AN AVERAGE DAY FOR DATA

August 4, 2017


I am sure you have heard the phrase “big data” and possibly wondered just what that terminology relates to.  Let’s get the “official” definition, as follows:

The amount of data that’s being created and stored on a global level is almost inconceivable, and it just keeps growing. That means there’s even more potential to glean key insights from business information – yet only a small percentage of data is actually analyzed. What does that mean for businesses? How can they make better use of the raw information that flows into their organizations every day?

The concept gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the four plus complexity:

  • Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden.
  • Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.
  • Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.
  • In addition to the increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. Is something trending in social media? Daily, seasonal and event-triggered peak data loads can be challenging to manage. Even more so with unstructured data.
  • Today’s data comes from multiple sources, which makes it difficult to link, match, cleanse and transform data across systems. However, it’s necessary to connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out of control.

AN AVERAGE DAY IN THE LIFE OF BIG DATA:

I picture is worth a thousand words but let us now quantify, on a daily basis, what we mean by big data.

  • U-Tube’s viewers are watching a billion (1,000,000,000) hours of videos each day.
  • We perform over forty thousand (40,000) searches per second on Google alone. That is approximately three and one-half (3.5) billion searches per day and roughly one point two (1.2) trillion searches per year, world-wide.
  • Five years ago, IBM estimated two point five (2.5) exabytes (2.5 billion gigabytes of data generated every day. It has grown since then.
  • The number of e-mail sent per day is around 269 billion. That is about seventy-four (74) trillion e-mails per year. Globally, the data stored in data centers will quintuple by 2020 to reach 915 exabytes.  This is up 5.3-fold with a compound annual growth rate (CAGR) of forty percent (40%) from 171 exabytes in 2015.
  • On average, an autonomous car will churn out 4 TB of data per day, when factoring in cameras, radar, sonar, GPS and LIDAR. That is just for one hour per day.  Every autonomous car will generate the data equivalent to almost 3,000 people.
  • By 2024, mobile networks will see machine-to-machine (M2M) connections jump ten-fold to 2.3 billion from 250 million in 2014, this is according to Machina Research.
  • The data collected by BMW’s current fleet of 40 prototype autonomous care during a single test session would fill the equivalent stack of CDs 60 miles high.

We have become a world that lives “by the numbers” and I’m not too sure that’s altogether troubling.  At no time in our history have we had access to data that informs, miss-informs, directs, challenges, etc etc as we have at this time.  How we use that data makes all the difference in our daily lives.  I have a great friend named Joe McGuinness. His favorite expressions: “It’s about time we learn to separate the fly s_____t from the pepper.  If we apply this phrase to big data, he may just be correct. Be careful out there.


One of the best things the automotive industry accomplishes is showing us what might be in our future.  They all have the finances, creative talent and vision to provide a glimpse into their “wish list” for upcoming vehicles.  Mercedes Benz has done just that with their futuristic F 015 Luxury in Motion.

In order to provide a foundation for the new autonomous F 015 Luxury in Motion research vehicle, an interdisciplinary team of experts from Mercedes-Benz has devised a scenario that incorporates different aspects of day-to-day mobility. Above and beyond its mobility function, this scenario perceives the motor car as a private retreat that additionally offers an important added value for society at large. (I like the word retreat.) If you take a look at how much time the “average” individual spends in his or her automobile or truck, we see the following:

  • On average, Americans drive 29.2 miles per day, making two trips with an average total duration of forty-six (46) minutes. This and other revealing data are the result of a ground-breaking study currently underway by the AAA Foundation for Traffic Safety and the Urban Institute.
  • Motorists age sixteen (16) years and older drive, on average, 29.2 miles per day or 10,658 miles per year.
  • Women take more driving trips, but men spend twenty-five (25) percent more time behind the wheel and drive thirty-five (35) percent more miles than women.
  • Both teenagers and seniors over the age of seventy-five (75) drive less than any other age group; motorists 30-49 years old drive an average 13,140 miles annually, more than any other age group.
  • The average distance and time spent driving increase in relation to higher levels of education. A driver with a grade school or some high school education drove an average of 19.9 miles and 32 minutes daily, while a college graduate drove an average of 37.2 miles and 58 minutes.
  • Drivers who reported living “in the country” or “a small town” drive greater distances (12,264 miles annually) and spend a greater amount of time driving than people who described living in a “medium sized town” or city (9,709 miles annually).
  • Motorists in the South drive the most (11,826 miles annually), while those in the Northeast drive the least (8,468 miles annually).

