ABIBLIOPHOBIA

January 10, 2018


Abibliophobia is the fear of running out of reading material.  Basically, just look up the Greek root-phobia and add whatever word you are afraid of, replace the ending with -o- and couple the results with phobia.  If you have any experience with libraries, the Internet, the back of soup cans, etc. you know there is more than enough material out there to be read and digested. It amazes me that this word has just “popped” up of the last few years.

Now, the World Wide Web is a cavernous source of reading material.  Indeed, it’s a bigger readers’ repository than the world has ever known, so it seems rather ironic that the term abibliophobia appears to have been coined on the Web during the last three or four years. It would seem impossible for anyone with regular access to the Internet to be an abibliophobe (someone suffering from a fear of running out of reading material) or to become abibliophobic when more and more reading matter is available by the hour.  Let’s look at just what is available to convince the abibliophobic individual that there is no fear of running out of reading material.

  • There Are More Than 440 Million Blogs In The World. By October 2011, there were an estimated 173 million blogs Nielsen estimates that by the end of 2011, that number had climbed to 181 million. That was four years after Tumblr launched, and in May 2011, there were just 17.5 million Tumblr blogs.  Today, there are over 360 million blogs on Tumblr alone, and there are millions more on other platforms. While there are some reliable statistics on the number of blogs in 2011, things have changed dramatically with the rise of services like Tumblr, WordPress, Squarespace, Medium and more. Exactly how many blogs there are in the world is difficult to know, but what’s clear is that blogs online number in the hundreds of millions. The total number of blogs on TumblrSquarespace, and WordPress alone equals over 440 million. In actuality, the total number of blogs in the world likely greatly exceeds this number. We do know that content is being consumed online more widely, more quickly, and more voraciously than ever before.
  • According to WordPress, 76.3 million posts are published on WordPress each month, and more than 409 million people view 22.3 billion blog pages each month. It’s interesting to see that there are about 1 billion websites and blogs in the world today. But that figure is not as helpful as looking at the other statistics involving blogging. For example, did you know that more than 409 million people on WordPress view more than 23.6 billion pages each month? Did you know that each month members produce 69.5 million new posts?
  • Websites with a blog have over 434% more indexed pages.
  • 76% of online marketers say they plan to add more content over the 2018 year.
  • There are an estimated 119,487 libraries of all kinds in the United States today.
  • It is estimated that there are 000 libraries in the world. Russia, India and China have about 50.000 each.

Thanks to Johannes Gensfleisch zur Laden zum Gutenberg, the written word flourished after he invented the printing press.  Gutenberg in 1439 was the first European to use movable type. Among his many contributions to printing are: the invention of a process for mass-producing movable type; the use of oil-based ink for printing books; adjustable molds; mechanical movable type; and the use of a wooden printing press similar to the agricultural screw presses of the period. His truly epochal invention was the combination of these elements into a practical system that allowed the mass production of printed books and was economically viable for printers and readers alike. Gutenberg’s method for making type is traditionally considered to have included a type metal alloy and a hand mold for casting type. The alloy was a mixture of lead, tin, and antimony melted at a relatively low temperature for faster and more economical casting.  His invention was a game-changing event for all prospective readers the world over.  No longer will there be a fear of or absence of material to read.

CONCLUSIONS:

I think the basic conclusion here is not the fear of having no reading material but the fear of reading.

  • If I read, I might miss my favorite TV programs.
  • If I read, I might miss that important phone call.
  • Why read when I can TWEET?
  • Why read when I can stream Netflix or HULU?
  • I’m such a slow reader. It just takes too much time.
  • I cannot find any subject I’m really that interested in.
  • I really have no quite place to read.
  • ___________________ Fill in the blanks.

Reading does take a commitment, so why not set goals and commit?

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Mom and Dad taught us how to read so why have I not heard about, until now, the Flesch-Kincaid Grade Level Readability Index?  I suppose better late than never.  Let’s take a look.

Rudolph Flesch, an author, writing consultant, and the supporter of Plain English Movement, is the co-author of this formula along with John P. Kincaid, thus the Flesch-Kincaid Grade Level Readability Test. Raised in Austria, Flesch studied law and earned a Ph.D. in English from the Columbia University. Flesch, through his writings and speeches, advocated a return to phonics. In his article, A New Readability Yardstick, published in the Journal of Applied Psychology in 1948, Flesch proposed the Reading Ease Readability Formula.

