The painting above is by Victorian-era painter, John Atkinson Grimshaw from Leeds, UK. Have you seen one of these around? Seems like a peculiar question, but that is part of the attraction of art. Art is unique. Art is stuff that you rarely see. If you look around you now, many things are familiar and plentiful. The grass, the wall, the cup, the ice--you've seen many examples of these objects and while these are interesting, the word "art" is synonymous with that which you do not often see. Art has another attribute: artists excel in viewing the world differently and so, unlike in mathematics where the emphasis is to use a globally social agreed-upon notation, with art, it is the opposite--finding new ways of seeing one thing. These two attributes, 1) seeing a thing in many different ways, and 2) celebrating unique things, are key to understanding the arts. But things get more interesting when we consider what we might learn from art. We might feel that to interpret art, to understand it, we need to be artists, art historians, art aficionados, or art teachers. But art goes much deeper. Grimshaw's painting shows what is possible. There are sailing ships in the dock, street lights, and buildings. We can learn about the engineering of ships, the mathematics of hull design, the chemistry of different lighting methods, the weather at the time and its effects on light. STEM subjects, Science, Technology, Engineering and Mathematics, are easily studied with Grimshaw's painting as a knowledge portal. The object is a literal gateway to all knowledge, not just knowledge normally associated with studying within an arts-based discipline. You can connect many types of knowledge to this object. That the object is unique makes it enticing for learning. That the object is one of many ways of seeing a dock provides the impetus for a new way of art interpretation based on STEM.
Pokemon Go has become a huge sensation in the mobile gaming world since July 2016. You can download the Go app onto your phone, and then proceed to stroll while engaging with virtual objects that the game gives you along your journey. You get a set of Pokemon (creatures with specific types and abilities) as well as potions, balls you throw, and many other items. That is me above having just taken over a "gym" with my Jolteon. The virtual gym is physically connected to a gazebo next to a lake. It didn't last long, though, before someone in a jeep stopped near the gym, defeated my Pokemon, and sped off to conquer the next gym. Ouch! Well, I controlled it for 2 minutes. While we can say many good and bad things about Pokemon Go, perhaps the most important aspect of the game is that it is integrated into experiencing the real world. You go out, explore, look at things that are not only on computer screens. This game is a harbinger of things to come in education even though the technology for augmented reality and GPS tracking has been with us for some time. Many Pokestops (designated physical objects, often public art or signs) were designated in a prior Nintendo game called Ingress, and carried forward into Go. A Pokestop may be a bronze statue in a garden. Imagine the possibilities: being at the statue (because of Go) but then being able to learn many things from it: chemistry (oxidization and the nature of bronze), art (who made it), craft (how it was made), math (how it could be modeled). We've been working on an app for about 2 months that does this using bluetooth beacons so that connections to objects can be made indoors. Outdoors we can use GPS, similar to Pokemon Go. Why is our learning rooted in stuffy rooms with flat boards? We can learn the basics there, but why is learning relegated to indoor conversations beyond kindergarten? In kindergarten and in early grades, we actually did go outside and learn. For some reason, we stopped doing this. We have a long way to go. Smell the Poke roses.
Elon Musk has recently been quoted as mentioning the simulation hypothesis formulated by Nick Bostrom, a professor at Oxford University. Bostrom's hypothesis was published in 2003 and is available online. Nobody knows currently whether we are in a simulation, but the topic of simulation is worthy of discussion from the standpoint of the posthuman possibilities of our species. A serious problem with the hypothesis would seem to be that one needs to be outside, literally, an environment to create a model of it or to be aware that a model exists. When we create models of something, we study it (in the case of science) or design it (in the case of engineering). It (the phenomenon) is separate from us. Phenomena are simulations of themselves by definition. So, to say that "the earth is simulating itself" is reasonable. Simulations tend to strip away all but essential aspects of what is being studied. In the limit, the simulation becomes the phenomenon where nothing is abstracted away. If a posthuman civilization were to create a simulation with a highly-advanced AI (Artificial Intelligence) component, that component has no way of knowing its simulated status because it is part of the simulated system. The intelligent components of a hypothetical simulated environment can look for something odd or peculiar in their world, and then know that they are part of a designed system. With things like quantum mechanics, though, how could things get weirder? And yet quantum behavior is part of our natural world and so does not imply that we are in someone else's simulation. The movie, The Matrix has the right idea in its script of Neo who chooses to ingest the blue pill offered by Morpheus. Only by swallowing this pill will Neo be able to figure out that he is part of a simulation. Unless we can track down the equivalent of Morpheus, I am afraid we'll never know whether or not we are simulated. Speculation along these lines may be no different than engaging in theology.
