All Things Techie With Huge, Unstructured, Intuitive Leaps

Looking For A Crowd-funding Project of Your Own, But Can't Think of One?

Your job sucks. You want to start a business but can't think of anything worthwhile. You want a surefire way to start a company, get rich and have an exit event where one of the big boys buys you. You see crowdfunding successes and wrack your brains for an idea, a spark, an inspiration of innovation for what the world will want in large quantities.  Well you have come to the right place. Here are some ideas for crowdfunding projects that will go big.  You snooze, you lose, so act fast. First to market wins.

Idea #1 Fixing The Chewing Gum Market With New Product Introductions

The chewing gum market has been flat for years. It is in decline. It really took off post-World War II and during the 1950's and 1960's, it was quite hip to chew gum. From the 1970's to the 1990's, chewing gum was seen as anti-social.

The last big hurrah for chewing gum was the sugarless kind that contained xylitol and proven to prevent cavities.  The way that chewing gum innovates now, is producing some more exotic chemical flavor, like Raspberry Melon Dream.

So I think that the time is right for a disruptive paradigm shift in the chewing gum market that will make you the Mogul of Mastication. And you can crowd fund it.

Let's revitalize the business in a couple of ways.  First of all, all gum now is made from synthetic rubber. In the early days, it was made naturally from chicle. ( ) In the Bahamas, and other Caribbean islands, there are dilly trees that produce a delicious sapodilla fruit that tastes like apple pie. These trees produce chicle as well, and NOBODY taps them. The Bahamas in particular is looking for economic diversification and growing chicle would be one way to do plus.  Plus, chicle is all natural, and not like the gum base today made from coal tar. While once stealing picking dilly fruit that was hanging over a fence on the sidewalk, the branch of the dilly tree broke and the chicle got all over my hand.  I licked it off and chewed it, and it was pleasant even without flavoring. AND IT IS ORGANIC! How about an organic gum. That is innovation number one. It would sell like hotcakes to the organic food crowd.

The second disruption in the chewing gum industry is flavor. I don't want chemical fruit flavors. Suppose you infused the gum with stuff that was good for you, like Acai berries, gingseng and herb and botanicals that were healthy. Suppose you made a gum that energized you like Red Bull. Red Bull came out of nowhere and mades its founder gazillionaires. So you would have an energy gum. You would have a St. John's Wort gum that is anti-depression and anxiety. Great for chewing during an exam. And speaking of exams, you would have a gum with ginko biloba to increase your brain power.

There are all sorts of things that you could do with chewing gum, and carve an empire to make you a Mandarin of Manducation.

Idea #2, The Virtual Window

I was driving in the downtown core a big city, and saw four luxury condo towers going up. The condos were thickly crammed into each of the twenty or so stories of the building, and since the towers were close to each other, some of the condos had a beautiful view of a brick wall, and another building.  Who would want to pay half-a-million dollars for a condo whose window look out on a brick wall, and yet many folks do.

So I had an idea to fix this. The Eureka moment is a virtual window. You make an LED display the size of window. As a matter of fact, you could enlarge one of those electronic photo frames, and enhance the display. The Light Emitting Diodes in the display would be the ultra-bright type, and the white ones would have sunlight spectrum built in.  Then images are loaded into memory, and the processor keeps the time to give you a diurnally-appropriate image. What this means is that you would see a sunrise, and a sunset, and the window would get darker in the evening and you would see a beautifully lighted cityscape at nights.

You could change your view to look out over Diamond Head in Waikiki. Or you could have a crystal blue lagoon view of French Polynesia. You could pick the view out of your window, according to your mood. Or the window would be part of the Internet of Everything, so you could link to a webcam in Paris for you window view.  Or you could link to art gallery images for something completely different. You never need to look at a brick wall again.

The goodies that you would offer the crowd funders for this one, would be a free static view of their choice.

Idea #3 The Private Internet Retreat Platform for Self-Forming Social Groups

Attention all tekkie geeks, this idea can be done by cobbling together existing technology.  What it is, is a private Internet Retreat platform for self-forming social groups.  Let me explain.

I got off LinkedIn because when 3 million accounts were hacked, my password was among them. My password was chosen with care so that it be very hard to break even with a rainbow table. I got off Facebook when I saw where it was going with my privacy violations (even though I managed to sneak with a pseudonym. However any decent data mining could have garnered my correct identity in 5 minutes by daughter calling me "Dad" in a post).   I don't like a lot of my information to be public.

My classmates are all huge successes in academia, industry and commerce, and I don't particularly want them looking me up. I don't want ex-girlfriends stalking me. I don't want my likes to be tracked and recorded.

What I do want, is a safe place to interact with my chosen contacts, where there is no chance of anything being public knowledge. I don't want to be a lab rat for a data miner like myself.

So, suppose I log into a platform that is like an intranet. I could VPN or SSH/SSL to the network, and it is a private little domain with no public access. I can have a Facebook-like app, because Facebook is the lazy man's way of keeping in touch, and it is damn handy for that. Forgot a birthday card? Send a Facebook-like message etc.

So the idea could grow. You have an area of the internet solely for a self-forming group. They can trade messages, ideas, shopping wants, product reviews, personal opinions, photos etc that would ensure their privacy.

Now the platform provider would have to provide some content safeguards, but that could be done with technology. If you had complete privacy, then you would attract child pornographers, money launderers, terrorists and criminals. I found that out the hard way. I have an ultra-secure app for High Networth Individuals to store their data in a secure bunker in the Bahamas, and we have to sell it by invitation only, because I like to travel to the US and don't want to be put on their "list".  I was caddying at the Michael Jordan Invitational in the Bahamas, and Bill Clinton was playing with Samuel L. Jackson and James Caan. One of President Clinton's caddies was the FBI director stationed in the embassy, and when I handed him my business card, he put in a section of his wallet and told me that he would keep it, because one day he might have to serve me with a subpoena. So, you have to be careful that you don't create a criminal garden like Silk Road did.  But the take-up for this idea would be fantastic, especially now that more and more people are becoming aware of intrusions in their privacy.

So there you have it. Three crowdfunding ideas that you can run with.  I have lots more of them.  You can get them by signing up for my very occasional, non-annoying email about tech ideas, futuristic things, artificial intelligence, machine learning and app ideas.

Need A Huge Expansion of Google Street View and Google Meander

I love Google Street View. I use it all the time. Sometimes it comes in handy. For example, a colleague in Los Angeles emailed me. She had received a business proposal for the Bahamas and my colleague was on the evaluation committee. The proposal had come from a person living in United Kingdom, who said that he represented a large, going concern that was capable of doing a multi-million dollar project. She sent me the individual's name and address. A Google search yielded little, so I plugged in the address into Street View. As it turned out, the building that this individual lived in, was far from the type of house that the CEO of a multi-million dollar company would live in. The address was for a rather dingy, run-down townhouse in London.