In the mid-seventies, the US Navy was looking for a method to measure the difficulty of technical manuals and documents used by Navy personnel.  These manuals were used for training on hardware and software installed on ships and land-based equipment.  Test results are not immediately meaningful and to make sense of the score requires the aid of a conversion table. So, the Flesch Reading Ease test was revisited and, along with other readability tests, the formula was amended to be more suitable for use in the Navy. The new calculation was the Flesch-Kincaid Grade Level (1975).  The methodology is given as follows:

Grade level classifications are based on the attainment of participants in the norming group on which the test was given.  The grade represents norming group participants’ typical score. So, if a piece of text has a grade level readability score of six (6), this is equivalent in difficulty to the average reading level of the norming group who were at grade six (6 ) when they took the test. This test rates text on a U.S. school grade level. For example, a score of 8.0 means an eighth grader can understand the document. For most documents, aim for a score of approximately 7.0 to 8.0.

The actual formula and classification of the individual grades may be seen below:

Now, with that out of the way, President Donald Trump—who boasted over the weekend that his success in life was a result of “being, like, really smart”—communicates at the lowest grade level of the last 15 presidents, according to a new analysis of the speech patterns of presidents going back to Herbert Hoover. 

 

I want to come to President Trump’s defense, somewhat, as an employee at General Electric, we were told to write our Use and Care Manuals at a fifth (5th) grade level AND use plenty of pictures—plenty of pictures.  This President will probably never win an award for public speaking, and he communicates in a rather unique manner:  He does get his point across.

The very painful fact is that we have basically slaughtered the “King’s English” and our presidents are playing to a much less sophisticated audience than ever before.  The following chart will explain.

Sad—very sad.

As always, I welcome your comments.

Heavens to Murgatroyd

January 8, 2018


Portions of this post are attributed to: tobeerndt@yahoo.com

Our English language is constantly evolving to match changes in culture, religion, technology and other areas of reality.  Over the past two or three years the Oxford Dictionary of the English Language has added many new words.  A few of these words are given below:

  • Adorbs
  • Binge-watch
  • Cray
  • humblebrag
  • listicle
  • side boob
  • vape
  • YOLO
  • live-tweet
  • second screen
  • sentiment analysis
  • cord cutting
  • hyperconnected
  • acquihire
  • clickbait
  • Deep Web
  • Dox
  • Fast follower
  • Geocache
  • In silico
  • Smartwatch
  • Tech-savvy
  • Vaping
  • E-cig
  • Bro hug
  • Hot-mess

These new words describe to some extent where we are today relative to technology and “pop-culture”.  These new words are entirely appropriate, but just as sure as we add words, we remove from daily usage words that just do not seem to fit. Lost Words from our childhood: Words gone as fast as the buggy whip! Sad really! Let’s take a look.

Murgatroyd!…

Do you remember that word? Would you believe the email spell checker did not recognize the word Murgatroyd?  Heavens to Mergatroyd!  If you are over fifty (50) or even forty (40) you have said, Heavens to Mergatroyd.

The other day a not so elderly sixty-five (65) or maybe seventy-five (75) year old lady said something to her son about driving a Jalopy and he looked at her quizzically and said “What the heck is a Jalopy?”

OMG (new phrase)! He never heard of the word jalopy!! She knew she was old….. but not that old. Well, I hope you are Hunky Dory after you read this and chuckle.

About a month ago, I illuminated some old expressions that have become obsolete because of the inexorable march of technology. These phrases included “Don’t touch that dial,” “Carbon copy,” “You sound like a broken record” and “Hung out to dry.”

Back in the olden days we had a lot of ‘moxie.’ We’d put on our best ‘bib and tucker’ to’ straighten up and fly right’.

Heavens to Betsy! Gee whillikers! Jumping Jehoshaphat! Holy moley!

We were ‘in like Flynn’ and ‘living the life of Riley”, and even a regular guy couldn’t accuse us of being a knucklehead, a nincompoop or a pill. Not for all the tea in China.

Back in the olden days, life used to be swell, but when’s the last time anything was swell?

Swell has gone the way of beehives, pageboys and the D.A.; of spats, knickers, fedoras, poodle skirts, saddle shoes and pedal pushers. AND DON’T FORGET…. Saddle Stitched Pants

Oh, my aching back! Kilroy was here, but he isn’t anymore.

We wake up from what surely has been just a short nap, and before we can say, Well, I’ll be ‘a monkey’s uncle!’ Or, This is a ‘fine kettle of fish’! We discover that the words we grew up with, the words that seemed omnipresent, as oxygen, have vanished with scarcely a notice from our tongues and our pens and our keyboards.

Poof, go the words of our youth, the words we’ve left behind.  We blink, and they’re gone.  Where have all those great phrases gone? (My Favorite)” Let’s all go to the beach Saturday”..

