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.
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.
I just Googled "rhetoric." The top search result defines rhetoric as "the art of effective or persuasive speaking or writing, especially the use of figures of speech and other compositional techniques." Rhetoric is one of those topics that is fundamental to our society, and one of three ancient arts of discourse. But, something interesting about rhetoric has been going on for the last half-century, and modeling and simulation (M&S) is at the core of the excitement. A quick diversion to Syria and Palmyra with Wired Magazine's article entitled "A Jailed Activist's 3-D Models could save Syria History from ISIS." Bassel Khartabil created 3-D models of the ancient ruins of Palmyra and is currently jailed in Syria. There is a group of online "activists, archivists, and archaeologists" releasing 3D models under the name The New Palmyra Project. First, this is a welcome project and a great humanitarian cause. Second, New Palmyra is an example of how rhetoric has been changing in the digital age. Rhetoric is no longer limited to videos, photographs, and written texts. Models, in the form of models of geometry and dynamics, represent the new rhetorical force. If you want strength in your argument, you rely on models. The Climate Change 2014 Synthesis Report Summary for Policymakers is based on multiple models of climate. While data charts are interesting, it is what is hidden behind the data that is even more interesting: models of how climate changes, with its many effects (e.g., flooding, wind damage). Speaking as a member of the modeling and simulation discipline, we need to embrace modeling with a capital "M", meaning models of information, dynamics, and 3D models like those initiated by Bassel Khartabil.
I started playing chess at a young age when my uncle in England sent me a tiny plastic chess set for Christmas. What were these strange pieces? How did they move? Before long, I learned that they could make interesting patterns on the checkered board. I followed Fischer vs. Spassky with an almost religious fervor. Over time, I became interested in computer science and followed those who made chess machines and software. And then came the inevitable day when the machine beat the reigning world champion (Kasparov). What were we to do now? I guess there goes chess out the window. But no. Humans continued to play chess, and the game is as popular, or more, than ever. There a lesson here. Just because we teach machines to excel at artificial intelligence and at machine learning doesn't mean we stop our quest for life-long learning and enjoyment. Big data is hot. The machine can run through an array of sophisticated algorithms so that, for instance, your search engine experience is more meaningful. I am grateful for this capability and the research that goes into it. Think of the massively complex data networks and automated inferences and patterns generated from them. And yet, I find myself interested in teaching students to draw small networks for things that they see around them. By doing this, students learn something about semantic networks and concept maps (ideas developed by artificial intelligence researchers in the 1970s). The learning that occurs is personal and in this case, does not require the big. It requires an attention to detail and a never-ending fascination with discovery.
What is computing? What is computer science? These questions would seem to have easy answers, but the field of computer science is still in its infancy compared with fields such as physics, and the mothership--mathematics. The term "computer science," somewhat unfortunately, seems inextricably linked to a family of artificial devices we call computers. But can you think of any other major discipline with this characteristic? We do not refer to astronomy as telescope science, chemistry as mass-spectrometry science, or mathematics as compass-and-rule science. In mathematics, for instance, educators will observe mathematics at play in nature. In the above image, the mathematical symmetry of the pagoda and the geometric branching structure of trees and bushes are all too evident. And yet, in computer science, we seem fixated on the box. However, computing, with its focus on information, has potentially a much larger role to play in our world. Recently, we created another video that dives into computing to illustrate three major paradigms, ways of seeing information management and flow outside of the box. None of this is to downplay the relevance of post 1940s growth in what we know as computer science today. Computers assist us with our daily chores, our workplace tasks, and our entertainment options. But if computer science is to approach the ubiquity of mathematics in our world, we must venture beyond "code" and back to the idea that computing can be just as much about describing what we see, and how we see it--in the wild through an information lens.
I recently engaged in a three-way podcast conversation covering research that we do in the CA lab, as well as activities in the Creative Automata class that I teach--if that is even the right word. Guide? The title of this post is gleaned from Christopher White who works with Elecia White. I engaged in dialogue with both of them, and thoroughly enjoyed our discussion. Elecia and Chris produce a podcast called Embedded where the main theme is embedded systems and electronics. But they tackle a wide variety of interesting topics around this central theme. This audio podcast name Bubblesort Yourself was invented by Elecia, and the hour long podcast can be found here. Their Embedded podcast can also be accessed using the Apple podcast app or the equivalent app on Android phones and tablets. I listen to their podcasts regularly, and also to other podcasts while I take long walks. For some of you, driving the car or working out in the gym may be good times for podcast listening. Chris White also posted an accompanying blog entry where he expands upon formalized synesthesia. Is that what we do when we model in simulation? It seems to be on the basis that we employ many models, each of which contains a hidden set of analogies. The models are encoded with respect to our senses [credit: artwork Synesthesia above is from Nuno de Matox].
