Edge Cases

Technology has changed our world in unprecedented ways. Ugh. That first sentence sounds like one of those standardized ‘suggestions’ of how to begin an essay you’re writing for a school assignment. It’s true, but it’s also drab.

 

Your standard computer as an example of technology? Boring. Maybe because of its ubiquity and its eclipse by tablets and smartphones.

 

What’s more exciting? What’s cutting-edge? What’s creative? The new buzzword is machine-learning, previously better known as artificial intelligence. Or if you wanted to take the learning and intelligence out of it, these are automated algorithmic systems programmed for a specific task. That sounds more boring, but it’s the boring you have to be careful of if you worry whether you’ll be replaced by a robot. See my previous blog post about Futureproof where author Kevin Roose hammers this point home.

 

Today’s blog post is about a different book: Artificial Unintelligence by Meredith Broussard. While presently an academic in the journalism school at NYU, Broussard is also a hacker and has the skillz, both in computation and communication. Her specialty is data journalism. She’s not afraid to get into the trenches even when it means riding a ‘startup bus’ packed with too many people, junk food, and all manner of cords for one’s technology – all while trying to create an app to win kudos at a hackathon. I admire her gumption. Her experience is one interesting story among others; I also learned about self-driving cars, campaign-finance rules, the history of computing, and how our obsession with rankings has obscured the difference between the popular and the good. Broussard is an excellent storyteller even as she peppers you with data tables and lines of Python code.

 

Broussard thinks that the general A.I. portrayed in Hollywood movies and dystopian fiction is a mirage. They make for exciting stories perhaps, but are unlikely to live up to their potential. No, she doesn’t think the apocalyptic Singularity is nigh. Narrow A.I., on the other hand, deserves our attention both journalistic and otherwise. What seems boring will change our lives in ways we might not like if we don’t pay attention. Many of our twenty-first centuries problems are intertwined with issues of seemingly boring technology coupled with human greed and indifference. But like Roose, Broussard provides a positive counterweight framing to the problem: the edge cases.

 

Automation can be a good thing in many cases. Broussard write: “[It] will handle a lot of the mundane work; it won’t handle the edge cases. The edge cases require hand curation. You need to build in human effort for the edge cases, or they won’t get done. It’s also important not to expect that technology will take care of the edge cases. Effective, human-centered design requires the engineer to acknowledge that sometimes, you’ll have to finish the job by hand if you want it done.” And here Broussard refers to doing something well in a broader sense of the word.

 

What can narrow A.I. do in my area of teaching and education? Certainly, automation can take care of many mundane tasks – record keeping for example. For many introductory math and science classes, it can deliver homework problems and grade them! It will even mix up the variables so different students get different numbers, which means when they help each other they’ll need to know how to solve a problem and can’t just copy each other’s answers (at least the numerical ones). If there’s a task that many teachers would like to avoid, grading ranks very high. So-called adaptive learning is the rising star with its claims of personalizing the learning process – the ever-patient tutor who curates questions at the right level to help you advance your learning and “knows” when you’ve progressed sufficiently to move you to the next level. What does this require? In short, the ability to atomize knowledge, which I’ve argued is a questionable assumption.

 

But there are many steps in the learning process that can be atomized. My job as a teacher is to break down complex material into digestible steps for the student. After doing it for many years, I have a good idea where students get stuck, where the tricky bits are, and how to use different analogies and models to help illuminate abstract ideas. Some of this can be parameterized into an A.I. tutor. We’re still in the early days of this revolution despite the occasional grandiose claim, and I expect to see more progress in this area.

 

Can an automated tutor system handle the edge cases? Not in all cases, but it is likely to make inroads into those edges as such systems improve. How sizable are the edges? It depends on what you mean by learning and how you determine whether a student has learned or not. One danger is letting the boring technology define and determine what constitutes learning. A follow-up danger is allowing the black box machines to categorize us into boxes (the irony!) – something that’s happening at an alarming rate in so-called dynamic pricing systems be it in retail or insurance. Data is not destiny. Increasing its density does not make data necessarily better if you don’t understand its blind spots. Broussard’s many examples show us why this is so and why narrow A.I. works as well as it does. The edge might be larger than you think if you don’t stop to look at the bigger picture. The narrow A.I. cannot see the big picture, hence you might call it artificially Unintelligent. This is a good distinction to keep in mind.