With this being the case, why not enjoy it?

The F 015 made its debut at the Consumer Electronics Show in Las Vegas more than two years ago. It’s packed with advanced (or what was considered advanced in 2015) autonomous technology, and can, in theory, run for almost 900 kilometers on a mixture of pure electric power and a hydrogen fuel cell.

But while countless other vehicles are still trying to prove that cars can, literally, drive themselves, the Mercedes-Benz offering takes this for granted. Instead, this vehicle wants us to consider what we’ll actually do while the car is driving us around.

The steering wheel slides into the dashboard to create more of a “lounge” space. The seating configuration allows four people to face each other if they want to talk. And when the onboard conversation dries up, a bewildering collection of screens — one on the rear wall, and one on each of the doors — offers plenty of opportunity to interact with various media.

The F 015 could have done all of this as a flash-in-the-pan show car — seen at a couple of major events before vanishing without trace. But in fact, it has been touring almost constantly since that Vegas debut.

“Anyone who focuses solely on the technology has not yet grasped how autonomous driving will change our society,” emphasizes Dr Dieter Zetsche, Chairman of the Board of Management of Daimler AG and Head of Mercedes-Benz Cars. “The car is growing beyond its role as a mere means of transport and will ultimately become a mobile living space.”

The visionary research vehicle was born, a vehicle which raises comfort and luxury to a new level by offering a maximum of space and a lounge character on the inside. Every facet of the F 015 Luxury in Motion is the utmost reflection of the Mercedes way of interpreting the terms “modern luxury”, emotion and intelligence.

This innovative four-seater is a forerunner of a mobility revolution, and this is immediately apparent from its futuristic appearance. Sensuousness and clarity, the core elements of the Mercedes-Benz design philosophy, combine to create a unique, progressive aesthetic appeal.

OK, with this being the case, let us now take a pictorial look at what the “Benz” has to offer.

One look and you can see the car is definitely aerodynamic in styling.  I am very sure that much time has been spent with this “ride” in wind tunnels with slip streams being monitored carefully.  That is where drag coefficients are determined initially.

The two JPEGs above indicate the front and rear swept glass windshields that definitely reduce induced drag.

The interiors are the most striking feature of this automobile.

Please note, this version is a four-seater but with plenty of leg-room.

Each occupant has a touch screen, presumably for accessing wireless or the Internet.  One thing, as yet there is no published list price for the car.  I’m sure that is being considered at this time but no USD numbers to date.  Also, as mentioned the car is self-driving so that brings on added complexities.  By design, this vehicle is a moving computer.  It has to be.  I am always very interested in maintenance and training necessary to diagnose and repair a vehicle such as this.  Infrastructure MUST be in place to facilitate quick turnaround when trouble arises–both mechanical and electrical.

As always, I welcome your comments.


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.


Various definitions of product lifecycle management or PLM have been issued over the years but basically: product lifecycle management is the process of managing the entire lifecycle of a product from inception, through engineering design and manufacture, to service and disposal of manufactured products.  PLM integrates people, data, processes and business systems and provides a product information backbone for companies and their extended enterprise.

“In recent years, great emphasis has been put on disposal of a product after its service life has been met.  How to get rid of a product or component is extremely important. Disposal methodology is covered by RoHS standards for the European Community.  If you sell into the EU, you will have to designate proper disposal.  Dumping in a landfill is no longer appropriate.

Since this course deals with the application of PLM to industry, we will now look at various industry definitions.

Industry Definitions

PLM is a strategic business approach that applies a consistent set of business solutions in support of the collaborative creation, management, dissemination, and use of product definition information across the extended enterprise, and spanning from product concept to end of life integrating people, processes, business systems, and information. PLM forms the product information backbone for a company and its extended enterprise.” Source:  CIMdata

“Product life cycle management or PLM is an all-encompassing approach for innovation, new product development and introduction (NPDI) and product information management from initial idea to the end of life.  PLM Systems is an enabling technology for PLM integrating people, data, processes, and business systems and providing a product information backbone for companies and their extended enterprise.” Source:  PLM Technology Guide

“The core of PLM (product life cycle management) is in the creation and central management of all product data and the technology used to access this information and knowledge. PLM as a discipline emerged from tools such as CAD, CAM and PDM, but can be viewed as the integration of these tools with methods, people and the processes through all stages of a product’s life.” Source:  Wikipedia article on Product Lifecycle Management

