Excited About Tech

Excitement in Life is usually like your dog's experience of going to the vet. They get all excited about the car ride until they find out where they are going. Unlike the mundanities of life, I can get excited about applying new concepts to things like blockchain and coming up with neat stuff that could disrupt so many things. If I wasn't busy earning a living, I would build stuff in a skunkworks. If I had money, I'd be dangerous (and rich)!

So what is some of the stuff that is whetting my excitement (and wetting my pants)? How about concepts in secure multi-party computation? This is a hugely under-exploited field. I would find/build/buy the hardware for a blockchain mobile phone. I would do some applications in Oblivious Transfer Cryptography (google it). I would invent a small footprint blockchain for personal use (Ken Olson, founder of DEC said in 1977 that there is no reason that anyone would want a computer in their home. Blockchain is at the same stage.) And who wouldn't be excited by conceptualizing Utility Fog ( https://lnkd.in/dJfjVpg ). So much to do and so little time.

Of course, it wouldn't preclude me from fun. I'd also make a lot of fun stuff too, like the electric dog polisher, instant water powder (just add water), and a fur-lined sink.

(originally appeared as a Linked In post: https://www.linkedin.com/in/ken-bodnar-57b635133/)

A Possible Technology Solution To Fake News

I just read an article in Finance Monthly about how fake news is killing the economy, and it is really disturbing to me, how various sectors of the political scene can assert a total untruth and have thousands of sheeple believe it to be gospel. I keep thinking that surely we are better as a people and more enlightened, but apparently that is not the case. We seemed to have regressed over the last couple of years. Fake news has turned into a monster, and certain classes of people cannot find direction their moral compass enough to fight it. So maybe technology can offer some solutions.

Thanks to some of my friends at @Microsoft , I once read a bulletin put out by them with a title that resonated with me. It was the differentiation between attackers and defenders. To be successful in fake news, you cannot just merely defend the truth, you have to attack the fake stuff. There is a difference between those two actions. Defenders use lists in their arsenal of tools. Attackers use graphs. That was the Microsoft assertion and it is true. All techies will have nodded when they have read the sentence about attackers using graphs.

When I talk about graphs, I don't mean those pretty pictures that Excel puts out of profit and loss, or the rise of the price of Bitcoin over the last year. I mean graph in the sense of mathematical graph theory. If you aren't up on this, let me explain. A graph is a theoretical structure amounting to a set of objects or ideas of which some pairs of objects or ideas are related. How they are related (or the lines connecting them) are called edges. The objects or ideas are called nodes or vertices. Graphs are a part of discrete mathematics that can translate easily into real life scenarios. A picture is worth a thousand words, so here is a picture of a graph.

You will notice in the above graph there are things (nouns) which are the vertices or nodes and there are states (is, lives, has) which are the edges. Edges have properties. and the properties can have sub-properties. A sub-property in this case is that the edge is directed with an arrowhead. This makes the information in the graph semantic -- or composed of meaning that is apparent by the structure of the graph. The nice part is that there are discrete mathematical methods for traversing the graph and extracting not only data, but knowledge. A graph is capable of creating a level of abstraction. For example, the discrete data is a news story. A level of abstraction is the assertion or inference that the particular news story is fake news.

When fake news appears, the defenders of the truth manage it in list form. Here is their list:

• How many untruths are there? List them.
• Find countervailing documented data to refute.
• Do for all untruths.
• Come to conclusion that the whole article is fake news.

In the meantime, the originator of the fake news uses a more complex graph-like function to promulgate the fake news. It starts with an inconvenient truth that is pejorative and an attempt is made to neutralize it. First they must define the audience that is ready, willing and able to uncritically accept any falsehood. They must also craft the "alternative facts" to be plausible, at least possible if high improbable. Then they have to find the opportunity and the medium to place the fake news. This involves a network of perfidy that is a graph of the underbelly of spreading falsehoods for personal, pecuniary or political gain.