 

One thing I can do well that an A.I. cannot at the moment, is answering questions from students, or eliciting the gaps in knowledge by asking follow-up questions. When you don’t know something, it’s hard to come up with a well-posed question. Human expert intelligence is particularly good at divining these cases and getting to the bottom of the root question efficiently. That’s why I’m still needed as a human educator – besides the human connection, which I think is just as if not more important. But will A.I. be able to increasingly handle that task well? It’s hard to make predictions. Especially about the future. And edge cases, by their very nature are the hardest to predict. Automated systems need predictability to be trained to work well. Real human behavior is not so predictable. Perhaps our humanity is the ultimate edge case.

Sciencery

I’m now three books into Terry Pratchett’s Discworld series involving the hapless wizard-protagonist Rincewind, who once again has to save the world. Grudgingly, of course. He’s the anti-hero and prefers a non-life-threatening life, but life seems to have different ideas. This time around, the magic of wizards will be threatened by the magic of sourcerers. No, that’s not a spelling typo. Sourcerers draw their magic directly from its source, bypassing the need for learning complicated incantations and hand-motions. I wonder if they somehow break the Law of Conservation of Reality, but sadly this is not addressed in the third book, aptly titled Sourcery. 

 

However, there is a remarkable side passage that deals with what I will dub “Sciencery”, that odd practice of its acolytes, of whom I am one of many – scientists. I will quote parts of it since paraphrasing Pratchett is nigh impossible and nowhere as fun.

 

It is a well-known and established fact throughout the many-dimensional worlds of the multiverse that most really great discoveries are owed to one brief moment of inspiration. There’s a lot of spadework first, of course, but what clinches the whole thing is the sight of, say, a falling apple or boiling kettle or the water slopping over the edge of the bath. Something goes click inside the observer’s head and then everything falls into place. The shape of DNA, it is popularly said, owes its discovery to the chance sight of a spiral staircase when the scientist’s mind was just at the right receptive temperature. Had he used the lift [elevator], the whole science of genetics might have been a good deal different.

 

Coincidentally, this week I was telling one of my research students about the tedious slog that accompanies most of research, but also the ‘high’ you get when something seems to just work out in one glorious gestalt moment. Pratchett’s prose, of course, is tongue-in-cheek. One might think of adages such as “Inspiration is 99% Perspiration”, but it is interesting how one gets these ‘aha’ moments. Chemistry has its famous iconic ones such as Kekule’s telling of his serpent-eating-its-tail dream. This past Friday, I told students about Archimedes and his eureka moment, upon which we proceeded to do calculations on gold-plated crowns and discussed the practicality of measuring water displacement when submerging said crowns.

 

But Pratchett has a twist on this story, so I’ll quote what follows.

 

This is thought of as somehow wonderful. It isn’t. It is tragic. Little particles of inspiration sleet through the universe all the time, traveling through the densest matter the way that a neutrino passes through a candyfloss haystack, and most of them miss. Even worse, most of the ones that hit the exact cerebral target hit the wrong one.

 

For example, the weird dream about a lead doughnut on a mile-high gantry, which in the right mind would have the catalyst for the invention of repressed-gravitational electricity generation (a cheap and inexhaustible and totally non-polluting form of power which the world in question had been seeking for centuries, and for the lack of which it was plunged into a terrible and pointless war) was in fact had by a small and bewildered duck.

 

Oh, well. There went our chances of a workable fusion reactor. We’ll have to wait until the real Iron Man comes along and invents his new element – a subject for one of my later classes on the periodic table. In the meantime, what are my chances of being hit by an ‘inspiration particle’? I might have had one this morning in my state of hypnagogia, just as I was waking up but still having strange thoughts. At the very least I sorta think I have a new idea to analyze some data if the current simpler approach fails. Not mind-bending or profound in any way. Too bad I don’t remember the wild dream that preceded it. Now if only the inspirational particles hit when I’m conscious and ready for it.

 

Sciencery seems like a lot more work compared to Sourcery. In that sense, it’s more like the Wizardry practiced by the denizens of Unseen University in Discworld. There’s a lot of studying by the lower echelons, and then a lot of backstabbing your way into the upper echelons. Or I should say it resembles dysfunctional academia. The strong and cunning survive, and if they bide their time, one might even become the Archchancellor of Unseen University, who as Pratchett says:

 

…was the official leader of all the Wizards on the Disc. Once upon a time it had meant that he would be the most powerful in the handling of magic, but times were a lot quieter now and, to be honest, senior wizards tended to look upon actual magic as a bit beneath them. They tended to prefer administration, which was safer and nearly as much fun, and also big dinners.

