GLOBAL IT SALARY SURVEY

Several months ago, before the COVID-19 virus hit, I was talking to a young man interested in the STEM (Science, Technology, Engineering, Mathematics) professions.  He was a graduating high school senior from Nashville and wondered what might be the future of technology as a profession. He asked me about IT and what I thought that course of work might provide in the way of a challenge, salary, stability, etc.  In other words, all of those qualities we look for in a job.  At that time, I did not have the information you are about to see regarding salary.  My advice to him was go for the challenge and the money will follow.  As it turns out, the bucks are there also.  I’m going to make this a two-part post because the 2020 IT Skills and Salary Survey is very detailed.  The first part involves salary.  The second part discusses job satisfaction, certifications, and rate of advancement. Let’s now take a look.

The 2020 IT Skills and Salary Survey was conducted online from September 2019 through November 2019 using the Qualtrics XM Platform. Global Knowledge and technology companies distributed survey invitations to millions of professionals around the world, primarily in their databases. The survey was made available in web articles, online newsletters and social media. The survey yielded 9,505 completed responses. The survey was tabulated using the Qualtrics XM Platform.

An individual’s salary is determined by a combination of factors, including education, responsibility level, job role, certification, tenure, industry, company size and location. In the 2020 IT Skills and Salary Survey, the respondents were about these factors. Respondents came from all over the globe, consequently the results reflect their answers.  Participants were required to convert their salaries into U.S. dollars while taking the survey to enable relevant comparisons.

BASE SALARY:

The average annual salary for global IT professionals is $85,115.

RAISES AND BONUSES: Nearly sixty percent (60%) of IT professionals received a raise in the past year. Raise percentages were consistent across all regions—four to six percent. The only outlier is decision-makers in Latin America whose salaries, on average, dropped two percent year over year. Thirty-seven percent (37%) of IT professionals who received a raise attribute it to job performance, while nearly thirty percent (30%) received a pay bump as part of a standard company increase. Fifteen percent (15%) saw their salary increase as part of a promotion. The reason for a raise impacts the amount of the raise. Twelve percent (12%) of individuals who received a raise attribute it to new skills—those same respondents earned nearly $12,000 more this year. IT professionals who obtained a new certification saw their salary increase nearly $13,000. Both of these numbers are strong indications that training pays off. Certifications are very important in the IT industry and form the basis for promotions.

Factors for increases salary are fairly standard and as you would expect with performance being the very first.

RECEIVED A BONUS:

Worldwide, sixty-six percent (66%) of eligible decision-makers and fifty-five percent (55%) of staff earned a bonus this year—both are up noticeably from 2019. In North America, seventy percent (70%) of decision-makers and fifty-seven percent (57%) of staff received bonuses. EMEA had the lowest numbers in this category, as only half of the eligible staff received a bonus.

SALARY BY RESPONSIBILITY LEVEL:

Even though the more senior IT personnel received the largest salaries, take a look at non-management “average” salary for North America.  A whopping $105,736.  That number goes down for other regions of the globe but still a big number.

CAREER EXPERIENCE:

Unsurprisingly, more tenured IT professionals have the highest salaries. Those with twenty-six (26) or more years of experience earn $120,098 a year—that’s more than double the average salary for first-year IT professionals. In North America, IT professionals cross the $100,000 threshold in years eleven to fifteen (11 to 15). The most tenured earn almost $140,000 a year.  Among our respondents, the highest percentage has between six and fifteen (15) years of experience. Only one percent are in their first year, while thirteen percent (13%) have worked in tech for over a quarter of a century.

JOB FUNCTION:

SALARIES BY INDUSTRY:

U.S. SALARIES BY STATE:

IT professionals in the U.S. have an average annual salary of $120,491. U.S. decision-makers earn $138,200 a year. While tenure, job function and industry affect pay, geography is also a major salary influencer. The cost of living in the Mid-Atlantic or New England, for example, is higher than the Midwest. Washington, D.C. has the highest IT salaries in the country at $151,896—a nineteen percent (19%) increase from 2019. New Jersey, California, Maryland and New York round out the top five U.S. salaries by state. California had the highest U.S. salaries in 2019. South Dakota has the lowest average salary ($61,867) this year.

