Perception of Value & Today’s Cryptocurrency “Crash”

Artist’s rendering of Bitcoin. THERE ARE NO ACTUAL COINS THAT LOOK LIKE THIS. Don’t ever let anyone sell you one.

Today, many cryptocurrencies lost ~35-50% of their value. Reddit even posted contact information for the National Suicide Prevention Hotline in /r/cryptocurrency, knowing how emotional investors were bound to be today. Bitcoin, which was nearly $20K in mid-December and has been hovering near $14K this past week, dropped nearly $4K and almost sunk below the $10K milestone. I usually track the price of Bitcoin at http://bitcointicker.co, which can show the posted prices from several exchanges (web locations where people go to buy and sell, like Ebay). There are hundreds of cryptocurrencies and many of them dropped in value today.

Why did the prices drop so much on Tuesday? Here are some likely influences:

Market prices are usually driven by supply and demand — for example, if there aren’t that many lobsters available in a particular area at a particular time, and you go to a restaurant hoping to order one — you’ll pay a premium. But that price is also influenced by the quality of the product, the image of the product, which influences your perception of its value. Quality reflects how well something satisfies stated and implied needs or expectations.

Value, however, is quality relative to price, and influenced by image. And people are not always rational: they’ll pay a premium for image, even if the value of a product isn’t particularly high. Just think of all the Macs on display at schools, coffee shops, and airports. Price is related to value… usually, price goes up as value goes up.

Where’s the value of cryptocurrency? A Bitcoin does not, on its own, have any inherent value — just like a dollar or a Euro (a “fiat currency”). But the prospect of an asset that will increase in perceived value — where you can buy low, hold (sometimes just for a few days), and sell high because there are lots of people willing to buy it from you — will have perceived value. Hundreds of early adopters — or “Bitcoin millionaires” — are getting people excited about the prospect of making small investments and reaping huge rewards. That this has happened so recently lends a mystique to ownership of cryptocurrencies and Altcoins (or “alternatives to Bitcoin,” like Ether) in addition to the novelty.

Value is attributed to things by people, and cryptocurrencies are no exception. The quality of the currency itself, and the technical solidity of the platform upon which one is based, isn’t really tied to the cryptocurrency price right now — although this will probably change as knowledge and awareness increases.

Is this the end of Bitcoin? That’s doubtful — there are too many innovators who insist on exploring the technological landscape of cryptocurrencies and blockchain technology, and lots of investors willing to fund them. In the meantime, there are unlikely benefits: because cryptocurrencies are not yet mainstream, a “crypto crash” is not as likely to ripple through the whole economy (no pun intended) like the subprime mortgage crisis of 2008. But if you do decide to buy cryptocurrency, don’t invest any more than you can afford to lose.

Writing a Great Article Review

We’re teaching a class on blockchain and cryptocurrencies this semester, and since the field is so new and changing rapidly, we’ve asked our students to make finding and reviewing articles part of their learning practice this semester. This is a particularly challenging topic for this task because there’s so much hype, marketing, and fluff around these topics. We want to slice through that, and improve the signal-to-noise for people new to learning about blockchain and cryptocurrencies. Here are some tips I just prepared for our students — they may be helpful to anyone writing article reviews, especially for technology-related areas.


0 – Type of Source. Reviews or articles from from arXiv, Google Scholar were strong; reviews from Coindesk, CNN were weak; reviews from WSJ and Hacker Noon went both ways. Here are two submissions that were publishable with only minor edits:

1 – Spelling & Grammar. Most of you are college seniors, and the few who aren’t… are juniors. Please use complete sentences that make sense, with words that are spelled correctly! If this is hard for you, remember that every one of you has spell check. One way to remember this is to draft your posts in Word, and then perform spell check before you copy and paste what you wrote into WordPress.

1 – Your job is to create the TL;DR. What’s the essential substance of the source you’re reviewing? What are the main lessons or findings? If you were taking notes for an exam, what elements would you capture? (Using this perspective, commentary about how good or bad you think the article was, or what it didn’t cover well, would not help you on an exam.)