 

Perhaps more Cornelius Fudge than Albus Dumbledore? But there’s always the power-hungry wizard who is also magically powerful, and who think that might makes right. But he’s also trying to avoid his date with Death – yes, Voldermort, that’s you. There’s a sort of symmetry between the antagonist in Sourcery and that of the Harry Potter series. But this stock character is true of many other stories – perhaps telling us something about human nature and the corruptibility of power. Perhaps then it’s a good thing that Sciencery is so much more difficult than Sourcery, and that the Law of Conservation of Reality kicks in when needed.

 

P.S. Here’s a reference for actual neutrinos, those ghostly particles. How do you trap ghosts anyway?

First Week: Masked Edition

Our science building is bustling with students in the hallways, wandering around looking for their classes – not helped by the arcane numbering system of rooms and doors. One also enters the main doors on the third floor, while most of our lecture classrooms are on the first and second floors. We’re all masked, so it’s hard for me to recognize students. Thankfully, they recognize me and at least some of them are not shy about greetings. In several of these encounters with students who I’ve never seen in person but only through a Zoom box, I was able to remember the student’s name before he or she had to tell me. In other instances, my puzzled look prompted an introduction – after which, it seemed obvious when you know what to look for.

 

My main worry was teaching while masked. Microphones were hurriedly installed over the past weekend into classrooms that lacked them. They worked okay, but they’re stationary, and I’m always walk back and forth across the board as I write with occasional stops at the projection screen to explain a figure. Thankfully, the rooms aren’t that large, and the students at the back gave me the thumbs-up when I asked if they could hear me fine. My P-Chem class is a little smaller this semester, and I was pleased to walk in the first day to see them occupying the front rows. (In my larger G-Chem sections, the rooms are more full.) My masked regularly dipped while I was talking and gesticulating so my left hand was busy pushing it back up so it wouldn’t drop below by nose.

 

I was rusty in-person after the two-year hiatus. Last year we were all remote. And the year before, I was on sabbatical and not teaching. I pontificated more than I needed do, and covered less material than I had planned. But there were also additional housekeeping items we had to talk about, for example, my new office hour protocol so that students don’t crowd the narrow hallways of faculty office suites. I’ve had my first student visits and so far the protocol seems to work fine. There were also LMS problems related to first-day-access of the e-Textbook and online homework system. I had hoped the relevant folks had ironed out the problems from last semester where it was a fiasco, but no such luck. Students are frustrated through no fault of their own. That being said, the problem only seems to affect a few students and not the majority even though everyone is following the same protocol. The reasons are unclear.

 

I’m teaching three classes on MWF. P-Chem first, then an early lunch break for an hour before two back-to-back G-Chem classes. (It’s my first time teaching back-to-back classes.) After the first day of classes, I was exhausted. My body was not used to it. Teaching while masked might be a contributor. Overall, my teaching wasn’t great, but it wasn’t terrible. The next class meetings were better; I’m slowly building back the stamina. Thankfully, we have a long weekend with the U.S. Labour Day holiday so I’ll have a shorter upcoming work week.

 

This week I also trained new research students. I’ve cut down the session to just one full day by streamlining certain parts, making better handouts, and moving some other topics to when-needed sometime later in the semester. I’m excited to be rolling out a couple of new projects, and I think I’ve done a better job preparing templates to help my students organize their results. It will also help me when the time comes to write up a research article.

 

For the first time in a while, I am not an academic adviser for new incoming first-year students. This would have involved additional meetings this week and much more prep the week before. I had the luxury of spending last week focusing on class prep so that things are not as frantic when the semester begins. Still, there was a lot of running around this week with all those little tasks that add up. But I survived. Hopefully next week, I’ll thrive, even if masked.

Grand Creation

When describing my research to a broader audience, I’ve been using the following mantra: “Things that persist, persist. Things that don’t, don’t.” It’s a good segue into describing simple chemical autocatalytic cycles. Anyway, I finally looked up the source of that quote and traced it to one Steve Grand in his book Creation, published back in 2000. I promptly went to the library, borrowed the book, and read it cover-to-cover. 