CANADIAN SALARIES:

The average annual salary in Canada is $77,580. IT professionals in British Columbia have the highest average salaries in the country at $85,801. The other top-paying Canadian provinces by salary are Quebec ($81,573), Ontario ($78,887), Alberta ($78,040) and Saskatchewan ($70,811). Provinces with fewer than 10 respondents were omitted from the list.

EUROPEAN SALARIES:

For the second straight year, Switzerland dominates European salaries with an annual average of $133,344. The average annual IT salary in Europe is $71,796. Germany has the second-highest salary at $88,195. Rounding out the top five is Ireland ($87,154), Belgium ($85,899) and the United Kingdom ($82,792). European countries with fewer than 30 respondents were omitted from the list.

CONCLUSIONS:

The next post will be tomorrow and will be Certifications and Job Satisfaction.

ARTIFICIAL INTELLIGENCE

Just what do we know about Artificial Intelligence or AI?  Portions of this post were taken from Forbes Magazine.

John McCarthy first coined the term artificial intelligence in 1956 when he invited a group of researchers from a variety of disciplines including language simulation, neuron nets, complexity theory and more to a summer workshop called the Dartmouth Summer Research Project on Artificial Intelligence to discuss what would ultimately become the field of AI. At that time, the researchers came together to clarify and develop the concepts around “thinking machines” which up to this point had been quite divergent. McCarthy is said to have picked the name artificial intelligence for its neutrality; to avoid highlighting one of the tracks being pursued at the time for the field of “thinking machines” that included cybernetics, automation theory and complex information processing. The proposal for the conference said, “The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

Today, modern dictionary definitions focus on AI being a sub-field of computer science and how machines can imitate human intelligence (being human-like rather than becoming human). The English Oxford Living Dictionary gives this definition: “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.”

Merriam-Webster defines artificial intelligence this way:

1. A branch of computer science dealing with the simulation of intelligent behavior in computers.
2. The capability of a machine to imitate intelligent human behavior.

About thirty (30) year ago, a professor at the Harvard Business School (Dr. Shoshana Zuboff) articulated three laws based on research into the consequences that widespread computing would have on society. Dr. Zuboff had degrees in philosophy and social psychology so she was definitely ahead of her time relative to the unknown field of AI.  Her document “In the Age of the Smart Machine: The Future of Work and Power”, she postulated the following three laws:

• Everything that can be automated will be automated
• Everything that can be informated will be informated. (NOTE: Informated was coined by Zuboff to describe the process of turning descriptions and measurements of activities, events and objects into information.)
• In the absence of countervailing restrictions and sanctions, every digital application that can be sued for surveillance and control will be used for surveillance and control, irrespective of its originating intention.

At that time there was definitely a significant lack of computing power.  That ship has sailed and is no longer a great hinderance to AI advancement that it certainly once was.

 

WHERE ARE WE?

In recent speech, Russian president Vladimir Putin made an incredibly prescient statement: “Artificial intelligence is the future, not only for Russia, but for all of humankind.” He went on to highlight both the risks and rewards of AI and concluded by declaring that whatever country comes to dominate this technology will be the “ruler of the world.”

As someone who closely monitors global events and studies emerging technologies, I think Putin’s lofty rhetoric is entirely appropriate. Funding for global AI startups has grown at a sixty percent (60%) compound annual growth rate since 2010. More significantly, the international community is actively discussing the influence AI will exert over both global cooperation and national strength. In fact, the United Arab Emirates just recently appointed its first state minister responsible for AI.

