The Data Provenance Project

It is a scientist's job to ask a lot of questions and to search for answers. Often this means collecting extensive data and studying it to generate meaning. Along the way, scientists may see an unusual output, such as described in the hydrology research project in my last post. To follow the trail leading eventually to our friend the moose, questions had to be asked: which sensor produced the anomalous data, when did this happen, what day, what time, how long did the change last, was there a similar rise in water level at nearby sensors in other streams? These answers come in the form of data: Provenance Data. Provenance comes from the French verb "provenir", meaning "to come from, to come forth".

But Provenance Data is not just for getting to the bottom of mysteries. Provenance Data is a key contributor to proving and justifying scientific conclusions.  This is where the challenging concept of "raw data" comes into play. What data exactly are we talking about when we ask for the "raw data"? How do we present that data to others such that it has meaning, given that a bunch of numbers without any interpretation is often meaningless? But once we interpret (manipulate) it, is it still "raw"? It is easy to get trapped in a circular conundrum.

An Example, returning to the hydrology project: is raw data the data about stream outflow at a given location? This outflow information has meaning, but was generated by a synthesis and filtering of other data. So, is raw data the average water weight generated every 15 minutes at various onshore loggers? Maybe. We can get yet more specific: is raw data the individual underwater sensor readings taken every few seconds? Maybe...but at this point would those readings make any sense to anyone other than a few highly trained specialists and engineers?

Probably not.  So how helpful would it actually be in proving and justifying claims of stream outflow volume to the concerned external evaluator or critic? We didn't even discuss the fact that there are enormous technological hurdles to maintaining every single sensor reading for any length of time. Not to mention that if you ask an ecologist they would probably present even more alternatives for the title of "raw data".

More than ever, in this day and age of constant challenging and questioning of scientific claims, something is needed to assist with obtaining a full picture of where results come from and what they mean.

As explained to me by Barbara Lerner, computer science faculty at Mount Holyoke College, Provenance Data is useful for answering many questions related to understanding, validation and accountability: to provide tracking of data, to enable a study of interacting actions inherent to any complex process, to facilitate investigation of deeper and broader questions generated by data inherent to complex processes.

Barbara is part of the multi-institutional Data Provenance Project which is developing a process system to aid scientists in collecting, storing and analyzing Provenance Data. She works with faculty at the University of Massachusetts at Amherst (Lee Osterweil) and at Harvard Forest (Emery Boose). The tool they are creating will provide a disciplined method to track how and when data was collected, and how it has been manipulated, all the way through to the development of descriptive models.  There are applications in diverse domains; her focus is the Harvard Forest ecology project measuring stream volume outflow we have been discussing. When the project is complete, the ecologists her team works with will be able to extensively query and manipulate their data - without having to learn a query language such as SQL. The current prototype is already able to produce Data Derivation Graphs (DDG) for the scientists.

Here is a very simple example of a  DDG describing the process for obtaining one stream discharge value, using a specialized processing language called Little-JIL:

Detailed explanations can be found in the team's published papers.*

There are challenges on many levels to building a Data Provenance tool. One of the biggest concerns is with balancing technical flexibility with ease of use for the non computer scientist. For this reason the computer scientists work closely with the ecologists, who think this project is "cool" and are happy to provide ongoing feedback. There are other challenges: those inherent to graph problems in general; all sorts of challenges to developing process systems that will be functional across disciplines. Other areas of interest range from processes tied to climate modeling, emergency room care, chemotherapy delivery and labor negotiations. Clearly, the long term benefits extend far beyond the ecology project. Theoretically, any science process, research or otherwise, will be able to use this system once it is fully developed.

As Barbara Lerner says, it is extremely rewarding to do outward looking things and obtain concrete results.  It is inspiring to work with other scientists who think this work is exciting. The field of computer science benefits, the overall cause of science benefits, and society benefits. Hard to argue with any of that.



