Okay, there will be a test after you read this post.  Here we go.  Do you know these people?

  • Beyoncé
  • Jennifer Lopez
  • Mariah Cary
  • Lady Gaga
  • Ariana Grande
  • Katy Perry
  • Miley Cyrus
  • Karen Uhlenbeck

Don’t feel bad.  I didn’t know either.  This is Karen Uhlenbeck—the mathematician we do not know.  For some unknown reason we all (even me) know the “pop” stars by name; who their significant other or others are, their children, their latest hit single, who they recently “dumped”, where they vacationed, etc. etc.  We know this. I would propose the lady whose picture shown below has contributed more to “human kind” that all the individuals listed above.  Then again, that’s just me.

For the first time, one of the top prizes in mathematics has been given to a woman.  I find this hard to believe because we all know that “girls” can’t do math.  Your mamas told you that and you remembered it.  (I suppose Dr. Uhlenbeck mom was doing her nails and forgot to mention that to her.)

This past Tuesday, the Norwegian Academy of Science and Letters announced it has awarded this year’s Abel Prize — an award modeled on the Nobel Prizes — to Karen Uhlenbeck, an emeritus professor at the University of Texas at Austin. The award cites “the fundamental impact of her work on analysis, geometry and mathematical physics.”   Uhlenbeck won for her foundational work in geometric analysis, which combines the technical power of analysis—a branch of math that extends and generalizes calculus—with the more conceptual areas of geometry and topology. She is the first woman to receive the prize since the award of six (6) million Norwegian kroner (approximately $700,000) was first given in 2003.

One of Dr. Uhlenbeck’s advances in essence described the complex shapes of soap films not in a bubble bath but in abstract, high-dimensional curved spaces. In later work, she helped put a rigorous mathematical underpinning to techniques widely used by physicists in quantum field theory to describe fundamental interactions between particles and forces. (How many think Beyoncé could do that?)

In the process, she helped pioneer a field known as geometric analysis, and she developed techniques now commonly used by many mathematicians. As a matter of fact, she invented the field.

“She did things nobody thought about doing,” said Sun-Yung Alice Chang, a mathematician at Princeton University who served on the five-member prize committee, “and after she did, she laid the foundations for that branch of mathematics.”

An example of objects studied in geometric analysis is a minimal surface. Analogous to a geodesic, a curve that minimizes path length, a minimal surface minimizes area; think of a soap film, a minimal surface that minimizes energy. Analysis focuses on the differential equations governing variations of surface area, whereas geometry and topology focus on the minimal surface representing a solution to the equations. Geometric analysis weaves together both approaches, resulting in new insights.

The field did not exist when Uhlenbeck began graduate school in the mid-1960s, but tantalizing results linking analysis and topology had begun to emerge. In the early 1980s, Uhlenbeck and her collaborators did ground-breaking work in minimal surfaces. They showed how to deal with singular points, that is, points where the minimal surface is no longer smooth or where the solution to the equations is not defined. They proved that there are only finitely many singular points and showed how to study them by expanding them into “bubbles.” As a technique, bubbling made a deep impact and is now a standard tool.

Born in 1942 to an engineer and an artist, Uhlenbeck is a mountain-loving hiker who learned to surf at the age of forty (40). As a child she was a voracious reader and “was interested in everything,” she said in an interview last year with Celebratio.org. “I was always tense, wanting to know what was going on and asking questions.”

She initially majored in physics as an undergraduate at the University of Michigan. But her impatience with lab work and a growing love for math led her to switch majors. She nevertheless retained a lifelong passion for physics, and centered much of her research on problems from that field.  In physics, a gauge theory is a kind of field theory, formulated in the language of the geometry of fiber bundles; the simplest example is electromagnetism. One of the most important gauge theories from the 20th century is Yang-Mills theory, which underlies the standard model of elementary particle physics. Uhlenbeck and other mathematicians began to realize that the Yang-Mills equations have deep connections to problems in geometry and topology. By the early 1980s, she laid the analytic foundations for mathematical investigation of the Yang-Mills equations.

