February 3, 2018

The list of the “most hated American companies” was provided by KATE GIBSON in the MONEYWATCH web site, February 1, 2018, 2:20 PM.  The text and narrative is this author’s.

Corporate America is sometimes, but not always, blamed for a number of misdeeds, swindles, “let’s bash the little guy”, etc. behavior.  Many times, those charges are warranted.   You get the picture.   Given below, is a very quick list of the twenty (20) most hated U.S. companies.  This list is according to 24/7 Wall St., which took customer surveys, employee reviews and news events into account in devising its list: ( I might mention the list is in descending order so the most-egregious offender is at the bottom.

  • The Weinstein Company. I think we can all understand this one but I strongly believe most of the employees of The Weinstein Company are honest hard-working individuals who do their job on a daily basis.  One big problem—you CANNOT tell me the word did not get around relative to Weinstein’s activities.  Those who knew are definitely complicit and should be ashamed of themselves.  This includes those holier-than-thou- actresses and actors pretending not-to-know.
  • United Airlines. The Chicago-based carrier is still in the dog housewith customers after a video of a passenger being forcibly removed from his seat on an overbooked flight went viral last year. You simply do NOT treat individuals, much less customers, in the manner in which this guy was treated.  I wonder how much money United has lost due to the video?
  • Fake news, deceptive ads, invasion of privacy.  You get the picture and YET millions subscribe.  This post will be hyperlinked to Facebook to improve readership.  That’s about the only reason I use the website.
  • I don’t really know these birds but apparently the telecom, one of the nation’s biggest internet and telephone service providers, reportedly gets poor reviews from customers and employees alike. I think that just might be said for many of the telecoms.
  • This one baffles me to a great extent but the chemical company has drawn public ire at a lengthy list of harmful products, including DDT, PCBs and Agent Orange. Most recently, it’s accused of causing cancer in hundreds exposed to its weed killer, Roundup.
  • I’m a Comcast subscriber and let me tell you their customer service is the WORST. They are terrible.  Enough said.
  • I have taken Uber multiple times with great success but there are individuals who have been harassed.  Hit by complaints of sexual harassment at the company and a video of its then-CEO Travis Kalanick arguing with an Uber driver, the company last year faced a slew of lawsuit and saw 13 executives resign, including Kalanick.
  • Sears Holdings. Sears plans to close more than one hundred (100) additional stores through the spring of 2018, with the count of Sears and Kmart stores already down to under 1,300 from 3,467 in 2007. Apparently, customer satisfaction is a huge problem also.  The retail giant needs a facelift and considerable management help to stay viable in this digital on-line-ordering world.
  • Trump Organization.  At this point in time, Donald Trumpis the least popular president in U.S. history, with a thirty-five (35) percent approval rating at the end of December. That disapproval extends to the Trump brand, which includes golf courses, a hotel chain and real estate holdings around the globe. One again, I suspect that most of the employees working for “the Donald” are honest hard-working individuals.
  • Wells Fargo. At one time, I had a Wells Fargo business account. NEVER AGAIN. I won’t go into detail.
  • The insurance industry is not exactly beloved, and allegations of fraud have not helped Cigna’s case. Multiple lawsuits allege the company inflated medical costs and overcharged customers.
  • Spirit Airlines. I’ve flown Spirit Airlines and you get what you pay for. I do not know why customers do not know that but it is always the case.  You want to be treated fairly, fly with other carriers.
  • Vice Media The media organization has lately been roiled by allegations of systemic sexual harassment, dating back to 2003. One of these day some bright individual in the corporate offices will understand you must value your employees.
  • The telecom gets knocked for poor customer experiences that could in part be due to service, with Sprint getting low grades for speed and data, as well as calling, texting and overall reliability.
  • Foxconn Technology Group. Once again, I’m not that familiar with Foxconn Technology Group. The company makes and assembles consumer electronics for entities including Apple and Nintendo. It’s also caught attention for poor working and living conditions after a series of employee suicides at a compound in China. It recently drew negative press for a planned complex in Wisconsin.
  • Electronic Arts. The video-game maker known for its successful franchises is also viewed poorly by gamers for buying smaller studios or operations for a specific game and then taking away its originality.
  • University of Phoenix. I would expect every potential student wishing to go on-line for training courses do their homework relative to the most-desirable provider. The University of Phoenix does a commendable job in advertising but apparently there are multiple complaints concerning the quality of services.
  • I’m a little burned out with the NFL right now. My Falcons and Titans have had a rough year and I’m ready to move on to baseball. Each club sets their own spring training reporting dates each year, though all camps open the same week. Pitchers and catchers always arrive first. The position players don’t have to show up until a few days later. Here are this year’s reporting dates for the 15 Cactus League teams, the teams that hold spring training in Arizona.
  • Fox Entertainment Group. If you do not like the channel—do something else.  I bounce back and forth across the various schedules to find something I really obtain value-added from.  The Food Network, the History Channel, SEC Network.  You choose.  There are hundreds of channels to take a look at.
  • The consumer credit reporting was hit by a massive hack last year, exposing the personal data of more than 145 million Americans and putting them at risk of identity theft. Arguably worse, the company sat on the information for a month before letting the public know.

