The convergence of “smart” microphones, new digital signal processing technology, voice recognition and natural language processing has opened the door for voice interfaces.  Let’s first define a “smart device”.

A smart device is an electronic device, generally connected to other devices or networks via different wireless protocols such as Bluetooth, NFC, Wi-Fi, 3G, etc., that can operate to some extent interactively and autonomously.

I am told by my youngest granddaughter that all the cool kids now have in-home, voice-activated devices like Amazon Echo or Google Home. These devices can play your favorite music, answer questions, read books, control home automation, and all those other things people thought the future was about in the 1960s. For the most part, the speech recognition of the devices works well; although you may find yourself with an extra dollhouse or two occasionally. (I do wonder if they speak “southern” but that’s another question for another day.)

A smart speaker is, essentially, a speaker with added internet connectivity and “smart assistant” voice-control functionality. The smart assistant is typically Amazon Alexa or Google Assistant, both of which are independently managed by their parent companies and have been opened up for other third-parties to implement into their hardware. The idea is that the more people who bring these into their homes, the more Amazon and Google have a “space” in every abode where they’re always accessible.

Let me first state that my family does not, as yet, have a smart device but we may be inching in that direction.  If we look at numbers, we see the following projections:

  • 175 million smart devices will be installed in a majority of U.S. households by 2022 with at least seventy (70) million households having at least one smart speaker in their home. (Digital Voice Assistants Platforms, Revenues & Opportunities, 2017-2022. Juniper Research, November 2017.)
  • Amazon sold over eleven (11) million Alexa voice-controlled Amazon Echo devices in 2016. That number was expected to double for 2017. (Smart Home Devices Forecast, 2017 to 2022(US) Forester Research, October 2017.
  • Amazon Echo accounted for 70.6% of all voice-enabled speaker users in the United States in 2017, followed by Google Home at 23.8%. (eMarketer, April 2017)
  • In 2018, 38.5 million millennials are expected to use voice-enabled digital assistants—such as Amazon Alexa, Apple Siri, Google Now and Microsoft Cortana—at least once per month. (eMarketer, April 2017.)
  • The growing smart speaker market is expected to hit 56.3 million shipments, globally in 2018. (Canalys Research, January 2018)
  • The United States will remain the most important market for smart speakers in 2018, with shipments expected to reach 38.4 million units. China is a distant second at 4.4 million units. (Canalys Research, April 2018.)

With that being the case, let’s now look at what smart speakers are now commercialized and available either as online purchases or retail markets:

  • Amazon Echo Spot–$114.99
  • Sonos One–$199.00
  • Google Home–$129.00
  • Amazon Echo Show–$179.99
  • Google Home Max–$399.00
  • Google Home Mini–$49.00
  • Fabriq Choros–$69.99
  • Amazon Echo (Second Generation) –$$84.99
  • Harman Kardon Evoke–$199.00
  • Amazon Echo Plus–$149.00

CONCLUSIONS:  If you are interested in purchasing one from the list above, I would definitely recommend you do your homework.  Investigate the services provided by a smart speaker to make sure you are getting what you desire.  Be aware that there will certainly be additional items enter the marketplace as time goes by.  GOOD LUCK.

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THE NEXT COLD WAR

February 3, 2018


I’m old enough to remember the Cold War waged by the United States and Russia.  The term “Cold War” first appeared in a 1945 essay by the English writer George Orwell called “You and the Atomic Bomb”.

HOW DID THIS START:

During World War II, the United States and the Soviet Union fought together as allies against the Axis powers, Germany, Japan and Italy. However, the relationship between the two nations was a tense one. Americans had long been wary of Soviet communism and concerned about Russian leader Joseph Stalin’s tyrannical, blood-thirsty rule of his own country. For their part, the Soviets resented the Americans’ decades-long refusal to treat the USSR as a legitimate part of the international community as well as their delayed entry into World War II, which resulted in the deaths of tens of millions of Russians. After the war ended, these grievances ripened into an overwhelming sense of mutual distrust and enmity. Postwar Soviet expansionism in Eastern Europe fueled many Americans’ fears of a Russian plan to control the world. Meanwhile, the USSR came to resent what they perceived as American officials’ bellicose rhetoric, arms buildup and interventionist approach to international relations. In such a hostile atmosphere, no single party was entirely to blame for the Cold War; in fact, some historians believe it was inevitable.

