Boy Scouts of America: Science Outreach that Lasts

I hiked around Ithaca, NY, the day before the 4th International Conference on Computational Sustainability at Cornell University, and happened upon a knickknack shop, where I found a cache of 1963 Boy Scout merit badge pamphlets. I bought two that I didn’t already have — Gardening and Bookbinding. The Gardening pamphlet was written by Professor Paul Work of Cornell University, probably in the 1940s when the material was copyrighted. Professor Work died in 1959, after a distinguished career that included The Tomato — if you scroll down a bit, you’ll see that Professor Work apparently liked to put faces to science.

I haven’t researched the history yet, but Boy Scout merit badges are my earliest recollection, as a scout myself, of formalized mechanisms of promoting lifelong and project-based learning through badging, and communicating science and technology to the public. Professor Work’s outreach on gardening may seem closer to hobbyist than to scientific material, but there is science outreach in that badge, and among the other original 1911 merit badges were those that were clearly science outreach and learning, including Astronomy, Ornithology (later Bird Study), Chemistry, and Electricity. Still others of the originals had additional sustainability connections, to include Conservation, Agriculture, and Forestry.

The Boy Scouts of America (BSA) are one of the very first environmental groups in America, and while BSA has been "dragged kicking and screaming" into inclusiveness on some social issues (see Treehugger article), though they are coming along, they have been environmentalists consistently. The current crop of sustainability-relevant merit badges are many: Animal Science; Architecture; Bird Study; Composite Materials; Energy; Environmental Science; Fish and Wildlife Management; Forestry; Geology; Insect Study; Landscape Architecture; Mammal Study; Mining in Society; Nature; Nuclear Science; Oceanography; Plant Science; Reptile and Amphibian Study; Soil and Water Conservation; and Sustainability. Moreover, among the required badges for Eagle Scout is either Environmental Science or Sustainability (choose at least one). A history of all merit badges, past and present, is an interesting read, ..., for those interested (like me!).  

After CompSust-2016, I went to Nashville’s Scout shop and picked up many of the study pamphlets for sustainability-related merit badges, and I was gratified to find attention to climate change in the most recent Sustainability merit badge (instituted 2013), and as importantly, global warming, climate change, and greenhouse effects have found their way into the study pamphlets of older merit badges like Chemistry, Weather, Environmental Science, and others. This article in Treehugger points to exactly the same satisfaction and mild surprise that I found in the BSA environmental record since I was last active.

BSA has a long history of technology-relevant merit badges too (e.g Machinery, 1911 - 1995). In “my day” there were badges on Computers (1967-2014), Electronics (1963 - ), Engineering (1967 - ), which has morphed and grown to include Digital Technology; Robotics; Programming; Geocaching; Game Design; Entrepreneurship; and Graphic Arts. And this brings me to a desire and goal of infusing computational sustainability (i.e., the application of computing to solve sustainability challenges) or CompSust for short, into the BSA merit badge system. While I have focused on BSA, which is integral to my personal story, I am learning about Girl Scouts of the USA (GSUSA) and their badging system, with goals for CompSust outreach in GSUSA as well.

Scouting has a long and proven history of science and engineering outreach (as well as Arts and Humanities outreach — just look at the merit badge list)! So its no surprise that as part of a network funded by the National Science Foundation, we are investigating the outreach possibilities with BSA and GSUSA -- we want science and engineering outreach mechanisms that will operate beyond the institutions of the network and that will persist beyond the period that we are funded. Web searches with keywords such as “NSF” (or “National Science Foundation”), “Boy Scouts” and “merit badge” show that NSF proposals include “outreach” activities with scouting, and merit badge workshops and study groups (e.g., “CAREER: Computational Modeling of Microstructure Evolution during Vapor Deposition”). Additional poking around finds that museums around the country work with scouts as part of the museum’s disciplinary outreach (e.g., Nashville’s Adventure Science Museum). Museums and other institutions can have their own (digital) badging systems, and so we are designing the desiderata, requirements, and graphic designs of CompSust badges.

Our network can aspire to create BSA and GSUSA merit badges on Computational Sustainability, but in the near term, our focus is on workshop materials that scouts and their mentors can use to integrate computing into satisfaction of sustainability-themed badge requirements, and to integrate sustainability into computing-themed badges.

I think that the “secret formula” of BSA is that the library and internet research involved in merit badges, ecology-themed and otherwise, are side by side with merit badges (and Eagle projects, and other activities) that get adherents out into the world, with “active study” in areas such as Backpacking; Cooking; Gardening; Scuba Diving; Search and Rescue; Climbing; Shooting; Fishing; and Citizenship in the Community, Nation, and the World. All of these activities bonded me with nature and my fellows, and BSA helped me amalgamate an appreciation of nature, citizenship, science, and humanities. BSA did its job very well.

Thanks to Professor Paul Work too, for being a pioneer in communicating science to the public. In part, it was serendipity that I discovered him, but it was serendipity that was made more probable by my curiosity about and appreciation for the place I was in.