2 – Choose solid source material — primary sources, e.g. research papers, if possible. If the article is less than ~400-500 words, it’s probably not detailed enough to write a 250-300 word summary/analysis. Your job in this class is to break down complex topics & help people understand them. If your article is short and already very easy to understand, there’s nothing for you to do.

3 – Avoid “weasel words” (phrases or sentences that sound like marketing or clickbait but actually say nothing) and words/sentences that sound like you’re writing a Yelp or Amazon review rather than a critical academic review. Here are a couple weaselly examples drawn from this week’s draft posts (see if you can spot what’s wrong):

  • It is clear how beneficial blockchain can be to smaller businesses.
  • Blockchain has the potential to change the world.
  • Each other the topics covered in the article deserve their own piece and could be augmented upon greatly.
  • There is a degree of uncertainty that comes with an emerging technology.
  • Blockchain can bring them into the 21st century to compete with larger corporations.
  • Many people are scared of the changes, and governments will seek to regulate it.

4 – Answer the “so what” question. Why is this topic interesting or compelling?

5 – Choose information-rich tags. For example, in our class, don’t include blockchain as a tag… pretty much everything we do will be related to blockchain, and everyone will tend to use it, so there won’t be much information contained in the tag.

Blockchain and Quality

Quality is all about satisfying stated and implied needs –now, or in the future. When we envision and design high-quality products and services for the future, that’s innovation. One of the most hyped innovations of 2017 was blockchain, which has the potential to transform business models and the way quality is managed. The purpose of this article is to explain this relationship in a simple way.

Blockchain is the innovative technology supporting the Bitcoin cryptocurrency. Bitcoin gained tremendous traction in 2017, starting at just over $1,000 in January and reaching nearly $20,000 by the end of the year.  It increased in value so much over this time that it’s been compared to the Dutch tulip market bubble of the 1630s.  After tulips were imported into Holland from Turkey, an alteration to the solid colors of the tulips caused the appearance of “flames” on the petals. This made people believe that the tulip bulbs held extreme value, and so many people traded their land and their savings to invest in what they felt was a “sure thing” – to lose everything not long after, when the market corrected itself.

Bitcoin (USD) prices, 1/1/17-12/13/17. Generated using https://www.coindesk.com/price/.

Bitcoin (USD) prices, 1/1/17-12/13/17. Generated using https://www.coindesk.com/price/.

The blockchain technology that supports Bitcoin is, at its core, a database. It’s a special kind of database, but no more magical, really – and easier to contextualize if you think about innovations in database technology over the past two decades.

Databases can be roughly classified into these categories:

  • Relational databases (Oracle, MySQL, PostgreSQL, Sybase): When you can organize your data in terms of tables, fields, and relationships between those entities, a relational database is often appropriate. For example, your customer data might be kept in the “people” table with fields like address, state, or gender. Each record in the people table might have a type – employee, partner, or customer. Although records can be changed, it’s easy to accidentally input bad data, and it’s also easy to accidentally generate duplicate records. Scaling a relational database can also be rather tricky.
  • Non-relational (NoSQL) databases (MongoDB, Cassandra, Redis): If most of your data comes in large blobs and you don’t want to split it up into fields and tables, these databases are useful. MongoDB is great for collections of documents, such as web pages, log data, or tweets. Cassandra works well for analytics applications. Sensor data and other data types that change frequently or need to be held in active memory (for example, in key-value stores) are handled well by databases like Redis. NoSQL databases are easier to scale than relational databases.
  • Other databases and data stores with special properties: Some databases are so unique they don’t feel or act like databases. Solr, for example, is traditionally used when you have to provide search functionality over a store of documents. Hadoop is a distributed file system, so it functions somewhat like a database even though it’s not one. Graph databases are designed for data stores where the relationships are the most important aspect, so they are gaining popularity for social networks. Large, institutional science projects often store their data in special binary files that have distinct formats, can be queried like databases, and in many ways act like databases – but they are not technically databases.

 

What Distinguishes Blockchain-based Databases from Ordinary Databases?