Long gone: Pshaw, The milkman did it. Hey! It’s your nickel. Don’t forget to pull the chain. Knee high to a grasshopper. Well, Fiddlesticks! Going like sixty. I’ll see you in the funny papers. Don’t take any wooden nickels. Wake up and smell the roses.

It turns out there are more of these lost words and expressions than Carter has liver pills. This can be disturbing stuff! (“Carter’s Little Liver Pills” are gone too!)

We of a certain age have been blessed to live in changeable times. For a child each new word is like a shiny toy, a toy that has no age. We at the other end of the chronological arc have the advantage of remembering there are words that once did not exist and there were words that once strutted their hour upon the earthly stage and now are heard no more, except in our collective memory. It’s one of the greatest advantages of aging. Leaves us to wonder where Superman will find a phone booth… See ya later, alligator! Okidoki

Personally, I like the “old” phrases.  They have meaning to me and to those I associate with but like the lady with her grandson, I use these words and my grandchildren look at me as though I have just fallen off a turnip truck—flown in from an alien planet—come down from the mountain.  I suppose times are a-changing.

DEEP LEARNING

December 10, 2017


If you read technical literature with some hope of keeping up with the latest trends in technology, you find words and phrases such as AI (Artificial Intelligence) and DL (Deep Learning). They seem to be used interchangeability but facts deny that premise.  Let’s look.

Deep learning ( also known as deep structured learning or hierarchical learning) is part of a broader family of machine-learning methods based on learning data representations, as opposed to task-specific algorithms. (NOTE: The key words here are MACHINE-LEARNING). The ability of computers to learn can be supervised, semi-supervised or unsupervised.  The prospect of developing learning mechanisms and software to control machine mechanisms is frightening to many but definitely very interesting to most.  Deep learning is a subfield of machine learning concerned with algorithms inspired by structure and function of the brain called artificial neural networks.  Machine-learning is a method by which human neural networks are duplicated by physical hardware: i.e. computers and computer programming.  Never in the history of our species has a degree of success been possible–only now. Only with the advent of very powerful computers and programs capable of handling “big data” has this been possible.

With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Artificial intelligence is finally getting smart.  The basic idea—that software can simulate the neocortex’s large array of neurons in an artificial “neural network”—is decades old, and it has led to as many disappointments as breakthroughs.  Because of improvements in mathematical formulas and increasingly powerful computers, computer scientists can now model many more layers of virtual neurons than ever before. Deep learning is a class of machine learning algorithms that accomplish the following:

With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Artificial intelligence is finally getting smart.  The basic idea—that software can simulate the neocortex’s large array of neurons in an artificial “neural network”—is decades old, and it has led to as many disappointments as breakthroughs.  Because of improvements in mathematical formulas and increasingly powerful computers, computer scientists can now model many more layers of virtual neurons than ever before. Deep learning is a class of machine learning algorithms that accomplish the following:

  • Use a cascade of multiple layers of nonlinear processingunits for feature extraction and transformation. Each successive layer uses the output from the previous layer as input.
  • Learn in supervised(e.g., classification) and/or unsupervised (e.g., pattern analysis) manners.
  • Learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts.
  • Use some form of gradient descentfor training via backpropagation.

Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas.  They may also include latent variables organized layer-wise in deep generative models such as the nodes in Deep Belief Networks and Deep Boltzmann Machines.

ARTIFICIAL NEURAL NETWORKS:

Artificial neural networks (ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn (progressively improve their ability) to do tasks by considering examples, generally without task-specific programming. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as “cat” or “no cat” and using the analytic results to identify cats in other images. They have found most use in applications difficult to express with a traditional computer algorithm using rule-based programming.

An ANN is based on a collection of connected units called artificial neurons, (analogous to axons in a biological brain). Each connection (synapse) between neurons can transmit a signal to another neuron. The receiving (postsynaptic) neuron can process the signal(s) and then signal downstream neurons connected to it. Neurons may have state, generally represented by real numbers, typically between 0 and 1. Neurons and synapses may also have a weight that varies as learning proceeds, which can increase or decrease the strength of the signal that it sends downstream.

Typically, neurons are organized in layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first (input), to the last (output) layer, possibly after traversing the layers multiple times.

The original goal of the neural network approach was to solve problems in the same way that a human brain would. Over time, attention focused on matching specific mental abilities, leading to deviations from biology such as backpropagation, or passing information in the reverse direction and adjusting the network to reflect that information.

Neural networks have been used on a variety of tasks, including computer vision, speech recognitionmachine translationsocial network filtering, playing board and video games and medical diagnosis.