CONCLUSIONS:  In looking at this survey, there are companies that deserve their most-hated-status and, in my opinion, some that do not.  Beauty is in the eye of the beholder.  As always, I welcome your comments.



January 6, 2018

OKAY, how many of you have said already this year?  “MAN, I have to lose some weight.”  I have a dear friend who put on a little weight over a couple of years and he commented: “Twenty or twenty-five pounds every year and pretty soon it adds up.”  It does add up.  Let’s look at several numbers from the CDC and other sources.

  • The CDC organization estimates that three-quarters (3/4of the American population will likely be overweight or obese by 2020. The latest figures, as of 2014, show that more than one-third (36.5%) of U.S. adults age twenty (20) and older and seventeen percent (17%) of children and adolescents aged two through nineteen (2–19) years were obese.
  • American ObesityRates are on the Rise, Gallup Poll Finds. Americans have become even fatter than before, with nearly twenty-eight (28%) percent saying they are clinically obese, a new survey finds. … At 180 pounds this person has a BMI of thirty (30) and is considered obese.

Now, you might say—we are in good company:  According to the World Health Organization, the following countries have the highest rates of obesity.

  • Republic of Nauru. Formerly known as Pleasant Island, this tiny island country in the South Pacific only has a population of 9,300. …
  • American Samoa. …
  • Tokelau
  • Tonga
  • French Polynesia. …
  • Republic of Kiribati. …
  • Saudi Arabia. …
  • Panama.

There is absolutely no doubt that more and more Americans are over weight even surpassing the magic BMI number of 30.  We all know what reduction in weight can do for us on an individual basis, but have you ever considered what reduction in weight can do for “other items”—namely hardware?

  • Using light-weight components, (composite materials) and high-efficiency engines enabled by advanced materials for internal-combustion engines in one-quarter of U.S. fleet trucks and automobiles could possibly save more than five (5) billion gallons of fuel annually by 2030. This is according to the US Energy Department Vehicle Technologies Office.
  • This is possible because, according to the Oak Ridge National Laboratory, The Department of Energy’s Carbon Fiber Technology Facility has a capacity to produce up to twenty-five (25) tons of carbon fiber per year.
  • Replacing heavy steel with high-strength steel, aluminum, or glass fiber-reinforced polymer composites can decrease component weight by ten to sixty percent (10-60 %). Longer term, materials such as magnesium and carbon fiber-reinforced composites could reduce the weight of some components by fifty to seventy-five percent (50-75%).
  • It costs $10,000 per pound to put one pound of payload into Earth orbit. NASA’s goal is to reduce the cost of getting to space down to hundreds of dollars per pound within twenty-five (25) years and tens of dollars per pound within forty (40) years.
  • Space-X Falcon Heavy rocket will be the first ever rocket to break the $1,000 per pound per orbit barrier—less than a tenth as much as the Shuttle. ( SpaceX press release, July 13, 2017.)
  • The Solar Impulse 2 flew 40,000 Km without fuel. The 3,257-pound solar plane used sandwiched carbon fiber and honey-combed alveolate foam for the fuselage, cockpit and wing spars.

So you see, reduction in weight can have lasting affects for just about every person and some pieces of hardware.   Let’s you and I get it off.


December 29, 2017

OK, it is once again time to make those New Year’s resolutions.  Health, finances, weight loss, quit smoking, cut out sugar, daily exercise, etc. You get the drill.   All of those resolutions we get tired of and basically forget by the end of February.  If you had all the money in the world, as some do, you might not even make resolutions.  You might sit back and watch it roll in.  Let’s take a quick look.

According to the Bloomberg Billionaires Index, 2017 proved to be an outstanding year for the world’s richest people, watching their net worth rise 23 percent from $4.4 trillion in 2016 to $5.3 trillion by the end of trading on Tuesday, December 26.