First, the blockchain is designed to handle transactions – it’s a digital ledger. So it’s not surprising that its first “successful” use cases are in the realm of cryptocurrency, where people engage in transactions with one another to exchange something of value.

Next, this database is immutable, meaning you can’t go back and change earlier records. Every time a new transaction occurs, a cryptographically sealed “snapshot” is taken of the entire database. When I first heard this, I was worried: so that means if we accidentally enter something incorrect into the database, it can never be changed, right? And its presence is memorialized forever? The answer to this question is: sort of. Thanks to “smart contracts”, we shouldn’t ever be in the situation where bad data gets entered into our blockchain-based system, because incoming data will be checked (by multiple agents) against the smart contract — and only allowed to join the blockchain database if it meets all the quality requirements specified by the contract. It’s like a fancy way to implement validation rules – with the added benefit of being totally traceable. Imagine how nice it would be to trace all the steps in the process that brought the fresh fruit into your kitchen – or any other product you use — just because all transactions in the production process were logged into a “supply blockchain.”

A blockchain database is also decentralized and distributed — you don’t just “buy a blockchain database” and install it at your company. Databases can be centralized, decentralized, or distributed. Most business databases in the past were centralized: there was one instance installed, and a database administrator (or team of them) ensured the performance and security of the database while everyone in the organization created and used applications that interacted with the data. Today, these databases are more commonly distributed: there’s not just one instance, but several – there is no central storage, but there may be storage on many computers, or over a network of connected computers (or “in the cloud”). 

Decentralized systems have many advantages – for example, nodes can join or leave the network at will. For example, you can create a web site or take it off the internet whenever you want, if you own and control it. In decentralized systems, there is no single point of control. If a business wants to implement blockchain but also wants to control all the nodes, that should be a big red flag. By its nature, blockchain is decentralized just like the internet itself.

Finally, blockchain is transparent. Any of the participants who own nodes can see all the transactions — so there should be fewer opportunities for fraud. This doesn’t mean that there isn’t opportunity for danger, though.

 

Why is Blockchain Potentially Useful for Quality Assurance?

In addition to enhancing provenance and traceability, one of the biggest envisioned applications of blockchain databases is to support machine to machine transactions. As intelligent agents grow in complexity and are trusted to handle more tasks, and as the Internet of Things (IoT) expands, there needs to be a high-quality record of how those objects and agents interact with other objects and agents – and with humans. Blockchain could also be used to support new business models like decentralized energy markets, where you can consume energy from the local power plant, but also potentially generate your own and contribute the excess energy to your local community for a fee. It could potentially transform middleware as well, which is software that allows different software systems to communicate with one another. (A long time ago, someone told me that it’s like “email for applications” – they can send messages to one another so they know how to react, for example, when a company receives an order and several systems need to be alerted that the order has arrived.)

In principle, transactions logged to a blockchain make it impossible to defraud participants in the process, and impossible to manipulate records after they are recorded. They are self-auditing and fully traceable. Blockchain won’t make quality assurance, tracking, or auditing EASY, but you should expect it to make the business landscape different – new business models will be possible, and it will be possible to entrust intelligent agents with more tasks.  

Blockchain can help us ensure that stated and implied needs are met, and do it in such a way that the integrity of our data is assured simply by its presence. But we’re not there yet. Developers still need to implement simple, demonstrable use cases to make it easier for managers and executives to map these technologies onto specific business needs. In addition, blockchain is slow compared to relational database systems, so this needs to be addressed as well before widespread adoption.

 

Read more in our December 2017 SQP article.

Quality 4.0 and Digital Transformation

The fourth industrial revolution is characterized by intelligence: smart, hyperconnected agents deployed in environments where humans and machines cooperate to achieved shared goals — and using data to generate value. Quality 4.0 is the name we give to the pursuit of performance excellence in the midst of technological progress, which are sometimes referred to as digital transformation.