Computer science is often taught by starting with computer programming. Programming is sometimes referred to as "coding" and a program, similarly, as "code." Whatever you call it, programming or coding, the activity permeates our lives. Just about everything has a microprocessor or microcontroller lurking within it, and these micro-machines execute programs. At a low-level, the program is "machine code" and at higher levels of human comprehension, the program is written in a language that you may have heard--or know--about such as Python, C, C++, or Java. But what if programming was something more--a way of thinking about the world rather than just a way to solve problems by constructing programs for computers? This represents an attitudinal change about programming and computer science. The above astronomical clock from the British Museum collection is a good example. This 18th century device has the key elements that are necessary to talk about programs. Programs are constructed of the following two key concepts: Memory (state,data) and Control (sequence, branching, iteration,feedback). If you saw the Imitation Game featuring Alan Turing, Turing not only designed physical machines, but he also designed an earlier mathematical machine that we now call the "Turing Machine." A Turing Machine contains just the right amount of Memory and Control required for doing universal computation. However, the key elements of memory and control can be interpreted in our world, and in the British Museum clock. The clock is not universal, but it contains the core abstractions that we prize in computer science. Want to learn computer science as more than a practical skill? Start interpreting.
Digital Humanities (DH)- what is it? If you look around within the humanities literature, you'll find lots of good examples of DH projects, and you'll also come across the ongoing debate: is DH a good or a bad thing? Does DH initiate a friction with traditional humanist approaches to scholarship? The problem is, without over-generalizing, that both proponents and critics of DH are failing to credit what is behind the DH movement. The research in DH has nothing intrinsically to do with technology, digital or otherwise. DH does, though, have a lot to do with augmenting humanistic methods of inquiry with scholarship in mathematics, science, and engineering (let's cluster these 3 areas to create SEM). Moretti with his book entitled Graphs, Maps, and Trees: Abstract Models for Literary History comes close to providing this perspective. Let's begin with graphs. Graphs originated through graph theory tracing its roots to 1735 through Leonhard Euler's writing. Trees are specific types of graphs (i.e., acyclic ones). If you are a DH practitioner using a network visualization tool, the tool is tangential--what is new is using discrete mathematics and computer science of networks which went into making the visualization tool. Let's give credit where it is due rather than this trend in framing DH in terms of technology and tools. The folks in mathematics and computer science produce scholarly work. DH is augmenting research methods in the humanities with research methods from mathematics and computer science. This non-technology argument comes to the rescue in common criticisms I have read about DH-- the critics usually frame an argument asking why "using Tool X" amounts to doing scholarship? Using Tool X is not scholarly. But, infusing computer science methods into humanities scholarship is scholarly. Forget the tool--the tool helps move things along quickly, but areas such as "database systems" define the theory that DHers use when they are using a database "tool." We all need tools, but tools are an outgrowth of more fundamental, and generally mature, SEM scholarship. The "T" is a practical outgrowth of SEM, most suited to what you will purchase next in Best Buy or on Amazon or Alibaba. Nice shiny tools. Let's not mistake the real contributions of DH with tools and technology. Time to credit the scholarship in SEM as one of the primary intellectual contributions to ground-breaking DH research.
