BatchQ - A social infrastructure app to encourage social distancing and maximize customer flow.

I'm pretty excited about this project and hope that you might give it a read. I've coined it BatchQ, if anything I hope it generates some inspiration for ideas for other solutions to our new normal lifestyles.

WHAT:

BatchQ is a community oriented software platform for businesses to help organize and orchestrate the entry of customers into establishments by a mobile ticketing system. This will be valuable by:

* Controlling the flow of people in order to to maintain safe distances
* Reducing or eliminating the amount of time close contact resulting by standing in a line.
* Keeping Essential item stock availability information available to the community

The funds will be used to create a business entity and facilitate the building of the application and maintain up to 3 months of hosting for the backers of this project. Customer support will be provided for the duration of the promised kick starter reward period during business hours. There will be options to continue support at a locked in rate determined by the backer level.

I plan to release this Friday as 12:00am because it isn't something that will have much value without a little urgency.
#entrepeneurship #coronavirus #socialdistancing #toiletpapercrisis #mobileapp

Applications of Machine Learning for Any Business: Classification

Machine learning (ML) is a modern innovation that has helped man improve not only many industrial and professional processes, but also promotes everyday life. But what is machine learning? It is a subset of artificial intelligence, which focuses on the use of statistical techniques to build intelligent computer systems in order to learn from the available databases. Currently, machine learning has been used in multiple fields and industries. For example, medical diagnosis, image processing, prediction, classification, learning association, regression, etc. Intelligent systems based on machine learning algorithms have the ability to learn from past experiences or historical data. Machine learning applications provide results based on past experience.

Machine learning has had many fruitful applications in finance long before the advent of mobile banking apps, competent chatbots or search engines. Given the high volume, accurate historical records, and quantitative nature of the financial world, few industries are better suited to artificial intelligence. There are more use cases for machine learning in finance than ever before, a trend that continues with more accessible computing power and machine learning tools.

Now with the tech culture of gathering and storing excesses of data, many more industries are setting themselves up to take advantage of Machine Learning as it can be applied to many other sets of data that have documented their sets of input and output combinations of variables and metrics.

Machine learning is set up to play an integral role in many phases of the financial ecosystem, marketing, and sales performance. However, few professionals with technical knowledge have an accurate view of how many ways machine learning finds its way into your daily financial life.

Classification Problems

Examples such as identifying credit worthiness, understanding who buys products for a company and determining good traffic from bad can all be generalized to classification problems, which a subset of Machine Learning applications.

Classification is the process of determining what category a set of observations belong.  The strategy Machine Learning approaches is using a set of observations with known outcomes to build a Mathematical model that can reliably reproduce the expected outcomes with the known input and output pairs.

This process of Machine Learning happens by using the observations and known outcomes to allow an algorithm to optimize its underlying mathematical model.  By using the known data, the algorithm over time becomes a specialist for this set of data and then uses the knowledge it built to classify new observations with unknown outcomes.

Some applications are outlined below:

Subscription of Loans / Insurance / Services

Subscription could be described as a perfect job for machine learning in finance, and in fact there is a great concern in the industry that machines will replace a large part of existing subscription positions today.

Especially in large companies (large banks and publicly traded insurance companies), machine learning algorithms can be trained in millions of examples of consumer data age, work, marital status and financial loans or insurance results, as if a person failed or not. or repaid your loans on time.

The underlying trends that can be evaluated with algorithms and analyzed continuously to detect trends that could influence loans and guarantee in the future (do more and more young people in a given state have car accidents? Are there increasing rates of default among a certain demographic population in the last 15 years)?

These results have tremendous tangible performance for companies, but today they are reserved primarily for larger companies with the resources to hire data scientists and the huge volumes of past and present data to train their algorithms.

Marketing and User Analysis

Similar to the application of ML to loans and credit worthiness, applying Machine Learning classification methodologies towards understanding trends of demographics, genders, and buying habits using consumer data.