Automation and digitalization have already had a radical effect on international systems and structures. And considering that this technology is still in its infancy, every new development will only deepen the effects. The question is: Which countries will lead the way, and which ones will follow behind?

If we look at criteria necessary for advancement, there are the seven countries in the best position to rule the world with the help of AI.  These countries are as follows:

• Russia
• The United States of America
• China
• Japan
• Estonia
• Israel
• Canada

The United States and China are currently in the best position to reap the rewards of AI. These countries have the infrastructure, innovations and initiative necessary to evolve AI into something with broadly shared benefits. In fact, China expects to dominate AI globally by 2030. The United States could still maintain its lead if it makes AI a top priority and charges necessary investments while also pulling together all required government and private sector resources.

Ultimately, however, winning and losing will not be determined by which country gains the most growth through AI. It will be determined by how the entire global community chooses to leverage AI — as a tool of war or as a tool of progress.

Ideally, the country that uses AI to rule the world will do it through leadership and cooperation rather than automated domination.

CONCLUSIONS:  We dare not neglect this disruptive technology.  We cannot afford to lose this battle.

COMPUTER SIMULATION

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.

 

 

OKAY, TELL ME THE DIFFERENCE

Portions of this post are taken from the January 2018 article written by John Lewis of “Vision Systems”.

I feel there is considerable confusion between Artificial Intelligence (AI), Machine Learning and Deep Learning.  Seemingly, we use these terms and phrases interchangeably and they certainly have different meanings.  Natural Learning is the intelligence displayed by humans and certain animals. Why don’t we do the numbers:

AI:

Artificial Intelligence refers to machines mimicking human cognitive functions such as problem solving or learning.  When a machine understands human speech or can compete with humans in a game of chess, AI applies.  There are several surprising opinions about AI as follows:

• Sixty-one percent (61%) of people see artificial intelligence making the world a better place
• Fifty-seven percent (57%) would prefer an AI doctor perform an eye exam
• Fifty-five percent (55%) would trust an autonomous car. (I’m really not there as yet.)

The term artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage.

Early AI research in the 1950s explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and began training computers to mimic basic human reasoning. For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Siri, Alexa or Cortana were household names. This early work paved the way for the automation and formal reasoning that we see in computers today, including decision support systems and smart search systems that can be designed to complement and augment human abilities.

While Hollywood movies and science fiction novels depict AI as human-like robots that take over the world, the current evolution of AI technologies isn’t that scary – or quite that smart. Instead, AI has evolved to provide many specific benefits in every industry.

MACHINE LEARNING:

Machine Learning is the current state-of-the-art application of AI and largely responsible for its recent rapid growth. Based upon the idea of giving machines access to data so that they can learn for themselves, machine learning has been enabled by the internet, and the associated rise in digital information being generated, stored and made available for analysis.

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level understanding. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

DEEP LEARNING:

Deep Learning concentrates on a subset of machine-learning techniques, with the term “deep” generally referring to the number of hidden layers in the deep neural network.  While conventional neural network may contain a few hidden layers, a deep network may have tens or hundreds of layers.  In deep learning, a computer model learns to perform classification tasks directly from text, sound or image data. In the case of images, deep learning requires substantial computing power and involves feeding large amounts of labeled data through a multi-layer neural network architecture to create a model that can classify the objects contained within the image.

CONCLUSIONS:

Brave new world we are living in.  Someone said that AI is definitely the future of computing power and eventually robotic systems that could possibly replace humans.  I just hope the programmers adhere to Dr. Isaac Asimov’s three laws:

 

• The First Law of Robotics: A robot may not injure a human being or, through inaction, allow a human being to come to harm.

 

• The Second Law of Robotics: A robot must obey the orders given to it by human beings, except where such orders would conflict with the First Law.

 

• The Third Law of Robotics: A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

With those words, science-fiction author Isaac Asimov changed how the world saw robots. Where they had largely been Frankenstein-esque, metal monsters in the pulp magazines, Asimov saw the potential for robotics as more domestic: as a labor-saving device; the ultimate worker. In doing so, he continued a literary tradition of speculative tales: What happens when humanity remakes itself in its image?