The following graph will indicate the progress of the world’s richest through the 2017 year.  As you can see, the world’s richest individuals added a very cool one trillion dollars ($1 trillion USD) to their individual wealth.  Now that’s the entire group of richest people but even that’s a huge sum of “dinero”.

Take a look at these duds below.  Do you know who they are?  I’m going to let you ponder this over the weekend but they all “look familiar” and they are all very very wealthy.


  • The U.S. has the largest presence on the index, with 159 billionaires. They added $315 billion, an eighteen (18%) percent gain that gives them a collective net worth of $2 trillion.
  • Russia’s twenty-seven (27) richest people put behind them the economic pain that followed President Vladimir Putin’s 2014 annexation of Crimea, adding $29 billion to $275 billion, surpassing the collective net worth they had before western economic sanctions began.
  • It was also a banner year for tech moguls, with the fifty-seven (57) technology billionaires on the index adding $262 billion, a thirty-five (35%) percent increase that was the most of any sector on the ranking.
  • Facebook Inc. co-founder Mark Zuckerberghad the fourth-largest U.S. dollar increase on the index, adding $22.6 billion, or forty-five (45%) percent, and filed plans to sell eighteen (18%) percent of his stake in the social media giant as part of his plan to give away the majority of his $72.6 billion fortune.
  • In all, the 440 billionaires on the index who added to their fortunes in 2017, gained a combined $1.05 trillion.
  • The Bloomberg index discovered sixty-seven (67) hidden billionaires in 2017.
  • Renaissance Technologies’ Henry Lauferwas identified with a net worth of $4 billion in April. Robert Mercer, 71, who plans to step down as co-CEO of the world’s most profitable trading fund on Jan. 1, couldn’t be confirmed as a billionaire.
  • Two fish billionaires were caught: Russia’s Vitaly Orlovand Chuck Bundrant of Trident Seafood.
  • A Brazilian tycoon who built a $1.3 billion fortune with Latin America’s biggest wind developer was interviewed in April.
  • Two New York real estate moguls were identified, Ben Ashkenazy and Joel Wiener.
  • Several technology startup billionaires were identified, including the chief executive officer of Roku Inc. and the two co-founders of Wayfair Inc.
  • Investor euphoria created a number of bitcoin billionaires, including Tyler and Cameron Winkelvoss, with the value of the cryptocurrency soaring to more than $16,000 Tuesday, up from $1,140 on Jan. 4. The leap came with a chorus of warnings, including from Janet Yellen, who called the emerging tender a “highly speculative asset” at her last news conference as chair of the Federal Reserve, on Dec. 13.

I’m not going to highlight the losers because even their monetary losses leave them as millionaires and billionaires.  I know this post makes your day but I tell you these things to indicate that maybe, just maybe it is possible to achieve monetary success in 2018.  I DO KNOW IT’S POSSIBLE TO TRY.  Now, when I say success, I’m not necessarily talking about millions and certainly not billions—enough to cover the basic expenses with a little left over for FUL.

Here’s hoping you all have a marvelous NEW YEAR.  Remember—clean slate.  Starting over. Have a great year.


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?


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.


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 (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”).


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


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.


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.


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.


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.


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 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.


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.


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.



  • 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.


  • 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.


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.

Elon Musk has warned again about the dangers of artificial intelligence, saying that it poses “vastly more risk” than the apparent nuclear capabilities of North Korea does. I feel sure Mr. Musk is talking about the long-term dangers and not short-term realities.   Mr. Musk is shown in the digital picture below.

This is not the first time Musk has stated that AI could potentially be one of the most dangerous international developments. He said in October 2014 that he considered it humanity’s “biggest existential threat”, a view he has repeated several times while making investments in AI startups and organizations, including Open AI, to “keep an eye on what’s going on”.  “Got to regulate AI/robotics like we do food, drugs, aircraft & cars. Public risks require public oversight. Getting rid of the FAA would not make flying safer. They’re there for good reason.”

Musk again called for regulation, previously doing so directly to US governors at their annual national meeting in Providence, Rhode Island.  Musk’s tweets coincide with the testing of an AI designed by OpenAI to play the multiplayer online battle arena (Moba) game Dota 2, which successfully managed to win all its 1-v-1 games at the International Dota 2 championships against many of the world’s best players competing for a $24.8m (£19m) prize fund.

The AI displayed the ability to predict where human players would deploy forces and improvise on the spot, in a game where sheer speed of operation does not correlate with victory, meaning the AI was simply better, not just faster than the best human players.

