Electrical and Computer Engineering Newsblog

This week I want to follow up on my column from two weeks ago, before Thanksgiving, on the various ways that we use the words associated with my line of work: science, computing, engineering, technology, and mathematics. This is mostly a random collection of observations on the strange ways that we use these words, without a whole lot of practical solutions. Think of it as the grammar maven (see FWF 06/16/2017) meeting the engineering professor.

A quick recap of the main points from the last column. Science is the human endeavor of figuring out how the world works, and creating mathematical models for it. Engineering is the human endeavor of designing tools to improve our way of life, often taking advantage of all that we have learned from science. Technology is the set of tools used by society, resulting from all our hard work as engineers. I made the point that engineering usually refers to the development of physical tools, and that there does not exist a good work in the English language to describe the process of designing cognitive tools; computing was the best I could come up with.

Of all the words listed above, it seems to me that science is the one that commands the most respect in academia and in the general public. There are many ways that we attach the word science to things to give them more gravitas. For example, what is the name of the degree that we confer to graduating engineers in the United States? Bachelor of Science. (Let’s set aside the use of the word bachelor for now – I’m not going there.) It seems to me that Bachelor of Engineering would make more sense, but you rarely hear that, if you did you would probably consider it “less than.” We do have both Master of Science (MS) and Master of Engineering (ME) degrees in engineering fields in the United States, but there is little consensus on what those degree titles mean. In many places, the MS program involves a thesis (which means there may be some actual science involved) whereas the ME program is coursework-based and may have more room for courses in business and management. However, at Michigan Tech we have coursework-based MS programs, particular in electrical engineering and mechanical engineering, because we have concluded that there is broader acceptance of that degree name among working professionals. The degrees Bachelor of Technology (B. Tech.) and Master of Technology (M. Tech.) are also fairly common outside the United States.

Confusion about the distinction between science and engineering, or perhaps between scientists and engineers, has always been a pet peeve of mine (just ask my family). If you ask the general public who is responsible for the highly complex tools that we use every day, such as computers, automobiles, and smart phones, chances are you will get the answer “scientists.” Popular culture doesn’t help to clear up the notion that there is a pecking order: in “The Big Bang Theory” the physicist Sheldon is always berating Wolowitz for being merely an engineer. Please do not misunderstand – I have the highest respect for good scientists and all that they do, my frustration has only to do with the misperceptions about what that enterprise is all about and how it relates to other activities.

Here at Michigan Tech our math department is called the Department of Mathematical Sciences. I should probably talk to someone over there before writing these words, but that mash-up makes no sense to me at all. Similar to the situation with engineering, science is one thing and mathematics is another. Mathematics is the world of pure logic, something that springs from the human imagination and where truth is absolute. Science is a little messier, where we try the understand how things work, where there is no absolute truth but only higher and higher levels of confidence in theories and hypotheses. Mathematics is of course the language of science, and it is what gives the ability to generalize what we have seen, to extrapolate from what we have seen to things that we have never seen before. For example, consider Newton’s Second Law, F=ma. We can conduct experiments to test the correctness of this hypothesis forever, applying a force F to a mass m and measuring the acceleration a, but we can never test it for all possible combinations of F and a. However, if we believe that our experiments confirm that validity of that mathematical model, then we can believe that it will hold true even for combinations of F and a that we have never seen before. [Aside: F=MA is also known as Newton’s Second Law of Graduate Education, applied to PhD students who fail their qualifying examinations. But I digress.] As with scientists, I have the highest respect for mathematicians and all that they do. Math is a beautiful subject, and I only wish I were better at it. I also wish that our engineering students, both undergraduate and graduate, would not find mathematics as something mysterious and unobtainable, and that they would not freak out every time they see the word “proof.” Maybe I wish too much.

Speaking of odd mash-ups, we also have the term engineering technology. I know what people mean when they use that term, but that does not mean it makes sense from an English usage point of view. Engineering is one thing (a human endeavor) and technology is another (a set of tools). I have no problem with our School of Technology, which teaches students about tools and prepares them to implement them in industry or society. I just don’t get where the modifier “Engineering” comes in. Instead of saying Electrical Engineering Technology it would make a lot more sense to say Electrical Technology or perhaps Electrical and Electronic Technology which is rare although I have seen it. No matter what I think, Engineering Technology is a thing; the term is used nationwide and it is recognized by our accreditation body ABET. We are stuck with it.