As always, I welcome your comments.

THE NEXT COLD WAR

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.

 

DARK NET

Most of the individuals who read my posting are very well-informed and know that Tim Berners-Lee “invented” the internet.  In my opinion, the Internet is a resounding technological improvement in communication.  It has been a game-changer in the truest since of the word.  I think there are legitimate uses which save tremendous time.  There are also illegitimate uses as we shall see.

A JPEG of Mr. Berners-Lee is shown below:

BIOGRAPHY:

In 1989, while working at CERN, the European Particle Physics Laboratory in Geneva, Switzerland, Tim Berners-Lee proposed a global hypertext project, to be known as the World Wide Web. Based on the earlier “Enquire” work, his efforts were designed to allow people to work together by combining their knowledge in a web of hypertext documents.  Sir Tim wrote the first World Wide Web server, “httpd“, and the first client, “WorldWideWeb” a what-you-see-is-what-you-get hypertext browser/editor which ran in the NeXTStep environment. This work began in October 1990.k   The program “WorldWideWeb” was first made available within CERN in December, and on the Internet at large in the summer of 1991.

Through 1991 and 1993, Tim continued working on the design of the Web, coordinating feedback from users across the Internet. His initial specifications of URIs, HTTP and HTML were refined and discussed in larger circles as the Web technology spread.

Tim Berners-Lee graduated from the Queen’s College at Oxford University, England, in 1976. While there he built his first computer with a soldering iron, TTL gates, an M6800 processor and an old television.

He spent two years with Plessey Telecommunications Ltd (Poole, Dorset, UK) a major UK Telecom equipment manufacturer, working on distributed transaction systems, message relays, and bar code technology.

In 1978 Tim left Plessey to join D.G Nash Ltd (Ferndown, Dorset, UK), where he wrote, among other things, typesetting software for intelligent printers and a multitasking operating system.

His year and one-half spent as an independent consultant included a six-month stint (Jun-Dec 1980) as consultant software engineer at CERN. While there, he wrote for his own private use his first program for storing information including using random associations. Named “Enquire” and never published, this program formed the conceptual basis for the future development of the World Wide Web.

From 1981 until 1984, Tim worked at John Poole’s Image Computer Systems Ltd, with technical design responsibility. Work here included real time control firmware, graphics and communications software, and a generic macro language. In 1984, he took up a fellowship at CERN, to work on distributed real-time systems for scientific data acquisition and system control. Among other things, he worked on FASTBUS system software and designed a heterogeneous remote procedure call system.

In 1994, Tim founded the World Wide Web Consortium at the Laboratory for Computer Science (LCS). This lab later merged with the Artificial Intelligence Lab in 2003 to become the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT). Since that time he has served as the Director of the World Wide Web Consortium, a Web standards organization which develops interoperable technologies (specifications, guidelines, software, and tools) to lead the Web to its full potential. The Consortium has host sites located at MIT, at ERCIM in Europe, and at Keio University in Japan as well as offices around the world.

In 1999, he became the first holder of 3Com Founders chair at MIT. In 2008 he was named 3COM Founders Professor of Engineering in the School of Engineering, with a joint appointment in the Department of Electrical Engineering and Computer Science at CSAIL where he also heads the Decentralized Information Group (DIG). In December 2004 he was also named a Professor in the Computer Science Department at the University of Southampton, UK. From 2006 to 2011 he was co-Director of the Web Science Trust, launched as the Web Science Research Initiative, to help create the first multidisciplinary research body to examine the Web.

In 2008 he founded and became Director of the World Wide Web Foundation.  The Web Foundation is a non-profit organization devoted to achieving a world in which all people can use the Web to communicate, collaborate and innovate freely.  The Web Foundation works to fund and coordinate efforts to defend the Open Web and further its potential to benefit humanity.