Dr. Uhlenbeck, who lives in Princeton, N.J., learned that she won the prize on Sunday morning.

“When I came out of church, I noticed that I had a text message from Alice Chang that said, Would I please accept a call from Norway?” Dr. Uhlenbeck said. “When I got home, I called Norway back and they told me.”

Who said women can’t do math?

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SMARTS

March 17, 2019


Who was the smartest person in the history of our species? Solomon, Albert Einstein, Jesus, Nikola Tesla, Isaac Newton, Leonardo de Vinci, Stephen Hawking—who would you name.  We’ve had several individuals who broke the curve relative to intelligence.   As defined by the Oxford Dictionary of the English Language, IQ:

“an intelligence test score that is obtained by dividing mental age, which reflects the age-graded level of performance as derived from population norms, by chronological age and multiplying by100: a score of100 thus indicates performance at exactly the normal level for that age group. Abbreviation: IQ”

An intelligence quotient or IQ is a score derived from one of several different intelligence measures.  Standardized tests are designed to measure intelligence.  The term “IQ” is a translation of the German Intellizenz Quotient and was coined by the German psychologist William Stern in 1912.  This was a method proposed by Dr. Stern to score early modern children’s intelligence tests such as those developed by Alfred Binet and Theodore Simin in the early twentieth century.  Although the term “IQ” is still in use, the scoring of modern IQ tests such as the Wechsler Adult Intelligence Scale is not based on a projection of the subject’s measured rank on the Gaussian Bell curve with a center value of one hundred (100) and a standard deviation of fifteen (15).  The Stanford-Binet IQ test has a standard deviation of sixteen (16).  As you can see from the graphic below, seventy percent (70%) of the human population has an IQ between eighty-five and one hundred and fifteen.  From one hundred and fifteen to one hundred and thirty you are considered to be highly intelligent.  Above one hundred and thirty you are exceptionally gifted.

What are several qualities of highly intelligent people?  Let’s look.

QUALITIES:

  • A great deal of self-control.
  • Very curious
  • They are avid readers
  • They are intuitive
  • They love learning
  • They are adaptable
  • They are risk-takers
  • They are NOT over-confident
  • They are open-minded
  • They are somewhat introverted

You probably know individuals who fit this profile.  We are going to look at one right now:  John von Neumann.

JON von NEUMANN:

The Financial Times of London celebrated John von Neumann as “The Man of the Century” on Dec. 24, 1999. The headline hailed him as the “architect of the computer age,” not only the “most striking” person of the 20th century, but its “pattern-card”—the pattern from which modern man, like the newest fashion collection, is cut.

The Financial Times and others characterize von Neumann’s importance for the development of modern thinking by what are termed his three great accomplishments, namely:

(1) Von Neumann is the inventor of the computer. All computers in use today have the “architecture” von Neumann developed, which makes it possible to store the program, together with data, in working memory.

(2) By comparing human intelligence to computers, von Neumann laid the foundation for “Artificial Intelligence,” which is taken to be one of the most important areas of research today.

(3) Von Neumann used his “game theory,” to develop a dominant tool for economic analysis, which gained recognition in 1994 when the Nobel Prize for economic sciences was awarded to John C. Harsanyi, John F. Nash, and Richard Selten.

John von Neumann, original name János Neumann, (born December 28, 1903, Budapest, Hungary—died February 8, 1957, Washington, D.C. Hungarian-born American mathematician. As an adult, he appended von to his surname; the hereditary title had been granted his father in 1913. Von Neumann grew from child prodigy to one of the world’s foremost mathematicians by his mid-twenties. Important work in set theory inaugurated a career that touched nearly every major branch of mathematics. Von Neumann’s gift for applied mathematics took his work in directions that influenced quantum theory theory of automation, economics, and defense planning. Von Neumann pioneered game theory, and, along with Alan Turing and Claude Shannon was one of the conceptual inventors of the stored-program digital computer .