American officials encouraged the development of atomic weapons like the ones that had ended World War II. Thus, began a deadly “arms race.” In 1949, the Soviets tested an atom bomb of their own. In response, President Truman announced that the United States would build an even more destructive atomic weapon: the hydrogen bomb, or “superbomb.” Stalin followed suit.

The ever-present threat of nuclear annihilation had a great impact on American domestic life as well. People built bomb shelters in their backyards. They practiced attack drills in schools and other public places. The 1950s and 1960s saw an epidemic of popular films that horrified moviegoers with depictions of nuclear devastation and mutant creatures. In these and other ways, the Cold War was a constant presence in Americans’ everyday lives.

SPACE AND THE COLD WAR:

Space exploration served as another dramatic arena for Cold War competition. On October 4, 1957, a Soviet R-7 intercontinental ballistic missile launched Sputnik (Russian for “traveler”), the world’s first artificial satellite and the first man-made object to be placed into the Earth’s orbit. Sputnik’s launch came as a surprise, and not a pleasant one, to most Americans. In the United States, space was seen as the next frontier, a logical extension of the grand American tradition of exploration, and it was crucial not to lose too much ground to the Soviets. In addition, this demonstration of the overwhelming power of the R-7 missile–seemingly capable of delivering a nuclear warhead into U.S. air space–made gathering intelligence about Soviet military activities particularly urgent.

In 1958, the U.S. launched its own satellite, Explorer I, designed by the U.S. Army under the direction of rocket scientist Wernher von Braun, and what came to be known as the Space Race was underway. That same year, President Dwight Eisenhower signed a public order creating the National Aeronautics and Space Administration (NASA), a federal agency dedicated to space exploration, as well as several programs seeking to exploit the military potential of space. Still, the Soviets were one step ahead, launching the first man into space in April 1961.

THE COLD WAR AND AI (ARTIFICIAL INTELLEGENCE):

Our country NEEDS to consider AI as an extension of the cold war.  Make no mistake about it, AI will definitely play into the hands of a few desperate dictators or individuals in future years.  A country that thinks its adversaries have or will get AI weapons will need them also to retaliate or deter foreign use against the US. Wide use of AI-powered cyberattacks may still be some time away. Countries might agree to a proposed Digital Geneva Convention to limit AI conflict. But that won’t stop AI attacks by independent nationalist groups, militias, criminal organizations, terrorists and others – and countries can back out of treaties. It’s almost certain, therefore, that someone will turn AI into a weapon – and that everyone else will do so too, even if only out of a desire to be prepared to defend themselves. With Russia embracing AI, other nations that don’t or those that restrict AI development risk becoming unable to compete – economically or militarily – with countries wielding developed AIs. Advanced AIs can create advantage for a nation’s businesses, not just its military, and those without AI may be severely disadvantaged. Perhaps most importantly, though, having sophisticated AIs in many countries could provide a deterrent against attacks, as happened with nuclear weapons during the Cold War.

The Congress of the United States and the Executive Branch need to “lose” the high school mentality and get back in the game.  They need to address the future instead of living in the past OR we the people need to vote them all out and start over.

 

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.


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.

DEGREE OR NO DEGREE

October 7, 2017


The availability of information in books (as always), on the Internet, through seminars and professional shows, scientific publications, pod-casts, Webinars, etc. is amazing in today’s “digital age”.  That begs the question—Is a college degree really necessary?   Can you rise to a level of competence and succeed by being self-taught?  For most, a college degree is the way to open doors. For a precious few, however, no help is needed.

Let’s look at twelve (12) individuals who did just that.

The co-founder of Apple and the force behind the iPod, iPhone, and iPad, Steve Jobs attended Reed College, an academically-rigorous liberal arts college with a heavy emphasis on social sciences and literature. Shortly after enrolling in 1972, however, he dropped out and took a job as a technician at Atari.