In June 2009 then Prime Minister Gordon Brown announced that he would work with the UK Government to help make data more open and accessible on the Web, building on the work of the Power of Information Task Force. Sir Tim was a member of The Public Sector Transparency Board tasked to drive forward the UK Government’s transparency agenda.  He has promoted open government data globally, is a member of the UK’s Transparency Board.

In 2011 he was named to the Board of Trustees of the Ford Foundation, a globally oriented private foundation with the mission of advancing human welfare. He is President of the UK’s Open Data Institute which was formed in 2012 to catalyze open data for economic, environmental, and social value.

He is the author, with Mark Fischetti, of the book “Weaving the Web” on the past, present and future of the Web.

On March 18 2013, Sir Tim, along with Vinton Cerf, Robert Kahn, Louis Pouzin and Marc Andreesen, was awarded the Queen Elizabeth Prize for Engineering for “ground-breaking innovation in engineering that has been of global benefit to humanity.”

It should be very obvious from this rather short biography that Sir Tim is definitely a “heavy hitter”.

DARK WEB:

I honestly don’t think Sir Tim realized the full gravity of his work and certainly never dreamed there might develop a “dark web”.

The Dark Web is the public World Wide Web content existing on dark nets or networks which overlay the public Internet.  These networks require specific software, configurations or authorization to access. They are NOT open forums as we know the web to be at this time.  The dark web forms part of the Deep Web which is not indexed by search engines such as GOOGLE, BING, Yahoo, Ask.com, AOL, Blekko.com,  Wolframalpha, DuckDuckGo, Waybackmachine, or ChaCha.com.  The dark nets which constitute the Dark Web include small, friend-to-friend peer-to-peer networks, as well as large, popular networks like Freenet, I2P, and Tor, operated by public organizations and individuals. Users of the Dark Web refer to the regular web as the Clearnet due to its unencrypted nature.

A December 2014 study by Gareth Owen from the University of Portsmouth found the most commonly requested type of content on Tor was child pornography, followed by black markets, while the individual sites with the highest traffic were dedicated to botnet operations.  Botnet is defined as follows:

“a network of computers created by malware andcontrolled remotely, without the knowledge of the users of those computers: The botnet was usedprimarily to send spam emails.”

Hackers built the botnet to carry out DDoS attacks.

Many whistle-blowing sites maintain a presence as well as political discussion forums.  Cloned websites and other scam sites are numerous.   Many hackers sell their services individually or as a part of groups. There are reports of crowd-funded assassinations and hit men for hire.   Sites associated with Bitcoin, fraud related services and mail order services are some of the most prolific.

Commercial dark net markets, which mediate transactions for illegal drugs and other goods, attracted significant media coverage starting with the popularity of Silk Road and its subsequent seizure by legal authorities. Other markets sells software exploits and weapons.  A very brief look at the table below will indicate activity commonly found on the dark net.

As you can see, the uses for the dark net are quite lovely, lovely indeed.  As with any great development such as the Internet, nefarious uses can and do present themselves.  I would stay away from the dark net.  Just don’t go there.  Hope you enjoy this one and please send me your comments.

OPEN SOURCE SOFTWARE AND WHY YOU SHOULD (MUST) AVOID AT ALL COSTS

OKAY first, let us define “OPEN SOURCE SOFTWARE” as follows:

Open-source software (OSS) is computer software with its source-code made available with a license in which the copyright holder provides the rights to study, change, and distribute the software to anyone and for any purpose. Open-source software may be developed in a collaborative public manner. The benefits include:

• COST—Generally, open source software if free.
• FLEXIBILITY—Computer specialists can alter the software to fit their needs for the program(s) they are writing code for.
• FREEDOM—Generally, no issues with patents or copyrights.
• SECURITY—The one issue with security is using open source software and embedded code due to compatibility issues.
• ACCOUNTABILITY—Once again, there are no issues with accountability and producers of the code are known.