Artificial Intelligence (AI) isn't a new phenomenon. For that matter much of what we value in the way of formalism in Computer Science, isn't new either since computers were analog (and human) before they were digital. Abstract concepts such as memory, state, event, iteration, and branching are ubiquitous in the real world. One exploring these concepts should not have to stare at a computer screen to learn them. The concepts are larger in scope than found in digital technologies. Jessica Riskin wrote a nice historical piece entitled Frolicsome Engines on the history of AI through mechanical automata, . In Computer Science (CS) we tend to be historically, if not culturally, illiterate. There are many issues at play here, with the main issue being that within Engineering, there are few electives since the goal is to educate students for specific skill sets. Maybe topics such as philosophy and history are tangential to CS? The core skill sets, theoretical or practical, stem from early mathematical research in the 1930s. Before the 1930s, I suppose we tend to think of the history of computing as non-existent. Do other areas in science or the liberal arts such as mathematics, physics, and chemistry suffer the same fate of removing history from their curricula? Do math teachers not talk of history when covering geometry, algebra, and calculus? I don't have a good answer to that, but I do need to start digging for answers.
Not a day goes by without reading about assaults on the arts and humanities. A recent New York Times article promotes STEM education and cuts liberal arts funding. But what is STEM and what is STEAM? I'd like to get at the heart of what the letters mean and then suggest we may wish to invest in SHAME. Let's begin with the last letter of STEM: "M" for Mathematics. Mathematics is a liberal art, and one of the original historical components of a liberal arts education. Math is relevant to just about everything, but often math is incorrectly made synonymous with symbolic notation. Those funny hieroglyphics. Math has nothing intrinsically to do with this notation; however. When we observe symmetry, color, or create abstract patterns of experience, we actively engage in mathematical thinking. Mathematics is the science of patterns, and through it we celebrate abstraction. Then we have "S"cience and "E"ngineering. Science is the study of nature and engineering is the study of manipulating nature to create products for human use. "T" is the odd one because "T" is not an academic discipline. Rather, "T"echnology is an end effect of science and engineering, flourishing in the marketplace of invention and commerce. You don't learn T as an academic subject, but you use T. Everyone does. Now, we have whittled the acronym down to create....SEM? But then here comes "A" for "A"rt (which is how folks get from STEM to STEAM). Each of these letters reflects a different way of looking at the world, using different lenses. If you go and talk to real people, they are hybrid mixes of all of these letters. People are not disciplines. But to achieve a better balance of letters, let's not forget the humanities. This gives us SHAME, because we should be ashamed not to use all of the letters to see the world in its diversity.
Last week, I had the good fortune to attend an exhibition at the Kimbell Art Museum in Fort Worth, Texas. The exhibition was entitled "Gustave Caillebotte: The Painter's Eye." The painting above is "Pont de L'Europe" (or "Europe bridge," because the bridge was situated at the crossroads of six roads, each named after a European capital). So, it is clear from (1) the title of the painting, and from (2) the subject (where the bridge trusses take up most of the painting), that the painting is about all about the bridge. Or it should be. We leave it to Joseph Black who created an interesting, and articulate, description of what we see. Black was a distinguished engineer. When I saw the painting, my gaze moved around the canvas but then focused on the iron bridge, since bridge construction is an interest of mine. The iron bridges were "high tech" in the 19th century--marvels of engineering. The man on the right hand side of the painting is looking at the rail yard for Gare St. Lazare. Looking at Caillebotte's painting and reading Black's description were treats. Contrast this with my experience of reading some of the other things about the painting that we find on Wikipedia. Let's see. "A dog walks away from the observer..", "She has been interpreted to be a prostitute...", "signaling his own homosexuality...". I don't think the Wikipedia editors get it. This is a painting about a bridge. The people are props. Leave it to an engineer to give us a reasonable interpretation when it comes to paintings containing beautiful structures.