Von Neumann did exhibit signs of genius in early childhood: he could joke in Classical Greek and, for a family stunt, he could quickly memorize a page from a telephone book and recite its numbers and addresses. Von Neumann learned languages and math from tutors and attended Budapest’s most prestigious secondary school, the Lutheran Gymnasium . The Neumann family fled Bela Kun’s short-lived communist regime in 1919 for a brief and relatively comfortable exile split between Vienna and the Adriatic resort of Abbazia. Upon completion of von Neumann’s secondary schooling in 1921, his father discouraged him from pursuing a career in mathematics, fearing that there was not enough money in the field. As a compromise, von Neumann simultaneously studied chemistry and mathematics. He earned a degree in chemical engineering from the Swiss Federal Institute in  Zurich and a doctorate in mathematics (1926) from the University of Budapest.

OK, that all well and good but do we know the IQ of Dr. John von Neumann?

John Von Neumann IQ is 190, which is considered as a super genius and in top 0.1% of the population in the world.

With his marvelous IQ, he wrote one hundred and fifty (150) published papers in his life; sixty (60) in pure mathematics, twenty (20) in physics, and sixty (60) in applied mathematics. His last work, an unfinished manuscript written while in the hospital and later published in book form as The Computer and the Brain, gives an indication of the direction of his interests at the time of his death. It discusses how the brain can be viewed as a computing machine. The book is speculative in nature, but discusses several important differences between brains and computers of his day (such as processing speed and parallelism), as well as suggesting directions for future research. Memory is one of the central themes in his book.

I told you he was smart!

COMPUTER SIMULATION

January 20, 2019


More and more engineers, systems analysist, biochemists, city planners, medical practitioners, individuals in entertainment fields are moving towards computer simulation.  Let’s take a quick look at simulation then we will discover several examples of how very powerful this technology can be.

WHAT IS COMPUTER SIMULATION?

Simulation modelling is an excellent tool for analyzing and optimizing dynamic processes. Specifically, when mathematical optimization of complex systems becomes infeasible, and when conducting experiments within real systems is too expensive, time consuming, or dangerous, simulation becomes a powerful tool. The aim of simulation is to support objective decision making by means of dynamic analysis, to enable managers to safely plan their operations, and to save costs.

A computer simulation or a computer model is a computer program that attempts to simulate an abstract model of a particular system. … Computer simulations build on and are useful adjuncts to purely mathematical models in science, technology and entertainment.

Computer simulations have become a useful part of mathematical modelling of many natural systems in physics, chemistry and biology, human systems in economics, psychology, and social science and in the process of engineering new technology, to gain insight into the operation of those systems. They are also widely used in the entertainment fields.

Traditionally, the formal modeling of systems has been possible using mathematical models, which attempts to find analytical solutions to problems enabling the prediction of behavior of the system from a set of parameters and initial conditions.  The word prediction is a very important word in the overall process. One very critical part of the predictive process is designating the parameters properly.  Not only the upper and lower specifications but parameters that define intermediate processes.

The reliability and the trust people put in computer simulations depends on the validity of the simulation model.  The degree of trust is directly related to the software itself and the reputation of the company producing the software. There will considerably more in this course regarding vendors providing software to companies wishing to simulate processes and solve complex problems.

Computer simulations find use in the study of dynamic behavior in an environment that may be difficult or dangerous to implement in real life. Say, a nuclear blast may be represented with a mathematical model that takes into consideration various elements such as velocity, heat and radioactive emissions. Additionally, one may implement changes to the equation by changing certain other variables, like the amount of fissionable material used in the blast.  Another application involves predictive efforts relative to weather systems.  Mathematics involving these determinations are significantly complex and usually involve a branch of math called “chaos theory”.

Simulations largely help in determining behaviors when individual components of a system are altered. Simulations can also be used in engineering to determine potential effects, such as that of river systems for the construction of dams.  Some companies call these behaviors “what-if” scenarios because they allow the engineer or scientist to apply differing parameters to discern cause-effect interaction.