Companies like Amazon and Walmart are already doing this with user data, but its is something that can be done by any size companies that are already capturing this data with their e-commerce orders.

Using sets of data like gender, age, salary, and sales (among other data points) a model can be derived to determine patterns of people that are likely to Buy (Category A) or Not Buy (Category B).   Since historical data has already been gathered from past purchases, this can be utilized to build targeting marketing strategies or targeted sales strategies focusing on people more likely to buy specific products.

Sales Performance

Another area that can utilize the generalization characteristics of classification is in analyzing and interpreting B2B sales data.  Data for improving lead generation, matching deals to customers, maximizing value to the customer,  and optimizing sales to the market and for the customer are already hiding in dormant data.  A wealth of knowledge can be generated using Machine Learning (ML).

An example set of variables that are invaluable for determining these metrics are previous products, customer retention, company size, business industries, and economic strength indicators.  Utilizing a combination of these variables, a ideal match can be made that makes as close to perfect mate of seller and consumer.

By utilizing ML business can make better decisions that maximize revenue and time.  Targeting specific customers and being able to advertise to just the right set of eyes can save money and also let sellers focus more specifically what their niche is.

Wrap Up

Classification is only one type of problem that Machine Learning is good at.  Big Data is a pop-term that is floating around, but overall technology hasn’t scratched the surface of making annoying meaningful out of it.  Many have coined the 2010 decade as the Age of Big Data.  Petabytes of knowledge

It hasn’t been until the last decade that computers have had the computation power to reasonably run these models and produce good results. There is a wealth of knowledge that is hiding inside historical data, accounting software, and traits of industry leaders.  

By using a combination of datasets and known outcomes, ML can optimize specific knowledge from data and generate models that produce consistent and correct results.  To an onlooker it may not be apparent how the model works, but by using proofs against we already know it is easy to empirically evaluate its correctness.

MaddLogic LLC is a company that specializes in ML and Neural Network solutions for small to medium size businesses.

Is Block Chain’s weakness the wallet?(Part 1)

A wallet on a block chain is the core way any end user interacts with it.  It establishes the end of the line of trust in the system, and using/accessing one is the edge of trust.  The wallet supports the means of making transactions between it and the chain to one or more parties.  A wallet can ensure anonymous use of the chain and provides trust between transactions without the need of validation of identity or cheating the system since all transactions are validated before applied.

To establish a baseline of a secure system these are a few important points to note.

Wallets in block chain technology check all the bullet points for a secure system.

• Authentication – If you have access to the wallet, it is assumed you are the owner and there isn’t much protection against attacks here.
• Authorization – If you have the pass phrase and the wallet address you are authorized to use it and interact with the services on the block chain.  It is very hard to random/brute force your way into this.
• Non-Repudiation – Having your wallet lets you make transactions on the chain.
• Integrity – Through the use of transactions via the wallet you are a trusted entity on the system.  Transactions are verified by consensus so you aren’t not going to have a invalid request (although it is more likely to be denied.)

Wallet check bullet points for Internet Privacy:

• Individual Privacy - Chain Specific but typically anonymous or pseudo anonymous only need an email or pass phrase to signup and access.
• Communication Anonymity - Trust between entities without revealing identities of the party.  Having an address is enough to establish trust with another party.

Moral of the story is, once you have access to the wallet, you pretty much have unrestricted access to use it to your desire.  There is no off switch or protection inside the wallet, and you are bound to the credits you have to use in the system.

Is that really what we wanted?

Attacks

• $5 Wrench Attack
• Keyloggers
• Wallet.dat imports
• Stolen Phones
• Mad Spouse
• Shared Logins

All of these are attacks that make getting access to a wallet obtainable, and once obtained you are a trusted entity on the system. 

Recovery

Lost Wallet

Forgot my password

 If you forget your pass phrase or lazily try and maintain your wallet, you risk loss.  There isn’t a great way to recover your assets if you have lost something to access it.