“Product life cycle management is the process of managing product-related design, production and maintenance information. PLM may also serve as the central repository for secondary information, such as vendor application notes, catalogs, customer feedback, marketing plans, archived project schedules, and other information acquired over the product’s life.” Source:  Product Lifecycle Management

“It is important to note that PLM is not a definition of a piece, or pieces, of technology. It is a definition of a business approach to solving the problem of managing the complete set of product definition information-creating that information, managing it through its life, and disseminating and using it throughout the lifecycle of the product. PLM is not just a technology, but is an approach in which processes are as important, or more important than data.” Source:  CIMdata

“PLM or Product Life Cycle Management is a process or system used to manage the data and design process associated with the life of a product from its conception and envisioning through its manufacture, to its retirement and disposal. PLM manages data, people, business processes, manufacturing processes, and anything else pertaining to a product. A PLM system acts as a central information hub for everyone associated with a given product, so a well-managed PLM system can streamline product development and facilitate easier communication among those working on/with a product. Source:  Aras

A pictorial representation of PLM may be seen as follows:

Hopefully, you can see that PLM deals with methodologies from “white napkin design to landfill disposal”.  Please note, documentation is critical to all aspects of PLM and good document production, storage and retrieval is extremely important to the overall process.  We are talking about CAD, CAM, CAE, DFSS, laboratory testing notes, etc.  In other words, “the whole nine yards of product life”.   If you work in a company with ISO certification, PLM is a great method to insure retaining that certification.

In looking at the four stages of a products lifecycle, we see the following:

Four Stages of Product Life Cycle—Marketing and Sales:

Introduction: When the product is brought into the market. In this stage, there’s heavy marketing activity, product promotion and the product is put into limited outlets in a few channels for distribution. Sales take off slowly in this stage. The need is to create awareness, not profits.

The second stage is growth. In this stage, sales take off, the market knows of the product; other companies are attracted, profits begin to come in and market shares stabilize.

The third stage is maturity, where sales grow at slowing rates and finally stabilize. In this stage, products get differentiated, price wars and sales promotion become common and a few weaker players exit.

The fourth stage is decline. Here, sales drop, as consumers may have changed, the product is no longer relevant or useful. Price wars continue, several products are withdrawn and cost control becomes the way out for most products in this stage.

Benefits of PLM Relative to the Four Stages of Product Life:

Considering the benefits of Product Lifecycle Management, we realize the following:

  • Reduced time to market
  • Increase full price sales
  • Improved product quality and reliability
  • Reduced prototypingcosts
  • More accurate and timely request for quote generation
  • Ability to quickly identify potential sales opportunities and revenue contributions
  • Savings through the re-use of original data
  • frameworkfor product optimization
  • Reduced waste
  • Savings through the complete integration of engineering workflows
  • Documentation that can assist in proving compliance for RoHSor Title 21 CFR Part 11
  • Ability to provide contract manufacturers with access to a centralized product record
  • Seasonal fluctuation management
  • Improved forecasting to reduce material costs
  • Maximize supply chain collaboration
  • Allowing for much better “troubleshooting” when field problems arise. This is accomplished by laboratory testing and reliability testing documentation.

PLM considers not only the four stages of a product’s lifecycle but all of the work prior to marketing and sales AND disposal after the product is removed from commercialization.   With this in mind, why is PLM a necessary business technique today?  Because increases in technology, manpower and specialization of departments, PLM was needed to integrate all activity toward the design, manufacturing and support of the product. Back in the late 1960s when the F-15 Eagle was conceived and developed, almost all manufacturing and design processes were done by hand.  Blueprints or drawings needed to make the parts for the F15 were created on a piece of paper. No electronics, no emails – all paper for documents. This caused a lack of efficiency in design and manufacturing compared to today’s technology.  OK, another example of today’s technology and the application of PLM.

If we look at the processes for Boeings DREAMLINER, we see the 787 Dreamliner has about 2.3 million parts per airplane.  Development and production of the 787 has involved a large-scale collaboration with numerous suppliers worldwide. They include everything from “fasten seatbelt” signs to jet engines and vary in size from small fasteners to large fuselage sections. Some parts are built by Boeing, and others are purchased from supplier partners around the world.  In 2012, Boeing purchased approximately seventy-five (75) percent of its supplier content from U.S. companies. On the 787 program, content from non-U.S. suppliers accounts for about thirty (30) percent of purchased parts and assemblies.  PLM or Boeing’s version of PLM was used to bring about commercialization of the 787 Dreamliner.