*Barbara Lerner, Emery Boose, Leon Osterweil, Aaron Ellison and Lori Clarke, "Provenance and Quality Control in Sensor Networks", Environmental Information Managemet 2011 Conference, Santa Barbara, California, September 2011.

Preparing for the Unexpected Moose in Your Hydrology Research Study

Let's say you are an ecologist studying a watershed high in the mountains. You care about the water flow through several streams that feed a pristine lake. Ultimately you want to understand the stream discharge process in this region - what is the volume? You need to collect a lot of complex data in order to build a realistic model of what happens day in and day out.

Water can enter the environment several ways, including rain and another body of water; water can exit the environment in several ways including evaporation, entering another body of water, or seeping underground. So you create small dams and place sensors in the water at well chosen locations. Each sensor measures the weight of the water (among other things) and feeds that data every few seconds to a data logger on the nearby shore. The data logger computes an average every 15 minutes and saves those values for you. Every so often you trek up the trail to your sensors, Palm Pilot in hand, download the data, take it back to the lab.

(Compressing the description of the scientific process for purposes of brevity) Run statistical analyses on the data, generate defensible behavioral models, write up the results and publish them.

Until the day that you notice a very strange reading. The water level is suddenly unusually high. Why might this be...

By running a few standard checks and conducting a little investigation you discover that a moose stepped in the water. If you are like me, when you first heard this all too real scenario, you almost fell off the chair laughing at the thought of a moose blithely wandering into the middle of a serious research project.

One unexpected and undetected moose could really mess up your data driven model of stream flow. Fortunately, the moose is reasonably easy to figure out. But other scenarios are a lot harder to get to the bottom of when you are dealing with complex natural phenomena and processes. What if you are measuring and modeling atmospheric carbon flow and sequestration in trees over that same expanse of forest? What if you include variables related to climate change, which is sure to bring in-depth scrutiny from peers and critics? You absolutely need to be able to explain and justify your conclusions to science and perhaps even to the wider public.

What you need is Provenance Data: the data about the data; the meta-data, whatever you want to call it. Provenance Data is the data that describes how those stream values were obtained, when they were obtained, what was done to that data. The contextual information surrounding the so-called Raw Data.

Computer Scientists are involved in a series of research projects to enable the gathering and clear presentation of Provenance Data. Next post, I will explain what they are doing, as well as why I said "so-called" Raw Data.

Assessing Value in the Corporate and Academic Worlds: An Interdisciplinary Problem?

During all of these ponderings about innovation and interdisciplinary computing I have been working with the question of where interesting ideas coming from the corporate world can (should? do?) apply in the academic world. Sometimes the cross over seems quite reasonable with a little twist in thinking, as in this, a summative statement from The Innovator's Dilemma book (the past 5 or so posts, in case you haven't been following the conversation):

"...historically, disruptive technologies involve no new technologies - rather they consist of components built around proven technology (ies) and put together in a novel product architecture that offers a set of attributes never before available".

It only takes a few word changes to see how this applies to interdisciplinary computing. I bet you can figure it out if you have been following this conversation (can you?).

A larger question that looms over me now, is - just how far and in how many directions, can experiences of successful or unsuccessful processes in the business world be applied in academia? And conversely, what can the corporate world learn from successful and unsuccessful processes in academia?

On the one hand the cultures are vastly different. On the other hand, I am becoming convinced that there is a lot that can transfer from one to the other. Because my reading came from a book about the corporate world, I was looking at how the academic world sometimes fits the author's theories of successful "product" innovation.

I am equally curious about how understandings of success and failure in academia can be useful to corporate success.

There are a lot of hard questions when you start from scratch. I'll start with a very basic one: the concept of "value".

The corporate world often places value upon monetary contributions (Person X saved the corporation $Y by doing XYZ, or Person X brought in $Y dollars  or Z physical resources to the corporation). Lately, I have been asking some professional acquaintances how they would place "value" upon academic contributions quantitatively.