AI ten years later

The 10 year anniversary of my interview on Artificial Intelligence (AI) with Adelyn Jones on Lightning 100 just passed.  I’m not really surprised that much of the interview is as relevant today as it was then, at least at the level of abstraction that we were talking. I think that some my attitudes have changed though -- I seem more open to some of the blue sky at the end of the interview today than I was then. Blue sky expectations are also what brings in so many new students to AI, who are then disappointed when AI is taught as a collection of disparate tools, rather than an integrative whole. I want to deliver integrated, blue sky AI to them -- I had the same expectation of AI as an undergraduate too! So part of my renewed openness is constrained wishful thinking. 

We didn’t plan the interview ahead of time, but editing is a great thing -- I remember helping with the edits after the interview -- the whole process was a revelation, which I remembered when I was Director of Vanderbilt Institute for Digital Learning. The interview audio recording is at  https://goo.gl/98oZHE .  

00:00 Cool music and Adelyn’s intro

00:45 What is AI? Exploring, evaluating, and acting on alternatives was my answer then, and I still think is definitional of intelligence, at least in part

01:35 What are AI applications? Medicine, surgery, military, space exploration, game playing, story telling were areas I highlighted. I would now stress environmental and energy applications.

02:30 How are AIs programmed? I talked about AI developers defining analogs to grammars by generalizing experience. This was an impromptu response that I nonetheless think is essentially true, at least if “grammars” is broadly construed. AI also includes other methodologies for which the “exploring alternatives” through “grammars” are less accurate descriptors of what happens.

03:30 Drawbacks of AI? The legal implications (e.g., can you hold an AI responsible for wrongful behavior?) were a concern then, in reference to medical diagnostic systems, for example. I just attended an AI and Law conference (http://watsonesq.org/) in which these same issues are discussed, albeit were not the focus of the conference.

04:35 What are you (me) working on? At the time I was working on machine learning for cancer informatics with Mary Edgerton, and military personal assistants, and I mentioned some other applications at Vanderbilt, including tutoring and story (cartoon) remixing. I went to the National Science Foundation not long after this interview, and it opened me up to a whole new set of possibilities, including AI storytelling and AI and sustainability -- being opened to all new possibilities comes with some pros and cons, but no regrets.

05:35 What is in its infancy? Self-driving cars as the infant that would become the adult of intelligent highways, was (and still is) a good example. Intelligent highways are still a long ways off though, in large part because of economics I think -- the technology is probably close to ready, but how long before large numbers of individuals can afford smart cars, and cities can afford to (and will) retrofit road networks and other infrastructure? A long time I think. I think thats the case with much of AI blue sky -- there are large disincentives for what doing is technically possible, or will be technically possible. That said, a lot more cars are “talking” to each other now than they were in 2006, broadcasting traffic conditions, and there are probably discernible smart herds of cars operating on dumb highways.

06:30 More on strategic choice of applications: Automation through AIs are attractive for support tasks, with across-domain recommender systems and personal assistants. And “support” for humans varies from diagnosis of ailments (in support of doctors) to “support” on routine tasks like vacuuming. 

08:19 How human-like can an AI become? We got into affective computing, particularly the mimicking, perceiving, and feeling of emotion. This was also a topic at the Law and AI conference, and affective computing is an area that I think has taken off in the last 10 years generally, though its introduction to AI certainly predates 2006 (e.g., http://affect.media.mit.edu/).

09:25 Are Phony emotions good? No way -- we have enough phoniness, and we don’t need machines to practice it -- it would have negative consequences for our perceptions of personhood, including disillusionment (http://www.vuse.vanderbilt.edu/~dfisher/ai.theology.html), but I think there are important caveats here.

10:18 Why is affective computing a goal? Among the caveats are that sensing emotion can be useful, when humans are in the loop. But here too, I am conflicted and there is otherwise plenty of nuance. For example, I saw PARO, the robotic seal (e.g., https://www.youtube.com/watch?v=2ZUn9qtG8ow) for hospice patients. The seal is phony, but it is still a positive presence for some -- and remember FERBY (http://www.radiolab.org/story/137469-furbidden-knowledge/)!

10:52 How is emotion perception programmed into a machine? We talked about machine learning of associations between emotions and facial expressions and physiological readings -- machine learning of this type (supervised, as clarified below) is still all the rage. I’m not as interested as I once was in the science of machine learning as it is currently practiced, but the applications of it are ubiquitous and often very interesting.

11:55 Clarification on Supervised learning of emotion recognition through sight and sound. We talked about the help window of a department of motor vehicles as example -- this was a fun exchange, with some of it undoubtedly edited out -- I was truly relaxed by this point in the interview.

13:25 How does the machine learn to respond in emotion charged interactions? A machine is good at “sitting there and taking it” :-)

14:50 What happens when we create a self-aware AI? What are its rights? This was right out of an earlier write up on the theological implications of artificial intelligence (http://www.vuse.vanderbilt.edu/~dfisher/ai.theology.html), something I may get more into one day.