The words I have referenced today – Science, Technology, Engineering, and Mathematics – have been put together in the popular acronym STEM. We hear all the time about the importance of STEM education, and we think of Michigan Tech as a STEM university. That is all well and good, but I have to come back to that fifth word – computing – and wonder where that fits in. Where is computing in STEM? Even though I have defined technology as a set of tools, twice now, I think that in the context of this particular acronym and its use in public policy discussions, “technology” is actually a code word for “computing.” When we talk about STEM, we really mean Science, Computing, Engineering, and Mathematics, all four of which are human endeavors, so they are parallel and go together nicely. The problem is, we can’t make a good acronym of those – SCEM? MESC? Stem is already a simple English word that is easy to pronounce, so I see the appeal. The scant evidence I have for this hypothesis lies in the images that people use to illustrate STEM. Just look up STEM on Google Images and you will find lots of examples of graphics like the one accompanying today’s column (for which I credit Student Affairs at Lehigh University.) If there are little icons associated with the four letters in STEM, the one that goes with T is almost always very computer-y, like the on-off button in the example above.

Again, nothing I say here is really an attempt to get people to change the way they use language. There are a lot of terms in English like “engineering technology” that are just idiomatic and are not built using a logical grammatical construction. However, it is good to be aware of that fact. The words we use and way we use them have a tremendous but often subtle influence on the way we think about things. I encourage all my readers to pay attention and to be as precise as possible whenever that makes the most sense – say what you mean, mean what you say.

– Dan

Daniel R. Fuhrmann
Dave House Professor and Chair
Department of Electrical and Computer Engineering
Michigan Technological University

This week, as our Computing and Information Sciences Working Group is building up some steam investigating how computing is handled academically and structurally at other universities across the country, I have been thinking about how computing here fits into our role as a technological university. That leads naturally to the question of “what is a technological university, anyway?” but today I mostly want to address some issues of language and ask the even more fundamental question “what is technology?” I will conclude with a few thoughts about the language we use to describe computing.

“Technology” is one of those things where everybody has some vague idea of what it means, but pinning down a definition is tricky. In the time-honored tradition of sermons and speeches everywhere I’ll start with a collection of dictionary definitions:

(Merriam-Webster)

1.a.  the practical application of knowledge especially in a particular area : ENGINEERING
b.  a capability given by the practical application of knowledge, e.g. a car’s fuel-saving technology
2.    a manner of accomplishing a task especially using technical processes, methods, or knowledge, e.g. new technologies for information storage.
3.   the specialized aspects of a particular field of endeavor, e.g. education technology

(Oxford English Dictionary)

1. a.  the branch of knowledge dealing with the mechanical arts and applied sciences; the study of this.
b. the application of such knowledge for practical purposes, esp. in industry, manufacturing, etc.; the sphere of activity concerned with this; the mechanical arts and applied sciences collectively.
c. the product of such application; technological knowledge or know-how; a technological process, method, or technique. Also: machinery, equipment, etc., developed from the practical application of scientific and technical knowledge; an example of this. Also in extended use.
2. A particular practical or industrial art; a branch of the mechanical arts or applied sciences; a technological discipline.

OED also list 3 obsolete definitions not included here.

(dictionary.com)

1. the branch of knowledge that deals with the creation and use of technical means and their interrelation with life, society, and the environment, drawing upon such subjects as industrial arts, engineering, applied science, and pure science.
2. the application of this knowledge for practical ends.
3. the terminology of an art, science, etc.; technical nomenclature.
4. a scientific or industrial process, invention, method, or the like.
5. the sum of the ways in which social groups provide themselves with the material objects of their civilization.

(The Free Dictionary)

1.a.  the application of science, especially to industrial or commercial objectives.
b.  the scientific method and material used to achieve a commercial or industrial objective.
2. electronic or digital products and systems considered as a group: a store specializing in office technology.
3. (Anthropology) the body of knowledge available to a society that is of use in fashioning implements, practicing manual arts and skills, and extracting or collecting materials.