If I wonder what a place looks like, I call up Google Street View. Recently I came across some literature about a hot springs spa in Eastern Europe, and I decided to call up Street View to see what the area looked at. I was surprised to find that one could (virtually) "drive" right into the resort and see the bubbling pools. This was a surprise to me as to how they got the Google camera car on a footpath, until I saw a shadow, shown in the above pic.  It is called Google Meander, and it is a backpack based Street View. Since then I have seen Google Meander at the Anne of Green Gables House on Prince Edward Island and other places.

The possibilities of Google Meander blew me away.  Right now, I can take a virtual road trip to anywhere -- say Ottumwa, Iowa, the fictional home of Radar O'Reilly of M*A*S*H fame. I can drive through the streets and to the highways leading to it, and it gives a fairly good idea of the area and what it looks like. But it isn't enough.

On a road trip, you stop and look around at what there is to see. Suppose people, businesses, attractions, restaurants, stores, museums, etc paid Google to do a meander along the way. For example, if you are virtually driving a highway and come up to a rest stop, you could meander inside to see if it were the place were you could buy your lunch, gas, souvenirs, and check out the bathroom facilities.

A meander through the public parts of a museum would give you a good idea if you were interested in paying admission. A meander through a souvenir shop would tell you quickly whether it was just a tourist trap with cheap Chinese junk or if it had stuff of cultural significance.

We live in an information world, and the Street View mapping has done wonders, but it could go a lot further. Google, are you listening? Being a Meandering Man would be a great job, and Bob Dylan would write a song about me.

Big Data, Data Mining and Machine Learning in Advertising

The advertising industry has been pretty much on the ball when it comes to exploiting audiences and corporations in the name of spreading a brand message. However Google has eaten their lunch when it come to advertising revenues and the sole reason is that Big Data is the most effective way to target a demographic, and Google is the king of Big Data. Google knows what people want to buy from the searches that the consumers do. They have the stats on what makes someone click, when they click, what best to make them click, where they are predicted to live and pretty much everything that an advertiser wants to know to reach their audience.

The laggards in the industry are the big and small advertising agencies and brokers. I can predict that the biggest, most profitable players that will be big in advertising in the next 5 years just by examining who is adopting data mining, big data and machine learning in the advertising field.

Advertising has been bought and sold using a very large degree of granularity. For example, its a slam dunk that if you want to get a brand message across, the quickest way is to buy a Super Bowl ad. But not everyone is a Fortune 500 company that can afford an ad in the ... the ... well .. Super Bowl of Advertising.

So how will advertising agencies monetize themselves and create huge and diverse revenue streams in the very near future?

Here is an example of how it will work. With Big Data and Machine Learning, the advertising industry can operate like a futures exchange for media placement. They will craft the brand message. Big Data and machine learning will not only tell them what the most effective demographic will be, but it can do it with a fine degree of granularity as well.  Advertising will be more goal oriented. If an advertiser wants to find new markets in a new demographic, Big Data and Machine Learning will tell the advertiser where to do it. It will also outline the optimal time, the optimal vehicle, the optimal message and the optimal leitmotif of the brand message. If they want to expand their market penetration with their current target demographic, they will know where to do that as well.

Finer degree of granularity will mean real time media monitoring with a control room full of Bloomberg-like stock market screens. If Reddit is trending with a million hits per hour with a content classification linked to the 25-35 year old demographic, it may be time to buy a spot from a ad placement futures database and pop the content onto the site within a couple of minutes. Advertising space will be bought in bulk and traded in smaller elements like a commodity exchange in real time.  It is media-adaptive advertising.

If CNN has an exclusive breaking story and millions are flocking to their site, the viewing demographic can be machine-analyzed in real time (a Bayesian probability, again gleaned from millions of training epochs) and a highly effective ad can be placed that will have an instant ROI or return on investment that would be stratospheric compared to a regular buy.

Amazon or Google will develop a real time ad engine platform (RTADS --- or they will buy one from guys like me), and it will be the next big thing. App developers will be paid to incorporated RTADS (Real Time Ad Delivery System) so that trending, topical media events can be exploited with embedded, dynamic advertising based on who is consuming the media.  As a matter of fact, the next Bloomberg millionaire will be the optimal ad monitoring station for advertising insertion. It is a function that is unknown today, and tomorrow the developers and maintainers of the system will have a coherent job description for it..

Machine Learning can predict good wine years and classical vintage. It can predict which films will win the Oscars. It can guide robots and munitions. Once the advertising agencies see the harnessed power of Big Data, Data Mining and Machine Learning, they will convert quickly, and eventually those players operating in the old paradigms will die. It is only a matter of time.

It's a great time to be alive if you are into Machine Learning, Artificial Intelligence, Data Mining and Big Data.

Re-Booting & Reforming Democracy With Big Data ~ The Box Carries the Vox

Winston Churchill stood in the British Parliament and spoke the following words:

"Many forms of Government have been tried and will be tried in this world of sin and woe. No one pretends that democracy is perfect or all-wise. Indeed, it has been said that democracy is the worst form of government except all those other forms that have been tried from time to time."

When the American Founding Fathers created a unique concept of Western Liberal Democracy, it was in fact a great experiment enshrining concepts operating under the principles of liberalism.  This includes protecting the enshrined  rights of the individual; fair, free, and competitive elections between multiple distinct political parties, a separation of powers into different branches of government, the rule of law in everyday life as part of an open society, and the equal protection of human rights, civil rights, civil liberties, and political freedoms for all persons. (quoted from Wikipedia).

However, the mechanism that they put into place to administer this democracy was very much a kludge or a compromise to best accommodate the will of the people, taking into account, the pragmatic aspects of their place and time in history.

Something has happened between then and now. Nowadays, the will of the people is being subverted and distorted by political partisanship and it is not for the good of the country and the people. The gridlock and dysfunction in the America Congress is a prime example (If con is the opposite of pro, then is Congress the opposite of progress?). And in the American Senate, when they do the roll call, half of them answer "Not Guilty!".  You don't have to go far to find examples of how the mechanism of government fails to democratically represent the will of the people. The idea of thousands of people being represented by a person whose vote and interest can be bought by a business lobby somehow sucks all of the air out of the room of democracy.

Well, times have changed since 1776, but the ways of the government have not. With the advent of technological age, it is time to update, enhance, and empower the forces of democracy through the judicious application of technology, communications and data management.