One great advantage a computer simulation has over a mathematical model is allowing a visual representation of events and time line. You can actually see the action and chain of events with simulation and investigate the parameters for acceptance.  You can examine the limits of acceptability using simulation.   All components and assemblies have upper and lower specification limits a and must perform within those limits.

Computer simulation is the discipline of designing a model of an actual or theoretical physical system, executing the model on a digital computer, and analyzing the execution output. Simulation embodies the principle of “learning by doing” — to learn about the system we must first build a model of some sort and then operate the model. The use of simulation is an activity that is as natural as a child who role plays. Children understand the world around them by simulating (with toys and figurines) most of their interactions with other people, animals and objects. As adults, we lose some of this childlike behavior but recapture it later on through computer simulation. To understand reality and all of its complexity, we must build artificial objects and dynamically act out roles with them. Computer simulation is the electronic equivalent of this type of role playing and it serves to drive synthetic environments and virtual worlds. Within the overall task of simulation, there are three primary sub-fields: model design, model execution and model analysis.

REAL-WORLD SIMULATION:

The following examples are taken from computer screen representing real-world situations and/or problems that need solutions.  As mentioned earlier, “what-ifs” may be realized by animating the computer model providing cause-effect and responses to desired inputs. Let’s take a look.

A great host of mechanical and structural problems may be solved by using computer simulation. The example above shows how the diameter of two matching holes may be affected by applying heat to the bracket

 

The Newtonian and non-Newtonian flow of fluids, i.e. liquids and gases, has always been a subject of concern within piping systems.  Flow related to pressure and temperature may be approximated by simulation.

 

The Newtonian and non-Newtonian flow of fluids, i.e. liquids and gases, has always been a subject of concern within piping systems.  Flow related to pressure and temperature may be approximated by simulation.

Electromagnetics is an extremely complex field. The digital above strives to show how a magnetic field reacts to applied voltage.

Chemical engineers are very concerned with reaction time when chemicals are mixed.  One example might be the ignition time when an oxidizer comes in contact with fuel.

Acoustics or how sound propagates through a physical device or structure.

The transfer of heat from a colder surface to a warmer surface has always come into question. Simulation programs are extremely valuable in visualizing this transfer.

 

Equation-based modeling can be simulated showing how a structure, in this case a metal plate, can be affected when forces are applied.

In addition to computer simulation, we have AR or augmented reality and VR virtual reality.  Those subjects are fascinating but will require another post for another day.  Hope you enjoy this one.

 

 

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.

THE ACCOUNTANT

October 23, 2016


My wife and I love to go to the movies.  When I say go, I mean GO.  We don’t download movies at home; we don’t subscribe to Netflix, Starz, HBO, HULU, etc.   We like to make our movie watching an event.  (OK, we are weird.)  I can barely remember one bad movie in my lifetime—something like David Letterman meets Godzilla.  We love movies.  You get the picture.

I generally do not write about movies because everyone has his or her own taste. I am the furthest thing from an experienced movie critic.   My thought is—if you like it, it’s good.  There is one movie we have seen lately I definitely can recommend—THE ACCOUNTANT.  Let’s look at several specifics to start with.

CAST: 

WRITER:  Bill Dubuque

DIRECTOR:  Gavin O’Conner

STUDIO:  Warner Brothers

RUNNING TIME: 128 Minutes

Christian Wolff is a mathematical genius who works as a forensic accountant at ZZZ Accounting in Plainfield, Illinois.  His primary responsibility is tracking insider financial deceptions for numerous criminal enterprises brokered to him by a mysterious figure known as “The Voice”.  The “Voice” contacts him by phone which is really spooky when initially encountered in the movie. As a child, Christian was diagnosed with autism and offered an opportunity to live at Harbor Neuroscience Institute in New Hampshire. Although Christian had bonded with Justine, the mute daughter of the institute’s director, his father, a decorated military officer, declined, believing that Christian should overcome the hardships inherent in his condition rather than expect the world to accommodate to them. The pressure of raising a special needs child drove Christian’s mother to abandon him and his younger brother, Braxton, who were left in their father’s care.  The movie indicates the family moved thirty-four (34) times in seventeen (17) years.  Each move was to introduce new experiences to Christian and his older brother Braxton with hopes of preparing both for adult life.

