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?

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.

I admit it. I got a free eBook.  I signed up with O’Reilly Media as a reviewer. The terms and conditions of this position were that when I get an  eBook,  I agree to write a review of it.  Doesn’t matter if the review is good or bad (so I guess, technically, this is NOT log rolling).  I just need to write a review.  And if I post the review, I get to choose another eBook to review.  And so on. So, here it is.  The first in what will likely be an irregular series.  My review.

The book under review is “The Basics of Web Hacking” subtitled “Tools and Techniques to Attack the Web” by Josh Pauli. The book was published in June, 2013 so it is fairly recent.  Alas, recent in calendar time is actually not quite that recent in Internet time – but more on this later.

First, a quick overview. The book provides an survey of hacking tools of the sort that might be used for either the good of mankind (to test and detect security issues in a website and application installation) or for the destruction of man and the furtherance of evil (to identify and exploit security issues in a website and application installation).  The book includes a several page disclaimer advising against the latter behavior suggesting that the eventual outcomes of such a path may not be pleasant.  I would say that the disclaimer section is written thoughtfully with the expectation that readers would take seriously its warnings.

For the purposes of practice, the book introduces the Damn Vulnerable Web Application (DVWA).  This poorly-designed-on-purpose web application allows you to use available tools and techniques to see exactly how vulnerabilities are detected and exploits deployed. While the book describes utilizing an earlier version of the application, figuring out how to install and use the newer version that is now available is a helpful and none-too-difficult experience as well.

Using DVWA as a test bed, the book walks you through jargon and then techniques and then practical exercises in the world of hacking. Coverage of scanning, exploitation, vulnerability assessment and attacks suited to each vulnerability including a decent overview of the vast array of available tools to facilitate these actions.  The number of widely available very well built applications with easy-to-use interfaces is overwhelming and quite frankly quite scary.  Additionally, a plethora of web sites provide a repository of information regarding already known to be vulnerable web sites and how they are vulnerable (in many cases these sites remain vulnerable despite the fact that they have been notified)

The book covers usage of applications such as Burp Suite, Metasploit, nmap, nessus, nikto and The Social Engineer Toolkit. Of course, you could simply download these applications and try them out but the book marches through a variety of useful hands-on experiments that exhibit typical real-life usage scenarios. The book also describes how the various applications can be used in combination with each other which can make investigation and exploitation easier.

In the final chapter, the book describes design methods and application development rules that can either correct or minimize most vulnerabilities as well as providing a relatively complete list of “for further study” items that includes books, groups, conferences and web sites.

All in all, this book provides a valuable primer and introduction to detecting and correcting vulnerabilities in web applications.  Since the book is not that old, changes to applications are slight enough that figuring out what the changes are and how to do what the book is describing is a great learning experience rather than simply an exercise in frustration. These slight detours actually serve to increase your understanding of the application.

I say 4.5 stars out of 5 (docked a star because these subject areas tend to get out-of-date too quickly but if you read it NOW you are set to grow with the field)

See you at DEFCON!

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?

Let me start by being perfectly clear.  I don’t have Google Glass.  I’ve never seen a pair live.  I’ve never held or used the device.  So basically, I just have strong opinions based on what I have read and seen.  And, of course, the way I have understood what I have read and seen.  Sergei Brin recently did a TED talk about Google Glass during which, after sharing a glitzy, well-produced video commercial for the product, he maintained that they developed Google Glass because burying your head in a smartphone was rude and anti-social.  Presumably staring off into the projected images produced by Google Glass but still avoiding eye-contact and real human interaction is somehow less rude and less anti-social.  But let that alone for now.

The “what’s in it for me” of Google Glass is the illusion of intelligence (or at least the ability to instantly access facts), Internet-based real-time social sharing, real-time scrapbooking and interactive memo taking amongst other Dick Tracy-like functions.

What’s in it for Google is obvious.  At its heart, Google is an advertising company – well – more of an advertising distribution company.  They are a platform for serving up advertisements for all manner of products and services.  Their ads are more valuable if they can directly target people with ads for products or services at a time and place when the confluence of the advertisement and the reality yield a situation in which the person is almost compelled to purchase what is on offer because it is exactly what they want when they want it.  This level of targeting is enhanced when they know what you like (Google+, Google Photos (formerly Picasa)), how much money you have (Google Wallet), where you are (Android), what you already have (Google Shopping), what you may be thinking (GMail), who you are with (Android) and what your friends and neighbors have and think (all of the aforementioned).  Google Glass, by recording location data, images, registering your likes and other purchases can work to build and enhance such a personal database.  Even if you choose to anonymize yourself and force Google to de-personalize your data, their guesses may be less accurate but they will still know about you as a demographic group (male, aged 30-34, lives in zip code 95123, etc.) and perhaps general information based on your locale and places you visit and where you might be at any time.  So, I immediately see the value of Google Glass for Google and Google’s advertising customers but see less value in its everyday use by ordinary folks unless they seek to be perceived as cold, anti-social savants who may possibly be on the Autistic Spectrum.

I don’t want to predict that Google Glass will be a marketplace disaster but the value statement for it appears to be limited.  A lot of the capabilities touted for it are already on your smartphone or soon to be released for it.  There is talk of image scanning applications that immediately bring up information about whatever it is that you’re looking at.  Well, Google’s own Goggles is an existing platform for that and it works on a standard mobile phone.  In fact, all of the applications touted thus far for Google Glass rely on some sort of visual analysis or geolocation-based look-up that is equally applicable to anything with a camera. It seems to me that the “gotta have the latest gadget” gang will flock to Google Glass as they always do to these devices but appealing to the general public may be a more difficult task.  Who really wants to wear their phone on their face?  If the benefit of Google Glass is its wearability then maybe Apple’s much-rumored iWatch is a less intrusive and less nerdy looking alternative.  Maybe Apple still better understands what people really want when it comes to mobile connectivity.

Ultimately, Google Glass may be a blockbuster hit or just an interesting (but expensive) experiment.  We’ll find out by the end of the year.