I still believe that political parties are necessary. As human beings we will always have ideological differences, and no matter how batsh*t crazy some people are, they still have a right to vote and express their opinion. Churchill again once said that the biggest argument against democracy is to speak to the average voter for five minutes. So you will get the weirdos who think that owning an assault rifle will protect them from a drone strike when their elected, democratic government chooses to attack them in their bunker amid the 10 years of rice stocks mixed with prepper gadgets stored on the shelves therein. There will always be the snake-handlers, the wanna-be polygamists, the Ovary Overlords who want to legislate women's reproductive rights, and the folks who want to throw out the science curriculum in schools and replace it with learned treatises on Adam and Eve domesticating the dinosaurs. All these have a right to a voice in the democracy.

Political parties also define policy, which is important in government. Policy the course by which the government steers by. We don't want a rudderless ship, so we still need legislators to debate policy. But when they come up with legislation specifics to policy implementation, I want my direct say.

In the days of 1776, it took a week to get from Philadelphia to New York. There were no telephones. You couldn't track people down on the farm for their views. Times have changed. We have Big Data. We have the technology and communications tools to hear from everyone. We have the infrastructure to empower all voices. With computer data collection, we can collect hundreds of millions of pieces of data in minutes. And we can machine-collate them in real time.

So, what if anyone with a Social Security Number had a private encryption key? Whenever legislation came up for a vote, we all vote on it? Vox populi. The voice of the people can speak and be heard. The legislation would be put to a vote, and we the people would respond. We could all directly vote for the legislation and the laws that we affect us. Being digital in this age has put a voice to the voiceless and nameless. Data Science can be our rescuers and our salvation.

This would make it harder for big business and lobbies to affect democracy. They would have to convince entire populations of their point of view, and it doing so, they would have to make it in the interest of the population. It would be the great leveling ground in the current incarnation of democracy.

Do we have the guts to change the way that we enact democracy? We still have a Digital Divide, where a significant portion of the population doesn't participate, and cannot participate in digital online life. We have education issues. We have a built-in inertial brake for radical change. The people who benefit from the current state of dysfunction want to keep it that way. So it will be an uphill battle, but Big Data can reform democracy and put the power back into the hands of the people, where it belongs.

Back to Winston Churchill one more time to close my argument.  Upon being offered The Order Of the Garter after a particularly humiliating defeat in the election of 1945, he said "Why should I accept the Order of the Garter, when I have already been given the Order of the Boot?"  It is time to give the old tired mechanisms of democracy a boot.

Data Mining And Ethics - Revenue Over Rights?

I was reading a WIRED magazine article where the author said that his liberal arts degree on his resume was viewed in the same way as a face tattoo in a job interview for an investment banking position. However, a liberal arts degree is probably the qualification of an up and coming job title for data mining companies -- the CEthO or the Chief Ethics Officer. Once data mining and machine learning gets to the next level, coupled with the Internet of Everything, there will be huge privacy and ethics issues to contend with.

The big question of ethics that will arise, is "Is is okay to make money off information about people that I glean from mining my data?"

Several far-fetched but not so far-fetched scenarios come to mind. I am reminded of the data mining done by Target Stores when they deduced that a 15 year old girl was pregnant by her purchases of face cream combined with a certain brand of vitamins.  Suppose that I was a data miner for a drug store chain, and I could find a strong correlation between a person buying certain antacids and a few months later being diagnosed with an ulcer requiring expensive stomach surgery. A health insurance company would be highly interested in knowing that. Should we sell the information on people that we discover? It would be an incredibly lucrative revenue stream.

Ethics was never a question in the good old days of business. McDonald's grew their fast food empire by putting toys into their Happy Meals and creating an obese America by targeting and hooking the children. One nutritionist noted that Chicken McNuggets in a Happy Meal were nutritionally worse than deep-fried cake. At the time, it was seen as a slick marketing move.

Data mining is in the same stage that McDonalds was fifty years ago. It is a solution looking for a problem to monetize. So it is like the Wild West until legislators, sober second-thought minds and ethicists added some groundwork rules to the field of endeavor. I personally know of a data miner who set up in a Caribbean jurisdiction that has little to no privacy laws. Big companies ship him their data complete with personal information, and he ships back everything bit of intelligence that he finds. His revenues are out of this world. It is almost the ethical equivalent of having consumer products made in the Third World by child labor. I can see the days, where data becomes a commodity that is bought, sold and traded. There will be export laws on data -- especially data with personal identification information in it.

But there are ethical questions closer to home. Suppose that my employer expects me to mine data, and I discover an untapped revenue stream that is extremely easy to exploit. Do I tell my employer, or give my notice and create a start-up to exploit that situation? What is the ethical course of action?

We have a long way to go with with applying ethics to data mining. Ethics is a lot like beauty -- it is in the eye of the beholder. I am reminded of a story that a shopkeeper told regarding ethics. He said "I was closing the store and a customer came in at the last minute and made a large purchase with a hundred dollar bill. After he had left and I closed the shop, as I was counting the money, I noticed that there was actually another hundred dollar bill stuck to the bill that the last customer had tendered. Immediately a question of ethics arose. I was wondering if I had to tell my business partner or not."

And that perfectly explains why we need some ethical boundaries in data mining.

The Fundamental Problem With Big Data Mining -- Missing Knowledge and ROI

Think back to high school calculus class. Calculus is the branch of mathematics invented by Isaac Newton (and Leibniz) that lets you do amazing things. If you take the derivative of distance, you get velocity and if you take the derivative of velocity, you get acceleration. You can go backwards and take the integral of acceleration to get velocity and do the same to velocity and get distance.

The reason that I bring this up is that the process of differentiation and integration is an inverse  similitude for what happens in the mining of Big Data Mining. Big data generally starts with a data entry -- a single point. That data entry (usually a column in a row in a database) in conjunction with other entries is integrate to create a fact. Facts are integrated with other facts to become information. Information integrated with other information, becomes knowledge. Mining Big Data usually stops at the information stage.

Knowledge is an ontological map of combined information to create both abstract and concrete ideations to create an amalgam of fact, belief, prediction, concepts, ideals and metaphors that gives a basis of understanding about any situation, object, proposition or relationship. The utilization of Big Data is nowhere near creating knowledge from its sources. It just creates the building blocks of knowledge without any underlying understanding of the wherefores and whys.

That ability alone is amazing in itself and not enough, but let me iterate that I am not talking about Machine Learning (although that can be put into the mix in the future). Data mining is done by a person, so you have an actual brain driving the process of trying to make sense of a huge pile of data. As such, you can have some intelligent advantage over machine learning.

Information gleaned from Data Mining can be extremely useful to any enterprise. However all data is not created equal, and dirty data creates a lot of noisy correlations and just plain wrong information. And some data is just not that rich in information potential. But even the best of datasets can produce spurious correlations.