I don’t know if you are familiar with autism but several symptoms are as follows:

  1. Not speak as well as his or her peers?
  2. Have poor eye contact?
  3. Not respond selectively to his or her name?
  4. Act as if he or she is in his or her own world?
  5. Seem to “tune others out?”
  6. Not have a social smile?
  7. Seem unable to tell you what he or she wants, preferring to lead you by the hand or get desired objects on his or her own, even at risk of danger?
  8. Have difficulty following simple commands?
  9. Show you things without bringing them to you?
  10. Not point to interesting objects to direct your attention to objects or events of interest?
  11. Have unusually long and severe temper tantrums?
  12. Have repetitive, odd, or stereotypic behaviors?
  13. Show an unusual attachment to inanimate objects, especially hard ones (e.g., flashlight or a chain vs. teddy bear or blanket)?
  14. Prefer to play alone?
  15. Demonstrate an inability to play with toys in the typical way?
  16. Not engage in pretend play (if older than age 2)?

Ben Affleck absolutely nails many of the traits and characteristics of an autistic adult although due to his father’s persistence with training Christian on how to overcome his difficulties, he is highly functional as a mathematician.  Affleck, in my opinion, will be nominated for an Oscar for this one.  His work is just that good.

The plot is anything but cookbook.  There are many twists and turns and the ending is really surprising.  You cannot guess as to how this one turns out. Many movies today are more of the same but The Accountant is quite the exception.  I’m not going to spoil it for you by divulging more of the plot but the entire movie is action-filled with a cast that certainly works very well together. It’s one of those movies complicated enough to see twice or even three times, each time discovering something you have missed previously.

I can definitely recommend this one to you.  Take a look.

LET’S DO THE NUMBERS

September 7, 2016


The convergence of mechanical and electronic engineering, coupled with embedded software, has produced an engineering discipline called mechatronics.  The “official” definition of mechatronics is as follows:

A multidisciplinary field of science that includes a combination of mechanical engineering, electronics, computer science, telecommunications engineering, systems engineering and control engineering”.

Technical systems have become more and more complex, requiring multiple disciplines for accomplishment of product designs that satisfy the needs of consumers and industrial markets.  If you read the technical literature, you have run across the phrase “the internet of things” or IoT. IoT is the interworking of physical devices, vehicles, buildings, airplanes, consumer appliances, and other items embedded with electronics, software, sensors, actuators and network connectivity enabling these devices and objects to collect and exchange data.

If I may, let’s now take a look at several fascinating numbers:

  • The world-wide public cloud services market is projected to grow 16.5% in 2016 to a total of $204 billion. This is up from $175 billion in 2015.  The world-wide X86 server virtualization market is expected to reach $5.6 billion in 2016, an increase of 5.7% from 2015 (Gartner, Inc.)
  • In July of this year, Apple announced it had recently sold its billionth iPhone since introduction in 2007.
  • The number of mobility devices managed from 2014 to 2015 increased 72%. (Citrix, “7 Enterprise Mobility Statistics You Should Know.”
  • 58% of consumers would consider eventually owning/riding in an autonomous automobile. (Deloitte 2015 Global Mobile Consumer Survey.)
  • The number of connected devices world-wide will rise from 15 billion today to 50 billion by 2020. (Cisco/DHL Trend Report, April 2015)
  • By 2020, 90% of cars will be online, compared with just 2% in 2012. (Telefonica, Connected Car Report 2013)
  • Nearly half (48%) of consumers check their phones up to 25 times per day. (Deloitte 2015 Global Mobile Survey)
  • The US mobile worker population will increase from 96.2 million in 2015 to 105.4 million mobile workers in 2020. (IDC, “US Mobile Worker Forecast,2015-2020.)
  • Mobile workers will account for nearly three quarters (72.3%) of the total U.S. workforce. (IDC,” U.S. Mobile Worker Forecast, 2015-2020.)
  • 86% of those ages 18-29 have a smartphone. 83% of those ages 30-49 have a smartphone. 87% if these percentages are for those living in households earning $75,000 and up.

