HAPPY NEW YEAR

December 31, 2017


I certainly want to thank all of you who, over this past year, taken time to access my posts.  I wish you the very best in 2018—HEALTH, HAPPINESS, PROFESSIONAL SUCCESS, AND A LITTLE MONEY LEFT IN YOUR POCKETS FOR FUN AT THE END OF THE DAY. Let’s see if we can make 2018 a banner year.

HAPPY NEW YEAR

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WORLD’S RICHEST

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.

WINNERS:

  • 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.

FOUR MOVIES

December 27, 2017


If you read my posts you know that my wife and I definitely enjoy going to the movies.  Please note—going to the movies, not downloading or streaming a movie.  We like the “going experience”.  Well, this past year has been a real bust for movie-goers, at least in my opinion.

“And the number of actual tickets sold this summer paints a bleaker picture, with total admissions likely to clock in at about 425 million, the lowest level since 1992, according to industry estimates.”

The quote above just about sums it up.  Two reasons for the exceptional drop in movie attendance is Franchise Fatigue and Bad Reviews.  Just how many “do-overs” can you stand before you say OKAY, I’m reading a book?

According to the movies critics, the ten (10) worst movies of 2017 are:

  1. Underworld: Blood Wars
  2. Fifty Shades Darker
  3. Power Rangers
  4. Transformers: The Last Knight
  5. The Shack
  6. Fist Fight
  7. Unforgettable
  8. The Dark Tower
  9. Rings
  10. Assassin’s Creed

I am very proud to tell you that we saw only one of these “worst movies” of the year—Dark Tower. Very forgettable.

Now, with that behind us, I would like to recommend to you four (4) movies that we thought were absolutely great.  Worth the price of a ticket.

  1. Three Billboards— THREE BILLBOARDS OUTSIDE EBBING, MISSOURI is a dark comic drama from Academy Award winner Martin McDonagh. After months without a culprit in her daughter’s murder case, Mildred Hayes (Academy Award winner Frances McDormand) makes a bold move, painting three signs leading into her town with a controversial message directed at William Willoughby (Academy Award nominee Woody Harrelson), the town’s revered chief of police. When his second-in-command Officer Dixon (Sam Rockwell), an immature mother’s boy with a penchant for violence, gets involved, the battle between Mildred and Ebbing’s law enforcement is only exacerbated.
  2. The Wonder Wheel-– WONDER WHEEL tells the story of four characters whose lives intertwine amid the hustle and bustle of the Coney Island amusement park in the 1950s: Ginny (Kate Winslet), an emotionally volatile former actress now working as a waitress in a clam house; Humpty (Jim Belushi), Ginny’s rough-hewn carousel operator husband; Mickey (Justin Timberlake), a handsome young lifeguard who dreams of becoming a playwright; and Carolina (Juno Temple), Humpty’s long-estranged daughter, who is now hiding out from gangsters at her father’s apartment. Cinematographer Vittorio Storaro captures a tale of passion, violence, and betrayal that plays out against the picturesque tableau of 1950s Coney Island. In my opinion, all performers deserve an Oscar nomination. This is Belushi’s best acting role to date.
  3. The Darkest Hour-– During the early days of World War II, with the fall of France imminent, Britain faces its darkest hour as the threat of invasion looms. As the seemingly unstoppable Nazi forces advance, and with the Allied army cornered on the beaches of Dunkirk, the fate of Western Europe hangs on the leadership of the newly-appointed British Prime Minister Winston Churchill (Academy Award nominee Gary Oldman). While maneuvering his political rivals, he must confront the ultimate choice: negotiate with Hitler and save the British people at a terrible cost or rally the nation and fight on against incredible odds. Directed by Joe Wright, DARKEST HOUR is the dramatic and inspiring story of four weeks in 1940 during which Churchill’s courage to lead changed the course of world history. Once again, Gary Oldman has Best Performer in a Dramatic Series “nailed”. He becomes Sir Winston.
  4. All the Money in the World-– ALL THE MONEY IN THE WORLD follows the kidnapping of sixteen (16)-year-old John Paul Getty III (Charlie Plummer) and the desperate attempt by his devoted mother Gail (Michelle Williams) to convince his billionaire grandfather (Christopher Plummer) to pay the ransom. When Getty Sr. refuses, Gail attempts to sway him as her son’s captors become increasingly volatile and brutal. With her son’s life in the balance, Gail and Getty’s advisor (Mark Wahlberg) become unlikely allies in the race against time that ultimately reveals the true and lasting value of love over money. As you may remember, Kevin Spacey was originally chosen to portray J. Paul Getty but due to his sexual harassment issues was dropped from the cast.  Christopher Plummer did a wonderful job in stepping in at the very last moment, and I do mean last moment, to fulfill that role.

I can recommend these movies to you without any hesitation at all.  The ensemble casts are marvelous and work together to provide the unfolding of great stories.  All four are definitely worth the ticket prices.

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”.

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.

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