16:00 Economic disincentives for creating genuinely feeling robots -- see 05:35.

16:19 Feeling robots as surrogates for humans in science fiction -- again, the AI and theology commentary at 14:50 is relevant here.

16:35 What are differences between AI hardware platforms and the human brain? (I was winging it, but I think I got the big picture right)

17:45 Many in AI are not interested in human intelligence, so much as they are interested in “alien intelligence” -- anything that gets job done (for the pragmatists) and/or a genuine curiosity with all the possible kinds of intelligences that can exist. Who is to say that a whale’s intelligence resembles ours in all or even most aspects?

18:28 Is downloading the mind a possibility? I think I would be more accepting of this possibility, which is blue sky for sure, but after 10 years, I am much more appreciative that the future is a big place. I don’t know what’s possible. And I want to build a planetary AI environmental consciousness -- almost as crazy.

Thanks for the memories, AJ!

Idea for a Class Exercise: Reverse Engineering Annoying and Embedded AI Software

I initially blamed age for a sudden increase in the number of "typos" that were actually correctly spelled words, but out of context. Then I started suspecting that stupid (or rather annoying!) word completion algorithms was responsible, and recently I've started catching it. My favorite artificial intelligence (AI) gaffs are

"wild west" ended up as "mild west" (the 'w' in "wild" changed to 'm' mid-way through the phrase though I didn't correct it until I was done ripping off text)

"dinner party" ==> "sinner party"

"cutting and pasting" ==> "cussing and pasting"

These mistakes lead to funny results -- in most cases the auto-correction on various platforms is just plain screwy, and in other cases of course it works well, but these are generally cases when I've completed the word, its flagged as an incorrect spelling with red underline and there is a change. It seems like some software is just trying to be TOO proactive and TOO helpful, perhaps like some people are some of the time.

I would prefer slightly more patient AI ... AI without the emotional needs.

Having my AI students do a thought experiment, reverse engineering such software, will be a neat exercise in my AI class, I think, because while this software is really, really annoying, and just plain messed up, it (or its developers) is a lot more interesting and instructive in its neediness than I first appreciated!

Playing with Pictures

I originally posted the following on my now extinguished Woodpress blog in
August 2011, and used the story in my Fall 2011 AI class as an example of problem solving that we'd eventually like computers to do.

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During a driving tour of the Midwest in July that Pat and I made in our new Honda Fit, I was continually posting pictures on Facebook in a vacation album. Less than midway through I exhausted the 200 picture limit per album and was tempted to start new albums, one for each day or two, but a 200 picture limit is plenty I thought, and I liked the idea of using the constraint to prune out all but my favorites and pictures that were not thematically redundant; I also constrained myself to keep those already receiving a thumbs up or comment, etc.  After the trip I was invited onto Google+ by Russ and Mary Lou, and Google (Picasa) has no album limit that I can tell, so there are 750+ pictures there!  (https://picasaweb.google.com/106374191437655932029/SummerVacation2011 ) Talk about a lack of discipline.

A very cool functionality is that I can locate these pictures on Google maps, using any of the modalities – maps, satellite view, street view, or Earth.  In locations with sufficient resolution I could place the photo right on the spot I was standing when I took the picture, though in some cases there appears to be some drift from the location I placed it when I look back. There didn’t appear to be a way to specify orientation of the photo – what direction I was facing when I took it, but I am guessing someone will do that in the near future. In any case, it’s very cool.

Since I was placing the pictures a couple of weeks after the trip, there were different heuristics I used to place them – sometimes it was straightforward – a particular highway junction, or something otherwise named like a school or a cemetery or a mountain peak on Google maps. The order in which pictures were taken offered some constraints, since having located one picture narrowed the possible locations for the next, but frankly, I have a good memory for such things as events and sequences. In one case though, even with some known restricted area stemming from sequencing information, I was trying to locate a picture in the tiny town of Scribner, Nebraska, but I saw no way to identify the precise location of a picture I had taken of an old church or the like, with a steeple (attached below). I was in the satellite view, maximum resolution, struggling to see some identifying visual cues, but the steeple itself was impossible to make out from a direct overhead view, …, and then I saw the SHADOW of the steeple in the satellite image !!! Amazing!! I’m attaching that image to this note. That was just neat.

I had so much fun that I created a couple of other albums on Picasa. One of these was from my trip to Copenhagen in 2009, while at NSF (https://picasaweb.google.com/106374191437655932029/Copenhagen2009 ; http://doughfisher.blogspot.com/2013/07/copenhagen-2009.html). I had taken a redeye from Dulles in DC to Copenhagen, arriving about 7 or 8 AM the day BEFORE the conference would start in a port city some ways away. I never sleep on planes, not even redeye flights, so I was pretty trashed when I arrived, but how often do you I go to Copenhagen! (OK, I’d been there is 2008 as well). So before taking the train to the Helsingor, I walked around Copenhagen. Even though it had been more than two years previously, I remembered the sequence well and was able to place the pictures in the same way I had for our recent vacation, including confirming the exact location of a picture of a statue from its shadow! Statue and shadow attached.