There are others, but this is enough for now. There are several take-aways from this collection, and the first is that the definitions are all over the map. This creates an opening for me to take issue with some of the definitions and furthermore describe my own perspective. For starters, that first Merriam-Webster definition that equates technology with engineering is just flat-out wrong and they should know better. The OED definition that equates technology with the “mechanical arts” seems stuck in the First Industrial Revolution and ignores all the advances in the past 200 years due to human knowledge of electricity, chemistry, materials, biology (not to mention computing – more on that shortly). There are some circular definitions in there, such as any of the ones that use the word “technical” which I assume has a similar etymology. However, the area that I think is most interesting is how these definitions describe a relationship with “science.” Let’s look at that.

Contrary to what a lot of people might think, science is not a collection of complicated and hard-to-understand laws of the universe. Rather, it is a human endeavor, by which we as a species attempt to figure out how the world works. At its heart is the scientific method, by which we look at the world, try to form a rough guess about what is happening in it, then form hypotheses which we attempt to prove or disprove using experiments. When I say “the world” I mean a lot of different things; it could be the hard physical laws of physics, like light or gravity or electromagnetism, or it could mean the nature of matter, as in chemistry, or it could mean complex life systems from biology. “The world” can also include systems that human beings are a part of it, such as in economics, social sciences, or psychology. The point is that science is all about uncovering the truth, to the best of our ability. The truth might be simple, or it might be complex, but it is what it is.

When the definitions above refer to the application of science to industry and commercial applications, they do not really mean science, they mean the laws of nature and our understanding of them. What we do as human beings is use our understanding of the laws of nature in order to exploit them (in a good way, usually) to make tools – things that did not exist before in nature – and thereby improve the human condition. This leads us into the territory of engineering.

Like science, engineering is a human endeavor, by which we take advantage of our understanding of the physical and natural world, combine that with our intellectual capacity, and create useful things that did not exist before. Where science is concerned with the truth, engineering deals with what is possible. Engineers are essentially tool builders, and as such the engineering profession embodies much of what it means to be human. These tools could be simple, or they can be enormously complex, but either way they are the product of human creativity. For most of the 250 years since the beginning of the First Industrial Revolution, engineering has meant the creation of physical things whose operation relied the principles from physical laws of nature like Newtonian statics and dynamics, thermodynamics, electromagnetism, chemistry, and the like. In the last 50 years or so the concept of systems engineering and control systems has brought an additional layer of complexity that is highly mathematical and is more about organization than the laws of physics, but nevertheless I think most people associate engineering with the development of physical tools – cars, airplanes, telephones, power grids, bridges, chemical plants, and so forth.

When I say that engineers build tools that did not exist previously, it sounds like I am saying that engineering is the same as invention, which is not really true. The distinction has been blurred in the past few decades, where there is so much emphasis placed on innovation and entrepreneurship in engineering schools. Even our own Michigan Tech tagline “Create the Future” suggests we want our students to go out and invent things. While engineering practice can lead to inventions that are, in the words of the U.S. Patent Office “new, useful, and nonobvious”, it does not always have to be that way. A lot of what engineers do is take well-known principles and skills and apply them in new situations, for example in the design of a new airplane or a power plant. The work can be evolutionary, not revolutionary. Imagine a skilled homebuilder: he or she may design and build beautiful houses all over town, but does not apply for a patent for each new house just because it is different than the one before. A lot of engineering is like that too, just with a different set of objectives.

This brings us back to the original question of “what is technology?” Looking over the set of definitions, the ones most closely aligned with what I mean when I use the word are Merriam-Webster 2 (“A manner of accomplishing a task…”), OED 1c (“The product of such application…”) and dictionary.com 5. This last one is interesting: “the sum of the ways in which social groups provide themselves with the material objects of their civilization.” That’s really catchy. What all of these are saying is that technology does not mean the knowledge or the process by which we build tools, it refers to the tools themselves. As that last definition makes clear, what we mean by tools covers a lot of territory, but the key element is that they are the products of our own human imagination, applied to the materials we find around us. Science provides us with the process for understanding how the world works, engineering is the process by which we create tools and extend what is available to us in nature, and technology is the result. These thoughts are echoed nicely and expanded further in http://researchpedia.info/difference-between-science-engineering-and-technology/.