I was not surprised by the reactions I have been receiving, which show just how differently "value" is determined in  higher education and in corporate America. The more difficult I find it to try and bridge this cultural divide, (I'm starting to think of it as an interdisciplinary computing problem of a new sort) the more I want to do it.

How can the academic and corporate worlds arrive at a common understanding of value that does not involve one unilaterally imposing its "definitions" on the other?

How is VALUE going to be defined?

• If you are an academic and you had to express your "value" to a corporate executive in a way they would appreciate, what would you say?

• If you are in the corporate world and you had to find "value" in an academic's career in a way that acknowledged their worth honestly, what would you say?

Ideas about these questions could lead to a really productive conversation.

Interdisciplinary Computing Meeting Number 2: Day 2, Part 1

The second (and final) day of our meeting was jam packed. I need a giant suitcase to contain all the notes I could write about our discussions. So... I'll start again with some of the items that jumped out at me from the day.

• Interdisciplinary need not be (as some have feared) a zero sum game. In fact, Interdisciplinary Computing (IC) can change the zero sum game (either you go into field X or field Y). With IC, one department does not lose students to another department ("student stealing"). Quite the opposite. Programs that implement IC programs, minors, (or the variety of forms I have discussed previously) often result in students having a foot in multiple departments. Depending upon how an institution calculates FTE  a student may be counted towards FTE in more than one department, thus giving "credit" to both departments - a good thing. (FTE = Full Time Equivalence, or to the layperson: a calculation of how many students are considered to be in class/department/division/school. FTE numbers often have a powerful effect on fund distribution, hiring etc.).

• Here is an interesting observation: look at the National Science Foundation Discoveries site. This is a site where the NSF highlights innovation; most of the featured projects are IC - computing and another science. 

• Big Science and Small Science. These terms were introduced and there was a bit of a discussion about what they mean and the implications. One table (I was not sitting there) later spent time discussing the topic in detail. But from the more general discussion, here is the gist I came away with. It started when someone posited the idea that computer science projects would more likely be "small science", i.e. local projects that produce a lot of data; "large science" (aka "long tail science") projects are large multi-institutional / multi-group / multi-person projects such as those conducted by NOAA (National Oceanic and Atmospheric Administration). The "problem" would be that larger projects are more likely to get large funding, and thus dissemination. What to do?

I'm not sure I totally buy this definition and the gloomy conclusion. There are certainly computing projects that obtain multi-million dollar funding - when I was working on my PhD some faculty I knew received that kind of funding. And even if we are talking smaller funding levels, I think multi-institutional grants of $500,000 or $1,000,000 are nothing to be sneezed at and can certainly produce results and wide dissemination. Those are available. I was recently one of several PIs on a grant in the first category. Sure it helps to be a government agency, but one does not have to be one in order to get your results "out there". You may have to work a little harder at dissemination, but there are channels. And in fact, sometimes, you know exactly who the groups are that have an interest in your type of work and can target them in a direct semi - personalized manner. And we haven't even mentioned the fact that Industry is working on IC projects and they have their own mechanisms for dissemination, sometimes working with academia, and other times going it alone.

But really, the question is: Why is "small" vs. "large" important for a discussion of IC? Our conversation turned to how small science (let's say 2 departments such as CS and History working together) can leverage large science. NASA, Historical Archives and a variety of organizations make large amounts of data publicly available; these data can be used and added to at the local level. Perceptions of both fields can be changed and information made available that otherwise might gather dust. Here is a great example: check out this project, which one of the meeting participants is working on: Digital Durham, a fascinating integration of computing and history.

Arg. I'm out of space (assuming the general protocol of not making a post tooooo long). To be continued...

IFF Computing == Cabbage

I am periodically asked "what is it like to write a book?" If you have written a book you may be having a small laugh to yourself all of a sudden. A sort of evil chuckle. Because you know, .... well, I don't need to tell you. It takes you over in strange and wonderful and unexpected ways.