Musk backed the non-profit AI research company OpenAI in December 2015, taking up a co-chair position. OpenAI’s goal is to develop AI “in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return”. But it is not the first group to take on human players in a gaming scenario. Google’s Deepmind AI outfit, in which Musk was an early investor, beat the world’s best players in the board game Go and has its sights set on conquering the real-time strategy game StarCraft II.

Musk envisions a situation found in the movie “i-ROBOT with humanoid robotic systems shown below.  Robots that can think for themselves. Great movie—but the time-frame was set in a future Earth (2035 A.D.) where robots are common assistants and workers for their human owners, this is the story of “robotophobic” Chicago Police Detective Del Spooner’s investigation into the murder of Dr. Alfred Lanning, who works at U.S. Robotics.  Let me clue you in—the robot did it.

I am sure this audience is familiar with Isaac Asimov’s Three Laws of Robotics.

  • First Law: A robot may not injure a human being, or, through inaction, allow a human being to come to harm.
  • Second Law: A robot must obey orders given it by human beings, except where such orders would conflict with the First Law.
  • Third Law: A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

Asimov’s three laws indicate there will be no “Rise of the Machines” like the very popular movie indicates.   For the three laws to be null and void, we would have to enter a world of “singularity”.  The term singularity describes the moment when a civilization changes so much that its rules and technologies are incomprehensible to previous generations. Think of it as a point-of-no-return in history. Most thinkers believe the singularity will be jump-started by extremely rapid technological and scientific changes. These changes will be so fast, and so profound, that every aspect of our society will be transformed, from our bodies and families to our governments and economies.

A good way to understand the singularity is to imagine explaining the internet to somebody living in the year 1200. Your frames of reference would be so different that it would be almost impossible to convey how the internet works, let alone what it means to our society. You are on the other side of what seems like a singularity to our person from the Middle Ages. But from the perspective of a future singularity, we are the medieval ones. Advances in science and technology mean that singularities might happen over periods much shorter than 800 years. And nobody knows for sure what the hell they’ll bring.

Author Ken MacLeod has a character describe the singularity as “the Rapture for nerds” in his novel The Cassini Division, and the turn of phrase stuck, becoming a popular way to describe the singularity. (Note: MacLeod didn’t actually coin this phrase – he says he got the phrase from a satirical essay in an early-1990s issue of Extropy.) Catherynne Valente argued recently for an expansion of the term to include what she calls “personal singularities,” moments where a person is altered so much that she becomes unrecognizable to her former self. This definition could include post-human experiences. Post-human (my words) would describe robotic future.

Could this happen?  Elon Musk has an estimated net worth of $13.2 billion, making him the 87th richest person in the world, according to Forbes. His fortune owes much to his stake in Tesla Motors Inc. (TSLA), of which he remains CEO and chief product architect. Musk made his first fortune as a cofounder of PayPal, the online payments system that was sold to eBay for $1.5 billion in 2002.  In other words, he is no dummy.

I think it is very wise to listen to people like Musk and heed any and all warnings they may give. The Executive, Legislative and Judicial branches of our country are too busy trying to get reelected to bother with such warnings and when “catch-up” is needed, they always go overboard with rules and regulations.  Now is the time to develop proper and binding laws and regulations—when the technology is new.

Portions of the following post were taken from the September 2017 Machine Design Magazine.

We all like to keep up with salary levels within our chosen profession.  It’s a great indicator of where we stand relative to our peers and the industry we participate in.  The state of the engineering profession has always been relatively stable. Engineers are as essential to the job market as doctors are to medicine. Even in the face of automation and the fear many have of losing their jobs to robots, engineers are still in high demand.  I personally do not think most engineers will be out-placed by robotic systems.  That fear definitely resides with on-line manufacturing positions with duties that are repetitive in nature.  As long as engineers can think, they will have employment.

The Machine Design Annual Salary & Career Report collected information and opinions from more than two thousand (2,000) Machine Design readers. The employee outlook is very good with thirty-three percent (33%) indicating they are staying with their current employer and thirty-six percent (36%) of employers focusing on job retention. This is up fifteen percent (15%) from 2016.  From those who responded to the survey, the average reported salary for engineers across the country was $99,922, and almost sixty percent (57.9%) reported a salary increase while only ten percent (9.7%) reported a salary decrease. The top three earning industries with the largest work forces were 1.) industrial controls systems and equipment, 2.) research & development, and 3.) medical products. Among these industries, the average salary was $104,193. The West Coast looks like the best place for engineers to earn a living with the average salary in the states of California, Washington, and Oregon was $116,684. Of course, the cost of living in these three states is definitely higher than other regions of the country.