As of 2017, neural networks typically have a few thousand to a few million units and millions of connections. Despite this number being several orders of magnitude less than the number of neurons on a human brain, these networks can perform many tasks at a level beyond that of humans (e.g., recognizing faces, playing “Go”).

APPLICATIONS:

Just what applications could take advantage of “deep learning?”

IMAGE RECOGNITION:

A common evaluation set for image classification is the MNIST database data set. MNIST is composed of handwritten digits and includes 60,000 training examples and 10,000 test examples. As with TIMIT, its small size allows multiple configurations to be tested. A comprehensive list of results on this set is available.

Deep learning-based image recognition has become “superhuman”, producing more accurate results than human contestants. This first occurred in 2011.

Deep learning-trained vehicles now interpret 360° camera views.   Another example is Facial Dysmorphology Novel Analysis (FDNA) used to analyze cases of human malformation connected to a large database of genetic syndromes.

The i-Phone X uses, I am told, uses facial recognition as one method of insuring safety and a potential hacker’s ultimate failure to unlock the phone.

VISUAL ART PROCESSING:

Closely related to the progress that has been made in image recognition is the increasing application of deep learning techniques to various visual art tasks. DNNs have proven themselves capable, for example, of a) identifying the style period of a given painting, b) “capturing” the style of a given painting and applying it in a visually pleasing manner to an arbitrary photograph, and c) generating striking imagery based on random visual input fields.

NATURAL LANGUAGE PROCESSING:

Neural networks have been used for implementing language models since the early 2000s.  LSTM helped to improve machine translation and language modeling.  Other key techniques in this field are negative sampling  and word embedding. Word embedding, such as word2vec, can be thought of as a representational layer in a deep-learning architecture that transforms an atomic word into a positional representation of the word relative to other words in the dataset; the position is represented as a point in a vector space. Using word embedding as an RNN input layer allows the network to parse sentences and phrases using an effective compositional vector grammar. A compositional vector grammar can be thought of as probabilistic context free grammar (PCFG) implemented by an RNN.   Recursive auto-encoders built atop word embeddings can assess sentence similarity and detect paraphrasing.  Deep neural architectures provide the best results for constituency parsing,  sentiment analysis,  information retrieval,  spoken language understanding,  machine translation, contextual entity linking, writing style recognition and others.

Google Translate (GT) uses a large end-to-end long short-term memory network.   GNMT uses an example-based machine translation method in which the system “learns from millions of examples.  It translates “whole sentences at a time, rather than pieces. Google Translate supports over one hundred languages.   The network encodes the “semantics of the sentence rather than simply memorizing phrase-to-phrase translations”.  GT can translate directly from one language to another, rather than using English as an intermediate.

DRUG DISCOVERY AND TOXICOLOGY:

A large percentage of candidate drugs fail to win regulatory approval. These failures are caused by insufficient efficacy (on-target effect), undesired interactions (off-target effects), or unanticipated toxic effects.  Research has explored use of deep learning to predict biomolecular target, off-target and toxic effects of environmental chemicals in nutrients, household products and drugs.

AtomNet is a deep learning system for structure-based rational drug design.   AtomNet was used to predict novel candidate biomolecules for disease targets such as the Ebola virus and multiple sclerosis.

CUSTOMER RELATIONS MANAGEMENT:

Deep reinforcement learning has been used to approximate the value of possible direct marketing actions, defined in terms of RFM variables. The estimated value function was shown to have a natural interpretation as customer lifetime value.

RECOMMENDATION SYSTEMS:

Recommendation systems have used deep learning to extract meaningful features for a latent factor model for content-based music recommendations.  Multiview deep learning has been applied for learning user preferences from multiple domains.  The model uses a hybrid collaborative and content-based approach and enhances recommendations in multiple tasks.

BIOINFORMATICS:

An autoencoder ANN was used in bioinformatics, to predict gene ontology annotations and gene-function relationships.

In medical informatics, deep learning was used to predict sleep quality based on data from wearables and predictions of health complications from electronic health record data.

MOBILE ADVERTISING:

Finding the appropriate mobile audience for mobile advertising is always challenging since there are many data points that need to be considered and assimilated before a target segment can be created and used in ad serving by any ad server. Deep learning has been used to interpret large, many-dimensioned advertising datasets. Many data points are collected during the request/serve/click internet advertising cycle. This information can form the basis of machine learning to improve ad selection.

ADVANTAGES AND DISADVANTAGES:

ADVANTAGES:

  • Has best-in-class performance on problems that significantly outperforms other solutions in multiple domains. This includes speech, language, vision, playing games like Go etc. This isn’t by a little bit, but by a significant amount.
  • Reduces the need for feature engineering, one of the most time-consuming parts of machine learning practice.
  • Is an architecture that can be adapted to new problems relatively easily (e.g. Vision, time series, language etc. using techniques like convolutional neural networks, recurrent neural networks, long short-term memory etc.