What was even more striking in this case than our recent vacation was the affect of reliving that walk as I placed the pictures – I saw the images; remembered roughly the walking sequence, using cues from Google views to fill in a few gaps and otherwise reduce uncertainties. A good friend had died not long before that trip, and that had been on my mind during the stroll of Copenhagen, and a hint of that emotion in the form of reflection came back.

I had so much fun doing the picture placement that I’m trying to think about how to formalize the activity as a project for my artificial intelligence class this coming semester. There is also a good human-computer interface problem here – in many cases I could only approximately place the pictures (e.g., highway shots on our driving vacation) and representing the variable uncertainty associated with physical location would be desirable.

I am writing this note on a whim – I watched the last installment of Ken Burns’ National Parks, and was remembering my Boy Scout days of backpacking through places like Tuolumne Meadows in Yosemite and Mt Whitney in Sequoia National Park. I’ve got some great stories of the Colorado river trip and bears raiding our campground in Yosemite, but I think my favorite trip was Kings Canyon, which probably started outside Mammoth, but in any case, we hiked among some incredible lakes, most above timberline: Thousand islands, Emerald, Ruby, Garnet and Shadow lakes. There is nothing like hiking above timberline along a ridge when a wind hits your back -- really an amazing feeling.

My crispest memories are probably of Garnet Lake – it was amazing when I was a boy scout, and I returned in graduate school with friends Rogers and Pete. In any case, I looked for some pictures on the Web and found these: http://www-personal.umich.edu/~jensenl/visuals/album/2006/thousand/ . And many others of course. Scroll down – there are some very nice pictures here and I remember more than a few scenes – I could point out the little island in Garnet Lake I swam too and almost died (joking, sort of) – it was freezing! The places we cooked and washed. And I can probably place some of the pictures on the trail map.

This recent play with pictures suggests some possibilities with immersion into virtual worlds – the technology is pretty primitive now, but because its piggy backing on memory of real experience, the affect is quite powerful.

Scribner Steeple (above)
Scribner Google Map image, with shadow! (below)

Copenhagen Statue (above)
Copenhagen Google Map image, with shadow! (below)

Reusing other Instructor's Assignments ... not! (or ?)

I am in the Educational Advances in Artificial Intelligence (EAAI-13), and we just concluded a session on educational repositories, particularly online repositories of homework assignments. Repositories of educational resources is a topic near and dear to my heart, but at least in the case of repositories of homework assignments, there appears to be no, little, or at best weak anecdotal evidence that assignments are being reused. At a minimum, don't we want repositories to be "instrumented,", like my (and everyone's) YouTube channel(s), so I can see downloads, likes, dislikes, and more sophisticated measures of usage that are specific to homework assignments?

Its hard to know if a homework assignment that has been posted in a educational repository is actually used by another instructor, unless an instructor who has used it, gets back to me and tells me so. There is some work in thinking about how to do this. But there is also low hanging fruit. First, we can measure downloads, but beyond this, as an educational community can take a small step towards a scholarly culture surrounding education materials by designing licenses specific to this kind of content.

For example, a license for usage of educational content could require that the material can be used by others (e.g., following any of the principles of creative commons licenses: http://creativecommons.org/), but additionally require that the user report back on the usage to the author (typically, the copyright holder), whether the use is as is, or derivative.

I think that this would be an incredible help to evaluating the extent and manner of use of educational material, going well beyond measuring downloads, and ultimately of evaluating the utility of educational materials to the educational community.

Let's ask people about their use, through a license that requires report back (and nothing else), rather than simply depending of the ability of inference by machine methods.

The Pumping Lemma

This was originally posted in October 2008 while I was at the National Science Foundation (NSF)  in Arlington, Virginia -- the 12th floor of NSF was the big room, but this post is primarily on the wonders of computer science.
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My talk on "the 12th floor" yesterday went well, and I'm here on the second floor of the Arlington library, looking out on a beautiful day ... maybe a matinee later, and DC tomorrow.



I've been remembering the Pumping Lemma, a little gem from computer science (http://en.wikipedia.org/wiki/Pumping_lemma). The Pumping Lemma says that for certain languages that have an infinite number of possible sentences, there is a fragment (e.g., "big") of a sentence that is 'long' enough ("You are making a big mistake"), which you can repeat an arbitrary number of times (or exclude in some cases), and you will still have a (legal) sentence -- so if "big" is the fragment, then "You are making a mistake" is a sentence (excluding "big" from the original), and "You are making a big, big mistake" and You are making a big, big, big, mistake" are sentences (if you know enough to note the comma, you know enough to know the fix; and I don't think that English is one of the "certain" languages for which the Pumping lemma covers "in all cases", but it's ok). More generally, if I've got an adjective in a sentence, I could repeat that adjective or any other for that matter an arbitrary number of times, and still end up with a (legal) sentence.