No Limits

As annoying as the banking system is.  I does add some sanity checks for thinks like limiting atm withdrawal, reviewing large transactions, serving as a mediator between transactions etc.  At some point we take these things for granted, and when they are barrier to something you want to do it is a inconvenience.  For situations when your credit card is stolen it is good there is someone there to monitor your transactions and limit the damage.  With block chain wallets there is no damage control.

Impact of the Internet on our Identity and how Block Chain can help us get it back (Part 1):

One of the generation divides between the retiring population and the population that is entering their prime is the concept of privacy and our expectations of how privacy is handled in the internet age. As a millennial I had pretty early exposure to computers and growing with technology, but when I was younger the devices were simplistic and weren’t connected to each other. I had a PC before the internet started becoming a thing and slowly started getting more access to it as the price of it lowered.

To express myself I gradually forfeited more and more of my personal self to the internet without really a second thought through sites like Facebook and Myspace. As I developed professionally in the tech field I realized how much of myself I was giving to the internet of things.

Google has a pretty good idea where I’m going, what I’m thinking and how much I have to get where I’m going, probably before I do. I’m aware of it, slightly bothered by it, but I move on. The only way I can be 100% certain of what I can keep to myself is something that isn’t passed outside of my thoughts

My parents hold their privacy to a much higher part of the self identity. I remember going through the process of getting a home loan with them while I was growing up. I wasn’t involved with the details of everything, but I remember their comments about how intruded upon they felt and how exposed they felt to secure the loan. Even now after I had went through the process of it in the last couple of years on my own, they commented about asking how I felt about the whole process and if I felt a loss of myself to the system for that.

I didn’t really feel violated, but I did lay every thing out on the table to prove my reliability in being able to make my payments. I don’t doubt that my desensitization from the peak of my childhood/young adulthood that lessened the impact of the process.

Comparing these two mentalities and thinking through what privacy means to me I have big opinions on what acceptable use of my privacy and what is abuse of my privacy. The trend of data and ourselves is a losing battle as companies want more and more of our behaviors and use patterns of their specific interests. As more companies adopt this mindset, I don’t know that we will know ourselves as well as the conglomerate of internet companies.

As a race are in the middle of an identity change. Are we a species of individuals or are we now simply a cog in a new type of mental ecosystem. To an extent there are examples of arguing either way, but as automation and intelligent computing continues we are trending towards losing that individuality.

As we move forward it will be hard to protect ourselves from this kind of divulgence of information.  It is almost impossible for us to prevent companies from taking information from us because they provide the platforms that enable us to interact with the things we want or need to.  To get ahead A.I.s are predicting what we need before we need them.

This rate of technology is advancing much faster than we as humans are willing to change.  The ethics of what is happening is still being figured out, but in the meantime, it is a buffet of data that is getting passed to any observant eyes.  

We take the control of our data back where we can.

Block Chain and Personal Privacy:

In the current culture of the internet and content providers, the use of Personally Identifiable Information (PII) is easily abused because of the ease of transfer, ability to be copied, and can be securely[1] stored.   Content providers, healthcare professionals, and the government all lazily protects this information for the people it serves once an actor agrees with the following enforcement principals: Enforcement of Validity (2) and Enforcement of Separation of Duty (2). 

The mentality that once an entity evaluates the integrity and background of its actors of interaction there is an assumption of Trust.  This establishes the Enforcement of Validity (2) and naively maintains that as long as an entity can guarantee that all actors maintains this level of Trust, Risk can be eliminated.  By establishing a base metric of Trust passing an artifact between actors, with equivalent baselines, allows for a network to be established and maintain a Secure System.  So long as an artifact stays within the rules defining the Enforcement of Separation of Duty, that there is little to no Risk of violations or risk of to be committed in a Secure System by un-trusted actors. 