If one buys this line of reasoning, then it means that the purpose of a technological university, if it is true to its name, would be education and scholarship in the ways that we as a society provide ourselves with the material objects of our civilization. I’m good with that.

So where does computing fit into all this? I think we would all agree that, if we are talking about the tools of 21st-century civilization, then computers, computation, artificial intelligence, information systems, and all the related things you want to put into that bucket, are a big part of what we would call technology today. But what are the words that we use to talk about that? Engineering is the well-established term we use to describe the process for creating physical tools, but what about the analogous process on the computing side? I assert that there is no word or phrase in the English language that adequately captures the process of creating new cognitive tools. This may very well be the reason why we have such a hard time recognizing computing as a discipline, when we compare and contrast it to the field of engineering.

Some of the options available to us are:

1. Computer Science. I think this term is a misnomer and its very existence is holding the field back. While one can easily argue that there are some scientific aspects to CS, where one actually does experiments to test hypotheses about algorithms or computer systems, by and large Computer Science is not science. Of the four terms in the STEM acronym, Computer Science is more mathematics, more engineering, and more technology than it is science. Unfortunately we are stuck with it.

2. Computer Engineering. This is an accurate descriptive term but its use is limited. It generally means the application of the tools of electronics, integrated with low-level software, to the creation of computers and computer systems. It can also mean the use of cognitive tools to control engineered physical systems (going the opposite direction, one might say) but these days I prefer the terms automation/ or cyber-physical systems for that.

3. Software Engineering. This is a wonderfully accurate term but again it refers to a narrow slice within computing. It means the process of creating computer code that meets modern industrial standards for large projects written by teams. Most CS majors would probably be better off as Software Engineering majors if they are looking to become professional programmers.

Before all my CS friends come to my door with torches and pitchforks, let me say that I have tremendous respect for everything that has been accomplished in the past 50 years under the name of Computer Science. Here we are in the Fourth Industrial Revolution where all our physical tools are connected, sensed, and controlled using cognitive tools, and that is the result of the extraordinary efforts of a lot of very smart people, many of whom are graduates of CS departments. I am only saying that the name is wrong. We need a word that describes all of the human effort and the human process that goes into building those cognitive tools, and Computer Science just doesn’t cut it for me.

My own wholly inadequate answer to this lexicon quandary is to use the word “Computing”, as I have already done in this column. I like it because it is simple, a one-word gerund that nicely parallels the word “Engineering.” It is its own word: it is not Science, it is not Technology, it is not Engineering, it is not Mathematics. The reason it is inadequate is that it sounds too much like what you do on a computer, and that is pretty narrow, but I don’t know what else to do. When I use the term Computing, I mean it to encompass the wide range of human activities that result in the creation of new cognitive tools. Here at Michigan Tech, we find that activity all over the place, in the CS Department, in the ECE Department, in the Math Department, the School of Business and Economics, in the School of Technology, pretty much every department in the College of Engineering, you name it. I am convinced that if we could find the right word to describe all that activity, our view of that field as a scholarly discipline would change entirely.

Next week is Thanksgiving. Among many other things I am thankful for those of you who read these random musings on a more-or-less weekly basis. FWF next week is doubtful, so unless inspiration strikes from a leftover turkey sandwich I’ll write again in two weeks. Have a wonderful holiday!

– Dan

Daniel R. Fuhrmann
Dave House Professor and Chair
Department of Electrical and Computer Engineering
Michigan Technological University

A couple of weeks ago I reported with barely contained glee that winter was on its way. I was right. We got a few inches of additional snow this past week, and for the time being it is sticking around. Thursday night temperatures plunged into the single digits in the western part of the Upper Peninsula. The cross-country ski trails at Michigan Tech are open, and I plan to be out there on Saturday. It’s a good time to make sure I have all the right gear, or at least remember where I put it at the end of the season last year. It’s nice to have the ski season start a little early for a change.