What does this have to do with anything? I first saw the pumping lemma(s) about age 21, my first or second quarter at UC Irvine, after transferring schools (UCSC, USNA) and changing majors twice before, having my heart broken a few times, etc :-) When I saw these lemmas and their proofs I was struck by the formal beauty of them, but I also saw them as descriptive metaphors of my life to date -- you can repeat the some mistakes over and over, but still have hope of completing a sentence -- I'm serious, I was wow'd by it, as well as by the metaphorical significance of other gems in computing and mathematics. 



There are some parts of a partial sentence, of course, that you can't repeat arbitrarily and have hope of completing a sentence; for example, you can't write "You are making a a " and have any hope of completing the sentence so it's legal -- once you repeat that 'a' in the way written, you end up in a 'dead' state. And you can of course keep repeating the "big" and never get to the final "mistake" before you "run out of time", and since "You are making a big, big,..., big" isn't a legal sentence, again, its a "dead" state -- both of these examples have formal interpretation. And of course, the repetition (or pumping) need not be metaphorical of mistakes, but successes, and a lot of in between.



The pumping lemmas are just concerned with syntax and not semantics -- you can understand and appreciate a string such as "Made you a mistake big" or even "You are making a big, ...., big", but I'm retaining hope for a sentence, and a compound, rich sentence at that. 



Thank goodness for the pumping lemmas -- I've reflected on their lessons since age 21 :-). Hallelujah.

Goldbach's Conjecture, Turing Machines, and Artificial Intelligence

When I was a graduate student I'd work on proving Goldbach's Conjecture when I needed a break from my real research. I'd focus on what this Wikipedia article (http://en.wikipedia.org/wiki/Goldbach's_conjecture) calls the strong form : every even natural number (aka even positive integer) greater than 5 can be expressed as the sum of two prime numbers. So, for example, 6 = 3 + 3, 8 = 5 + 3, 10 = 5 + 5 (and 7 + 3), 12 = 7 + 5, .... Again, this is a conjecture that is believed to be true by virtually everone and its truth has been demonstrated with computers up to huge even numbers, but no one has proved its truth for all even numbers, and there are an infinity of them.

The really attractive thing about number theory is that so many of the problems are so easy to understand by so many -- you may not be able to solve the problem, but you sure understand what's being asked! An approach I hit upon to prove Goldbach's conjecture (or I suppose disprove it, or perhaps that you could'nt prove it one way or the other!) was essentially this, write a computer program that ran forever (if you were to run it), generating the even natural numbers one after the other, and write another computer program that ran forever (again, only if you were to actually run it), that generated all the sums of two primes "in sequence", and then show that the two programs were equivalent. Unfortunately, that last step is REALLY, REALLY hard, if doable at all, but fortunately my PhD research took off about this time and I did that instead, much to the relief of my wife, parents, and in-laws!

But now, just as I want my artificial intelligence students to find projects of interest, this is the project that I want to return to. Its been about 3 years since I've done my own substantive computer programming, and its probably been 15 years since I've done substantive programming in the LISP language. So this will be fun! I can trivially write a program that generates all even natural numbers greater than 5: (defun GenEven () (do ((i 3 (+ 1 i))) (t (princ (* 2 i))))). A program that generates the sum of all pairs of primes is a good deal more complicated, because in general each addend needs to be verified as prime (http://en.wikipedia.org/wiki/Prime_number). In fact, one way to write this second program is simply to write a program that generates all prime numbers, and then "append" it to a copy of itself, and as each copy produces a prime the sum is output. However we write the second, what we imagine is something remarkable -- that the latter very complicated program is equivalent to the former very simple program.

It would be tempting to spend a good deal of time making each of these programs as concise or as efficient as possible, but you see, I am never going to run either program. If I am biased in any direction it is that each program be as "unstructured" and as "primitive" as possible, because once these programs are defined, a third program, an AI program, is going to search for a sequence of rewrites that will transform one program into the other, while provably maintaining the original functionality of each. The third (AI) program is the one that will actually be run, and I'll be writing this program in Lisp. But the two programs, one for generating the even numbers and one for generating the sums of prime pairs, I'm imagining will be written in the most primitive of languages -- the language for programming (or defining) a Turing Machine -- a simple form of computer, but not a computer that you would ever power up -- a Turing Machine is strictly a theoretical device (http://en.wikipedia.org/wiki/Turing_machine).

The reason for the bias of starting with as unstructured and primitive as programs as possible is that though there are optimizations in the test for primality, for example, which I could reflect in my initial programs, these optimizations reflect patterns that almost certainly have been exploited in explorations of Goldbach’s conjecture by better minds than mine. It may be that any proof, if one is possible, has to rely on reasoning that is just off (human-conceived) map.

