LOVE, PEACE & BURGER GREASE

December 17, 2017


I always like to highlight our home town, Chattanooga, Tennessee.  One of the very best ways to do so is by indicating the HUGE number of locally-owned restaurants that exist within the city limits. One of our favorite hangouts is “SLICKS”.  As you drive into the parking lot, you see the mural below painted on an adjoining building.  Now really, who could pass up an opportunity to try this place once seeing “LOVE, PEACE & BURGER GREASE”?  (Makes a man proud to be an American.)

The main entrance is shown above.  As you can see—very welcoming and accessible.

The interior is very welcoming.  You order first then seat yourself.  Please note the food truck area where orders are placed.  It, at one time, was actually a food truck used during summer months at the Chattanooga Market.  (Another story for another day.)

It’s somewhat difficult to see the menu above the truck so I’m print a short copy as follows:

A menu for the kids is also displayed above the order bar.

Very “homey”, down-to-earth, straight-forward seating.  The main focus is the food served.  Highest quality meats, fish and chicken served in this place.  I had the fish sandwich: today grouper.  Excellent choice on my part.  Really delicious.

CONCLUSIONS:

Come on now—it’s time to admit you need to come to Chattanooga to put some “south in your mouth”.

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YOU KNOW YOU’RE OLD WHEN

December 16, 2017


Your grandchildren start graduating from college or a university system.  One of our oldest granddaughters graduated this past Wednesday from Georgia State University in Atlanta.  Magna Cum Laude.  The commencement program is shown below.

QUICK FACTS ABOUT GEORGIA STATE UNIVERSITY:

There are several very interesting facts about Georgia State as follows:

  • 7 campuses
  • 10 colleges and schools
  • 51,000+ students from every county in Georgia, every state in the U.S. and 170 countries. (This one blew my mind. Fifty-one thousand students?????? I’m sure that includes part-time, online, and night students but fifty-one thousand?)
  • 3,000+ international students. All you have to do is look at the graduating class and try to pronounce the names to see there is a significant international presence.
  • This graduating class, forty-four (44%) percent were culturally diverse or from backgrounds being non-native-born American.
  • 250+ degree programs in 100 fields of study at the Atlanta Campus — the widest variety in the state
  • 30+ associate degree pathways at five campuses and through the largest online program in the state
  • $2.5 billion annual economic impact on metro Atlanta*
  • 84 research centers
  • 72 study-abroad programs in 45 countries
  • 400+ student organizations, including 31 fraternities and sororities
  • 9,500+ degrees conferred each year
  • 240,276 all-time degrees conferred
  • 88% of faculty with the highest degree in their field
  • A campus without boundaries in downtown Atlanta, the leading economic center of the Southeast with the world’s busiest airport and third most Fortune 500 companies of any U.S. city, where internships, jobs and connections to the world’s business, government, healthcare, nonprofit and cultural communities are just blocks away.
  • One more fact that I would like to throw in. Traffic in Atlanta is the worst—the very worst on the planet.  Orange barrels everywhere with associated road work.  If you plan on taking a look at the campus, plan your trip then double or triple the time for transit when in “hot—Lanta”.

The graduation ceremony was held in the Georgia Tech McCamish Pavilion.  This is a beautiful stadium. Ground was broken for the construction of Tech’s new on-campus arena on May 5, 2011, and eighteen (18) months later, the Yellow Jackets had a state-of-the-art building with 8,600 seats and a luxurious club area, which provides a cozy view of the court. The lower level seating bowl has 6,935 seats, and the balcony level seats 1,665.  There were approximately fifteen hundred graduates that walked that day so the pavilion was just about full of family, friends, faculty, and assorted people getting in from the thirty-two (32) degree cold weather.  It was a beautiful day though.

(NOTE:  I want to apologize for the quality of the digital pictures below.  The lighting was not very good and our vantage point gave us a great overall view of the ceremony but at a distance.)

You can see from the picture above the size of the pavilion.  McCamish is the site of Yellow Jacket basketball.  Note the vacant seats behind the overhead screen.  Other than these vacant seats, the auditorium was absolutely full.

In the picture above, all prospective graduates were standing for the opening ceremonies.  Hopefully, you can see the bagpipers coming down the isle to open the event.

Georgia State used the overhead screen in a marvelous way by posting the name and college of the graduate.   Excellent use of the overhead and those of us in the upper seats were allowed to get a great look at our graduate.