Saturday is also Veterans Day, and so in what has become a bit of a tradition I would like to recognize those members of the ECE Department who have served in the U.S. Armed Forces:

Glen Archer
Mike Chase
Roger Kieckhafer
Chris Middlebrook
Mike Roggemann
Mark Sloat

I thank these gentlemen for their service to the Nation. I also want to acknowledge these individuals for their service to the ECE Department in their current civilian careers. I try not to do this too much, but on those occasions when I need to ask people to put the interests of Michigan Tech and the ECE Department above their own personal interests, they have always answered the call, and I am deeply grateful. They serve as an example for all of us.

We are in the middle of preregistration for the Spring 2018 semester, and because we are in the second year of an increase in the number of new ECE students, we are seeing particularly large enrollments in key sophomore courses like Digital Logic and Circuits II. Right now Associate Chair Glen Archer and I are working to make sure that we put the right instructors in these courses, and even though we still have a couple of question marks I am confident that these courses will be expertly taught. I have always been a big believer in putting our best instructors in these critical early courses, where pedagogy matters as much as the material.

I have to believe that the increase in enrollment is due in part to increased awareness of the demand for computing professionals, exemplified by the data I showed last week. Judy Donahue, our academic advisor for the BSEE program, tells me she has been processing an unusually large number of requests for transfers into the ECE Department. I’m not really surprised, but we will have to make sure we are prepared to offer our new students everything they are looking for. Coincidentally, just as I was preparing to write this, I got wind of a recent report from the National Academy of Science about the growth in Computer Science enrollment and what universities all across the country are doing in response. I am looking forward to reading that and sharing some of it here. The topic fits squarely within the interests of our Computing and Information Sciences Working Group, which has started its own study of how computing is approached, as a broad academic and research area, at 40 U.S. universities.

November 10 is the anniversary of the sinking of the Edmund Fitzgerald, in 1975. I’m not sure what that has to do with anything else in today’s column, but I suppose I can use it as an opportunity to remind everyone to be careful while traveling, especially a week from now when people take off for Thanksgiving. I don’t know anyone going out on Lake Superior, but the roads can be a little dicey this time of year with fresh snow. Take it easy and don’t get in a hurry!

– Dan

Daniel R. Fuhrmann
Dave House Professor and Chair
Department of Electrical and Computer Engineering
Michigan Technological University

Any questions?

– Dan

Daniel R. Fuhrmann
Dave House Professor and Chair
Department of Electrical and Computer Engineering
Michigan Technological University

I have mentioned before the old canard that there are two seasons in the Upper Peninsula: Winter’s Here and Winter’s Coming. While this obviously does not do justice to our beautiful summers, there is definitely a Winter’s Coming season, and it just arrived. After a rather late, warm, and wet fall, we had a storm blow through here on Tuesday with cold rain and 50-60 mph winds that was like a shot across the bow. Although the landscape is still dotted with a few bright yellow trees that weren’t quite ready to quit, for the most part our woods are bare. The forecast now includes rain/snow mix and other reminders of what’s to come. It’s the time when we put the driveway stakes in the ground, tune up the snowblowers, and make sure our snowplowing contracts are in place.

The change of seasons coincides as it often does with a change in the vibe in the ECE office. For some reason, it seems that the first half of the fall semester is just frenetic, with one deadline after another and lots of visitors who like to come to campus during that short window of time when the fall colors are at their peak. This year was made even a little more hectic with our ABET accreditation visit earlier this week. (I’m not supposed to tell you how that turned out, so I won’t.) Now all of a sudden it feels like we can take a little bit of breather. More importantly, we can turn our attention to some important longer-range issues that keep getting putting off on those days when all one thinks about is the agenda for tomorrow’s meeting.

The biggest long-range project on my plate this year is leading an effort to look at the role of computing at Michigan Tech. Before describing that further, I should give some context. This year we are looking at a major transition in the leadership at Michigan Tech, with ongoing searches for the president and the deans for four out of our five academic units: the College of Engineering, the College of Sciences and Arts, the School of Technology, and the School of Forest Resources and Environmental Science. This is not the result of any crisis, but just a bizarre coincidence where everyone hit retirement age at about the same time. The “last man standing” is Dean Johnson from the School of Business and Economics (whose first name really is Dean, leading to no end of jokes and explanations.) While it is difficult to predict exactly what will happen, I think it is safe to say that Michigan Tech will look a lot different at this time next year.