Legendary industrialist Howard Hughes is often said to have graduated from Cal Tech, but the truth is that the California school has no record of his having attended classes there. He did enroll at Rice University in Texas in 1924, but dropped out prematurely due the death of his father.

Arguably Harvard’s most famous dropout, Bill Gates was already an accomplished software programmer when he started as a freshman at the Massachusetts campus in 1973. His passion for software actually began before high school, at the Lakeside School in Seattle, Washington, where he was programming in BASIC by age 13.

Just like his fellow Microsoft co-founder Bill Gates, Paul Allen was a college dropout.

Like Gates, he was also a star student (a perfect score on the SAT) who honed his programming skills at the Lakeside School in Seattle. Unlike Gates, however, he went on to study at Washington State University before leaving in his second year to work as a programmer at Honeywell in Boston.

Even for his time, Thomas Edison had little formal education. His schooling didn’t start until age eight, and then only lasted a few months.

Edison said that he learned most of his reading, writing, and math at home from his mother. Still, he became known as one of America’s most prolific inventors, amassing 1,093 U.S. patents and changing the world with such devices as the phonograph, fluoroscope, stock ticker, motion picture camera, mechanical vote recorder, and long-lasting incandescent electric light bulb. He is also credited with patenting a system of electrical power distribution for homes, businesses, and factories.

Michael Dell, founder of Dell Computer Corp., seemed destined for a career in the computer industry long before he dropped out of the University of Texas. He purchased his first calculator at age seven, applied to take a high school equivalency exam at age eight, and performed his first computer teardown at age 15.

A pioneer of early television technology, Philo T. Farnsworth was a brilliant student who dropped out of Brigham Young University after the death of his father, according to Biography.com.

Although born in a log cabin, Farnsworth quickly grasped technical concepts, sketching out his revolutionary idea for a television vacuum tube while still in high school, much to the confusion of teachers and fellow students.

Credited with inventing the controls that made fixed-wing powered flight possible, the Wright Brothers had little formal education.

Neither attended college, but they gained technical knowledge from their experiences working with printing presses, bicycles, and motors. By doing so, they were able to develop a three-axis controller, which served as the means to steer and maintain the equilibrium of an aircraft.

Stanford Ovshinsky managed to amass 400 patents covering subjects ranging from nickel-metal hydride batteries to amorphous silicon semiconductors to hydrogen fuel cells, all without the benefit of a college education. He is best known for his formation of Energy Conversion Devices and his pioneering work in nickel-metal hydride batteries, which have been widely used in hybrid and electric cars, as well as laptop computers, digital cameras, and cell phones.

Preston Tucker, designer of the infamous 1948 Tucker sedan, worked as a machinist, police officer and car salesman, but was not known to have attended college. Still, he managed to become founder of the Tucker Aviation Corp. and the Tucker Corp.

Larry Ellison dropped out of his pre-med studies at the University of Illinois in his second year and left the University of Chicago after only one term, but his brief academic experiences eventually led him to the top of the computer industry.

A Harvard dropout, Mark Zuckerberg was considered a prodigy before he even set foot on campus.

He began doing BASIC programming in middle school, created an instant messaging system while in high school, and learned to read and write French, Hebrew, Latin, and ancient Greek prior to enrolling in college.

CONCLUSIONS:

In conclusions, I want to leave you with a quote from President Calvin Coolidge:

Nothing in this 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.

NATIONAL TELEPHONE DAY

April 25, 2017


OK, are you ready for a bit of ridiculous trivia?  Today, 25 April 2017, is National Telephone Day.  I do not think there will be any denial that the telephone has revolutionized communication the world over.

It was February 14, 1876, when Marcellus Bailey, one of Alexander Graham Bell’s attorneys rushed into the US Patent office in Boston to file for what would later be called the telephone. Later that same day, Elisha Gray filed a patent caveat for a similar device. A caveat is an intent to file for a patent. There is also a third contender, Antonio Meucci.  Mr. Meucci filed a caveat in November of 1871 for a talking telegraph but failed to renew the caveat due to hardships. Because Bell’s patent was submitted first, it was awarded to him on March 7, 1876. Gray contested this decision in court, but without success.