The characteristics of Quality 4.0 were first described in the 2015 American Society for Quality (ASQ) Future of Quality Report. This study aimed to uncover the key issues related to quality that could be expected to evolve over the next 5 to 10 years. In general, the analysts expected that the new reality would focus not so much on individual interests, but on the health and viability of the entire industrial ecosystem.

Some of the insights from the 2015 ASQ Future of Quality Report were:

  • A shifting emphasis from efficiency and effectiveness, to continuous learning and adaptability
  • Shifting seams and transitions (boundaries within and between organizations, and how information is shared between the different areas)
  • Supply chain omniscience (being able to assess the status of any element of a global supply chain in real time)
  • Managing data over the lifetime of the data rather than the organization collecting it

The World Economic Forum (WEF) has also been keenly interested in these changes for the past decade. In 2015, they launched a Digital Transformation Initiative (DTI) to coordinate research to help anticipate the impacts of these changes on business and society. They recognize that we’ve been actively experiencing digital transformation since the emergence of digital computing in the 1950’s:

 

Because the cost of enabling technologies has decreased so much over the past decade, it’s now possible for organizations to begin making them part of their digital strategy. In general, digital transformation reveals that the nature of “organization” is changing, and the nature of “customer” is changing as well. Organizations will no longer be defined solely by their employees and business partners, but also by the customers who participate – without even explicitly being aware of their integral involvement — in ongoing dialogues that shape the evolution of product lines and new services.

New business models will not necessarily rely on ownership, consumption, or centralized production of products or provision of services. The value-based approach will accentuate the importance of trust, transparency, and security, and new technologies (like blockchain) will help us implement and deploy systems to support those changes.

 

What is Quality 4.0?

Image Credit: Doug Buckley of http://hyperactive.to

My first post of the year addresses an idea that’s just starting to gain traction – one you’ll hear a lot more about from me in 2018: Quality 4.0.  It’s not a fad or trend, but a reminder that the business environment is changing, and that performance excellence in the future will depend on how well you adapt, change, and transform in response. Although we started building community around this concept at the ASQ Quality 4.0 Summit on Disruption, Innovation, and Change, held in November 2017 in Dallas, the truly revolutionary work is yet to come.

The term “Quality 4.0” comes from “Industry 4.0” – referring to the “fourth industrial revolution” – originally addressed at the Hannover (Germany) Fair in 2011. That meeting emphasized the increasing intelligence and interconnectedness in “smart” manufacturing systems and reflected on the newest technological innovations in historical context.

In the first industrial revolution (late 1700’s), steam and water power made it possible for production facilities to scale up and expanded the potential locations for production. By the late 1800’s, the discovery of electricity and development of associated infrastructure enabled the development of machines for mass production. In the US, the expansion of railways made it easier to obtain supplies and deliver finished goods. The availability of power also sparked a renaissance in computing, and digital computing emerged from its analog ancestor. The third industrial revolution came at the end of the 1960’s, with the invention of the Programmable Logic Controller (PLC). This made it possible to automate processes like filling and reloading tanks of liquids, turning engines on and off, and controlling sequences of events based on changing environmental conditions.

Although the growth and expansion of the internet accelerated innovation in the late 1990’s and 2000’s, we are just now poised for another industrial revolution. What’s changing?

  • Production & Availability of Information: More information is available because people and devices are producing it at greater rates than ever before. Falling costs of enabling technologies like sensors and actuators are catalyzing innovation in these areas.
  • Connectivity: In many cases, and from many locations, that information is instantly accessible over the internet. Improved network infrastructure is expanding the extent of connectivity, making it more widely available and more robust. (And unlike the 80’s and 90’s, there are far fewer communications protocols that are commonly encountered so it’s a lot easier to get one device to talk to another device on your network.)
  • Intelligent Processing: Affordable computing capabilities (and computing power!) are available to process that information so it can be incorporated into decision making. High-performance software libraries for advanced processing and visualization of data are easy to find, and easy to use. (In the past, we had to write our own… now we can use open-source solutions that are battle tested.
  • New Modes of Interaction: The way in which we can acquire and interact with information are also changing, in particular through new interfaces like Augmented Reality (AR) and Virtual Reality (VR), which expand possibilities for training and navigating a hybrid physical-digital environment with greater ease.
  • New Modes of Production: 3D printing, nanotechnology, and gene editing (CRISPR) are poised to change the nature and means of production in several industries. Technologies for enhancing human performance (e.g. exoskeletons, brain-computer interfaces, and even autonomous vehicles) will also open up new mechanisms for innovation in production. (Roco & Bainbridge (2002) describe many of these, and their prescience is remarkable.) New technologies like blockchain have the potential to change the nature of production as well, by challenging ingrained perceptions of trust, control, consensus, and value.