I'd actually started this process as a grad, exploring the ways to bridge these two programs, via an AI program that searched through billions of possible rewrites. I'm essentially an experimentalist and I start with code and looking for data -- that's my bread and butter. I think that what I am really doing is shaping my retirement 20 years from now (or less, for Pete's sake). When friends visit and ask Pat where I am, she'll point to the shed and tell them that I'm working on "that proof". More likely, I’ll be tinkering with the AI program itself, making sure that there are no bugs in it — can you imagine my despair, if near the end of my life and after searching billions of rewrites, my program comes back with “Proof Found!”, and I didn’t correctly save the path my AI program took to get there!?

The older I get the more I remind myself of my father.

(originally posted Thursday, August 20, 2009 on Wordpress)

Pedagogical benefits of bottlenecks

Back in the day of my initial computer programming classes we used punch cards (http://en.wikipedia.org/wiki/Punched_card). One card held one line of computer code, so that a 100-line program, which is a small program, required a deck of 100 cards.

To prepare and run my program required I get in a line for the next available key punch machine -- I typed my program out on a keyboard and coded holes were punched into cards. I might have to wait 30 minutes for a free machine on the night before an assignment was due, then taking another 15-30 minutes to punch my cards. When I was done, I went to another line, with a 5-10 min wait, and ran my deck through the mainframe-computer's card reader -- it flipped through the card deck like a card shark flips through playing cards, reading the holes in the cards as they sped by. After this, my program waited in the computer's internal queue until its turn came, about 20 minutes, and the computer "ran" (aka executed) my program, printing out the results *IF* my program "worked" (but, sigh :-( even then the results or OUTPUT was often wrong, a result of semantic or "runtime" errors), or the "line" printer (a massive thing that printed on large rolls of paper) would print out my program, flagging syntax errors that had prevented my program from running at all. If this all seems like a drag, it wasn't really -- the night before an assignment was due was a party -- computer science was probably the most social major on campus. More recently than back-in-the-day, personal laptops have come to dominate and students often work in their dorm rooms (sigh), but I hope that computer science education is as social as it once was, albeit in different forms.

The simple physical operations that I had to perform to correct and run my program and get the results back was about (45min+5min+20min =) 70 minutes on a busy night!! Before I got back in that line for the key punch machine, I rolled out my printout and studied it for at least 30-60 minutes, maybe longer, maybe much longer -- if I found the bugs that appeared to be the problem, I didn't stop there, but studied the entire program looking for more, because there surely were more bugs and its usually the case that the "real bug" is NOT in the vicinity of its manifestation. No professor or textbook berated me to take a global view of the code, to go beyond the immediate symptoms and look for causes -- it was the time bottleneck, the 70 minute response time, that encouraged, even demanded extensive thought on my part. In writing even a several-hundred line program, I actually ran the program only a handful of times.

Since back-in-the-day, programming development environments have gotten much better and computer response times have decreased drastically. A student can make a change to a program, hit "run", and have the results of the run back before they've finished blinking, at least for the programs of complexity that novice-to-intermediate students will run (in contrast, while in grad school and not that long ago as a faculty member, I wrote and ran programs that might take a week).

But lesser response times (i.e., faster) and friendlier programming environments are not all good news -- not for novice programmers trying to become expert anyways, though they might think otherwise. Unfortunately, it seems that the decrease in response time is accompanied by a decrease in thought time. The removal of a time bottleneck encourages a local change, hit run to see what happens, change, run, change, run, change, run ... anyways, this is my experience as an instructor. A student might (try to) run the program a hundred times using this knee-jerk debugging strategy, and because the strategy focuses on local changes, not benefiting from reorganizations that stem from a global view, the code is far less elegant and more brittle.

Among the experienced programmer, fast response is a godsend, but its a bane to the novice programmer in training, whether the student knows it or not.

I want to know whether this correlation between computer response time and programmer think time is really true, particularly among novices. And I'm very concerned with what analog correlations exist with other technologies and the influence of such correlations with respect to human sustainability.

When I have the time, so wonderful to think about (having time), I'll be contemplating bottlenecks, how they promote the long view, the global view, particularly as they relate to computing and sustainability. I like the idea of bottlenecks that actively teach and reason with you, even as they slow you down -- another note.

BTW -- one of the greats in CS, Edsger Dijkstra, went so far as to suggest that the new CS programmer shouldn't be able to access a computer for a year, so I recall. You ought to be able to write correct code for even complicated tasks without getting feedback from a computer at all -- amazing, but I believe it.

(originally posted on Wordpress blog: May 20, 2009)

Update on MOOCs in support of blended courses

In October 2012 I gave several talks on my experiences with using MOOCs (or simply using material from MOOCs) in my regular Vanderbilt courses (e.g., http://vimeo.com/53361649, with my presentation starting at about 26:40, speaking from slides at https://my.vanderbilt.edu/douglasfisher/files/2012/02/ITHAKA-Presentation-10-16-12.pdf); a text summary of my experience up to that time can be found on the Chronicle of Higher Education's ProfHacker blog at http://chronicle.com/blogs/profhacker/warming-up-to-moocs/44022.