Born March 3, 1847, in Edinburgh, United Kingdom, Bell was an instructor at a boys’ boarding school. The sounds of speech were an integral part of his life. His father developed a “Visible Speech” system for deaf students to communicate. Bell would later become friend and benefactor of Helen Keller. Three days after his patent was approved, Bell spoke the first words by telephone to his assistant. “Mr. Watson, come here! I want to see you!”  By May of the same year, Bell and his team were ready for a public demonstration, and there would be no better place than the World’s Fair in Philadelphia. On May 10, 1876, in a crowded Machinery Hall a man’s voice was transmitted from a small horn and carried out through a speaker to the audience. One year later, the White House installed its first phone. The telephone revolution began. Bell Telephone Company was founded on July 9, 1877, and the first public telephone lines were installed from Boston to Sommerville, Massachusetts the same year.  By the end of the decade, there were nearly 50,000 phones in the United States.  In May of 1967, the 1 millionth telephone was installed.

Growing up in in the 50’s, I remember the rotary telephone shown by the digital picture below.  We were on a three-party line.  As I recall, ours was a two-ring phone call.  Of course, there was snooping.  Big time snooping by the other two families on our line.

Let’s take a quick look at how the cell phone has literally taken over this communication method.

  • The number of mobile devices rose nine (9) percent in the first six months of 2011, to 327.6 million — more than the 315 million people living in the U.S., Puerto Rico, Guam and the U.S. Virgin Islands. Wireless network data traffic rose 111 percent, to 341.2 billion megabytes, during the same period.
  • Nearly two-thirds of Americans are now smartphone owners, and for many these devices are a key entry point to the online world. Sixty-four percent( 64) ofAmerican adults now own a smartphone of some kind, up from thirty-five percent (35%) in the spring of 2011. Smartphone ownership is especially high among younger Americans, as well as those with relatively high income and education levels.
  • Ten percent (10%) of Americans own a smartphone but do not have any other form of high-speed internet access at home beyond their phone’s data plan.
  • Using a broader measure of the access options available to them, fifteen percent (15% of Americans own a smartphone but say that they have a limited number of ways to get online other than their cell phone.
  • Younger adults — Fifteen percent (15%) of Americans ages 18-29 are heavily dependent on a smartphone for online access.
  • Those with low household incomes and levels of educational attainment — Some thirteen percent (13%) of Americans with an annual household income of less than $30,000 per year are smartphone-dependent. Just one percent (1%) of Americans from households earning more than $75,000 per year rely on their smartphones to a similar degree for online access.
  • Non-whites — Twelve percent (12%) of African Americans and thirteen percent (13%) of Latinos are smartphone-dependent, compared with four percent (4%) of whites
  • Sixty-two percent (62%) of smartphone owners have used their phone in the past year to look up information about a health condition
  • Fifty-seven percent (57%) have used their phone to do online banking.
  • Forty-four percent (44%) have used their phone to look up real estate listings or other information about a place to live.
  • Forty-three percent (43%) to look up information about a job.
  • Forty percent (40%) to look up government services or information.
  • Thirty percent (30%) to take a class or get educational content
  • Eighteen percent (18%) to submit a job application.
  • Sixty-eight percent (68%) of smartphone owners use their phone at least occasionally to follow along with breaking news events, with thirty-three percent (33%) saying that they do this “frequently.”
  • Sixty-seven percent (67%) use their phone to share pictures, videos, or commentary about events happening in their community, with 35% doing so frequently.
  • Fifty-six percent (56%) use their phone at least occasionally to learn about community events or activities, with eighteen percent (18%) doing this “frequently.”

OK, by now you get the picture.  The graphic below will basically summarize the cell phone phenomenon relative to other digital devices including desktop and laptop computers. By the way, laptop and desktop computer purchases have somewhat declined due to the increased usage of cell phones for communication purposes.

The number of smart phone users in the United States from 2012 to a projected 2021 in millions is given below.

CONCLUSION: “Big Al” (Mr. Bell that is.) probably knew he was on to something.  At any rate, the trend will continue towards infinity over the next few decades.