 

I had never heard of Steve Grand who became famous for creating the popular computer game Creatures in the 1990s. I don’t recall ever hearing about Creatures, a game where players nurture alien species named Norns, helping them to grow and learn within a virtual environment. I did hear about tamagotchis, so-called handheld “digital pets”, which function on a similar premise. Never having played with either, I plead ignorance to the aficionados who can point out the dissimilarities between the two.

 

Creation is a strange book, at least when reading it twenty years later having spent a bunch of time reading and thinking about complex systems while probing the question “what is life?”. I recognized much of what Grand was trying to describe. I agree with him that reductionism is flawed when it comes to describing biology, and that one needs to analyze life at different functional levels rather than just focus on its material mechanistic aspects – especially tempting to a chemist who is trained to think at the molecular level of action. As I work my way through Rosen’s much heavier treatise on the nature of dynamics and what it means when we try to model a system, it seems that Grand has worked his way to similar conclusions without the heavy theory.

 

But not having the theory means that Grand struggles to describe what seems indescribable, and one gets the sense that he is grasping whatever straw-like metaphors he can come up with. I say this with the utmost respect for his book, since I don’t think I could put into coherent words the swirl of thoughts I have about similar questions. I appreciate how challenging it is, and that’s why I find Creation to be a strange book. It attempts to appeal to a general audience with an interesting hook (designing life-like organisms in a virtual world), but feels nebulous when you read it. If I read it twenty years ago, I don’t think I would appreciate how challenging it is to describe what Grand attempts. The subtitle of his book is “Life and How to Make It”. It’s a grand goal (pun intended), but not an easy one because the notion of life, and in particular organic life, is more slippery than an eel.

 

For those Creature gaming enthusiasts who read Creation, Grand does a nice job describing what goes into his mind as a designer to try and give the user-player the feel of life. For someone like me who has never played the game, I found it interesting because his approach is quite different from how I would approach the problem. Our goals are different, of course, but I think game designers and scientists can learn from each other in this respect. Grand doesn’t worry about the mechanics at the molecular level; what he does is use a neural net to mimic feedback and feedforward loops. It’s elegant in its own way, and I’ve been pondering how some of his ideas can be married into graph theory to solve the problems I’m working on. I particularly appreciated the way he approached the analog-digital divide. And with his computer-programmer perspective, seeing movement as a copy-and-erasure process when applied in the virtual space of bits, was eye-opening to me.

 

I enjoy having my mind stretched. Grand’s book does that in a way that’s different from most of what I’ve read, which has tended towards the dense and academic. But it’s still a strange book that doesn’t fit any particular niche. Perhaps I did read it twenty years too late. Grand doesn’t worry about getting things right, like the scientist would. It’s as if he’s feeling his way towards the solution of a problem like going into a dark room arms stretched out to figure out what’s around you. Like an explorer! Perhaps it is not so different from a broader notion of research after all.

 

P.S. The title of this blog post comes from the book spine which essentially reads “Grand Creation”

Fading Summer

Classes start next week. I think I’m ready. Or at least I’ve spent most of the last two weeks getting ready. Syllabi are done. LMS content is ready-to-go. I had a training session yesterday with media services to learn how to use the new technology in our classrooms, given that I was teaching completely remotely last year and was away on sabbatical the previous year. We now have fancy cameras installed in each classroom. I don’t plan on using them because my college is planning all in-person classes (masked). But there needs to be contingency plan if a student had to quarantine, so I have one in theory which I hope I won’t have to put into practice.

 

Office hours will be interesting. I am offering them hybrid, i.e., I will be logged on to Zoom but students can also visit me in my office (masked). However, before they can enter the suite leading to my office, they’ll have to Zoom in first before getting permission to physically enter so that we can all maintain lower human density in the vicinity of a bunch of small rooms. Not sure yet how this will work. One of my classes is the math-symbol-heavy P-Chem, and I’m encouraging my students to come in-person when possible. My other two classes are G-Chem and online Q&A generally works fine, with only slightly reduced efficiency.

 

My original plan over the summer was to spend about two thirds of my time on research and a third on class prep because I am overhauling my P-Chem class. We won’t be using a textbook and I’m making worksheets for every class meeting. At this point I’ve done the bare minimum; I have enough material prepared to get me through the first four weeks. I only started working on this in earnest several weeks ago rather than starting in June like I originally planned.