Without background knowledge, spurious correlations have no value.  As an example, here are some spurious correlations from

The above graph shows that US spending on science, space, and technology correlates with Suicides by hanging, strangulation and suffocation.

You need knowledge behind the mining to find intelligent, meaningful relationships.  Here are other examples of absurd correlations:
  • Number people who drowned by falling into a swimming-pool correlates with Number of films Nicolas Cage appeared in
  • Per capita consumption of cheese (US) correlates with Number of people who died by becoming tangled in their bedsheets
  • Divorce rate in Maine correlates with Per capita consumption of margarine (US)
But the biggest fundamental problem with Big Data that with the lack of background knowledge, you could be finding local information and nuggets that do not translate into a universal picture, or vice versa. For example, a colleague was telling me of mining big data to determine the best days to run special promotions for dairy products for a small supermarket chain. When the data was dimensioned for day-of-week, it was found that there was a statistically significant decrease in demand for dairy products on a Friday. The data showed that dairy product sales were way down for that day, and past promotions did not work as well as projected.

What the data mining was missing, was that there was a statistically significant dip in the sales curve that was due to local conditions. Data was aggregated from all of the chains into a central database, and the sales database was mined. However the chain of stores had many locations, and in some locations a major dairy was co-located in the same city. That dairy had retail operations just on weekdays, and not on weekends, and on Friday, the dairy would run huge sales to clear its stocks over the weekend closure. Folks would buy their dairy products at huge savings at the dairy on Fridays at a subset of locations and it was enough to skew the data past a statistically significant point. However statistical significance did not negate increased sales overall, and it appeared that the data mining exercise was a failure when the null hypothesis was tested.  The issue would have been avoided if the data has been dimensioned by geographical location, but who would have thought that a supermarket demand for milk on a Friday in Cincinnati would be different from that supermarket's Friday dairy demand in Steubenville? 

To recap, the fundamental problem with Big Data, is that you get information, but rarely do you get knowledge. You know the who, where, what and when, but you don't know the why. And that may be the Achilles Heel of a lot of Big Data projects that do not deliver a promised ROI or Return On Investment. 

In many companies, you have to mine Big Data to improve the bottom line. If you find spurious correlations, or information that is just plain wrong in either a local or universal sense, then the exercise has been a waste of money, time and resources.

So how do you map a Big Data Project to a reasonable ROI, especially since a fundamental flaw of mining Big Data is missing background knowledge? The answer lies in Total Data Point Dimensioning, coupled with Dynamic Cognitive Modeling, and the subject of a future blog post on how I see it evolving and the toolsets necessary for it. Stay tuned.

Watch out for the "What's App Web" Spam Virus/Malware

I just got this piece of crap spam malware/virus injector in my mail.  It came directly from a friend's gmail account so obviously he picked up the malware from somebody.

Don't click on it.  Notice the spelling error in the word "length".  The domain with the link comes from That doesn't mean anything, because typically these spammers hack a relatively untended website, inject their crap from there without the owner being the wiser.

Interesting that they would try to play off the Whats App name.  Don't be fooled.

How To Be The Next Big Data, Machine-Learning Millionaire (in 3 Easy Paradigms)

I have a special talent. I make other people rich -- extremely rich! The first time that it happened, was in the early 1990's. I invented a new type of golf tee. The lawyer that I hired patented it under an umbrella proxy over which he had power of attorney. He said it was necessary for the financing of it. It was a long story, but I never saw a dime. He is retired in Turks & Caicos. It was particularly painful to find one of my designs on the golf course, now that the patent is expired.

The second time, I made a pile myself. It was during the tech boom, and the tech crash took us out with the speed of a tsunami. The third time was when I was when I was consulting as a technical architect to a G8 government. We were sitting in a scrum, and one of the team members mentioned that the telecom giant Nortel was trading at .75 cents a share. A few short months ago, it was at $130 per share. This team member said that it might be worthwhile to throw ten grand at it.  I said "yeah, yeah, lets do it" and ultimately forgot.  A young programmer on our team, believed in my endorsement of the stock and threw much more than that at it.  He got out when it reached $16 a share. Do the math. Nortel eventually collapsed, but our intrepid friend made such a pile that he bought a BMW and never got out his pajamas for the next few years.

The last time that I made someone rich, was that I was in idle conversation with an elderly Manhattan-based writer last May (May 2014). He was a meditating Buddhist who lived simply and had a pile of cash to bet on the next big thing. He asked me what the next big thing would be. I told him that it would be the Internet of Everything.

He asked me who would be the big player in the Internet of Everything. I told him that Sierra Wireless (stock symbol SWIR) had foundation patents and had the potential to be the next Google or Apple. Since May ( a short 9 months ago) he has doubled his money. He thinks that I am genius. Needless to say, I didn't get on the ride with him.

And now, you too can benefit from my largess and become a millionaire in the field of Big Data and Machine Learning. You can do it in three easy paradigms.

Paradigm 1: Write a Universal Lightweight Data Inter-change Universal Sensor Data Transfer Protocol. Use JSON or XML. It is dead easy. And actually, you don't have to do it. I did it for you in this blog entry! And for the ultra-lazy putative millionaire, here is an example of it:

For that, I propose my handy-dandy XML based Universal Sensor Transfer Protocol, but instead of XML it is STML or Sensor Transfer Markup Language.  Here is what it looks like:
<?stml version="1.0" encoding="utf-8"?>
      <name>Caliente Temp Sensor</name>

Paradigm 2: Use some open source stuff like Apache Tomcat, MySQL and open source stuff to write a RESTful service to pop all of the sensor readings into a database.

Paradigm 3: Using your favorite machine-learning platform, input the data and train the living crap out of the data, preferably in real time to make ultra-smart houses, ultra-smart factories, ultra-smart utilities, etc etc etc.  Everyone will want one of your platforms, because the system will be fire-up-and-forget as the military guys say of intelligent systems.  The machine will learn what is normal, call someone when it ain't, and send back feedback to optimize whatever the sensor controls and make life, smarter, easier and better. It will save everyone time, energy, human work hours and time.    AND IT WILL MAKE YOU FRIGGIN' RICH.  Everybody will want one of these systems.

And here is the disrupter idea for the disruptive idea:

The cutesy coder guys will offload the training to the cloud and push the results to a smartphone.

There you go. You are welcome. This idea is a sure-fire winner to make you a millionaire.  I would do this project, except that I am too busy with being Chief Technology Officer of our company.  Also I am working on a recreational pharmaceutical company with a new designer drug offering. We are combining birth control pills with LSD so that you can take a trip without the kids. Oughta be a slam-dunk as well!