Back to the computing initiative. For many years, a number of people, both on-campus (including me) and some key external advisors have been looking at Michigan Tech’s position with regard to computing and information sciences, and wondering what we can do or should do to elevate our impact and our visibility in this key technology area. For several years now I have been a part of the Alliance for Computing, Information, and Automation (ACIA) which brings together the ECE Department, the Department of Computer Science, and the School of Technology, as we look for ways to cooperate in our academic programs and collaborate in research. The most successful outgrowth of that partnership has been the establishment of the Institute of Computer and Cybersystems, a research center led by CS department chair Min Song. However, there is more to be done, and with the upcoming transitions at Michigan Tech now is the time to do it.

In April of 2017 I made a presentation to the Michigan Tech Board of Trustees on behalf of the ACIA, where we made the case that computing is a key technology driver in the 21st century and that Michigan Tech should have a larger presence in order to stay true to its mission. With the encouragement of the Board of Trustees, this was followed up with a Computing and Information Sciences retreat on August 18, led by Provost Jackie Huntoon, where more than 60 member of the Michigan Tech community came together for a day and got a lot of issues out on the table. There was an excellent keynote address by Michigan Tech alumnus and benefactor Dave House, really driving home the point that the world has changed and that Michigan Tech needs to be paying attention. The retreat was a success, I believe, for raising awareness and getting people to think about what we might do. Of course, there were as many ideas about that as there were people in the room.

This brings us to present. Provost Huntoon has formed a Computing and Information Sciences (CIS) Working Group and asked me to lead it, and of course I jumped at the opportunity. The other members are: Min Song (CS), Jim Frendewey (SoT), Laura Brown (CS), Tim Havens (ECE/CS), Roger Kieckhafer (ECE), Myounghoon Jeon (CLS/CS), and Benjamin Ong (Math). Our charge is to use the time we have this year to develop recommendations designed to promote growth in size and quality of the degree programs and the University’s research portfolio in computing and information sciences, in the broadest sense. The recommendations are due to the Provost prior to the end of the 2017-2018 academic year. She will review those recommendations and use them to provide guidance to the future University president and the University’s Board of Trustees. Throughout the year we will periodically engage with a broad-based Advisory Group to share ideas and receive feedback. We have already gotten started, but now that some of the early-semester tasks are behind us I hope to really gather some momentum.

Most likely the topic of computing and information sciences at Michigan Tech will be the theme for this column, for much of the rest of this semester. The reader might wonder why I led off this story with a description of the change of seasons in the Upper Peninsula, which on the surface sounds a little ominous. You have to understand: I love the winters here at Michigan Tech. I am energized by the snow and all the winter sports that come with it. For me, this is not a time of hibernation, it’s a time of joy and rejuvenation, even during the shortest days and the darkest nights. I hope to bring some of that enthusiasm to the important task before us, and if we make any progress you will read about it right here.

– Dan

Daniel R. Fuhrmann
Dave House Professor and Chair
Department of Electrical and Computer Engineering
Michigan Technological University

Last weekend Michigan Tech was privileged to host Silicon Valley entrepreneur and writer Martin Ford, author of the NY Times bestseller Rise of the Robots, which is all about the disruptive changes in the recent past and future in the areas of robotics, control, and automation, and the implications for our society and our economy. I was able to join Mr. Ford for a couple of different question-and-answer sessions with interested faculty, and to attend his presentation at the Rozsa Center which was open to the general public. I found the entire day to be stimulating and compelling, and I was very happy about the fact that Career Services and the Rozsa Center were able to work together and pull this off. The evening presentation was very well attended and included a lot of students. I was impressed that so many people were willing to give up their Saturday to hear a PowerPoint presentation about automation – but it really was that good.