If you go to Google Images, and you type in the word "modeling," if you are like me, you might expect to see all sorts of equations, diagrams, and software interfaces allowing scientists and engineers to model complicated things. Instead, from the public's perspective, or perhaps from the perspective of the advertising industry, modeling means to model clothing. Fashion. Runways. The above image is from Top Ten Modeling Agencies. At first glance, this type of modeling is something we might be tempted to dismiss as irrelevant to our supposedly higher ambitions within mathematics, science, and engineering. But, this type of modeling is ubiquitous and serves as a good way to talk about modeling to others. Why not talk about models by covering fashion? You probably have heard of a "model house" or a "model kitchen." This type of model refers to model as prototype. Rather than modeling a pre-existing phenomenon like automobile traffic or heat exchange, model as prototype brings in design as a type of modeling. The model house may be one you'd like to live in. The model kitchen gives you ideas of how you'd like your kitchen to look and function. So, if you want to explain what you do as a modeler to others, begin with common terms and experiences. Meet people on their ground, with their understanding of model. And wear something suitable like a well-designed pair of jeans. You might be surprised when the next day, your friend shows up wearing the same clothing. You have become a model. [Source concept: from a column that I did long ago for the Society for Modeling & Simulation International].
In a recent audio podcast, three of us were discussing personalized modeling from different angles--including using art and craft-inspiration, and engineering culture. Karen Doore, Sharon Hewitt, and I engaged in a short conversation that is part of a series of podcasts called Creative Disturbance. Anyone who has been to a modeling and simulation conference notices that...the people attending are all quite different. Often having different degrees and from different departments and schools. There is good reason for this: modeling is inherently an area that connects different people and things together. This diversity plays out, also, in our modeling choices. What is your favorite modeling system or language? What underlying analogies are used?
Most of our research in the Creative Automata Lab is devoted to better understanding mathematics and dynamic system modeling through multiple modalities and representations. This strategy is partially art-based, and stresses an individual orientation toward education rather than one based on standard notations pushed to the masses. The lab stresses having more people understand modeling. Last month, I was intrigued by news of someone in the UK holding a professorship entitled the Public Understanding of Philosophy. And I found information on two faculty (Richard Dawkins and Marcus du Sautoy) who hold the title of Simonyi Professor for the Public Understanding of Science at Oxford. The emphasis on public understanding of an academic area has a strong fit with our lab goals. But, there is something deeper happening: Ideally, all university faculty should strive toward a public understanding of their disciplinary topics. Engaging the public directly, and speaking more broadly about an area, should be explicitly encouraged and rewarded by university administration at all levels. As faculty, we need to maintain deep disciplinary depth, but we must also strive to gently establish tendrils throughout the university knowledge infrastructure. A justification for this need can be seen in the latest version of National Geographic entitled "Why do Many Reasonable People Doubt Science?" Perhaps fewer people would doubt science if universities made a stronger effort at public outreach and communication. Public outreach is not a speciality; it should be a job requirement within the academy. Publishing in a society transactions moves a field forward, expanding our essential knowledge base. Talking and publishing to a wider audience brings more people into our fields. More people on the planet become better educated. If we try, we can achieve both breadth and depth of knowledge. If you are a faculty member at a college or university, you find yourself in a park on one side of a bridge. The public is waiting for you on the other side. Meet in the middle?
Much of what we do in the Creative Automata (CA) Lab is oriented around multiple representations of a single abstract mathematical concept--such as integration in calculus or sorting in computer science. How can we personalize approaches for learning something like integration? Is it possible to leverage our multiple cultures to engage and motivate the learner? The lab just submitted our video entry to the National Academy of Engineering (NAE) Grand Challenges for Engineering Video Contest called E4U2. Sharon Hewitt from the CA Lab designed and produced this video. The video segments include representations of a virtual analog computer based on the sand-like flow in PowderToy, as well as several personalized models of the Lotka Volterra model. Instead of making models for other people, consider that you can learn about modeling by making these wonders for yourself. In this arts-based approach, you will also interest other people in modeling.