Oh, and be sure to sign up in the box to the lower right for my occasional non-obtrusive emails with further app ideas, cogitation on Deep Learning and AI, and futurism thoughts on tech. There will be a few monetizable ideas there as well.

Conquering The Time Domain in Marketing With Big Data & Analytics

Our platform sells big ticket items -- it remarkets and wholesales used cars.  The supply chain is well defined. A new car dealer takes in a car on trade. He really doesn't want to do it, because most used cars are not moneymakers. If it sits on his or her used car lot forever, it loses money for him/her  instead of making money. That is because the new car inventory underlying that trade-in is usually financed.  To complete the deal cycle of used car trade-in -> new car purchase -> used car sale for recouping money, the used car has to sell quickly.

Secondhand car dealers in small markets are experts at what sells and for how much, and what the market is willing to pay. They have intense local knowledge of their geographic domain.  A lot of the time, new car dealers do not have that expertise and/or knowledge.

Coupled to this fact, is that in spite of the parameters of make, model, year and condition, there is no uniform valuation for a used vehicle. It varies by area, time of year, color of vehicle, geographical location, local economy and a million and one different factors. Folks like Black Book try to standardize the valuation for the process, but at best, they are only a rough guide based on auction prices around the continent.

As we have shown in this article, the Black Book paradigm of gleaning value from auctions is not  accurate because up to two-thirds of all vehicles are remarketed through relationship-based wholesaling, and never hit the auction floor.

Coupled to that, there is no "real price" for any used vehicle. What a vehicle sells for is based on what the new car dealer has in it (a combination of what he thinks the vehicle is worth and the discount that he has allowed on the new car that was bought with this trade-in). A good example of this is that on our platform recently, a dealer had $9,000 in an SUV. That's the reserve price that he put on his vehicle, because that is what he needed to make the deal profitable. He let the market forces dictate the ultimate price, but he needed $9,000. The SUV sold for $27,000 in the fair and equitable marketplace on our platform. So what was the vehicle worth? It was worth $9000 to one person and triple that to another. This is why we introduced crowd-sourcing valuations into our platform.

But there is one other element in marketing that transcends specific sectors, and that is the time element.  Currently, a light manufacturer will do a run of product, and try to flog it off to wholesalers, retailers, online markets etc.  It costs money to hold the product in inventory.

Technology such as 3D printing and print on demand for digital books alleviates some inventory build-up, but generally the time domain is huge in merchandising and marketing.  What I mean by that, is that inventory is built up, and disposed of over time at ever-changing prices based on supply and demand. There is a measurable, considerable cost to storing inventory.

As pointed out in the automobile remarketing industry that we are in, the domain of time is a negative one. The longer an item stays in inventory, the less it is worth, and the larger the drag on the bottom line. Positive revenue stream is based on timely sales.

To conquer the time domain, we used Big Data to our advantage. We coupled it with our relationship-based sales paradigm described in the above link, and as it turns out, the piece of technology was patentable, and we have foundation patents pending in that area.

This is how it works. The whole idea is to move inventory quickly. We have mapped the buyer/seller network relationships (a social network media type of construct) with trusted buyer zones based on previous commercial relationships. This is the first step in the process that we have created.  The product is offered to this trusted network group for a limited time (in our case, four hours is a norm). If the product does not sell, what then? As the clock ticks, money is lost.

The second step involves Analytics.  We use Big Data to find in our customer base, and in other databases, who is the best and most frequent kind of buyer for this product. The machine assembles a top-10 list based on a proprietary algorithm of sifting through Big Data, and offers it to that ad hoc group of buyers for a limited time.  The really nice part, is that once buyers find out about the top ten, we have a potential revenue stream where they will pay for early market information and a chance at a deal.

When that time expires, the platform has the smarts to move the inventory to the next phase of selling. In our case, it goes to general auction to the open group of buyers, and if that fails, the platform has the technology and ability to transfer the inventory to a classified type of listing.

Our competitive advantage, is that we have conquered the time domain with relationship-based social network selling for the first step, and the use of Big Data for the second step. Our competitors use the third step as their first step.

Big Data has a huge advantage in conquering the time domain.  Suppose as a manufacturer, or even a retailer you had a platform to sell all of your inventory in a specified time-frame. With a platform such as ours, adapted to other fields, you could commoditize your inventory, and using relationship-based selling coupled with Big Data, you could have your inventory dispersed just as it was about to leave the factory floor, or arrive on a shipping dock.  Big Data will even tell you how much inventory to order and make.

Merchandising and selling will all change drastically in the next few years, and those that don't adopt the Analytics/Machine Learning paradigm, will bite the dust.

Asynchronous Neural Nets Are Primitive Cave Man Neural Nets

The first electronic circuit that I ever designed was a logic fall-through. (The circuit was for a team quiz-show type of game which determined which button was pressed first by what member of what team.)  It was asynchronous. That means that when a signal arrived at the inputs to the silicon chip, it was processed right away. It wasn't held for a state change of the chip like modern digital systems are today.

Modern digital systems have a bus architecture. That means that every chip on the circuit board is connected to a central set of traces or wires called a bus. The chips share the traffic or signals on the bus. The way this happens, is that there is a regular clock signal generator which controls timing. So if an input receives a signal, it in turn sends a signal to the bus controller that it needs to put its data on the bus, and that needs to go to a certain scratchpad register or memory unit to hold that data.

The bus controller signals all of the other chips that it is going to commandeer the bus. All of the other chips finish up what they are doing, and clear the decks. The bus controller then signals the necessary registers to receive the data, and signals the originating input to load its data on the bus.  Once the data is loaded, it signals the register to process the data, and clears the decks for the next operation.  All operations are controlled by a nice orderly clock signal, that is a square wave that rises up and down. And that square wave represents computer binary language of zeros and ones.  So a clock signal in computer talk, always looks like this: 01010101010101010101 etc,
but the important thing is that there is a frequency or a timing between the state changes of zero and one, or the up and down of the waveform.

This frequency is important. If you remember back to the days of dial-up internet (if you are old enough), you would remember the distinctive sound of the modem connecting to the internet through the phone line. It would be an oscillating sound. The binary signals were converted to a certain frequency that the modem that the other end understood to be a zero or a one. This was called frequency shift keying, and it was a way to turn the binary computer language into a sound that could traverse a telephone line. And it could preserve the coherent computer data by having a set frequency, and a set timing of that frequency. It was all rather ingenious, and it was baby steps to where we are today with  high-speed internet.

Well a good idea can always be re-used. FSK or frequency shift keying took us from asynchronous to synchronous systems, and it could be used to make Artificial Neural Networks a lot smarter.