DISADVANTAGES:

  • Requires a large amount of data — if you only have thousands of examples, deep learning is unlikely to outperform other approaches.
  • Is extremely computationally expensive to train. The most complex models take weeks to train using hundreds of machines equipped with expensive GPUs.
  • Do not have much in the way of strong theoretical foundation. This leads to the next disadvantage.
  • Determining the topology/flavor/training method/hyperparameters for deep learning is a black art with no theory to guide you.
  • What is learned is not easy to comprehend. Other classifiers (e.g. decision trees, logistic regression etc.) make it much easier to understand what’s going on.

SUMMARY:

Whether we like it or not, deep learning will continue to develop.  As equipment and the ability to capture and store huge amounts of data continue, the machine-learning process will only improve.  There will come a time when we will see a “rise of the machines”.  Let’s just hope humans have the ability to control those machines.

BITCOIN

December 9, 2017


I have been hearing a great deal about Bitcoin lately specifically on the early-morning television business channels. I am not too sure what this is all about so I thought I would take a look.    First, an “official” definition.

Bitcoin is a cryptocurrency and worldwide payment system. It is the first decentralized digital currency, as the system works without a central bank or single administrator. … Bitcoin was invented by an unknown person or group of people under the name Satoshi Nakamoto and released as open-source software in 2009.

The “unknown” part really disturbs me as well as the “cryptocurrency” aspects, but let’s continue.  Do you remember the Star Trek episodes in which someone asks, ‘how much does it cost and the answer is _______ credits’?  This is specifically what Bitcoin does, it is digital currency. No one controls Bitcoin; they aren’t printed, like dollars or euros – they’re produced by people, and increasingly businesses, running computers all around the world, using software that solves mathematical problems. A Bitcoin looks as follows-if you acquire a physical object representing“coin”.

Bitcoin transactions are completed when a “block” is added to the blockchain database that underpins the currency however, this can be a laborious process.  Segwit2x proposes moving bitcoin’s transaction data outside of the block and on to a parallel track to allow more transactions to take place. The changes happened in November and it remains to be seen if those changes will have a positive or negative impact on the price of bitcoin in the long term.

It’s been an incredible 2017 for bitcoin growth, with its value quadrupling in the past six months, surpassing the value of an ounce of gold for the first time. It means if you invested £2,000 five years ago, you would be a millionaire today.

You cannot “churn out” an unlimited number of Bitcoin. The bitcoin protocol – the rules that make bitcoin work – say that only twenty-one (21) million bitcoins can ever be created by miners. However, these coins can be divided into smaller parts (the smallest divisible amount is one hundred millionth of a bitcoin and is called a ‘Satoshi’, after the founder of bitcoin).

Conventional currency has been based on gold or silver. Theoretically, you knew that if you handed over a dollar at the bank, you could get some gold back (although this didn’t actually work in practice). But bitcoin isn’t based on gold; it’s based on mathematics. To me this is absolutely fascinating.  Around the world, people are using software programs that follow a mathematical formula to produce bitcoins. The mathematical formula is freely available, so that anyone can check it. The software is also open source, meaning that anyone can look at it to make sure that it does what it is supposed to.

SPECIFIC CHARACTERISTICS:

  1. It’s decentralized

The bitcoin network isn’t controlled by one central authority. Every machine that mines bitcoin and processes transactions makes up a part of the network, and the machines work together. That means that, in theory, one central authority can’t tinker with monetary policy and cause a meltdown – or simply decide to take people’s bitcoins away from them, as the Central European Bank decided to do in Cyprus in early 2013. And if some part of the network goes offline for some reason, the money keeps on flowing.

  1. It’s easy to set up

Conventional banks make you jump through hoops simply to open a bank account. Setting up merchant accounts for payment is another Kafkaesque task, beset by bureaucracy. However, you can set up a bitcoin address in seconds, no questions asked, and with no fees payable.

  1. It’s anonymous

Well, kind of. Users can hold multiple bitcoin addresses, and they aren’t linked to names, addresses, or other personally identifying information.

  1. It’s completely transparent

Bitcoin stores details of every single transaction that ever happened in the network in a huge version of a general ledger, called the blockchain. The blockchain tells all. If you have a publicly used bitcoin address, anyone can tell how many bitcoins are stored at that address. They just don’t know that it’s yours. There are measures that people can take to make their activities opaquer on the bitcoin network, though, such as not using the same bitcoin addresses consistently, and not transferring lots of bitcoin to a single address.