As is the ongoing trend in engineering, the profession is dominated by male engineers, with seventy-one percent (71%) being over fifty (50) years of age. However, the MD report shows an up-swing of young engineers entering the profession.  One effort that has been underway for some years now is encouraging more women to enter the profession.  With seventy-one percent (71%) of the engineering workforce being over fifty, there is a definite need to attract participants.    There was an increase in engineers within between twenty-five (25) and thirty-five (35).  This was up from 5.6% to 9.2%.  The percentage of individuals entering the profession increased as well, with engineers with less than fourteen (14) years of experience increasing five percent (5%) from last year.  Even with all the challenges of engineering, ninety-two percent (92%) would still recommend the engineering profession to their children, grandchildren and others. One engineer responds, “In fact, wherever I’ll go, I always will have an engineer’s point of view. Trying to understand how things work, and how to improve them.”


When asked about foreign labor forces, fifty-four percent (54%) believe H1-B visas hurt engineering employment opportunities and sixty-one percent (61%) support measures to reform the system. In terms of outsourcing, fifty-two percent (52%) reported their companies outsource work—the main reason being lack of in-house talent. However, seventy-three percent (73%) of the outsourced work is toward other U.S. locations. When discussing the future, the job force, fifty-five percent (55%) of engineers believe there is a job shortage, specifically in the skilled labor area. An overwhelming eighty-seven percent (87%) believe that we lack a skilled labor force. According to the MD readers, the strongest place for job growth is in automation at forty-five percent (45%) and the strongest place to look for skilled laborers is in vocational schools at thirty-two percent (32%). The future of engineering is dependent on the new engineers not only in school today, but also in younger people just starting their young science, technology, engineering, and mathematic (STEM) interests. With the average engineer being fifty (50) years or old, the future of engineering will rely heavily on new engineers willing to carry the torch—eighty-seven percent (87%) of our engineers believe there needs to be more focus on STEM at an earlier age to make sure the future of engineering is secure.

With being the case, let us now look at the numbers.

The engineering profession is a “graying” profession as mentioned earlier.  The next digital picture will indicate that, for the most part, those in engineering have been in for the “long haul”.  They are “lifers”.  This fact speaks volumes when trying to influence young men and women to consider the field of engineering.  If you look at “years in the profession”, “work location” and years at present employer” we see the following:

The slide below is a surprise to me and I think the first time the question has been asked by Machine Design.  How much of your engineering training is theory vs. practice? You can see the greatest response is almost fourteen percent (13.6%) with a fifty/fifty balance between theory and practice.  In my opinion, this is as it should be.

“The theory can be learned in a school, but the practical applications need to be learned on the job. The academic world is out of touch with the current reality of practical applications since they do not work in

that area.” “My university required three internships prior to graduating. This allowed them to focus significantly on theoretical, fundamental knowledge and have the internships bolster the practical.”


The demands made on engineers by their respective companies can sometimes be time-consuming.  The respondents indicated the following certifications their companies felt necessary.




The lowest salary is found with contract design and manufacturing.  Even this salary, would be much desired by just about any individual.

As we mentioned earlier, the West Coast provides the highest salary with several states in the New England area coming is a fairly close second.



This one should be no surprise.  The greater number of years in the profession—the greater the salary level.  Forty (40) plus years provides an average salary of approximately $100,000.  Management, as you might expect, makes the highest salary with an average being $126,052.88.



As mentioned earlier, outsourcing is a huge concern to the engineering community. The chart below indicates where the jobs go.



Most engineers will tell you they stay in the profession because they love the work. The euphoria created by a “really neat” design stays with an engineer much longer than an elevated pay check.  Engineers love solving problems.  Only two percent (2%) told MD they are not satisfied at all with their profession or current employer.  This is significant.

Any reason or reasons for leaving the engineering profession are shown by the following graphic.


As mentioned earlier, engineers are very worried about the H1-B visa program and trade policies issued by President Trump and the Legislative Branch of our country.  The Trans-Pacific Partnership has been “nixed” by President Trump but trade policies such as NAFTA and trade between the EU are still of great concern to engineers.  Trade with China, patent infringement, and cyber security remain big issues with the STEM profession and certainly engineers.



I think it’s very safe to say that, for the most part, engineers are very satisfied with the profession and the salary levels offered by the profession.  Job satisfaction is great making the dawn of a new day something NOT to be dreaded.

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