving accounts, photos, posts and comments thereby providing a window into a life lived (albeit online).

In an odd way, this creates an avatar that is a more accurate representation of yourself than anything you could possibly create on Second Life or any similar virtual world. One could certainly imagine, though, taking all that data input and using it to create a sort of stilted avatar driven by the content entered over the course of your life.  It might only have actions based on what was collected about you but a more sophisticated variation would derive behaviors or likely responses based on projections of your “collected works”.

Immortality?  Not exactly.  But an amazing simulation.