 

The other unexpected activity this summer was doing a chunk of data analysis on “student success” in my department particularly where it relates to different student groups (race/ethnicity, socioeconomic status, whether a student transferred from a different institution or is a first-generation college student, etc.) I was able to obtain a reasonably sized data set from the institutional repository and I spent probably a solid two weeks writing scripts to analyze the data. I’m not done yet, but I did put together a presentation for my department. I think it was time well-spent even though it wasn’t originally in my summer plan.

 

The rest of my summer was earmarked for two things: writing a paper and learning some new research methodology. I’m pleased to say I succeeded in the first endeavor, and I submitted the paper last week. The figures took a long time to make, but I’m quite proud of this paper because I think I pulled together some creative ideas amidst the research data. We’ll see what the reviewers think, but I won’t know for another month or two. The second item on my list is still ongoing. I haven’t made as much progress as I would like although I’ve slogged through some challenging reading with many equations I don’t understand. I also spent a solid few weeks trying to make progress on a new project – I ran a bunch of test simulations and collected a bunch of data, but I’m not sure if I made much of a dent on the problem I’m working on. I have a suspicion that my current approach is flawed.

 

I did enjoy working mostly from home all summer (I went into the office once a week for a half-day or so). I’m pleased that I was rather disciplined about partitioning my time between work and relaxation at home. I suppose having no travel plans thanks to Covid meant that there was simply a lot of staying in. I enjoyed having lunch at home, and occasionally walking in the park after lunch. Not having to commute saved me 40 minutes every day, and probably several tanks of gasoline.

 

But summer is fading and the new semester is upon me. I’m actually excited about being back in-person, having run into several students on campus that I had only met in class over Zoom. (I recognized some, but not others; we’re harder to identify when we’re all masked.) I’m not looking forward to teaching while masked, but my classrooms aren’t very big so I hope my voice carries sufficiently. Otherwise I’ll have to get a wireless microphone and amplifier. I have new research students that I’ll be training next week. And soon there will be meetings aplenty. I’ve enjoyed the quietness of the summer but I suppose it’s time to get back into the bustle. I have one more weekend to enjoy before summer fades!

Futureproof

Can I be replaced by a robot? Maybe. If you can break my job down into discrete repetitive tasks that can be encapsulated into an algorithm such that it satisfies a particular goal that can be assessed by measurement. Sounds like a test. I mean that literally – one tests the algorithmic system to see if it matches the desired outcomes. Multiple tests preferably, so you can collect statistics to see if your test is both valid and reliable.

 

How might I prevent myself from being replaced by a robot? That’s the subject of Futureproof by Kevin Roose, where he provides “9 Rules for Humans in the Age of Automation”. Roose does not classify himself as an optimist or pessimist, but rather as a sub-optimist, which he defines as “while our worst fears about A.I. and automation may not play out, there are real, urgent threats that require our attention.” He probably wouldn’t classify that as a sub-optimal position to take.

 

While A.I. in its many guises is much discussed in his book, Roose makes a useful distinction between A.I. (the subset) from its broader realm of Automation. The latter does not necessarily involve machine-learning – today’s new catchphrase for grant applications. Whatever can be automated soon will be. We’ve seen many examples from the first industrial revolution to today. However, it will also depend on the acceptable error rate. Who determines this or how it will be determined is a matter of debate that should involve a much wider group beyond technologists and technocrats.

 

A distinction I found most useful in Roose’s telling of the tale is the difference between machine-assisted and machine-managed processes. The former is job-enhancing, and frequently used examples come from techno-optimists. The latter is soul-sucking, and provides the necessary pessimistic counterpoint. I certainly use technology in my job, and that use has increased over time, taking a quantum leap thanks to Covid-19. I’ve blogged about the pros and cons of how I spent my Covid year; in summary, I didn’t like it but it wasn’t as bad as I had anticipated.

 

So, which parts of my job are machine-assisted and which parts are machine-managed? Let’s try a few examples. E-mail has changed how I communicate with students and colleagues – there’s a lot more of it. What’s nice: it’s efficient, it keeps records, it allows me to think before responding asynchronously, and I don’t have to be spatially co-located. What’s not-so-nice: it can be a taskmaster in more ways than one because of its advantages. What do I do? I don’t have it open all the time, checking only once every hour or so during “work hours” and not at all other times. I happen to be in a job which hardly ever involves a life-or-death situation. And students and colleagues can be trained to not expect replies during evenings and weekends.