A very detailed article written by Jacob Beningo has seven (7) excellent points for avoiding, like the plague, open source software.  Given below are his arguments.

REASON 1—LACKS TRACEABLE SOFTWARE DEVELOPMENT LIFE CYCLE–Open source software usually starts with an ingenious developer working out their garage or basement hoping to create code that is very functional and useful. Eventually multiple developers with spare time on their hands get involved. The software evolves but it doesn’t really follow a traceable design cycle or even follow best practices. These various developers implement what they want or push the code in the direction that meets their needs. The result is software that works in limited situations and circumstances and users need to cross their fingers and pray that their needs and conditions match them.

REASON 2—DESIGNED FOR FUNCTIONALITY AND NOT ROBUSTNESS–Open source software is often written for functionality only. Accessed and written to an SD card for communication over USB connections. The issue here is that while it functions the code, it generally is not robust and is never designed to anticipate issues.  This is rarely the case and while the software is free, very quickly developers can find that their open source software is just functional and can’t stand up to real-world pressures. Developers will find themselves having to dig through unknown terrain trying to figure out how best to improve or handle errors that weren’t expected by the original developers.

REASON 3—ACCIDENTIALLY EXPOSING CONFIDENTIAL INTELLECTURAL PROPERTY–There are several different licensing schemes that open source software developers use. Some really do give away the farm; however, there are also licenses that require any modifications or even associated software to be released as open source. If close attention is not being paid, a developer could find themselves having to release confidential code and algorithms to the world. Free software just cost the company in revealing the code or if they want to be protected, they now need to spend money on attorney fees to make sure that they aren’t giving it all away by using “free” software.

REASON 4—LACKING AUTOMATED AND/OR MANUAL TESTING–A formalized testing process, especially automated tests are critical to ensuring that a code base is robust and has sufficient quality to meet its needs. I’ve seen open source Python projects that include automated testing which is encouraging but for low level firmware and embedded systems we seem to still lag behind the rest of the software industry. Without automated tests, we have no way to know if integrating that open source component broke something in it that we won’t notice until we go to production.

REASON 5—POOR DOCUMENTATION OR DOCUMENTATION THAT IS LACKING COMPLETELY–Documentation has been getting better among open source projects that have been around for a long time or that have strong commercial backing. Smaller projects though that are driven by individuals tend to have little to no documentation. If the open source code doesn’t have documentation, putting it into practice or debugging it is going to be a nightmare and more expensive than just getting commercial or industrial-grade software.

REASON 6—REAL-TIME SUPPORT IS LACKING–There are few things more frustrating than doing everything you can to get something to work or debugged and you just hit the wall. When this happens, the best way to resolve the issue is to get support. The problem with open source is that there is no guarantee that you will get the support you need in a timely manner to resolve any issues. Sure, there are forums and social media to request help but those are manned by people giving up their free time to help solve problems. If they don’t have the time to dig into a problem, or the problem isn’t interesting or is too complex, then the developer is on their own.

REASON 7—INTEGRATION IS NEVER AS EASY AS IT SEEMS–The website was found; the demonstration video was awesome. This is the component to use. Look at how easy it is! The source is downloaded and the integration begins. Months later, integration is still going on. What appeared easy quickly turned complex because the same platform or toolchain wasn’t being used. “Minor” modifications had to be made. The rabbit hole just keeps getting deeper but after this much time has been sunk into the integration, it cannot be for naught.

CONCLUSIONS:

I personally am by no means completely against open source software. It’s been extremely helpful and beneficial in certain circumstances. I have used open source, namely JAVA, as embedded software for several programs I have written.   It’s important though not to just use software because it’s free.  Developers need to recognize their requirements, needs, and level of robustness that required for their product and appropriately develop or source software that meets those needs rather than blindly selecting software because it’s “free.”  IN OTHER WORDS—BE CAREFUL!