I was just reading some of the latest in brain science research with real neural networks in our brain. They are fall-through asynchronous in general, meaning that when a nerve sends a signal, it is immediately fed into a massively parallel network of neurons. However, it has been discovered that frequency also plays a factor in the neural network.

The stimulation is almost like a palimpsest that Isaac Asimov talked about in his book "Contact". In its truest sense a palimpsest is an ancient manuscript of a book, back in the day before paper, that had another book written on it. The old words were scraped off, and new words were written.  However you could still read the old words, and the book carried double the information. In the book and movie "Contact", the information to build the time machine was a palimpsest where additional information was encoded in the polarity or the rotation of the wave form.

Apparently the brain in humans uses frequency as an additional information encoder. It has been measured in studying emotion response in the brain, where frequency plays a huge part.  This component is entirely missing in computer Artificial Neural Networks. All computer neurons are asynchronous fall-through.

I am by no means suggesting that they become synchronous in the sense of a clock system in a computer (although that could be a possible paradigm), but that somehow frequency be incorporated as an additional tool, paradigm, algorithm or species for the neurons.  A good start would be to incorporate Frequency Shift Keying into Artificial Neural Networks. I don't have an exact methodology on how to do this yet, but you can be sure that I will devote some of my internal brain cycles to try and figure this thing out.

As a matter of fact, it is a fascinating thought experiment to contemplate on how a Von Neumann machine might behave if it were frequency-aware. New, ingenious compute dynamics such as frequency awareness are fascinating to think about.

Obviously a lot more research needs to happen, but here is a venue worth exploring for Machine Learning, Deep Learning, Artificial Neural Networks, and Artificial Consciousness.  More ruminations on this topic to come.

An End To Dangerous Big Data Stalking

You are being stalked. Every website that you visit may add a stalker in the form of tracking cookies to your browser. They know where you have been.  And with just a modicum of inference they know who you are.

This web tracking is pervasive. It all goes into a big database. If for some reason, you enter your name on a form, and the form is transmitted to the website in what is known as an HTTP Post, they will harvest your name. But even without your name, they will know what demographic you belong to. They will know your financial standing and how much you earn. They will know what music you listen to and what clothes you buy. And all of this information is processed without the benefit of human eyes sorting and classifying this data. Machine Learning is pervasive.

But here is what is most dangerous about these stalkers.  They can make the wrong inference, and put you on a watch list that may be impossible to get off, or you may not even know about.  Here is a scenario that could make you a terrorist according to Big Data and Machine Learning.

You are sipping your morning coffee looking at Facebook, and you see a heartbreaking picture of a child caught in the clutches of war in the Middle East.  You "Like" the photo.  Then it is time for you to go to the airport. You are flying business class and are given a choice of food. There are Halal meals. You are an adventurous foodie, so you tick it to try it.   Coupled to that, is that you have an aisle seat.  Then you check your Twitter feed.  Someone posts about "Freedom of Religion",  You favorite the tweet. In the business section of a European website, you see the add for a hedge fund that promises great returns. You click for more information.  What you don't know, is that you have put the Big Data Digital Stalkers into overdrive, and you are now a person of interest to several agencies.

As it turns out, the photo that you "Liked" was posted by a terrorist group to garner sympathy.  All of the "Likes" are collected as possible links to these terrorists. You are in another database because you chose Halal food instead of the bacon cheeseburger.  The aisle seat is problematic. Hijackers do not take window seats.  The "Freedom of Religion" tweet was sponsored by the Muslim Anti-Defamation League. Into another database you go.  The hedge fund promising great returns is headquartered in the Cayman Islands. The IRS is suddenly interested in you.

The most dangerous thing about Big Data Stalkers, that that they make Bayesian Inferences which are probabilities.  Probabilities are just that. They are not certainty. Even with a 99% probability, the next event in the sample space could be wrong -- not what the probability predicts.  Machine Learning and Big Data Stalkers are a clear and present danger to personal privacy.

The other intrusion on your life from Big Data Stalking is the stuff done with commercial enterprises. They aim to learn absolutely everything they can about you, because they can sell that data.  Big Data can produce new or enhanced revenue streams.  Is there a way out of this?

I say that there can be.  With a paradigm shift, the consumers of Big Data can get what they want, and your privacy can be protected. How you ask? With a little dash of technology.

Let's suppose that you turn the tables and consent to limited data tracking. That data tracking is now bowdlerized, meaning that sensitive personal stuff is obfuscated or removed. This is done by an app on your device, cell phone, tablet or computer.  Then you are paid for that data to the highest bidder.  Everyone is happy, and you the consumer benefit from the data collection.

As for the other stuff, technology can help too.  I am a huge proponent of Artificial Intelligence.  Suppose that you had a proxy entity digital assistant called Blocker.  Blocker would surf the web for you, executing your Likes and Dislikes while retaining your anonymity. Blocker would run on a proxy service, so that even IP addresses would be hidden. On top of that, it would surf in anonymous mode.  If there wasn't any personal user data to be had, your privacy would be protected. The data flow wouldn't entirely be impeded because through content analysis, you could still make pretty good inferences of the humans behind any wall. For example, a grandma living in Norway wouldn't be listening to rap music, but her grandson might be.

So, with a bit of different thinking, we can mitigate the dangers of Big Data Stalkers. The unfortunate thing, is that many denizens of the Internet, do know or don't care about the Stalkers.

The Future of Television is William Larkham Jr.

I was an early adopter of the Internet.  At the time, I was working in the research branch of a major telecom. In 1992, they put the internet on my desktop.  There were no search engines. The most amazing thing was a picture of a coffee pot at a British university and the pic was updated every 5 minutes.  It was state of the art.  I didn't think that the internet would go anywhere fast.  A couple of months, I put up a personal website after seeing the light. It was called Chezken Uppe (pronounced Shaken Up) and it was a pun on Chez Ken as the French would say.

Since making that initial mistake of thinking that the proto-internet wouldn't go anywhere (who wanted to read a bunch of CERN research papers?), my spider senses have been considerably sharpened by what hath technology wrought.

Television is a changing medium.  It is possible to have 900 satellite channels and nothing to watch on TV.  As a result, when I do feel like kicking back and letting my mind idle while sipping my favorite brewski (a St. Ambroise Oatmeal Stout -- like having an espresso, dark chocolate and black truffle goodness in a beer -- and I wasn't paid to shill this):

anyway, I watch Youtube.  The Youtube folks could use the Amazon algorithm as to what I like because it is rarely right.  However I stumbled onto the Youtube channel of William Larkham Jr, and it has never left my mind since.  His channel is a primordial incarnation of the future of television.