As part of each of these presentations (ITHAKA S+R, UNCC, CUMU-12), I illustrated the breadth of computer science course offerings online with 4 slides (https://my.vanderbilt.edu/douglasfisher/files/2012/02/CSMajorOnline.pdf ), showing that one could come close to fulfilling the course requirements of a typical CS major online and free. I was also presenting this material from the perspective of an instructor, so my focus was on how instructors could add to online content, allowing others still to customize, drawing from expanding online content.

Since these presentations and the ProfHacker post, my graduate "Individual Studies" course (CS 390) in Fall 2012 on Machine Learning has finished; this course was a "wrapper" around Andrew Ng's  COURSERA (Stanford) course. The requirements of the CS 390 included the requirements (quizzes, homeworks) of the COURSERA course. There were 10 graduate students who completed the CS 390 course. You can look at the details of the course organization, roughly a cross between a more structured upper division undergrad class (the COURSERA component) and a graduate seminar course (the face-to-face component), at https://my.vanderbilt.edu/cs390fall2012/, should you wish. Rather than feeling more like a TA (it has been suggested by some that faculty roles might morph into glorified TAs under a blended model), I felt LESS like a TA, and more like an "old-school" prof (I suppose as portrayed in 1940s and 50s movies :-), interacting closely with students in class. But there are models of blended learning besides the one that I used, and probably better fits to different preferences.

The CS 390 OVERALL RATINGs (as coded on Vanderbilt's forms, each on a 1...5 range, 5 being "best") for the instructor/course (4.16/4.16) were comparable to the "regular" Spring 2012 Machine Learning (CS 362) offering (4.33/4.22) and my other Fall 2012 courses of CS 360 (4.50/4.25) and CS 260 (4.25/4.00 ). Derek Bruff and others at Vanderbilt/s Center for Teaching did a mid semester evaluation, we have identified ways to improve, and we are working on further evaluation. On the whole it seems that the wrapper was a well appreciated course.

Despite this CS 390's more structured (COURSERA) component, the CS 390 required no more than 1/4 the time as my upper-division AI course (CS 260), probably considerably less than that, because the COURSERA platform was doing work of lectures and grading. A very specific consequence of my CS 390 experience is that I may advocate offering CS 362 (Machine Learning) yearly (instead of every other year), if it can be done as a wrapper (all or some of the time). Coincidentally, there is at least one other ML MOOC coming online soon, enabling more customization across the two MOOCs, to say nothing of the material that I will put up (e.g., on YouTube), as I have done for CS 260; in fact, I have a couple of "best sellers", which resulted from students of yet another AI MOOC looking for clarification on a couple of algorithms (i.e., generalized arc consistency and iterative deepening): https://www.youtube.com/channel/UCWOFdpEfNuQP3O_JUiwhT8A.

More generally, most commentaries point out that reduced faculty workload per course (nonetheless, regarded by students and faculty as strong courses!!!) would enable more high-quality electives for a fixed staff level, and it would allow a finer granularity in assessing faculty workload; finer granularity in characterizing course workload could translate into finer granularity in course buyouts, enabling even a heavily committed research faculty member to lead a (blended) course, etc. I haven't seen the increased flexibility of faculty buyouts mentioned in previous commentaries, but I suspect that some of the most grant-committed faculty would like to get in front of a class of undergrads if it just didn't take so much time, and of course, I'm sure undergrads would love this too.

A GRADUATE-LEVEL INDIVIDUAL STUDIES course, such as my CS 390, seemed like the most conservative next step into formal blended learning courses, but the online CS offerings through COURSERA particularly (but EdX and Udacity too, and to include "opensource" initiatives), are large, giving lots of opportunities for blended courses --  again, recalling the "CS major online" (https://my.vanderbilt.edu/douglasfisher/files/2012/02/CSMajorOnline.pdf ). And because CS enrollments are busting at the seams, CS is perhaps an ideal program to be designing and vetting blended learning courses.

My initial thoughts are that "standard format" ("the way" we have always done it) might remain ideal for core classes (we don't want our faculty's skills to atrophy!!!), as well as for electives that correspond to the primary expertise of instructors, but offering electives that blended courses, for which there is limited existing faculty expertise, but for which there would be student (and faculty) interest. My limited experience is that students would like and appreciate this stretch; and I, for one, would love to learn with a group of students in an area that I was not expert in; faculty member as "lead learner" is only one blended model, albeit much different than the CS390 model I have experience with.

zen of computing

WAY back in the day, when I was a college senior, I was a peer co-advisor for the computer science (CS) major at the University of California, Irvine (UCI). My partner’s name was also Doug – he was a great guy, a wise-guy gamer type, who complemented my lower-key inner-nerd well. Before subjecting our peers to our advice, UCI put us (and co-advisors of all majors) through mass orientation and training. There was one exercise where each pair of same-major co-advisors was put sitting down, back to back – one person in each pair was handed a strange geometric object, with many faces, different composite shapes on each face, and the other partner was given a sketch pad and pencil. The pair’s task was to transfer understanding of the object from one partner to the other, through verbal question and answers, so that the partner with the pad could later answer (post-training) pre-specified questions, but these questions were unknown a priori to either co-advisor.