So, the solution is that there must be an impartial, balanced methodology of determining and labeling fake news. This is the nub of the problem. Other problems are that the sheer volume of news coming out, makes human content moderation almost an impossible job, unless you have deep pockets like Google or Facebook. Although, from past experience, Facebook is for sale to anyone who wants to buy ad space, Russian trolls and democracy-destabilizers and all. The obvious answer is machine learning and artificial intelligence monitoring and labeling fake news. You can't suppress fake news, no matter how egregious the lies are, because of the First Amendment and Freedom of Speech, but you can label it with the Scarlet Letter of fake news and those who cite it, are obviously lacking some cognitive ability.

What does the Fake News BS Detector technology stack look like? First you have to give the system some context for current events. This is where AI comes in. Graphs have to be created and semantically understood. Luckily for this, we have wonderful graph databases. My current favorite is @Neo4j. Some of the graphs that your AI machine will create will be something like this:

CREATE (djt:Person {name:"Donald J. Trump"})
RETURN djt
MATCH (djt:Person {name:"Donald J. Trump"})
CREATE (djt)-[status: HOLDS_OFFICE] -> (potus:Position {name:"President"} 
RETURN djt, status, potus

The above happens to be a simplistic example of Cypher, the language used to create graphs in Neo4j. You get the idea. The AI machine does lexical, syntactical and semantic analysis to create the graphs,

So you run the AI machine, and you get a bunch of graphs. I was a little stumped as to how to teach the machine true from false one the semantic analysis was complete. You need some human intervention somewhere at the beginning of the cycle, and I wondered how to do it. However, just recently, I read a seminal article by Dimitri De Jonghe about Curated Governance with Stake Machines and the light bulb switched on, and I got the Eureka moment.

I wasn't totally unfamiliar with Dimitri. He is one of the key members of the @BigchainDB team, and I had communicated with him on smart contracts, and they graciously granted me access to the Github on smart contracts before it was released.

The article on Curated Governance with Stake Machines is a perfect example of how our lives will be tokenized by blockchain. Essentially what you do is steer token holders to earn more tokens by curating items (graphs) that are or are not fake. The token holders themselves have their opinion rated by reputation and bias that are empiricized by the curation automata. Essentially, you have created a token-curated registry of graphs. These curators could be reporters and news media types, just like Reddit editors. Let me quote from the article linked above: So long as there are parties which would desire to be curated into a given list, a market can exist in which the incentives of rational, self-interested token holders are aligned towards curating a list of high quality.

Naturally these verified graphs would be stored in a data-centric blockchain like BigchainDB, which could also handle the tokenization of the curation. The data payload of BigchainDB is well suited to textual or tuple key:pair representation of graphs.

Now onto the automatic part. Suppose you built the machine and Twitter bought it to scan posted items and put a red stop sign icon if it is fake news. You have the consensus of the curators for a graph, for example, that Entity: "Russia" -> Action: "Interfered" -> Object: "US Election 2016". The fake news article is read by the platform and feed into the stack. The algorithm to check for fakeness can be a method like Latent Dirichlet Allocation. This throws data as a document at the platform and allows the platform to sort it out, as opposed to having a manual model. If you are a techie and have done eCommerce recommender systems, you will see that this is similar matrix factorization models. If the previous sentence is Greek to you, essentially you have a matrix where the rows are documents and the columns are words. These matrices are not exactly a sequence of words, but rather of the index of the words found in either the nodes or edges of graphs that you already have. Thus, you can calculate a probability (known as a Bayesian process) of the new item being fake or not. This methodology is a generative model, meaning that you can generate examples of fake and real news and it knows the difference.

This type of architecture can be extended as the number of meme and graphs grow, using an algorithm called Hierarchical Dirichlet Process where the number of topics chooses itself automatically and grows according to the input data (that can be assisted token curated when necessary) via non-parametric machine learning.