 

Example Two: A web site to deliver course materials. Until Covid which forced my complete use of the LMS, I delivered materials through my simple HTML-hacked website. It was easy to change things on the fly without having to print or re-print, be it class notes, problem sets, quizzes, syllabus items, and many more. Coupled with something like Microsoft Word that allows me to modify and reuse documents, this has been a huge time-saver. I remember the old days when I would handwrite most things – faster and easier than using a typewriter. (Having weak fingers, I was a one-fingered typist on those older machines, but I’m very quick on the modern QWERTY soft-touch keyboard where multiple digits are used.) I can’t think of drawbacks to my simple use of these tools, but I do not like the enforced LMS categories which might look fancier but turn out to be less efficient at least for the way I teach, presumably referred to as old-school.

 

Now let’s tackle whether my job can be atomized and algorithm-ized. I’m in the business of helping students learn chemistry. The end goal is that students have learned the chemistry I wanted them to know. Often that chemistry is “standardized”, i.e., chemistry college courses all over the country have similar content and skills we want the students to master, at least for standard courses such as G-Chem, O-Chem, P-Chem, etc. How do we assess whether students have learned the material? Typically through some final assessment that may be an exam or project or portfolio or paper. Could a machine conduct and “score” that final assessment? For a multiple-choice exam, certainly. For other formats, let’s just say that machines are getting much, much better. Whether the assessment fairly evaluates student knowledge (a rubric is like Swiss Cheese focusing on the holes!) is a different matter altogether and a much longer discussion.

 

Instead, let’s ask whether given the assessment tool, an algorithm can now be devised that leads the student through a process that improves their score on that assessment. I suspect the answer to this is yes, and that the error rate is reducing over time. We call this “teaching to the test”. I don’t mean that in a derogatory way. In a sense, all teaching is to the test. We have final goals in mind that we want to assess, and we want to train our students to reach those goals. If the test is standardized, it’s likely that the learning process to reach it can be atomized and algorithmized – minimally in a Swiss Cheese manner that assumes reduced parts capture the whole. So, could a robot do my job? Under the circumstances and constraints I have proposed, I think the answer is yes.

 

What is my added value as a human instructor? Is it presumptuous to think I add value? I’d like to think that knowledge and life cannot be ultimately atomized and algorithmized, and therefore cannot be automated. Parts of it can – the parts that are reducible – but others cannot be because they can’t be part-itioned. Living systems are likely one of those irreducible things. Hence, part of my job (oh, the irony) is to constantly allude to those complex things. They’re never easy to define – fundamental things never are – but we can get at such systems with many different examples that complement each other to some extent.

 

Roose’s book suggests habits-of-mind to do so in his nine rules, the most useful of which is Rule #1: “Be Surprising, Social, and Scarce”. A.I. is not so good in these areas, at least for now, and perhaps for a long time if indeed these are parts of what it means to be complex and not just complicated. I can see how being Surprising or being Social can be construed as complex. I’d replace Scarce with Unique or Rare, which captures its meaning better. Roose provides examples for these, but the question is how this applies to my specific job. Thinking about how I teach, and how this has evolved over the years, has energized me for the upcoming semester. I feel I’ve unconsciously gone in the direction of providing the extra sauce that machines cannot provide in thinking more deeply about the conceptual parts of my classes and conveying that in multiple exams that defy a simple definition or description. Chemistry looks supremely organized from a bird’s-eye-view, when you think about an entity such as the Periodic Table, but as you take a closer look it becomes so much more messy, complicated, and interesting!

 

The other rules take different angles; I’ll highlight a few with a short sentence or two.

·      Rule #2: “Resist Machine Drift” reminds us to not let machine-recommended systems drive what we read or consume online. Reset your browser. Venture out of your comfort zone.

·      Rule #5: “Don’t Be an Endpoint” asks you to take a close look at whether your job involves helping two machines talk to each other simply because different systems haven’t perfected direct communication yet. You might think teaching would be immune to this, but I was recently at a vendor presentation where they were giving you as a teacher everything that you needed electronically, so that a new teacher could just use their materials out-of-the-box. You’re there to help connect the learning system and the assessment system by being a friendly face and answering some questions the system can’t yet handle.