ARTIFICIAL INTELLIGENCE & ELON MUSK

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.

AUGMENTED REALITY (AR)

Depending on the location, you can ask just about anybody to give a definition of Virtual Reality (VR) and they will take a stab at it. This is because gaming and the entertainment segments of our population have used VR as a new tool to promote games such as SuperHot VR, Rock Band VR, House of the Dying Sun, Minecraft VR, Robo Recall, and others.  If you ask them about Augmented Reality or AR they probably will give you the definition of VR or nothing at all.

Augmented reality, sometimes called Mixed Reality, is a technology that merges real-world objects or the environment with virtual elements generated by sensory input devices for sound, video, graphics, or GPS data.  Unlike VR, which completely replaces the real world with a virtual world, AR operates in real time and is interactive with objects found in the environment, providing an overlaid virtual display over the real one.

While popularized by gaming, AR technology has shown a prowess for bringing an interactive digital world into a person’s perceived real world, where the digital aspect can reveal more information about a real-world object that is seen in reality.  This is basically what AR strives to do.  We are going to take a look at several very real applications of AR to indicate the possibilities of this technology.

• Augmented Reality has found a home in healthcare aiding preventative measures for professionals to receive information relative to the status of patients. Healthcare giant Cigna recently launched a program called BioBall that uses Microsoft HoloLense technology in an interactive game to test for blood pressure and body mass index or BMI. Patients hold a light, medium-sized ball in their hands in a one-minute race to capture all the images that flash on the screen in front of them. The Bio Ball senses a player’s heartbeat. At the University of Maryland’s Augmentarium virtual and augmented reality laboratory, the school is using AR I healthcare to improve how ultrasound is administered to a patient.  Physicians wearing an AR device can look at both a patient and the ultrasound device while images flash on the “hood” of the AR device itself.
• AR is opening up new methods to teach young children a variety of subjects they might not be interested in learning or, in some cases, help those who have trouble in class catching up with their peers. The University of Helsinki’s AR program helps struggling kids learn science by enabling them to virtually interact with the molecule movement in gases, gravity, sound waves, and airplane wind physics.   AR creates new types of learning possibilities by transporting “old knowledge” into a new format.
• Projection-based AR is emerging as a new way to case virtual elements in the real world without the use of bulky headgear or glasses. That is why AR is becoming a very popular alternative for use in the office or during meetings. Startups such as Lampix and Lightform are working on projection-based augmented reality for use in the boardroom, retail displays, hospitality rooms, digital signage, and other applications.
• In Germany, a company called FleetBoard is in the development phase for application software that tracks logistics for truck drivers to help with the long series of pre-departure checks before setting off cross-country or for local deliveries. The Fleet Board Vehicle Lense app uses a smartphone and software to provide live image recognition to identify the truck’s number plate.  The relevant information is super-imposed in AR, thus speeding up the pre-departure process.
• Last winter, Delft University of Technology in the Netherlands started working with first responders in using AR as a tool in crime scene investigation. The handheld AR system allows on-scene investigators and remote forensic teams to minimize the potential for site contamination.  This could be extremely helpful in finding traces of DNA, preserving evidence, and getting medical help from an outside source.
• Sandia National Laboratories is working with AR as a tool to improve security training for users who are protecting vulnerable areas such as nuclear weapons or nuclear materials. The physical security training helps guide users through real-world examples such as theft or sabotage in order to be better prepared when an event takes place.  The training can be accomplished remotely and cheaply using standalone AR headsets.
• In Finland, the VTT Technical Research Center recently developed an AR tool for the European Space Agency (ESA) for astronauts to perform real-time equipment monitoring in space. AR prepares astronauts with in-depth practice by coordinating the activities with experts in a mixed-reality situation.
• The U.S. Daqri International uses computer vision for industrial AR to enable data visualization while working on machinery or in a warehouse. These glasses and headsets from Daqri display project data, tasks that need to be completed and potential problems with machinery or even where an object needs to be placed or repaired.