To keep pace with the design of complex, connected products requires engineers from different disciplines working closely together.  These engineers will be from different disciplines and will coordinate on design, simulation, prototyping and testing.  It also requires real-time input from co-workers outside engineering departments. For this reason, our schools and universities MUST alter their teaching methods to attract and train individuals capable of working to bring the U.S. population these marvelous advances in technology.  This not only means in the product development area but in manufacturing also.  Many companies see technology as a means to redefine what it means to be a manufacturer.  Thanks to the growing popularity of IoT in industrial and consumer products, design complexity shown no signs of slowing.

I cannot wait to see what the future holds.

THE WORLD’S BEST

October 3, 2015


Data for each university was taken from Wikipedia.  I checked information for each school relative to authenticity and found Wikipedia to be correct in every case.

USA Today recently published an article from the London-based “Times Higher Education World University Rankings”.  This organization was founded in 2004 for the sole purpose of evaluating universities across the world.  Evaluations are accomplished using the following areas of university life:

  • Teaching ability and qualification of individual teachers
  • International outlook
  • Reputation of university
  • Research initiatives
  • Student-staff ratios
  • Income from industries
  • Female-male ratios
  • Quality of student body
  • Citations

There were thirteen (13) performance criteria in the total evaluation.  The nine (9) above give an indication as to the depth of the investigation. Eight hundred (800) universities from seventy (70) countries were evaluated.  This year, there were only sixty-three (63) out of two hundred (200) schools that made the “best in the world” list. Let’s take a look at the top fifteen (15).  These are in order.