When the ceremony was over and all of the graduates having walked, balloons were released from overhead netting.  That’s when the mortar boards started flying.

Our granddaughter, her mother and father are shown in this picture.  Next year, our oldest granddaughter—this young ladies’ sister, will graduate from Georgia State.  Both granddaughters have two degrees indicating hard, intense, focused work over four and five years.  We are certainly proud of their considerable efforts.  Remarkable work ethic for both.  Their futures look very bright.

 

HALF SMART

December 12, 2017


The other day I was visiting a client and discussing a project involving the application of a robotic system to an existing work cell.  The process is somewhat complex and we all questioned which employee would manage the operation of the cell including the system.  The system is a SCARA type.  SCARA is an acronym for Selective Compliance Assembly Robot Arm or Selective Compliance Articulated Robot Arm.

In 1981, Sankyo SeikiPentel and NEC presented a completely new concept for assembly robots. The robot was developed under the guidance of Hiroshi Makino, a professor at the University of Yamanashi and was called the Selective Compliance Assembly Robot Arm or SCARA.

SCARA’s are generally faster and cleaner than comparable Cartesian (X, Y, Z) robotic systems.  Their single pedestal mount requires a small footprint and provides an easy, unhindered form of mounting. On the other hand, SCARA’s can be more expensive than comparable Cartesian systems and the controlling software requires inverse kinematics for linear interpolated moves. This software typically comes with the SCARA however and is usually transparent to the end-user.   The SCARA system used in this work cell had the capability of one hundred programs with 100 data points per program.  It was programmed by virtue of a “teach pendant” and “jog” switch controlling the placement of the robotic arm over the material.

Several names were mentioned as to who might ultimately, after training, be capable of taking on this task.  When one individual was named, the retort was; “not James, he is only half smart.  That got me to thinking about “smarts”.  How smart is smart?   At what point do we say smart is smart enough?

IQ CHARTS—WHO’S SMART

The concept of IQ or intelligence quotient was developed by either the German psychologist and philosopher Wilhelm Stern in 1912 or by Lewis Terman in 1916.  This is depending on which of several sources you consult.   Intelligence testing was initially accomplished on a large scale before either of these dates. In 1904 psychologist Alfred Binet was commissioned by the French government to create a testing system to differentiate intellectually normal children from those who were inferior.

From Binet’s work the IQ scale called the “Binet Scale,” (and later the “Simon-Binet Scale”) was developed. Sometime later, “intelligence quotient,” or “IQ,” entered our vocabulary.  Lewis M. Terman revised the Simon-Binet IQ Scale, and in 1916 published the Stanford Revision of the Binet-Simon Scale of Intelligence (also known as the Stanford-Binet).

Intelligence tests are one of the most popular types of psychological tests in use today. On the majority of modern IQ tests, the average (or mean) score is set at 100 with a standard deviation of 15 so that scores conform to a normal distribution curve.  This means that 68 percent of scores fall within one standard deviation of the mean (that is, between 85 and 115), and 95 percent of scores fall within two standard deviations (between 70 and 130).  This may be shown from the following bell-shaped curve:

Why is the average score set to 100?  Psychometritians, individuals who study the biology of the brain, utilize a process known as standardization in order to make it possible to compare and interpret the meaning of IQ scores. This process is accomplished by administering the test to a representative sample and using these scores to establish standards, usually referred to as norms, by which all individual scores can be compared. Since the average score is 100, experts can quickly assess individual test scores against the average to determine where these scores fall on the normal distribution.

The following scale resulted for classifying IQ scores:

IQ Scale

Over 140 – Genius or almost genius
120 – 140 – Very superior intelligence
110 – 119 – Superior intelligence
90 – 109 – Average or normal intelligence
80 – 89 – Dullness
70 – 79 – Borderline deficiency in intelligence
Under 70 – Feeble-mindedness

Normal Distribution of IQ Scores

From the curve above, we see the following:

50% of IQ scores fall between 90 and 110
68% of IQ scores fall between 85 and 115
95% of IQ scores fall between 70 and 130
99.5% of IQ scores fall between 60 and 140

Low IQ & Mental Retardation

An IQ under 70 is considered as “mental retardation” or limited mental ability. 5% of the population falls below 70 on IQ tests. The severity of the mental retardation is commonly broken into 4 levels:

50-70 – Mild mental retardation (85%)
35-50 – Moderate mental retardation (10%)
20-35 – Severe mental retardation (4%)
IQ < 20 – Profound mental retardation (1%)

High IQ & Genius IQ

Genius or near-genius IQ is considered to start around 140 to 145. Less than 1/4 of 1 percent fall into this category. Here are some common designations on the IQ scale:

115-124 – Above average
125-134 – Gifted
135-144 – Very gifted
145-164 – Genius
165-179 – High genius
180-200 – Highest genius

We are told “Big Al” had an IQ over 160 which would definitely qualify him as being one the most intelligent people on the planet.