Let me explain. My information intake is mostly non-fiction.  My most recent book that I have read is "The Innovators".  When watching television, I usually watch the Discovery Channel, PBS or the food channel.  I like assimilating information, especially about exotic places and people doing exotic things.  Like most human beings, I have a deep curiosity about how other humans live.  I think that it is an inborn trait.  We are all nosey gawkers when it comes to other human beings.  For me, I even like looking in the windows as I am driving down a road in the dark and I see a lit up house. I catch a glimpse as I pass of how other people live their lives.

The Discovery Channel capitalizes on this innate curiosity about other human beings.  They produce shows like Dangerous Catch, Swamp Loggers, Pawn Stars and even Here Comes Honey Boo Boo.  However, a lot of the show is drivel. It is becoming more and more scripted.  As such, they try to build up suspense before going to a commercial break, and when you see the denouement of the scripted suspense, it is a cheesy deus ex machina, or a total fake situation enhanced by the script writers.  All reality shows eventually become parodies of themselves.  However, there is a certain magnetism to them, and hence the Duck Dynasty figurines at the local discount store (where they belong).

So when I stumbled on the channel of William Larkham Jr., I was surprised, entertained, enlightened and hooked. William lives in the extreme outback of Labrador. He calls it The Big Land, and the name of his channel is Big Land Trapper. He has an incredibly quaint Newfie or fish accent from the Maritimes that is endearing ( spice becomes "spoice" and weasel becomes "wizzle").  He films himself and his life from the Christmas celebrations to trapping to harvesting fish and game to gathering berries and playing with his children.  In spite of the fact that he shows seal hunting, trapping and fishing, it is an incredibly classy show.  Tasteful as well.  He doesn't do the hunting gore.  All of his efforts, whether they be working on a North Atlantic shrimp trawler, or crab fishing, or hunting ptarmigan, are all done to eke out a living in a place where you can't drive to.  There are roads and snowmobiles, but getting to the Big Land is a challenge best served going by ferry.  It is a fascinating look into the lives of people who live in remote North American places.

The production is visibly home made. At times he has covered the microphone of the camera and the sound dies out.  When things get exciting, the camera shifts away as he deals with the situation. He recently got a Go-Pro camera that enhanced the filming.  But you don't mind these things, because it is pure authenticity.  It is obvious from the videos, that he is a nice, classy, hard-working guy, and the most improbable reality star that you will ever see.  And his channel is addictive.

When I get to thinking about the future of work and jobs and such, I often wonder what comes next in the Brave New World. With 3D printers, we won't need to buy cheap crap from China. With artificial intelligence and robots, many jobs will be lost in manufacturing and manual labor.  The future of work for many, can be providing content and getting paid for it.  In this way, people create their own jobs, in their own niches.  It makes sense with the aid of technology, that people can create their own television channels.  They are already doing it on Youtube, and there could be further technology incarnations (developers take note, here is an opportunity).

Sponsors are already jumping on the bandwagon.  They find these Youtube channels and send product which gets featured by the grateful recipients.  This will be a burgeoning aspect of creating your own television channel in terms of generating a revenue stream and supporting yourself.  You don't need a million viewers.  You need 500-5000 views per video, and you could be self-sustaining.  Several woodworking channels are already making a living from Youtube views.

So if you are looking for some reality entertainment, mosey over to William's channel:

It is wholesome, hearty, informative and highly entertaining. The embedded video above shows an Innuit (Eskimo) winter or Christmas tradition in Labrador.    Click on his advertising links to generate some income for him, and if you like his stuff, send him some product or beer or spoices.  You will be contributing to the future of television.

Note: I haven't met or communicated with William Larkham yet, but his channel has sharpened my curiosity about Labrador, but probably only during the summer months.

Will Computers Be Able To Have Children?

Dr. Stephen Hawking says that we should be afraid of creating Artificial Intelligence that can become a threat to man.  My contention, is that we are already on that path. That Pandora's Box or Can of Worms is already opened. The only way to close a can of worms is with a bigger can, and nobody has one when it comes to the progress of technology.

When Ray Kurzweil's book, "The Age of Spiritual Machine's" came out, I thought that it was a bunch of bosh -- until I got to a seminal part of the book for me.  It was a small appendix of a few pages about building an intelligent machine in three easy paradigms.  That book changed my life. One of my daughter's gave the book for Christmas, and it was the book that started me on the path to programming artificial intelligence and playing with machine learning.  I never once thought that I would use Machine Learning in my job, and I was wrong.

Machine Learning has a long way to go, as does Artificial Intelligence, but we are making great headway.  In previous blog entries, I make the case for every Operating System, or OS to have an artificial neural network embedded in it. I also make the case for standardized neural network notation so that I can transfer, or sell what my machine has learned to your machine.  And I make the case in this blog post, that we can evolve smarter and smarter machines, if every time that we need to load a new operating system, we let an existing operating system impart its neural nets to the new machine.  One of the differences between humans and other animals, is that knowledge is not passed from generation to generation.  If we do that with computers, we are well on the way to make scary intelligent machines.

So if a computer can pass on knowledge to a new generation of computers, by passing down knowledge embedded in Artificial Neural Networks, can one say that the new computer is a child of the old computer?

I have opined on how to create Artificial Consciousness (more in a later blog topic on how I can make a computer have the worry emotion). I also have talked about Computational Creativity and Dr. Stephen Thaler's work.  So if we evolve computer intelligence to the point that it can seed other computer's with that intelligence, then we are on the way to computers having virtual children.

The way that I see Artificial Intelligence evolving, is that no computer can be an expert on everything. As computers become more and more intelligent, there will be specialization among the ranks of computers, as there is in human endeavor. Some computers will trade securities. Some will diagnose illness. Others will run power plants.  There will be a hierarchy of computer intelligence as there is in humans now.  And the progeny of each computer will be a mirror of its parents.  It's hard to imagine, but if computers do acquire consciousness, intelligence, personality and creativity, then the internet will become a computer society mirroring human society.  And that is when we will have to fear it.

Alan Turing never knew what he was getting into when he proposed his machines and the capability of passing a Turing Test.  We are on the cusp of something mind boggling, but at the moment, I would be content on creating an Artificial Neural Network that makes money for me while I ruminate about Artificial Intelligence.

Harnessing The Power of Social Relationships in Buying and Selling

As a tech company, we don't do something very sexy. We sell used cars. Our parent organization is a large bricks-and-mortar auto auction that has been doing it for years, and sells millions of dollars worth of cars a year. They are the biggest on the East Coast where they conduct their business.  Being a progressive organization, they decided to move the business to the canvas of the internet. The question of course, was what the technology solution would look like in it final incarnation.

I am the chief technology officer, and my job is to creative industry-disruptive applications as specified by the chief executive officer and the chief product officer. Technology is merely a tool to leverage business. The innate power of technology, is communications, and its ability to enhance networking. So we created a tool to do just that.