 


If you work or have worked in manufacturing you know robotic systems have definitely had a distinct impact on assembly, inventory acquisition from storage areas and finished-part warehousing.   There is considerable concern that the “rise of the machines” will eventually replace individuals performing a verity of tasks.  I personally do not feel this will be the case although there is no doubt robotic systems have found their way onto the manufacturing floor.

From the “Executive Summary World Robotics 2016 Industrial Robots”, we see the following:

2015:  By far the highest volume ever recorded in 2015, robot sales increased by 15% to 253,748 units, again by far the highest level ever recorded for one year. The main driver of the growth in 2015 was the general industry with an increase of 33% compared to 2014, in particular the electronics industry (+41%), metal industry (+39%), the chemical, plastics and rubber industry (+16%). The robot sales in the automotive industry only moderately increased in 2015 after a five-year period of continued considerable increase. China has significantly expanded its leading position as the biggest market with a share of 27% of the total supply in 2015.

In looking at the chart below, we can see the sales picture with perspective and show how system sales have increased from 2003.

It is very important to note that seventy-five percent (75%) of global robot sales comes from five (5) countries.

There were five major markets representing seventy-five percent (75%) of the total sales volume in 2015:  China, the Republic of Korea, Japan, the United States, and Germany.

As you can see from the bar chart above, sales volume increased from seventy percent (70%) in 2014. Since 2013 China is the biggest robot market in the world with a continued dynamic growth. With sales of about 68,600 industrial robots in 2015 – an increase of twenty percent (20%) compared to 2014 – China alone surpassed Europe’s total sales volume (50,100 units). Chinese robot suppliers installed about 20,400 units according to the information from the China Robot Industry Alliance (CRIA). Their sales volume was about twenty-nine percent (29%) higher than in 2014. Foreign robot suppliers increased their sales by seventeen percent (17%) to 48,100 units (including robots produced by international robot suppliers in China). The market share of Chinese robot suppliers grew from twenty-five percent (25%) in 2013 to twenty-nine percent (29%) in 2015. Between 2010 and 2015, total supply of industrial robots increased by about thirty-six percent (36%) per year on average.

About 38,300 units were sold to the Republic of Korea, fifty-five percent (55%) more than in 2014. The increase is partly due to a number of companies which started to report their data only in 2015. The actual growth rate in 2015 is estimated at about thirty percent (30%) to thirty-five percent (35%.)

In 2015, robot sales in Japan increased by twenty percent (20%) to about 35,000 units reaching the highest level since 2007 (36,100 units). Robot sales in Japan followed a decreasing trend between 2005 (reaching the peak at 44,000 units) and 2009 (when sales dropped to only 12,767 units). Between 2010 and 2015, robot sales increased by ten percent (10%) on average per year (CAGR).

Increase in robot installations in the United States continued in 2015, by five percent (5%) to the peak of 27,504 units. Driver of this continued growth since 2010 was the ongoing trend to automate production in order to strengthen American industries on the global market and to keep manufacturing at home, and in some cases, to bring back manufacturing that had previously been sent overseas.

Germany is the fifth largest robot market in the world. In 2015, the number of robots sold increased slightly to a new record high at 20,105 units compared to 2014 (20,051 units). In spite of the high robot density of 301 units per 10,000 employees, annual sales are still very high in Germany. Between 2010 and 2015, annual sales of industrial robots increased by an average of seven percent (7%) in Germany (CAGR).

From the graphic below, you can see which industries employ robotic systems the most.

Growth rates will not lessen with projections through 2019 being as follows:

A fascinating development involves the assistance of human endeavor by robotic systems.  This fairly new technology is called collaborative robots of COBOTS.  Let’s get a definition.

COBOTS:

A cobot or “collaborative robot” is a robot designed to assist human beings as a guide or assistor in a specific task. A regular robot is designed to be programmed to work more or less autonomously. In one approach to cobot design, the cobot allows a human to perform certain operations successfully if they fit within the scope of the task and to steer the human on a correct path when the human begins to stray from or exceed the scope of the task.