As you can see, the percentage of individuals considered to be genius is quite small. 0.50 percent to be exact.  OK, who are these people?

  1. Stephen Hawking

Dr. Hawking is a man of Science, a theoretical physicist and cosmologist.  Hawking has never failed to astonish everyone with his IQ level of 160. He was born in Oxford, England and has proven himself to be a remarkably intelligent person.   Hawking is an Honorary Fellow of the Royal Society of Arts, a lifetime member of the Pontifical Academy of Sciences, and a recipient of the Presidential Medal of Freedom, the highest civilian award in the United States.  Hawking was the Lucasian Professor of Mathematics at the University of Cambridge between 1979 and 2009. Hawking has a motor neuron disease related to amyotrophic lateral sclerosis (ALS), a condition that has progressed over the years. He is almost entirely paralyzed and communicates through a speech generating device. Even with this condition, he maintains a very active schedule demonstrating significant mental ability.

  1. Andrew Wiles

Sir Andrew John Wiles is a remarkably intelligent individual.  Sir Andrew is a British mathematician, a member of the Royal Society, and a research professor at Oxford University.  His specialty is numbers theory.  He proved Fermat’s last theorem and for this effort, he was awarded a special silver plaque.    It is reported that he has an IQ of 170.

  1. Paul Gardner Allen

Paul Gardner Allen is an American business magnate, investor and philanthropist, best known as the co-founder of The Microsoft Corporation. As of March 2013, he was estimated to be the 53rd-richest person in the world, with an estimated wealth of $15 billion. His IQ is reported to be 170. He is considered to be the most influential person in his field and known to be a good decision maker.

  1. Judit Polgar

Born in Hungary in 1976, Judit Polgár is a chess grandmaster. She is by far the strongest female chess player in history. In 1991, Polgár achieved the title of Grandmaster at the age of 15 years and 4 months, the youngest person to do so until then. Polgar is not only a chess master but a certified brainiac with a recorded IQ of 170. She lived a childhood filled with extensive chess training given by her father. She defeated nine former and current world champions including Garry Kasparov, Boris Spassky, and Anatoly Karpov.  Quite amazing.

  1. Garry Kasparov

Garry Kasparov has totally amazed the world with his outstanding IQ of more than 190. He is a Russian chess Grandmaster, former World Chess Champion, writer, and political activist, considered by many to be the greatest chess player of all time. From 1986 until his retirement in 2005, Kasparov was ranked world No. 1 for 225 months.  Kasparov became the youngest ever undisputed World Chess Champion in 1985 at age 22 by defeating then-champion Anatoly Karpov.   He held the official FIDE world title until 1993, when a dispute with FIDE led him to set up a rival organization, the Professional Chess Association. In 1997 he became the first world champion to lose a match to a computer under standard time controls, when he lost to the IBM supercomputer Deep Blue in a highly publicized match. He continued to hold the “Classical” World Chess Championship until his defeat by Vladimir Kramnik in 2000.

  1. Rick Rosner

Gifted with an amazing IQ of 192.  Richard G. “Rick” Rosner (born May 2, 1960) is an American television writer and media figure known for his high intelligence test scores and his unusual career. There are reports that he has achieved some of the highest scores ever recorded on IQ tests designed to measure exceptional intelligence. He has become known for taking part in activities not usually associated with geniuses.

  1. Kim Ung-Yong

With a verified IQ of 210, Korean civil engineer Ung Yong is considered to be one of the smartest people on the planet.  He was born March 7, 1963 and was definitely a child prodigy .  He started speaking at the age of 6 months and was able to read Japanese, Korean, German, English and many other languages by his third birthday. When he was four years old, his father said he had memorized about 2000 words in both English and German.  He was writing poetry in Korean and Chinese and wrote two very short books of essays and poems (less than 20 pages). Kim was listed in the Guinness Book of World Records under “Highest IQ“; the book gave the boy’s score as about 210. [Guinness retired the “Highest IQ” category in 1990 after concluding IQ tests were too unreliable to designate a single record holder.