If the first industrial revolution was characterized by steam-powered machines, the second was characterized by electricity and assembly lines. Innovations in computing and industrial automation defined the third industrial revolution.  The fourth industrial revolution is one of intelligence: smart, hyperconnected cyber-physical systems in environments where humans and machines cooperate to achieved shared goals, and use data to generate value.

These enabling technologies originate in the physical, digital, and biological domains, and include the following:

  • Information
    • Affordable Sensors and Actuators
    • Big Data infrastructure (e.g. MapReduce, Hadoop, NoSQL databases)
  • Connectivity
    • 5G Networks
    • IPv6 Addresses (which expand the number of devices that can be put online)
    • Internet of Things (IoT)
    • Cloud Computing
  • Processing
    • Predictive Analytics
    • Artificial Intelligence
    • Machine Learning (incl. Deep Learning)
    • Data Science
  • Interaction
    • Augmented Reality (AR)
    • Mixed Reality (MR)
    • Virtual Reality (VR)
    • Diminished Reality (DR)
  • Construction
    • 3D Printing
    • Additive Manufacturing
    • Smart Materials
    • Nanotechnology
    • Gene Editing
    • Automated (Software) Code Generation
    • Robotic Process Automation (RPA)
    • Blockchain

Today’s quality profession was born during the middle of the second industrial revolution, when methods were needed to ensure that assembly lines ran smoothly – that they produced artifacts to specifications, that the workers knew how to engage in the process, and that costs were controlled. As industrial production matured, those methods grew to encompass the design of processes which were built to produce to specifications. In the 1980’s and 1990’s, organizations in the US started to recognize the importance of human capabilities and active engagement in quality as essential, and TQM, Lean, and Six Sigma gained in popularity. 

How will these methods evolve in an adaptive, intelligent environment? The question is largely still open, and that’s the essence of Quality 4.0.

Roco, M. C., & Bainbridge, W. S. (2002). Converging technologies for improving human performance: Integrating from the nanoscale. Journal of nanoparticle research4(4), 281-295. (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.465.7221&rep=rep1&type=pdf)

How to Assess the Quality of a Chatbot

Image Credit: Doug Buckley of http://hyperactive.to

Quality is the “totality of characteristics of an entity that bear upon its ability to meet stated and implied needs.” (ISO 9001:2015, p.3.1.5) Quality assurance is the practice of assessing whether a particular product or service has the characteristics to meet needs, and through continuous improvement efforts, we use data to tell us whether or not we are adjusting those characteristics to more effectively meet the needs of our stakeholders.

But what if the entity is a chatbot?

In June 2017, we published a paper that explored that question. We mined the academic and industry literature to determine 1) what quality attributes have been used by others to determine chatbot quality, we 2) organized them according to the efficiency, effectiveness, and satisfaction (using guidance from the ISO 9241 definition of usability), and 3) we explored the utility of Saaty’s Analytic Hierarchy Process (AHP) to help organizations select between one or more versions of chatbots based on quality considerations. (It’s sort of like A/B testing for chatbots.)

“There are many ways for practitioners to apply the material in this article:

  • The quality attributes in Table 1 can be used as a checklist for a chatbot implementation team to make sure they have addressed key issues.
  • Two or more conversational systems can be compared by selecting the most significant quality attributes.
  • Systems can be compared at two points in time to see if quality has improved, which may be particularly useful for adaptive systems that learn as they as exposed to additional participants and topics.”