  1. Transaction fees are miniscule

Your bank may charge you a £10 fee for international transfers. Bitcoin doesn’t.

  1. It’s fast

You can send money anywhere and it will arrive minutes later, as soon as the bitcoin network processes the payment.

  1. It’s non-reputable

When your bitcoins are sent, there’s no getting them back, unless the recipient returns them to you. They’re gone forever.

WHERE TO BUY AND SELL

I definitely recommend you do your homework before buying Bitcoin because the value is roller coaster in nature, but given below are several exchanges in which Bitcoin can be purchased or sold.  Good luck.

CONSLUSIONS:

Is Bitcoin a bubble? It’s a natural question to ask—especially after Bitcoin’s price shot up from $12,000 to $15,000 this past week.

Brent Goldfarb is a business professor at the University of Maryland, and William Deringer is a historian at MIT. Both have done research on the history and economics of bubbles, and they talked to Ars by phone this week as Bitcoin continues its surge.

Both academics saw clear parallels between the bubbles they’ve studied and Bitcoin’s current rally. Bubbles tend to be driven either by new technologies (like railroads in 1840s Britain or the Internet in the 1990s) or by new financial innovations (like the financial engineering that produced the 2008 financial crisis). Bitcoin, of course, is both a new technology and a major financial innovation.

“A lot of bubbles historically involve some kind of new financial technology the effects of which people can’t really predict,” Deringer told Ars. “These new financial innovations create enthusiasm at a speed that is greater than people are able to reckon with all the consequences.”

Neither scholar wanted to predict when the current Bitcoin boom would end. But Goldfarb argued that we’re seeing classic signs that often occur near the end of a bubble. The end of a bubble, he told us, often comes with “a high amount of volatility and a lot of excitement.”

Goldfarb expects that in the coming months we’ll see more “stories about people who got fabulously wealthy on bitcoin.” That, in turn, could draw in more and more novice investors looking to get in on the action. From there, some triggering event will start a panic that will lead to a market crash.

“Uncertainty of valuation is often a huge issue in bubbles,” Deringer told Ars. Unlike a stock or bond, Bitcoin pays no interest or dividends, making it hard to figure out how much the currency ought to be worth. “It is hard to pinpoint exactly what the fundamentals of Bitcoin are,” Deringer said.

That uncertainty has allowed Bitcoin’s value to soar a 1,000-fold over the last five years. But it could also make the market vulnerable to crashes if investors start to lose confidence.

I would say travel at your own risk.

 

DARK NET

December 6, 2017


Most of the individuals who read my posting are very well-informed and know that Tim Berners-Lee “invented” the internet.  In my opinion, the Internet is a resounding technological improvement in communication.  It has been a game-changer in the truest since of the word.  I think there are legitimate uses which save tremendous time.  There are also illegitimate uses as we shall see.

A JPEG of Mr. Berners-Lee is shown below:

BIOGRAPHY:

In 1989, while working at CERN, the European Particle Physics Laboratory in Geneva, Switzerland, Tim Berners-Lee proposed a global hypertext project, to be known as the World Wide Web. Based on the earlier “Enquire” work, his efforts were designed to allow people to work together by combining their knowledge in a web of hypertext documents.  Sir Tim wrote the first World Wide Web server, “httpd“, and the first client, “WorldWideWeb” a what-you-see-is-what-you-get hypertext browser/editor which ran in the NeXTStep environment. This work began in October 1990.k   The program “WorldWideWeb” was first made available within CERN in December, and on the Internet at large in the summer of 1991.

Through 1991 and 1993, Tim continued working on the design of the Web, coordinating feedback from users across the Internet. His initial specifications of URIs, HTTP and HTML were refined and discussed in larger circles as the Web technology spread.

Tim Berners-Lee graduated from the Queen’s College at Oxford University, England, in 1976. While there he built his first computer with a soldering iron, TTL gates, an M6800 processor and an old television.

He spent two years with Plessey Telecommunications Ltd (Poole, Dorset, UK) a major UK Telecom equipment manufacturer, working on distributed transaction systems, message relays, and bar code technology.

In 1978 Tim left Plessey to join D.G Nash Ltd (Ferndown, Dorset, UK), where he wrote, among other things, typesetting software for intelligent printers and a multitasking operating system.

His year and one-half spent as an independent consultant included a six-month stint (Jun-Dec 1980) as consultant software engineer at CERN. While there, he wrote for his own private use his first program for storing information including using random associations. Named “Enquire” and never published, this program formed the conceptual basis for the future development of the World Wide Web.