These ideas need some research and development, but they could point to a way where we have "trusted" news adjudicated by machines that were "taught" by trusted token curated registries.

We really need to do something about how we have degenerated as a human species from the ethical and altruistic, moral high ground of the truth, to a third of the American people willing to believe lies in spite of what rational evidence tells them. Perhaps it is time for the machines to step in.

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.

The Seven Deadly Sins That Startups Commit

I got to thinking about the startups that I was involved with, and what went wrong when they failed.  I am an analyst by nature, so I did a postmortem on all of my startup failures.  Here are some nuggets from those postmortems outlining mistakes made.

1) Just because it is a neat idea, employs the latest technology and nobody is doing it, doesn't mean that customers will want to pay money for it.  Your startup product must have a distinct value proposition to its users.

2) No matter how much money will be saved by your product, it won't sell unless people want to use it.  People want to use products that fit into existing processes in their businesses or lives.  If it doesn't fit, then it won't be a commercial success.

3) Then there is "Lipstick on a Pig".  Oftentimes when our product was not adopted, we sat around the table and decided that we needed a sexier feature, or an edgier look and feel, or the latest in a UX consultant to come and do a new design for us.  You can't put lipstick on a pig to dress it up and sell it.  Customers will see through that.  If they don't use your barebones product, they are not going use one dressed up with new features or a snazzier look.  There is way too much emphasis on Look and Feel and looking sharp and UIX and UX and all of the buzzwords that bring in consulting money.  The ultimate success of an app is shown by Google which is simple, clean and beautiful, and would never pass design review with these so-called UIX experts.

4) That brings us to minimum viable product.  You have to have something that people are willing to use, willing to buy, and willing to share with their friends.  Too often we have built apps that everyone said was good and no one used them.  We then decided that perhaps we needed more lipstick on the pig.  If that didn't work, then we decided that we needed to shave the pig and sell it as something else.  All the while, we never had a minimum viable product.

5) We never had a feedback loop.  When someone bought our product and never used it extensively, we did go back and ask for feedback, but we never really listened to our customers.  When they would bring up objections like "It takes up too much time to use" we thought that they were just making excuses.  We never modified the app to fit their time constraints and their business.  The correct methodology is to embed someone with an early adopter and keep adapting until they used it, loved it and shared it among peer businesses.  We never did that.

6) Before you go and build something and a business around it, you should be able to pre-sell it.  It should be that good of an idea.  You should be able to articulate the value proposition in ten seconds.  The value proposition should include savings in both time and money and/or ease of doing business which is time and money.

7) You should be culturally appropriate to your customer base.  We had a designer create wire frames for an app that was aimed at 50 year old, non-computer literate business owners.  He designed it like a social media app with gaming, individual photos, reputation scores and the latest in device integration on smart phones.  No fifty year old, computer illiterate is going to upload his pic to a business app that is supposed to help him make money.  They don't give a damn about being 5 Star users and they don't react to gamification the way that millenials do.  Their favorite device is the Blackberry with the full QWERTY keyboard.

So there is the seven deadly sins of startups.  How do you know if you have a viable product? Simple.  It will be a product that people will want to use.  If you get widespread usage, you will find a way to monetize it later.  Usage is the key indicator of success.

What the Chinese Are Looking To Invest In

It is interesting to see what the Chinese where to place investments in terms of technology.

I belong to a Mobile Technology group online, and today, the following advertisement was posted:

Looking for possible acquisition targets in US & Europe

A large Chinese investment company is looking for possible acquisition targets in mobile applications, games, internet, eCommerce, mobile & online advertising, 3D technology, animation, comics and traditional media such as newspaper and magazines. The company must be profitable. Targets size between $10m - $100m.

What I find interesting is that they want to invest in newspapers and magazines as well as technology media -- these are instruments for spheres of influence. This has the potential of being frightening to Americans, considering that the Chinese state probably owns this investment company.