You remember the library. It is where you can find lots of information about everything. But the library would not be anywhere near as fun and interesting without the librarians. This is because librarians guide us toward knowledge. But, sadly, we find items in the news such as this one from last year where libraries are struggling to survive. Take a step back from libraries and librarians to look at our landscape for knowledge delivery--specifically places of learning like K-12 schools, community colleges, universities, and museums. Those of us who work in these institutions are becoming more like librarians, and that is a cause for celebration since we are entering a new era for learning, and encountering new modes of knowledge engagement. Let me tell you about my evolution in teaching. At one time, I used to stand in front of the classroom and deliver discrete packets of information. This used to be called teaching, but this mode of learning is dead except that many of us have not yet realized it. We are still living in a dream from the last century. The flipped classroom is a sign of the future where we are becoming more like guides to facilitate learning. Students are assigned things to think about ahead of time, and the classroom experience becomes a place for active engagement. I recently visited the Plano ISD Academy High School, and was impressed because there were no teachers, but rather, facilitators. But it's not just about the flipped classroom where class time is devoted to real personalized learning. It is also about where the knowledge comes from. The knowledge in libraries is in books, journals, and media that come from outside of the library. This idea is rapidly occurring everywhere. Consider all of the online resources and digital academies -- we must let go of the idea that inside of our brick institutions, that we generate all of the knowledge. Forget that-- this mindset is unsustainable for the future of learning. This goes for museums as well as places for primary, secondary, and higher education. Embrace outside knowledge. Guide rather than dictate to the learner. With all of the diverse knowledge on the web, we cannot hire people fast enough to keep up using a not-invented-here (NIH) approach to knowledge. So, are librarians needed? Absolutely. You are evolving into one.
Full steam ahead. Or should I say STEAM ahead? STEM stands for Science, Technology, Engineering, and Mathematics and has been a driving force initiated by the National Science Foundation to focus education policy within technical areas and their associated disciplines. More recently, the letter "A" has been added to create a new movement called STEAM. The "A" stands for the arts, and according to a leading site devoted to STEAM, STEM + Art = STEAM. Since I spend much of my time thinking about the interconnections between STEM and the Arts, I welcome the STEAM movement. And yet, I have deep concerns about the movement's three published policy goals stated on the STEAM site: (1) transform research policy to place Art + Design at the center of STEM; (2) encourage integration of Art + Design in K–20 education; and (3) influence employers to hire artists and designers to drive innovation. These are worthwhile goals, but notice how all three goals seem to be about getting STEM-oriented folks to hire artists and designers, and placing art & design at the middle of STEM? Let's flip this. What about having STEM at the center of Art and Design? I am not suggesting doing away with the three STEAM goals, but I am recommending some sort of balance by extending or broadening these goals; the current ones are lopsided. I strongly advocate new ways of starting with design and the arts, and then surfacing STEM concepts from within art and design. For the STEM subset of computing, this advocacy resulted in the aesthetic computing movement. Recently, this approach has taken root in learning systems thinking in the art museum. I am not the first to suggest this if we consider the larger literature base of blending STEM with the Arts. Take Martin Kemp's book The Science of Art where he explores mathematics and optics via art. Also, the MIT Press Leonardo journals edited by Roger Malina has extensive historical coverage of intersections of STEM and the arts. Leonardo was founded in 1968, and so its publications contain a treasure trove of knowledge, suggesting new ways to get to the heart of STEAM. To advocates of STEAM, my suggestion is to rethink of STEAM as two-way traffic: two steam locomotives, two tracks, perhaps with some switches here and there.
Programming is a subset of system thinking and modeling. System models are often displayed in diagrams, but not always. Sometimes, a model may reflect a bi-directional flow, in which case, we may use an equation. The equals sign (=) in any equation is shorthand for two opposing arrows. I've collected four types of models in Pinterest. These are labeled FlowMixed, FlowRule, FlowData, and FlowControl. We really have only one type of flow since control flow is a type of data flow where the datum is a simple signal (such as a wave containing "squares", pulses, leading, or trailing edges). Still, control vs. data flow is a common distinction within the computer science literature. Here is a brief guide to the three major flow types: (1) control flow is like a relay race: a flow of control that is frequently in the form of a sequence. Food recipes or cartoons are good examples of control flow: 1, 2, 3, .... Branching occurs based on a decision or a comparison (e.g., in engineering, we have comparators, and humans or control systems use decision blocks). If the flow is all about decisions, we call it a decision tree. (2) data flow is more general since data are generally processed along the way. Most computers prior to World War II were analog, and thus, data flow. The ancient Antikythera mechanism was data flow. Mechanical clocks are data flow machines. The real world is saturated with data flow, which is why most science and engineering disciplines employ these types of models in physical systems. (3) Rules suggest "micro flows." If this then that. Often rules are good for modeling sensing, controlling, actuating in that order. Do you like thinking in terms of diagrams? If you do, go into engineering or system modeling. It is a great way of interpreting the world around you.