  1. Christopher Hirata

Christopher Hirata’s  IQ is approximately 225 which is phenomenal. He was genius from childhood. At the age of 16, he was working with NASA with the Mars mission.  At the age of 22, he obtained a PhD from Princeton University.  Hirata is teaching astrophysics at the California Institute of Technology.

  1. Marilyn vos Savant

Marilyn Vos Savant is said to have an IQ of 228. She is an American magazine columnist, author, lecturer, and playwright who rose to fame as a result of the listing in the Guinness Book of World Records under “Highest IQ.” Since 1986 she has written “Ask Marilyn,” a Parade magazine Sunday column where she solves puzzles and answers questions on various subjects.

1.Terence Tao

Terence Tao is an Australian mathematician working in harmonic analysis, partial differential equations, additive combinatorics, ergodic Ramsey theory, random matrix theory, and analytic number theory.  He currently holds the James and Carol Collins chair in mathematics at the University of California, Los Angeles where he became the youngest ever promoted to full professor at the age of 24 years. He was a co-recipient of the 2006 Fields Medal and the 2014 Breakthrough Prize in Mathematics.

Tao was a child prodigy, one of the subjects in the longitudinal research on exceptionally gifted children by education researcher Miraca Gross. His father told the press that at the age of two, during a family gathering, Tao attempted to teach a 5-year-old child arithmetic and English. According to Smithsonian Online Magazine, Tao could carry out basic arithmetic by the age of two. When asked by his father how he knew numbers and letters, he said he learned them from Sesame Street.

OK, now before you go running to jump from the nearest bridge, consider the statement below:

Persistence—President Calvin Coolidge said it better than anyone I have ever heard. “Nothing in the world can take the place of persistence. Talent will not; nothing is more common than unsuccessful men with talent.   Genius will not; unrewarded genius is almost a proverb. Education will not; the world is full of educated derelicts. Persistence and determination alone are omnipotent.  The slogan “Press on” has solved and always will solve the problems of the human race.” 

I personally think Calvin really knew what he was talking about.  Most of us get it done by persistence!! ‘Nuff” said.

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.

 

WHY I DRINK WINE

December 6, 2017


Over the years I have developed a taste for wine.  Please note, I am definitely NOT an expert and do not even come close to being an expert.  As a matter of fact, when the waiter brings the wine list I immediately hand it to my wife to ponder and ultimately place the order. One of the things on my “bucket list” (and I had better get to it) is taking a wine-appreciation course—preferably in Italy.

The wine industry is a fascinating commercial enterprise and not without hazards; namely, weather and disease.  Even with that being the case, most individuals can appreciate production efforts on an annual basis.   Let’s take a look.

In April 2017 the International Organization of Vine and Wine (OIV) released its annual report on the state of the wine industry for the year 2016.  Last year, the global output definitely declined slightly over three (3%) percent from the production of 2015. Bad weather created the issues with production.  According to the OIV, the top ten (10) producers were as follows:

  • Italy—50.9 million hector-liters
  • France—43.5 million hector-liters
  • Spain—39.3 million hector-liters
  • United States—23.9 million hector-liters
  • Australia—13.0 million hector-liters
  • China—11.4 million hector-liters
  • South Africa—10.5 million hector-liters
  • Chile—10.1– million hector-liters
  • Argentina—9.4 million hector-liters
  • Germany—9.0 million hector-liters

Now, if we go from supply to demand, we find the following:

  • United States—31.8 million hector-liters
  • France—27.0– million hector-liters
  • Italy—22.5 million hector-liters
  • Germany—20.2 million hector-liters
  • China—17.3 million hector-liters
  • United Kingdom—12.9 million hector-liters
  • Spain—9.9 million hector-liters
  • Argentina—9.4 million hector-liters
  • Russia—9.3 million hector-liters
  • Australia—5.4 million hector-liters

A partial list of countries and associated consumption is given as follows:

Now, there may be other reasons and experiences when drinking wine as shown by the digital pictures below.

GREAT LOGIC ON THIS ONE.

This is called supreme rationalization.

I truly believe Mr. Handy has his head in the right place.  He is doing humanity a great service.

SELF-EXPLANATORY!!!!!!!!

We’ve all been there.

This very well could be the result of enjoying a glass (or several glasses) much too much.

As always, I welcome your comments.

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.

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