While great lengths have been made by agencies to maintain the integrity of PII and ensuring that the use of it maintains low risk, the core facets of a system measured by its ability to maintain Reliability, Availability, Maintainability, and Redundancy (3) can be violated when evaluating current implementations of PII as a system(4).  For the context of this post, PII will be thought of as a Secure System. 

An application of the Block Chain can serve as a Trusted System of Personally Identifiable Information, can establish a framework that minimizes risk and adds Reliability, Availability, and Maintainability to a system with its current implementation that cannot do so.

Here is what that application could look like:

The framwork enables the system to establish a Secure System for Personally Identifiable Information (PII).  There are three interactive roles for the system.

• Actors
• Requestors
• Source of Identity[JM1] 
• Transaction definitions are handled via contracts.

An actor is the lowest privileged of all the users that represents an individual or taxable identity.  This type of user can be used to login throughout the network against sites defined as requestors or request transactions between other actors or requestors.

Requestors have no login privileges.  Requestors can ask for validation against actors and/or requestors to verify their identities.  Requestors can only see information that they have contracted between the privacy application and the requestee.  Requestors must also establish a contract between the Source of Identity they are choosing to use.

Source of Identities(SOI) are the entities that are the true rule of authority.  These entities define what/who PII are and can define their own identifiers.  An example of these entities would be Governments.  New sources are needed but not yet defined.  This type of entity is useful for private networks or to gate access to certain parts of the digital world.  Source of Identities must maintain their contracts between them and the requestors.

Contracts set the rules between the communications.  At a basic level all requestors can use an alias of the privacy application as a method of authentication, but the Requestor cannot get any additional information for the Actor it is serving.  Contracts between requestors that need to use information from a Source of Identity

Requestors can ask for authentication requests between users and if they need to check details about a user, the Source of Identity must have already given them permission to be able to view that information as well as having an agreement between the user and the requestor.  If any link is broken a request is automatically denied.

When a requestor is created, they may choose a Source of Identity, doing so will give the Requestor and the Source of Identity to negotiate what information the Requestor will be able to see about a User or another Requestor.

The goals of this system is to:

Authentication and Verification:

•  Authenticate actors to actors or actors to requestors while never passing PII
• Be the Authority of validation to prove identity.
• Creation of unique links between actors and actors or actors and requestors.
• Ensure that enforcement of validity is maintained in the system between actors and/or requestors at all times
• Allow explicit control of actor’s permission of their information between other actors or requestors.

Communication:

• Allow for validation of information without passing identity revealing information
• Maintain channels of communication over insecure mediums
• Ensure transparency of the system and maintaining individual privacy
• State transitions of the system only act in consensus

Storage:

• Ledger of interactions between entities.
• Eliminate the need of PII to be stored externally on system (either the privacy application or by Sources of Identity). 
• Limit the ability of requestors to store PII.

Recoverability:

• Recover from PII breaches.
• Delete aliases between actors and requestors.

Redundancy:

• No single point of failure.  Distributed processing and storage.

1. Jøsang, Audun and Lo Presti, Stephane. Analysing the Relationship between Risk and Trust. Trust Management. Berlin, Heidelberg : Springer Berlin Heidelberg, 2004, pp. 135--145.

2. A Comparison of Commercial and Military Computer Security Policies. Clark, D. D and Wilson, D. R. s.l. : IEEE, 1987. 1987 IEEE Symposium on Security and Privacy. pp. 184-194.

3. Jackson, Yvonne, et al. The new Department of Defense (DoD) guide for achieving and assessing RAM. Reliability and Maintainability Symposium, 2005. Proceedings. Annual. s.l. : IEEE, 2005, pp. 1--7.

4. Corresponding Security Level with the Risk Factors of Personally Identifiable Information through the Analytic Hierarchy Process. Lin, Iuon-Chang, Lin, Yung-Wang and Wu, Yu-Syuan. 10.1770, s.l. : Journal of Computers, 2016, Vol. 11.