It was at the live auctions that gave us a clue as to how to build our platform. Car dealers, and indeed any business people like to do business with people that they know and trust. Humans are creatures of habit who don't like surprises. They also value relationships. They are also human, so they like a deal, and they respond to the power of the auction and the art of the deal. At the bricks-and-mortar auction, it is easy to see the networks and the social grooves. The people self-sort into various groups. Some like to buy trade-ins from a luxury car dealer. Some know that a particular dealer in a far-away city that has no auto auctions, always has good value cars with a low reserve price. You learn to know who under-rates a vehicle and who over-rates one. You learn the peccadilloes of each unique human being. Being observant of what goes on, led us to create Trusted Buyer Zones where each dealer sells first to his or her social network that has self-sorted and self-identified. They are also a competitive bunch so in addition to the trusted buyer zones, we still kept the 20 minute auction. However we put the control of it into the hands of those at the top of the supply chain -- the new car dealers who supply the trade-ins to the industry. They can schedule the auctions for a regular time each week, or they can sell a trade-in before a customer has signed the papers for a new car.

We also created stuff like proxy bidders, where software robots bid for you. They are time aware, and they can competively bid against humans and get nervous as time goes winds down. We have anti-snipe technology. We have the latest in communications with email and SMS.  We have a key patent in private buyer networks and auto escalation to buyers groups.  We have held auctions where no humans were present. The system sells the vehicle and generates the paperwork.  Our platform is geographically aware and we connect social circles on distance parameters.  We have collaborated with two Computer Science Departments of eastern universities. One of them is developing a machine-learning evaluation tool for us with big data and artificial neural networks. We have looked at semantic web and buyer cues. We are doing data mining, and machine-assembled buyers groups to get both sides, buyers and sellers a fair market price. In short, we are developing the future of automobile re-marketing.  In in our quest to do so, we have made some significant discoveries about the power of the crowd, and how to apply technology harnessing the power in social capital, and the social networks that self-sort in any business environment.

Auto auctions in North America are a multi-billion dollar business. There are some publicly traded companies who are the big, big players in the field. But what we have discovered, is that there is a hidden economy that the industry hasn't monetized yet. Like an iceberg, we have discovered that in some markets, as much as two-thirds of the re-marketed vehicles don't make it to auction. They are sold in relationship-based buying and selling. They are sold in informal social networks, that have self-sorted into their own groups.

A used car sitting on a lot for a long time, represent a bag of spent money to a car dealer. It turns into an expense rather than a profit center. This is especially true if the inventory is financed. The margins on used cars can be thin at times, so it makes sense for a new car dealer to wholesale out his trade-ins before they become an expense. The average new car dealer has two or three go-to wholesalers that he deals with on a fairly exclusive basis. This is relationship buying and selling. On some deals the dealer takes a bath and on some the wholesaler takes a bath, but it evens out and they trust each other. And they move cars. These cars never make it initially to a remarket auction.  And as we discovered, this is the segment of the marketplace that is untallied, unknown, unseen, and it is the major venue of remarketing automobiles. The billions of dollars that goes through the auctions, is the smaller part of this economy. It was staggering to find this out.

So our job was to use technology to aid this process. One of the most onerous tasks, is to enter the vehicle into any system. We created an onboarding app to do it with a mobile phone, and can be done in a minute or two. We created a reliable, systematized condition report that can be trusted. But there was one more step that required refinement in this relationship-based model, and that was the establishment of a fair market price for the vehicle. And that is where the relationship-based model, aided by our technology has the answer.

The usual industry metric for valuating automobiles, is the wholesale auction price.  Black Book and other valuators gather metrics, meta-data and averages from everywhere, and puts out a valuation guide that almost everyone uses, but personally discounts. The aggregation of price data is an art and not an exact science.  Same model and mileage cars vary in wholesale price from market to market. This is true of most products including food where in some markets hot dogs are bigger than in other markets.  When it comes to automobiles, one obvious parameter that goes to condition, is winter where heavily salted roads make the bodies of the cars deteriorate more rapidly. But there is a myriad of geographic factors. And when a dealer looks up a Black Book value, as an industry insider, he knows that it is merely a guide, and adds a local discount or co-efficient. The value in the book rarely matches what happens in the local marketplace.

But in the relationship models of buying and selling, the valuation is done at the extreme local level by the trusted buyer zone.  Our principals regularly get phone calls from dealers asking what a particular car was worth. Smart and savvy second-hand car dealers know what they can sell a particular vehicle for, and what the margins are.  And they know that if they low-ball a wholesale price, their frenemy (friend-enemy) compatriots in the trusted buyer zone will give a realistic value to move the vehicle and make some cash. Cars don't make money unless they sell.

So we made the technology to harness this and put the power into the dealers hands. They can scan the VIN number, have that VIN number exploded to tell all about the car in seconds, take a pic or two, and press a button, and their trusted buyer zone will appraise the vehicle, and can add an offer to buy with the appraisal.  If the economics works for the new car dealer, then another trade-in is moved in minutes and everyone makes money using our platform.  That is the power of relationship buyer and selling, and the vehicle never enters the auction lane.

The value proposition, is that the vehicle is fairly valuated for the current market conditions, the geographic location and the million and one different variables that make it so hard to valuate a car anywhere in the first place. A fair marketplace is an efficient marketplace, and we have discovered a way to fairly value vehicles for a particular marketplace. We have cracked that nut.

You are going to hear a lot about relationship buying in the future as it relates to technology, and I am pleased to be on the bleeding edge. The satisfying part is that our company has foundation patents in the works for this.

Appstore Developer Select: Program Discontinuation

I got this email from Amazon about their Developer Select program where developers embedded the Ad API into the apps to deliver ads.

Dear Developer,

Thank you for your participation in the Appstore Developer Select Program (ADS). Effective February 15, 2015, we will be discontinuing the program. All apps qualifying for ADS prior to February 15, 2015 will continue to receive their mobile ad impressions, Amazon Coins, and AWS credits benefits through March 31, 2015. 

We encourage you to visit our Developer Portal to learn more about the many ways you can engage and delight customers using Amazon’s services. You’ll find information about Amazon Web Services, Amazon’s In-App Purchasing and Mobile Ads APIs, the Amazon Coins program, opportunities such as the “Free App of the Day” program, as well as other self-service promotional capabilities through the Developer Promotions Console. We also offer technical tips, guidance on the different ways developers can distribute their apps, and discovery and monetization best practices on the Appstore Developer blog.

Thank you for your continued support of the Amazon Appstore. If you have questions, please contact us.

Best Regards,
Amazon Apps & Services Distribution Team