The CS team was the only team to complete the task successfully – all correct answers – I don’t think that others were even close!! Yes, I felt good about that and still do  , but beyond pumping up my ego (and it did), it started me thinking about what was special about my chosen field of computer science – what kind of thinking and communication, even values, did CS promote that were somewhat unique?

As CEers, we (Doug and Doug) were trained to communicate with the world’s stupidest entity capable of response – we were trained to program computers, and that training gave us a sense of when assumptions were problematic, to spell things out, to reflect on what we said, to recognize where there was ambiguity and seek to disambiguate, and like lessons on communication. The peer-advisor training task described above was made for us, and as we were told afterward, this was a task to illustrate how easy miscommunication was. I had read ‘Zen and the Art of Motorcycle Maintenance’ a few years before as a freshman at UC Santa Cruz, and I naturally thought that there was a zen of Computer Programming (lower case ‘z’, if there is such a thing).

CS was and is a rich source of metaphor – I took its strange and beautiful theoretical abstractions, like the Pumping Lemma, as bare-bone illustrations of life lessons (e.g., that you could screw up repeatedly and still “complete a sentence” — perhaps more later). But the concrete activity of computer programming was rich in metaphor as well. One lesson I internalized from programming is that my thinking is almost always incomplete and/or wrong, if only in the small. When I program a computer I’ll only start writing code when I think that I have a sufficient understanding of the problem and the solution, but even so I’ll produce a sequence of programs that are incorrect in some way, until ideally, I reach the last one in the sequence, which I hope is (entirely) ‘correct’ (maybe I’ll post later on implications of agile design methodology). Any experienced programmer knows though that this ideal of perfect code is never achieved for complex programs. Despite the complexity of the computer programs I’ve written or participated as part of a team in writing, their complexity is trivial compared to the complexity of the world’s real challenges, where there is no doubt that my knowledge is always incomplete – my knee jerk reaction to a fix, in the real world or when programming, is pitifully myopic. I’ll save for another day my thoughts on this shortsightedness, and how it has worsened, even in the limited context of programming, and even in the face (because?) of more powerful technology. In any case, though intellectual humility may grow from programming (we can hope), so does tenacity to see a solution through. Humility and tenacity make for a powerful partnership, one that I think computer scientists can have something of a unique perspective on.

A cousin to my-thinking-is-always-imperfect, again learned from programming, is to strive to see something for what it is, not (prematurely) what I would have it be. I can’t tell you how many times I have looked at computer code I’ve written and missed, over and over again, a bug, because I was projecting what I thought the code “should” be doing rather than what the code actually instructed that the computer do. I’ve had students look at their code for hours trying to debug a program, show the code to someone else, who then spots problems almost immediately – a fresh set of eyes is so important sometimes, and in principle they need not be someone else’s eyes, but often I’m just in a rut and can’t find my way out alone.

Clearly I have the goal of taking the code to an ideal state, but it can’t happen until I see the code for what it actually is. This lesson and generalization from code to other built entities is something I knew as an undergraduate, but it was many years and with much help from others, who sometimes bore sledgehammers  , that I internalized this as a lesson about self – seeing who and what I really was as a prerequisite to productive change. Some of these helpers became dear friends.

I’m not sure whether this account seems manufactured – its not – I saw and see grand life lessons, stripped bare but recognizable, in computer programming and computer science generally – recognizing these lessons excited me back in the day, and still does. I hope that CS excites its new practitioners in like ways.

AIs, robots, and humans

In the opening lecture of intro AI (CS 260), I discussed some contrasts between what AIs and robots were capable of, having come a long way, but have a long way to go -- I used these videos to illustrate some points:

   Bigdog (http://www.youtube.com/watch?v=cNZPRsrwumQ); 
   Asimo (http://www.youtube.com/watch?v=Q3C5sc8b3xM); 
   Dr. J (http://www.youtube.com/watch?v=f7njB1T-Xjk); 
   Willie Mays (http://www.youtube.com/watch?v=7dK6zPbkFnE ); 

I'm not sure Asimo could get back up if it fell!!! 

There are differences in mental abilities too (e.g., categorization and pattern matching:  http://www.youtube.com/watch?v=eq-AHmD8xz0 ), but the cognitive prothesis view suggests that AI can be used to augment myopic and otherwise limited human reasoning (e.g., Dan Ariely http://www.youtube.com/watch?v=9X68dm92HVI ) to yield a powerful human/AI hybrid intelligence.