Ford’s basic premise was twofold. First, although there have always been concerns raised about changes in employment and the economy due to technological advances, going all the way back to the Luddite movement in 1811, this time things are different due to the nature of the technological advances themselves, primarily in the area of artificial intelligence and deep learning. Second, there has been a marked shift in the relationship between worker productivity and worker compensation, that has led to increased inequality and that will probably continue into the foreseeable future.

The argument that “this time it’s different” centers around the sudden relevance of artificial intelligence and machine learning in engineered systems. Artificial intelligence has been around a long time, and for most of that time I have thought of it as the technology of the future – always has been, always will be. Now, in the past 5-10 years or so, it is becoming the technology of the present. This is due to a couple of factors. One is, the raw computing horsepower needed to carry out artificial intelligence calculations is starting to become a reality, due to the inexorable march of Moore’s Law (which says that, essentially, computing power per unit area on integrated circuits doubles every 1.5 to 2 years.) The second is the algorithms themselves, which have been steadily improving in academic research labs for many years, and which are now getting a turbo boost of innovation in industrial research labs like those of Google and Facebook, who recognize the importance to their bottom line. As evidence that we have turned a corner in artificial intelligence, Ford and many others love to point to the IBM Watson 2011 victory in an exhibition match of the TV game show “Jeopardy” over two human champions Ken Jennings and Brad Rutter. More recently, a program called AlphaGo was developed by Google DeepMind in London to play the enormously complex game of Go, and in May of this year it defeated the No. 1 player in the world in a 3-game match in Wuzhen. What is particularly interesting about AlphaGo is that it is not based on a set of rules or heuristics, but rather it simply (perhaps not so simply) trained itself to play the game through a process of trial and error using the techniques of machine learning. This whole field of “deep learning”, based on artificial neural networks made bigger and better as a result of Moore’s Law, is taking Silicon Valley by storm and has really transformed the economic focus there from electronics to software.

[Aside: I have always maintained that the IBM Watson Jeopardy match was not a fair fight. To make it fair, the entire computing platform and its database would have to fit into a box no bigger than 1500 cubic centimeters, consume no more than 20W of power, and be silent when others are speaking. Watson was a very large computer consisting of multiple servers in a separate room with a very loud air conditioning system, and it had access to huge databases of information. Human players are not allowed to “phone a friend” during the match. The counterargument, I suppose, is that the human players had the advantage of 30+ years of training.]

The starting point for Ford’s argument on the economic disruption of automation is in the relationship between worker productivity and worker compensation. In what is considered by the many the “golden age” of American manufacturing, post-WWII, advances in tools and technology allowed workers to become more and more productive, according to a metric of goods and services produced per unit time. As a result, workers became more and more valuable and thus wages went up in lock-step with productivity. Sometime in the mid-1970s, however, this coupling was broken. Worker productivity continued to go up and up, but wages became flat. Ford often made the statement that, adjusted for inflation, American workers have not received a raise in 40 years. He attributes this to a shift from a situation where tools helped human workers be more productive, to a situation in which tools can simply replace the human workers. The situation continues to this day, and the outlook is for it to continue even more rapidly, leading to greater levels of income and wealth inequality and hence social disruption.

Asked whether he was an optimist or a pessimist, Ford responded that he was a pessimist in the short term, based on the realities on the ground, but that he is still an optimist in the long term when he thinks about human resilience and ingenuity. There are some serious problems we are going to have to come to grips with, but if we can work together to recognize and solve those problems, and maybe even get out in front of them, then there is still hope. He is realistic, but not all doom and gloom. I did find his approach different, and more down to earth, from that of other futurist authors I have read lately, some of whom are wildly optimistic about the future of the human race and its relationship to the machines we are creating.

I found myself nodding in agreement with most of the Ford’s points, and had my own takeaway messages. The first is, and I realize this may sound a bit selfish, this is a fantastic time to be an electrical or computer engineer or computer scientist, about to be entering those fields. The technologies of robotics, control, and automation are advancing rapidly, and the advances are not about to stop. We are the ones who are creating this technology, and thus we are the ones who are going to be in demand in the next few decades. Ford himself, knowing that he was at a technological university, made a couple of offhand remarks to the effect that “you guys are going to be OK for a while.” Those who are losing out socially and economically could say that we are part of the problem, and they may very well have a point, although I think as well-educated problem solvers there is every reason to think we can be part of the solution as well. But, setting that aside for a moment, from the individual point of view I would have to say that I cannot imagine a better career to be considering right now than something in the intersection of EE, CpE, and CS.