·      Rule #6: “Treat A.I. Like a Chimp Army” is obvious.

 

I thought that reading Futureproof would make be despondent about the potential of losing my job to a robot. However, it galvanized me to think about teaching and learning at a fundamental level and the key role played by human-to-human connection (mediated or not by technology), and how to continue leverage technology in a machine-assisted manner to improve the process. And this makes me excited about meeting my students face-to-face again this upcoming semester! What makes me futureproof is to continue engaging in the conversation of what’s important and why in my field of teaching and learning more broadly. Even more exciting, my research now involves thinking about systems, algorithms, complexity, and the limits of reductionism. How exciting!

Reality and Chaos

It’s been a while since I blogged about the intersection of science and magic, the reason I started writing Potions for Muggles. And while this is something I still find interesting, in reality I have too many interests and read too many books. While randomly coming across The Science of Dune, which I wrote about recently, I also noticed there’s a Science of Discworld series. I’ve only read one Pratchett book, and it was so long ago (two to three decades), I’m not sure which one. (I suspect Guards! Guards!)  I did remember it being wacky and chaotic. I had not learned to appreciate it yet.

 

Thanks to the internet and Wikipedia, I discovered a flowchart for the Discworld novels. The Science series is an offshoot of the Rincewind series. I suppose this is fitting, since the Rincewind series begins with The Colour of Magic, also the first book about Discworld (I think). Thanks to my local library, I borrowed a battered old copy (cover shown below) and read it this weekend. It’s a short novel and a riot to read, as in chaotic riot. Or should there be riotous chaos? It reminded me of Jasper Fforde’s series that I discovered several years ago. This has helped me appreciate the chaos amidst a reality of some sort.

 

The main protagonist of The Colour of Magic is named Rincewind, a wizard who flunked out of magical school – for reasons that are (partially) explained in the book. In a series of unfortunate events, he takes up with a clueless tourist named Twoflower who hails from a different realm and is looking for adventure after apparently reading about the adventurous deeds of colourful characters in Rincewind’s realm. Twoflower travels with a strange many-legged magical Luggage box that stores his belongings, and sports a Demon-in-the-Box polaroid camera. Yes, there is a homunculus-like demon who operates the box. He remains unnamed, but I vote for Maxwell.

 

What is the colour of magic? Given my interest in how magic might be transduced via the electromagnetic spectrum, and the fact that Pratchett-fans lobbied for a new element to be named after this colour – I mentioned this in a previous post – it was fun and interesting to explore the wacky world of science and magic in Discworld. Reality meets chaos is how I would summarize Pratchett’s formula. But there are rules. Of a sort. Here’s Rincewind trying to explain some theory:

 

[Rincewind] tried to explain that magic had indeed once been wild and lawless, but had been tamed back in the mists of time by the Olden Ones, who had bound it to obey among other things the Law of Conservation of Reality; this demanded that the effort to achieve a goal should be the same regardless of the means used. In practical terms this meant that, say, creating the illusion of a glass of wine was relatively easy, since it involved merely the subtle shifting of light patterns. On the other hand, lifting a genuine wineglass a few feet in the air by sheer mental energy required several hours of systematic preparation if the wizard wished to prevent the simple principle of leverage from flicking his brain out through his ears. He went on to add that some of the ancient magic could still be found it in its raw state, recognizable – to the initiated – by…

 

You’ll have to read the book for yourself if you want to know more. Today’s quotes are found in Part 2, “The Sending of Eight” – yes, there is a magic number too (cubed). Interestingly, I’ve had my students go through a similar exercise, but I didn’t have the wit to call it the Law of Conservation of Reality. I’m stealing that phrase for the next time I facilitate a similar discussion. In any case, the adventures of Rincewind and Twoflower get progressively wackier, but there are limits. Ummm… conservation limits of some sort where reality smacks you in the face. I don’t pretend to understand how this works (maybe the Science series will be illuminating), but here’s another colourful description by Pratchett:

 

But Time, having initially gone for the throat, was now setting out to complete the job. The boiling interface between decaying magic and ascendant entropy roared down…

 

Yet amidst all the chaos:

 

There was a sound on the edge of Rincewind’s hearing. It sounded like several skulls bouncing down the steps of some distant dungeon.

 

Perhaps gods do play dice with the universe. I should remember this every time I sit down to play a boardgame.