A Simple Intro to Q-Learning in R: Floor Plan Navigation

This example is drawn from “A Painless Q-Learning Tutorial” at http://mnemstudio.org/path-finding-q-learning-tutorial.htm which explains how to manually calculate iterations using the updating equation for Q-Learning, based on the Bellman Equation (image from https://www.is.uni-freiburg.de/ressourcen/business-analytics/13_reinforcementlearning.pdf):

The example explores path-finding through a house:

The question to be answered here is: What’s the best way to get from Room 2 to Room 5 (outside)? Notice that by answering this question using reinforcement learning, we will also know how to find optimal routes from any room to outside. And if we run the iterative algorithm again for a new target state, we can find out the optimal route from any room to that new target state.

Since Q-Learning is model-free, we don’t need to know how likely it is that our agent will move between any room and any other room (the transition probabilities). If you had observed the behavior in this system over time, you might be able to find that information, but it many cases it just isn’t available. So the key for this problem is to construct a Rewards Matrix that explains the benefit (or penalty!) of attempting to go from one state (room) to another.

Assigning the rewards is somewhat arbitrary, but you should give a large positive value to your target state and negative values to states that are impossible or highly undesirable. Here’s the guideline we’ll use for this problem:

  • -1 if “you can’t get there from here”
  • 0 if the destination is not the target state
  • 100 if the destination is the target state

We’ll start constructing our rewards matrix by listing the states we’ll come FROM down the rows, and the states we’ll go TO in the columns. First, let’s fill the diagonal with -1 rewards, because we don’t want our agent to stay in the same place (that is, move from Room 1 to Room 1, or from Room 2 to Room 2, and so forth). The final one gets a 100 because if we’re already in Room 5, we want to stay there.

Next, let’s move across the first row. Starting in Room 0, we only have one choice: go to Room 4. All other possibilities are blocked (-1):

Now let’s fill in the row labeled 1. From Room 1, you have two choices: go to Room 3 (which is not great but permissible, so give it a 0) or go to Room 5 (the target, worth 100)!

Continue moving row by row, determining if you can’t get there from here (-1), you can but it’s not the target (0), or it’s the target(100). You’ll end up with a final rewards matrix that looks like this:

Now create this rewards matrix in R:

R <- matrix(c(-1, -1, -1, -1, 0, -1,
       -1, -1, -1, 0, -1, 100,
       -1, -1, -1, 0, -1, -1, 
       -1, 0, 0, -1, 0, -1,
        0, -1, -1, 0, -1, 100,
       -1, 0, -1, -1, 0, 100), nrow=6, ncol=6, byrow=TRUE)

And run the code. Notice that we’re calling the target state 6 instead of 5 because even though we have a room labeled with a zero, our matrix starts with a 1s so we have to adjust:

source("https://raw.githubusercontent.com/NicoleRadziwill/R-Functions/master/qlearn.R")

results <- q.learn(R,10000,alpha=0.1,gamma=0.8,tgt.state=6) 
> round(results)
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    0    0    0    0   80    0
[2,]    0    0    0   64    0  100
[3,]    0    0    0   64    0    0
[4,]    0   80   51    0   80    0
[5,]   64    0    0   64    0  100
[6,]    0   80    0    0   80  100

You can read this table of average value to obtain policies. A policy is a “path” through the states of the system:

  • Start at Room 0 (first row, labeled 1): Choose Room 4 (80), then from Room 4 choose Room 5 (100)
  • Start at Room 1: Choose Room 5 (100)
  • Start at Room 2: Choose Room 3 (64), from Room 3 choose Room 1 or Room 4 (80); from 1 or 4 choose 5 (100)
  • Start at Room 3: Choose Room 1 or Room 4 (80), then Room 5 (100)
  • Start at Room 4: Choose Room 5 (100)
  • Start at Room 5: Stay at Room 5 (100)

To answer the original problem, we would take route 2-3-1-5 or 2-3-4-5 to get out the quickest if we started in Room 2. This is easy to see with a simple map, but is much more complicated when the maps get bigger.

« Older Entries Recent Entries »