“The term ‘collaborative’ is used to distinguish robots that collaborate with humans from robots that work behind fences without any direct interaction with humans.  “In contrast, articulated, cartesian, delta and SCARA robots distinguish different robot kinematics.

Traditional industrial robots excel at applications that require extremely high speeds, heavy payloads and extreme precision.  They are reliable and very useful for many types of high volume, low mix applications.  But they pose several inherent challenges for higher mix environments, particularly in smaller companies.  First and foremost, they are very expensive, particularly when considering programming and integration costs.  They require specialized engineers working over several weeks or even months to program and integrate them to do a single task.  And they don’t multi-task easily between jobs since that setup effort is so substantial.  Plus, they can’t be readily integrated into a production line with people because they are too dangerous to operate in close proximity to humans.

For small manufacturers with limited budgets, space and staff, a collaborative robot such as Baxter (shown below) is an ideal fit because it overcomes many of these challenges.  It’s extremely intuitive, integrates seamlessly with other automation technologies, is very flexible and is quite affordable with a base price of only $25,000.  As a result, Baxter is well suited for many applications, such as those requiring manual labor and a high degree of flexibility, that are currently unmet by traditional technologies.

Baxter is one example of collaborative robotics and some say is by far the safest, easiest, most flexible and least costly robot of its kind today.  It features a sophisticated multi-tier safety design that includes a smooth, polymer exterior with fewer pinch points; back-drivable joints that can be rotated by hand; and series elastic actuators which help it to minimize the likelihood of injury during inadvertent contact.

It’s also incredibly simple to use.  Line workers and other non-engineers can quickly learn to train the robot themselves, by hand.  With Baxter, the robot itself is the interface, with no teaching pendant or external control system required.  And with its ease of use and diverse skill set, Baxter is extremely flexible, capable of being utilized across multiple lines and tasks in a fraction of the time and cost it would take to re-program other robots.  Plus, Baxter is made in the U.S.A., which is a particularly appealing aspect for many of our customers looking to re-shore their own production operations.

The digital picture above shows a lady work alongside a collaborative robotic system, both performing a specific task. The lady feels right at home with her mechanical friend only because usage demands a great element of safety.

Certifiable safety is the most important precondition for a collaborative robot system to be applied to an industrial setting.  Available solutions that fulfill the requirements imposed by safety standardization often show limited performance or productivity gains, as most of today’s implemented scenarios are often limited to very static processes. This means a strict stop and go of the robot process, when the human enters or leaves the work space.

Collaborative systems are still a work in progress but the technology has greatly expanded the use and this is primarily due to satisfying safety requirements.  Upcoming years will only produce greater acceptance and do not be surprised if you see robots and humans working side by side on every manufacturing floor over the next decade.

As always, I welcome your comments.


At one time in the world there were only two distinctive branches of engineering, civil and military.

The word engineer was initially used in the context of warfare, dating back to 1325 when engine’er (literally, one who operates an engine) referred to “a constructor of military engines”.  In this context, “engine” referred to a military machine, i. e., a mechanical contraption used in war (for example, a catapult).

As the design of civilian structures such as bridges and buildings developed as a technical discipline, the term civil engineering entered the lexicon as a way to distinguish between those specializing in the construction of such non-military projects and those involved in the older discipline. As the prevalence of civil engineering outstripped engineering in a military context and the number of disciplines expanded, the original military meaning of the word “engineering” is now largely obsolete. In its place, the term “military engineering” has come to be used.

OK, so that’s how we got here.  If you follow my posts you know I primarily concentrate on STEM (science, technology, engineering and mathematics) professions.  Engineering is somewhat uppermost since I am a mechanical engineer.

There are many branches of the engineering profession.  Distinct areas of endeavor that attract individuals and capture their professional lives.  Several of these are as follows:

  • Electrical Engineering
  • Mechanical Engineering
  • Civil Engineering
  • Chemical Engineering
  • Biomedical Engineering
  • Engineering Physics
  • Nuclear Engineering
  • Petroleum Engineering
  • Materials Engineering

Of course, there are others but the one I wish to concentrate on with this post is the growing branch of engineering—Biomedical Engineering. Biomedical engineering, or bioengineering, is the application of engineering principles to the fields of biology and health care. Bioengineers work with doctors, therapists and researchers to develop systems, equipment and devices in order to solve clinical problems.  As such, the possibilities of a bioengineer’s charge are as follows:

Biomedical engineering has evolved over the years in response to advancements in science and technology.  This is NOT a new classification for engineering involvement.  Engineers have been at this for a while.  Throughout history, humans have made increasingly more effective devices to diagnose and treat diseases and to alleviate, rehabilitate or compensate for disabilities or injuries. One example is the evolution of hearing aids to mitigate hearing loss through sound amplification. The ear trumpet, a large horn-shaped device that was held up to the ear, was the only “viable form” of hearing assistance until the mid-20th century, according to the Hearing Aid Museum. Electrical devices had been developed before then, but were slow to catch on, the museum said on its website.