From 1981 until 1984, Tim worked at John Poole’s Image Computer Systems Ltd, with technical design responsibility. Work here included real time control firmware, graphics and communications software, and a generic macro language. In 1984, he took up a fellowship at CERN, to work on distributed real-time systems for scientific data acquisition and system control. Among other things, he worked on FASTBUS system software and designed a heterogeneous remote procedure call system.

In 1994, Tim founded the World Wide Web Consortium at the Laboratory for Computer Science (LCS). This lab later merged with the Artificial Intelligence Lab in 2003 to become the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT). Since that time he has served as the Director of the World Wide Web Consortium, a Web standards organization which develops interoperable technologies (specifications, guidelines, software, and tools) to lead the Web to its full potential. The Consortium has host sites located at MIT, at ERCIM in Europe, and at Keio University in Japan as well as offices around the world.

In 1999, he became the first holder of 3Com Founders chair at MIT. In 2008 he was named 3COM Founders Professor of Engineering in the School of Engineering, with a joint appointment in the Department of Electrical Engineering and Computer Science at CSAIL where he also heads the Decentralized Information Group (DIG). In December 2004 he was also named a Professor in the Computer Science Department at the University of Southampton, UK. From 2006 to 2011 he was co-Director of the Web Science Trust, launched as the Web Science Research Initiative, to help create the first multidisciplinary research body to examine the Web.

In 2008 he founded and became Director of the World Wide Web Foundation.  The Web Foundation is a non-profit organization devoted to achieving a world in which all people can use the Web to communicate, collaborate and innovate freely.  The Web Foundation works to fund and coordinate efforts to defend the Open Web and further its potential to benefit humanity.

In June 2009 then Prime Minister Gordon Brown announced that he would work with the UK Government to help make data more open and accessible on the Web, building on the work of the Power of Information Task Force. Sir Tim was a member of The Public Sector Transparency Board tasked to drive forward the UK Government’s transparency agenda.  He has promoted open government data globally, is a member of the UK’s Transparency Board.

In 2011 he was named to the Board of Trustees of the Ford Foundation, a globally oriented private foundation with the mission of advancing human welfare. He is President of the UK’s Open Data Institute which was formed in 2012 to catalyze open data for economic, environmental, and social value.

He is the author, with Mark Fischetti, of the book “Weaving the Web” on the past, present and future of the Web.

On March 18 2013, Sir Tim, along with Vinton Cerf, Robert Kahn, Louis Pouzin and Marc Andreesen, was awarded the Queen Elizabeth Prize for Engineering for “ground-breaking innovation in engineering that has been of global benefit to humanity.”

It should be very obvious from this rather short biography that Sir Tim is definitely a “heavy hitter”.

DARK WEB:

I honestly don’t think Sir Tim realized the full gravity of his work and certainly never dreamed there might develop a “dark web”.

The Dark Web is the public World Wide Web content existing on dark nets or networks which overlay the public Internet.  These networks require specific software, configurations or authorization to access. They are NOT open forums as we know the web to be at this time.  The dark web forms part of the Deep Web which is not indexed by search engines such as GOOGLE, BING, Yahoo, Ask.com, AOL, Blekko.com,  Wolframalpha, DuckDuckGo, Waybackmachine, or ChaCha.com.  The dark nets which constitute the Dark Web include small, friend-to-friend peer-to-peer networks, as well as large, popular networks like FreenetI2P, and Tor, operated by public organizations and individuals. Users of the Dark Web refer to the regular web as the Clearnet due to its unencrypted nature.

A December 2014 study by Gareth Owen from the University of Portsmouth found the most commonly requested type of content on Tor was child pornography, followed by black markets, while the individual sites with the highest traffic were dedicated to botnet operations.  Botnet is defined as follows:

“a network of computers created by malware andcontrolled remotely, without the knowledge of the users of those computers: The botnet was usedprimarily to send spam emails.”

Hackers built the botnet to carry out DDoS attacks.

Many whistle-blowing sites maintain a presence as well as political discussion forums.  Cloned websites and other scam sites are numerous.   Many hackers sell their services individually or as a part of groups. There are reports of crowd-funded assassinations and hit men for hire.   Sites associated with Bitcoinfraud related services and mail order services are some of the most prolific.

Commercial dark net markets, which mediate transactions for illegal drugs and other goods, attracted significant media coverage starting with the popularity of Silk Road and its subsequent seizure by legal authorities. Other markets sells software exploits and weapons.  A very brief look at the table below will indicate activity commonly found on the dark net.

As you can see, the uses for the dark net are quite lovely, lovely indeed.  As with any great development such as the Internet, nefarious uses can and do present themselves.  I would stay away from the dark net.  Just don’t go there.  Hope you enjoy this one and please send me your comments.