  1. California Institute of Technology–The California Institute of Technologyor Caltech is a private research university located in Pasadena, California, United States.   The school was founded as a preparatory and vocational institution by Amos G. Throop in 1891.  Even from the early years, the college attracted influential scientists such as George Ellery HaleArthur Amos Noyes, and Robert Andrews Millikan. The vocational and preparatory schools were disbanded and spun off in 1910, and the college assumed its present name in 1921. In 1934, Caltech was elected to the Association, and the antecedents of NASA‘s Jet Propulsion Laboratory, which Caltech continues to manage and operate, were established between 1936 and 1943 under Theodore von Kármán. The university is one among a small group of Institutes of Technology in the United States which tends to be primarily devoted to the instruction of technical arts and applied sciences.
  2. Oxford University–The University of Oxford(informally Oxford University or simply Oxford) is a collegiate research university located in Oxford, England. While having no known date of foundation, there is evidence of teaching as far back as 1096, making it the oldest university in the English-speaking world and the world’s second-oldest surviving university.  It grew rapidly from 1167 when Henry II banned English students from attending the University of Paris.  After disputes between students and Oxford townsfolk in 1209, some academics fled northeast to Cambridge where they established what became the University of Cambridge. The two “ancient universities” are frequently jointly referred to as “Oxbridge“.
  3. Stanford University–Stanford University(officially Leland Stanford Junior University) is a private research university in StanfordCalifornia.  It is definitely one of the world’s most prestigious institutions, with the top position in numerous rankings and measures in the United States. Stanford was founded in 1885 by Leland Stanford, former Governor and S. Senator from California.  Mr. Stanford was a railroad tycoon.  He and his wife, Jane Lathrop Stanford, started the school in memory of their only child, Leland Stanford, Jr., who had died of typhoid fever at age 15 the previous year. Stanford was opened on October 1, 1891 as a coeducational and non-denominational institution. Tuition was free until 1920. The university struggled financially after Leland Stanford’s 1893 death and after much of the campus was damaged by the 1906 San Francisco earthquake. Following World War II, Provost Frederick Terman supported faculty and graduates’ entrepreneurialism to build self-sufficient local industry in what would later be known as Silicon Valley. By 1970, Stanford was home to a linear accelerator, and was one of the original four ARPANET nodes (precursor to the Internet).
  4. Cambridge University–The University of Cambridge (abbreviated as Cantabin post-nominal letters, sometimes referred to as Cambridge University) is a collegiate public research university in Cambridge, England. Founded in 1209, Cambridge is the second-oldest university in the English-speaking world and the world’s fourth-oldest surviving university.   It grew out of an association of scholars who left the University of Oxford after a dispute with townsfolk. The two ancient universities share many common features and are often jointly referred to as “Oxbridge“.
  5. Massachusetts Institute of Technology–The Massachusetts Institute of Technology(MIT) is a private research university in Cambridge, Massachusetts. Founded in 1861 in response to the increasing industrialization of the United States, MIT adopted a European polytechnic  university model and stressed laboratory instruction in applied science and engineering. Researchers worked on computersradar, and inertial guidance during World War II and the Cold War. Post-war defense research contributed to the rapid expansion of the faculty and campus.  The current 168-acre campus opened in 1916 and now covers over one (1) mile along the northern bank of the Charles River basin.
  6. Harvard University–Harvard Universityis a private Ivy League research university in Cambridge, Massachusetts and was established in 1636. Its history, influence and wealth have made it one of the most prestigious universities in the world. Established originally by the Massachusetts legislature and soon thereafter named for John Harvard, its first benefactor.  Harvard is the  oldest institution of higher learning in the United States.  The Harvard Corporation (formally, the President and Fellows of Harvard College) is its first chartered corporation. Although never formally affiliated with any denomination, the early College primarily trained Congregation­alist and Unitarian Its curriculum and student body were gradually secularized during the 18th century, and by the 19th century Harvard had emerged as the central cultural establishment among Boston elites.  Following the American Civil War, President Charles W. Eliot‘s long tenure (1869–1909) transformed the college and affiliated professional schools into a modern research university; Harvard was a founding member of the Association of American Universities in 1900.   James Bryant Conant led the university through the Great Depression and World War II and began to reform the curriculum and liberalize admissions after the war. The undergraduate college became coeducational after its 1977 merger with Radcliffe College.
  7. Princeton University–Princeton Universityis a private Ivy League research university in Princeton, New Jersey.  It was founded in 1746 as the College of New Jersey. Princeton was the fourth chartered institution of higher education in the Thirteen Colonies and thus one of the nine Colleges established before the American Revolution. The institution moved to Newark in 1747, then to the current site nine years later, where it was renamed Princeton University in 1896.
  8. Imperial College of London— Imperial College Londonis a public research university, located in London, United Kingdom. The Imperial College of Science and Technology was founded in 1907, as a constituent college of the federal University of London, by merging the City and Guilds College, the Royal School of Mines and the Royal College of Science. The college grew through mergers including with St Mary’s Hospital Medical SchoolCharing Cross and Westminster Medical School, the Royal Postgraduate Medical School and the National Heart and Lung Institute to be known as The Imperial College of Science, Technology and Medicine. The college established the Imperial College Business School in 2005, thus covering subjects in science, engineering, medicine and business. Imperial College London became an independent university in 2007 during its centennial celebration.