CONCLUSIONS:

Augmented Reality merges real-world objects with virtual elements generated by sensory input devices to provide great advantages to the user.  No longer is gaming and entertainment the sole objective of its use.  This brings to life a “new normal” for professionals seeking more and better technology to provide solutions to real-world problems.

ARTIFICIAL INTELLIGENCE (AI)

Portions of the following post were taken from an article by Rob Spiegel publishing through Design News Daily.

Two former Apple design engineers – Anna Katrina Shedletsky and Samuel Weiss have leveraged machine learning to help brand owners improve their manufacturing lines. The company, Instrumental , uses artificial intelligence (AI) to identify and fix problems with the goal of helping clients ship on time. The AI system consists of camera-equipped inspection stations that allow brand owners to remotely manage product lines at their contact manufacturing facilities with the purpose of maximizing up-time, quality and speed. Their digital photo is shown as follows:

Shedletsky and Weiss took what they learned from years of working with Apple contract manufacturers and put it into AI software.

“The experience with Apple opened our eyes to what was possible. We wanted to build artificial intelligence for manufacturing. The technology had been proven in other industries and could be applied to the manufacturing industry,   it’s part of the evolution of what is happening in manufacturing. The product we offer today solves a very specific need, but it also works toward overall intelligence in manufacturing.”

Shedletsky spent six (6) years working at Apple prior to founding Instrumental with fellow Apple alum, Weiss, who serves Instrumental’s CTO (Chief Technical Officer).  The two took their experience in solving manufacturing problems and created the AI fix. “After spending hundreds of days at manufacturers responsible for millions of Apple products, we gained a deep understanding of the inefficiencies in the new-product development process,” said Shedletsky. “There’s no going back, robotics and automation have already changed manufacturing. Intelligence like the kind we are building will change it again. We can radically improve how companies make products.”

There are number examples of big and small companies with problems that prevent them from shipping products on time. Delays are expensive and can cause the loss of a sale. One day of delay at a start-up could cost $10,000 in sales. For a large company, the cost could be millions. “There are hundreds of issues that need to be found and solved. They are difficult and they have to be solved one at a time,” said Shedletsky. “You can get on a plane, go to a factory and look at failure analysis so you can see why you have problems. Or, you can reduce the amount of time needed to identify and fix the problems by analyzing them remotely, using a combo of hardware and software.”

Instrumental combines hardware and software that takes images of each unit at key states of assembly on the line. The system then makes those images remotely searchable and comparable in order for the brand owner to learn and react to assembly line data. Engineers can then take action on issues. “The station goes onto the assembly line in China,” said Shedletsky. “We get the data into the cloud to discover issues the contract manufacturer doesn’t know they have. With the data, you can do failure analysis and reduced the time it takes to find an issue and correct it.”

WHAT IS AI:

Artificial intelligence (AI) is intelligence exhibited by machines.  In computer science, the field of AI research defines itself as the study of “intelligent agents“: any device that perceives its environment and takes actions that maximize its chance of success at some goal.   Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. For instance, optical character recognition is no longer perceived as an example of “artificial intelligence”, having become a routine technology.  Capabilities currently classified as AI include successfully understanding human speech,  competing at a high level in strategic game systems (such as chess and Go), autonomous cars, intelligent routing in content delivery networks, military simulations, and interpreting complex data.

FUTURE:

Some would have you believe that AI IS the future and we will succumb to the “Rise of the Machines”.  I’m not so melodramatic.  I feel AI has progressed and will progress to the point where great time saving and reduction in labor may be realized.   Anna Katrina Shedletsky and Samuel Weiss realize the potential and feel there will be no going back from this disruptive technology.   Moving AI to the factory floor will produce great benefits to manufacturing and other commercial enterprises.   There is also a significant possibility that job creation will occur as a result.  All is not doom and gloom.