As evidence of that I would point to our very own Career Fair, which was held this week. Over 340 companies were on campus recruiting Michigan Tech students for co-ops, internships, and full-time. My friends over in Career Services tell me that everybody – everybody – is looking for more electrical engineers and computer engineers. We cannot fill the demand right now of all the companies looking to hire our students. This story is reflected also in national starting salary data. According to the Spring 2017 report of the National Association of Colleges and Employers (NACE) which covers hiring of the Class of 2016, the top starting salaries by major in the nation, for groups with sample sizes of 500 or more, were:

Computer Science $78,199
Computer Engineering $74,439
Electrical Engineering $70,950

In the interest of full disclosure, Petroleum Engineering and Operations Research were higher, with sample sizes of 184 and 64 respectively. Petroleum Engineering used to be much higher, like over $100,000, but that has come way down and is now comparable to Computer Science.

These salary numbers are echoed locally. According to our Career Services 2016 Annual Report, Michigan Tech electrical and computer engineers (which were lumped together) had a 99% placement rate and an average starting salary of $65,951, which was highest among majors in the College of Engineering. ECE was second only to Computer Science, which did very well with an average starting salary of $78,333. The ECE figure is lower than the national average, but it is worth pointing out that many of our graduates take positions in the upper Midwest which has a lower cost of living than California. Our starting salaries are very close to what is reported in the NACE survey for the Great Lakes Region: EE $65,815 and CpE $67,610. Our Career Services numbers are self-reported and must be taken with a grain of salt; nevertheless there is no question in my mind that our graduates are doing very well. I never hear complaints otherwise.

A second point I want to make that was sparked by Martin Ford’s presentation, although tangential to his primary message, has to do with the ascendancy of the overall field of computing relative to engineering. He said it right out of the gate, that all the action in Silicon Valley right now is in artificial intelligence and deep learning. Silicon Valley got its name and its reputation from the design and manufacture of integrated circuits, but that is now taking a back seat to software engineering. The four U.S. corporations with the largest market cap right now are Apple ($791B), Google/Alphabet ($662B), Facebook ($490B) and Amazon ($459B). Apple still manufactures products, and Amazon manages a massive product distribution system, but even so the backbone and the core competency of these companies is essentially software. There are areas where software engineering intersects traditional engineering, to be sure, and the most visible example of that right now is in autonomous vehicles. The reason that Google can get into this game in the first place is that they do not have to design the power train. The value added by taking a traditional vehicle and making it autonomous comes from a suite of sensors, a trunk full of computing hardware, and all the cognitive data processing and artificial intelligence algorithms that end up controlling the accelerator, the brakes, and the steering. I predict that over the next 10-20 years we are going to see a lot more of these systems where the technological advances are primarily on the computational side, not on the physical side. I also believe that we need to be doing more to prepare our engineering students for a world that will be dominated by computing and software, and I will have much more to say about that in future columns.

Clearly my two take-away messages above were not really what Martin Ford came to talk to us about. In the end he advocated for a couple of things. One was more education in the social and economic impact of robotics and automation, which is certainly something I support and which would make all the sense in the world as part of our general education program. The second was starting a conversation around the idea of a guaranteed universal income. I think he is an proponent for this idea, but he recognizes the enormous political challenges and was content on this trip just to get people to start thinking about it. So, I am starting to think about it. I’m not ready to jump up and down arguing on either side, but am willing to learn more and have the conversation.

Fall is coming slowly to the Keweenaw this year. It’s been a wet summer and fall, so the colors should be pretty good as long as we can get a good cold snap to bring them out. Not seeing that in the forecast yet. Have a great weekend everyone!

– Dan

Daniel R. Fuhrmann
Dave House Professor and Chair
Department of Electrical and Computer Engineering
Michigan Technological University