  9. ETH Zurich— ETH Zürich(Swiss Federal Institute of Technology in Zurich, German:Eidgenössische Technische Hochschule Zürich) is an engineering, science, technology, mathematics and management university in the city of Zürich, Switzerland. Like its sister institution EPFL, it is an integral part of the Swiss Federal Institutes of Technology Domain (ETH Domain) that is directly subordinate to Switzerland’s Federal Department of Economic Affairs, Education and Research.
  10. University of Chicago— The University of Chicago(U of C, Chicago, or U Chicago) is a private research university in ChicagoIllinois. Established in 1890, the University of Chicago consists of The College, various graduate programs, interdisciplinary committees organized into four academic research divisions and seven professional schools. Beyond the arts and sciences, Chicago is also well known for its professional schools, which include the Pritzker  School of Medicine, the University of Chicago Booth School of Business, the Law School, the School of Social Service Administration, the Harris School of Public Policy Studies, the Graham School of Continuing Liberal and Professional Studies and the Divinity School. The university currently enrolls approximately 5,000 students in the College and around 15,000 students overall.
  11. Johns Hopkins— The Johns Hopkins University(commonly referred to as Johns Hopkins, JHU, or simply Hopkins) is a private research university in Baltimore, Maryland. Founded in 1876, the university was named after its first benefactor, the American entrepreneur, abolitionist, and philanthropist Johns Hopkins.   His $7 million bequest—of which half financed the establishment of The Johns Hopkins Hospital—was the largest philanthropic gift in the history of the United States at the time.   Daniel Coit Gilman, who was inaugurated as the institution’s first president on February 22, 1876,led the university to revolutionize higher education in the U.S. by integrating teaching and research.
  12. Yale University Yale Universityis a private Ivy League research university in New Haven, Connecticut. Founded in 1701 in Saybrook Colony as the Collegiate School, the University is the third-oldest institution of higher education in the United States. In 1718, the school was renamed Yale College in recognition of a gift from Elihu Yale, a governor of the British East India Company and in 1731 received a further gift of land and slaves from Bishop Berkeley.   Established to train Congregationalist ministers in theology and sacred languages, by 1777 the school’s curriculum began to incorporate humanities and sciences and in the 19th century gradually incorporated graduate and professional instruction, awarding the first D. in the United States in 1861 and organizing as a university in 1887.
  13. University of California Berkeley— The University of California, Berkeley(also referred to as Berkeley, UC Berkeley, California or simply Cal) is a public research university located in BerkeleyCalifornia. It is the flagship campus of the University of California system, one of three parts in the state’s public higher education plan, which also includes the California State University system and the California Community Colleges System.
  14. University College of London— University College London(UCL) is a public research university in London, England and a constituent college of the federal University of London. Recognized as one of the leading multidisciplinary research universities in the world, UCL is the largest higher education institution in London and the largest postgraduate institution in the UK by enrollment.  Founded in 1826 as London University, UCL was the first university institution established in London and the earliest in England to be entirely secular, to admit students regardless of their religion and to admit women on equal terms with men. The philosopher Jeremy Bentham is commonly regarded as the spiritual father of UCL, as his radical ideas on education and society were the inspiration to its founders, although his direct involvement in its foundation was limited. UCL became one of the two founding colleges of the University of London in 1836. It has grown through mergers, including with the Institute of Neurology (in 1997), the Eastman Dental Institute (in 1999), the School of Slavonic and East European Studies (in 1999), the School of Pharmacy (in 2012) and the Institute of Education (in 2014).
  15. Columbia University— Columbia University(officially Columbia University in the City of New York) is a private Ivy League research university in Upper ManhattanNew York City. Originally established in 1754 as King’s College by royal charter of George II of Great Britain, it is the oldest institution of higher learning in New York State, as well as one of the country’s nine colonial colleges.   After the revolutionary war, King’s College briefly became a state entity, and was renamed Columbia College in 1784. A 1787 charter placed the institution under a private board of trustees before it was further renamed Columbia University in 1896 when the campus was moved from Madison Avenue to its current location in Morningside Heights occupying land of 32 acres (13 ha). Columbia is one of the fourteen founding members of the Association of American Universities, and was the first school in the United States to grant the D. degree.

 

As you can see, individuals in leadership positions across the world consider formal education as being one the great assets to an individual, a country and our species in general.  Higher education can, but not always, drives us to discover, invent, and commercialize technology that advances our way of life and promotes health.  The entire university experience is remarkably beneficial to an individual’s understanding of the world and world events.

It is very safe to assume the faculty of each school is top-notch and attending students are serious over-achievers. (Then again, maybe not.)  I would invite your attention to the web site listing the two hundred schools considered—the top two hundred.  Maybe your school is on the list.  As always, I invite your comments.

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