OKAY first, let us define “OPEN SOURCE SOFTWARE” as follows:

Open-source software (OSS) is computer software with its source-code made available with a license in which the copyright holder provides the rights to study, change, and distribute the software to anyone and for any purpose. Open-source software may be developed in a collaborative public manner. The benefits include:

  • COST—Generally, open source software if free.
  • FLEXIBILITY—Computer specialists can alter the software to fit their needs for the program(s) they are writing code for.
  • FREEDOM—Generally, no issues with patents or copyrights.
  • SECURITY—The one issue with security is using open source software and embedded code due to compatibility issues.
  • ACCOUNTABILITY—Once again, there are no issues with accountability and producers of the code are known.

A very detailed article written by Jacob Beningo has seven (7) excellent points for avoiding, like the plague, open source software.  Given below are his arguments.

REASON 1—LACKS TRACEABLE SOFTWARE DEVELOPMENT LIFE CYCLE–Open source software usually starts with an ingenious developer working out their garage or basement hoping to create code that is very functional and useful. Eventually multiple developers with spare time on their hands get involved. The software evolves but it doesn’t really follow a traceable design cycle or even follow best practices. These various developers implement what they want or push the code in the direction that meets their needs. The result is software that works in limited situations and circumstances and users need to cross their fingers and pray that their needs and conditions match them.

REASON 2—DESIGNED FOR FUNCTIONALITY AND NOT ROBUSTNESS–Open source software is often written for functionality only. Accessed and written to an SD card for communication over USB connections. The issue here is that while it functions the code, it generally is not robust and is never designed to anticipate issues.  This is rarely the case and while the software is free, very quickly developers can find that their open source software is just functional and can’t stand up to real-world pressures. Developers will find themselves having to dig through unknown terrain trying to figure out how best to improve or handle errors that weren’t expected by the original developers.

REASON 3—ACCIDENTIALLY EXPOSING CONFIDENTIAL INTELLECTURAL PROPERTY–There are several different licensing schemes that open source software developers use. Some really do give away the farm; however, there are also licenses that require any modifications or even associated software to be released as open source. If close attention is not being paid, a developer could find themselves having to release confidential code and algorithms to the world. Free software just cost the company in revealing the code or if they want to be protected, they now need to spend money on attorney fees to make sure that they aren’t giving it all away by using “free” software.

REASON 4—LACKING AUTOMATED AND/OR MANUAL TESTING–A formalized testing process, especially automated tests are critical to ensuring that a code base is robust and has sufficient quality to meet its needs. I’ve seen open source Python projects that include automated testing which is encouraging but for low level firmware and embedded systems we seem to still lag behind the rest of the software industry. Without automated tests, we have no way to know if integrating that open source component broke something in it that we won’t notice until we go to production.

REASON 5—POOR DOCUMENTATION OR DOCUMENTATION THAT IS LACKING COMPLETELY–Documentation has been getting better among open source projects that have been around for a long time or that have strong commercial backing. Smaller projects though that are driven by individuals tend to have little to no documentation. If the open source code doesn’t have documentation, putting it into practice or debugging it is going to be a nightmare and more expensive than just getting commercial or industrial-grade software.

REASON 6—REAL-TIME SUPPORT IS LACKING–There are few things more frustrating than doing everything you can to get something to work or debugged and you just hit the wall. When this happens, the best way to resolve the issue is to get support. The problem with open source is that there is no guarantee that you will get the support you need in a timely manner to resolve any issues. Sure, there are forums and social media to request help but those are manned by people giving up their free time to help solve problems. If they don’t have the time to dig into a problem, or the problem isn’t interesting or is too complex, then the developer is on their own.

REASON 7—INTEGRATION IS NEVER AS EASY AS IT SEEMS–The website was found; the demonstration video was awesome. This is the component to use. Look at how easy it is! The source is downloaded and the integration begins. Months later, integration is still going on. What appeared easy quickly turned complex because the same platform or toolchain wasn’t being used. “Minor” modifications had to be made. The rabbit hole just keeps getting deeper but after this much time has been sunk into the integration, it cannot be for naught.

CONCLUSIONS:

I personally am by no means completely against open source software. It’s been extremely helpful and beneficial in certain circumstances. I have used open source, namely JAVA, as embedded software for several programs I have written.   It’s important though not to just use software because it’s free.  Developers need to recognize their requirements, needs, and level of robustness that required for their product and appropriately develop or source software that meets those needs rather than blindly selecting software because it’s “free.”  IN OTHER WORDS—BE CAREFUL!

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