 

RISE OF THE MACHINES

March 20, 2017


Movie making today is truly remarkable.  To me, one of the very best parts is animation created by computer graphics.  I’ve attended “B” movies just to see the graphic displays created by talented programmers.  The “Terminator” series, at least the first movie in that series, really captures the creative essence of graphic design technology.  I won’t replay the movie for you but, the “terminator” goes back in time to carry out its prime directive—Kill John Conner.  The terminator, a robotic humanoid, has decision-making capability as well as human-like mobility that allows the plot to unfold.  Artificial intelligence or AI is a fascinating technology many companies are working on today.  Let’s get a proper definition of AI as follows:

“the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.”

Question:  Are Siri, Cortana, and Alexa eventually going to be more literate than humans? Anyone excited about the recent advancements in artificial intelligence (AI) and machine learning should also be concerned about human literacy as well. That’s according to Protect Literacy , a global campaign, backed by education company Pearson, aimed at creating awareness and fighting against illiteracy.

Project Literacy, which has been raising awareness for its cause at SXSW 2017, recently released a report, “ 2027: Human vs. Machine Literacy ,” that projects machines powered by AI and voice recognition will surpass the literacy levels of one in seven American adults in the next ten (10) years. “While these systems currently have a much shallower understanding of language than people do, they can already perform tasks similar to simple text search task…exceeding the abilities of millions of people who are nonliterate,” Kate James, Project Literacy spokesperson and Chief Corporate Affairs and Global Marketing Officer at Pearson, wrote in the report. In light of this the organization is calling for “society to commit to upgrading its people at the same rate as upgrading its technology, so that by 2030 no child is born at risk of poor literacy.”  (I would invite you to re-read this statement and shudder in your boots as I did.)

While the past twenty-five (25) years have seen disappointing progress in U.S. literacy, there have been huge gains in linguistic performance by a totally different type of actor – computers. Dramatic advances in natural language processing (Hirschberg and Manning, 2015) have led to the rise of language technologies like search engines and machine translation that “read” text and produce answers or translations that are useful for people. While these systems currently have a much shallower understanding of language than people do, they can already perform tasks similar to the simple text search task above – exceeding the abilities of millions of people who are nonliterate.

According to the National National Centre for Education Statistics machine literacy has already exceeded the literacy abilities of the estimated three percent (3%) of non-literate adults in the US.

Comparing demographic data from the Global Developer Population and Demographic Study 2016 v2 and the 2015 Digest of Education Statistics finds there are more software engineers in the U.S. than school teachers, “We are focusing so much on teaching algorithms and AI to be better at language that we are forgetting that fifty percent (50%)  of adults cannot read a book written at an eighth grade level,” Project Literacy said in a statement.  I retired from General Electric Appliances.   Each engineer was required to write, or at least the first draft, of the Use and Care Manuals for specific cooking products.  We were instructed to 1.) Use plenty of graphic examples and 2.) Write for a fifth-grade audience.  Even with that, we know from experience that many consumers never use and have no intention of reading their Use and Care Manual.  With this being the case, many of the truly cool features are never used.  They may as well buy the most basic product.

Research done by Business Insider reveals that thirty-two (32) million Americans cannot currently read a road sign. Yet at the same time there are ten (10) million self-driving cars predicted to be on the roads by 2020. (One could argue this will further eliminate the need for literacy, but that is debatable.)  If we look at literacy rates for the top ten (10) countries on our planet we see the following:

Citing research from Venture Scanner , Project Literacy found that in 2015 investment in AI technologies, including natural language processing, speech recognition, and image recognition, reached $47.2 billion. Meanwhile, data on US government spending shows that the 2017 U.S. Federal Education Budget for schools (pre-primary through secondary school) is $40.4 billion.  I’m not too sure funding for education always goes to benefit students education. In other words, throwing more money at this problem may not always provide desired results, but there is no doubt, funding for AI will only increase.

“Human literacy levels have stalled since 2000. At any time, this would be a cause for concern, when one in ten people worldwide…still cannot read a road sign, a voting form, or a medicine label,” James wrote in the report. “In popular discussion about advances in artificial intelligence, it is easy

CONCLUSION:  AI will only continue to advance and there will come a time when robotic systems will be programmed with basic decision-making skills.  To me, this is not only fascinating but more than a little scary.

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