The works of Alexander Graham Bell and Thomas Edison on sound transmission and amplification in the late 19th and early 20th centuries were applied to make the first tabletop hearing aids. These were followed by the first portable (or “luggable”) devices using vacuum-tube amplifiers powered by large batteries. However, the first wearable hearing aids had to await the development of the transistor by William Shockley and his team at Bell Laboratories. Subsequent development of micro-integrated circuits and advance battery technology has led to miniature hearing aids that fit entirely within the ear canal.

Let’s take a very quick look at several devices designed by biomedical engineering personnel.

MAGNETIC RESONANCE IMAGING:

POSITION EMISSION TOMOGRAPHY OR (PET) SCAN:

NOTE: PET scans represent a different technology relative to MRIs. The scan uses a special dye that has radioactive tracers. These tracers are injected into a vein in your arm. Your organs and tissues then absorb the tracer.

BLOOD CHEMISTRY MONOTORING EQUIPMENT:

ELECTROCARDIOGRAM MONITORING DEVICE (EKG):

INSULIN PUMP:

COLONOSCOPY:

THE PROFESSION:

Biomedical engineers design and develop medical systems, equipment and devices. According to the U.S. Bureau of Labor Statistics (BLS), this requires in-depth knowledge of the operational principles of the equipment (electronic, mechanical, biological, etc.) as well as knowledge about the application for which it is to be used. For instance, in order to design an artificial heart, an engineer must have extensive knowledge of electrical engineeringmechanical engineering and fluid dynamics as well as an in-depth understanding of cardiology and physiology. Designing a lab-on-a-chip requires knowledge of electronics, nanotechnology, materials science and biochemistry. In order to design prosthetic replacement limbs, expertise in mechanical engineering and material properties as well as biomechanics and physiology is essential.

The critical skills needed by a biomedical engineer include a well-rounded understanding of several areas of engineering as well as the specific area of application. This could include studying physiology, organic chemistry, biomechanics or computer science. Continuing education and training are also necessary to keep up with technological advances and potential new applications.

SCHOOLS OFFERING BIO-ENGINEERING:

If we take a look at the top schools offering Biomedical engineering, we see the following:

  • MIT
  • Stanford
  • University of California-San Diego
  • Rice University
  • University of California-Berkley
  • University of Pennsylvania
  • University of Michigan—Ann Arbor
  • Georgia Tech
  • Johns Hopkins
  • Duke University

As you can see, these are among the most prestigious schools in the United States.  They have had established engineering programs for decades.  Bio-engineering does not represent a new discipline for them.  There are several others and I would definitely recommend you go online to take a look if you are interested in seeing a complete list of colleges and universities offering a four (4) or five (5) year degree.

SALARY LEVELS:

The median annual wage for biomedical engineers was $86,950 in May 2014. The median wage is the wage at which half the workers in an occupation earned more than that amount and half earned less. The lowest ten (10) percent earned less than $52,680, and the highest ten (10) percent earned more than $139,350.  As you might expect, salary levels vary depending upon several factors:

  • Years of experience
  • Location within the United States
  • Size of company
  • Research facility and corporate structure
  • Bonus or profit sharing arrangement of company

EXPECTATIONS FOR EMPLOYMENT:

In their list of top jobs for 2015, CNNMoney classified Biomedical Engineering as the 37th best job in the US, and of the jobs in the top 37, Biomedical Engineering 10-year job growth was the third highest (27%) behind Information Assurance Analyst (37%) and Product Analyst (32%). CNN previously reported Biomedical Engineer as the top job in the US in 2012 with a predicted 10-year growth rate of nearly 62% ‘Biomedical Engineer’ was listed as a high-paying low-stress job according to Time magazine.  There is absolutely no doubt that medical technology will advance as time go on so biomedical engineers will continue to be in demand.

As always, I welcome your comments.

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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