You probably also know that no matter what you say it may not convince the conversant that conceiving cognitively comprehensible and convivial concepts takes considered concentration. Even if you say "it is Computer Science!" (people often assume fiction for reasons I know not why) there is ofttimes a clinging to the concept that you must spend much of your time continuously cavorting. Come again? If we waited to compose for when we felt suitably inspired by The Muse ... my Editor could conceivably consider calling out the Costa Nostra (uh, just kidding...right, Randi and John?)

Perhaps my more-often-than-usual far away look, prompted not by my new glasses so much as by a looming deadline, brings on the question with greater frequency lately. However, I find myself contemplating a different question: "How do you know when you are really and truly becoming one with interdisciplinary computing?" (with which topic my book most certainly is concerned).

I have the answer. It came to me this evening as I took a break and sat looking out over a neighborhood canyon just breathing calmly. Even in stillness, everywhere I looked I saw things that reminded me of a computer. "Canyon - how lovely...oh, Canyon starts with the same letter as Computer." "Critter poo on the sidewalk leading up to the bench....Critter reminds me of Computer". "I Cannot see the stars because there are Clouds in the sky. Clouds? Computer!" "Concentrate on your breathing ...Concentrate. Concentrate. Computer Computer Computer".

A Colleague gave me a red Cabbage from their garden. Oh my gosh that was good - a fresh Cabbage tastes like Candy Compared to Cabbage from the grocery store". Candy? Compared? More "C" words! Cabbage. Is an excellent source of Vitamin C and beta-Carotene. Consuming large amounts of Cabbage reduces the Chances of getting Colon Cancer because it Contains Chemicals that protect Cells against free radicals. All those words Commence with the same letter as COMPUTER! Perhaps I have passed the threshold and am now officially (Crazy?) Coalescing with interdisciplinary Computing and Computer science and Computational thinking.

The Cabbage Convinced me. Computers are truly everywhere. All you have to do is Consider it.

International Women's Day Spawns Important "Science Magazine" Post

I have a "new and different" blog post in the wings that I hope to post tomorrow - I am waiting for confirmation of some information I'm very excited about.

Meanwhile, I want to reference an interesting blog entry in the Science Careers Blog (part of Science Magazine online) posted yesterday as part of acknowledgment of International Women's Day.

The post discusses the significant positive improvement in retention of women when applied contexts, in particular real world social contexts are presented as an integral part of computer science coursework. Given that this is Science Magazine, there is more to the post than unsupported opinion and commentary. I quote a few lines:

" "The faculty initially did not think that the students who dropped out could hack it," Huang said. "But, on closer examination... they found that women had lost interest because they did not see what algorithms were good for or why they needed to learn how to design a variety of complicated algorithms." The faculty decided to focus the first session of the course on how algorithms may be used to help social causes. [my added emphasis]" Once this began, the retention rate for women increased so much so that now all professors spend the first class introducing their courses by discussing the applied relevance of the material that will be presented," she added. "I admit, I was really relieved to find that the women could hack it." "

(I wonder if there was an intended play on words there. Probably not.)

The post goes on to report that the male students did not respond in the same way. Interesting, however a very intelligent response followed:

"Assuming that men and women continue to have predominantly different interests in how their research is applied later in life, here's my thought: There are differences between individuals of the same gender of course, but couldn't women scientists use these differences to find a niche for themselves that their male colleagues may not necessarily have thought of? It is still difficult for women to work in male-dominated fields in many ways, but the culture has changed drastically in the last several decades and there is now more space for new ideas and individuality."

All I can say is YES. There is space. There will continue to be more space. I happen to think that there may have always been space for new ideas and individuality but that it was not sufficiently recognized or acknowledged or supported. I am so glad to see this recognition coming along. I am even more glad to see research in support of the notion that including social relevance in computing coursework is good for the computing field itself.

Computing faculty, what do you think in reaction to this?

Full Science Careers Blog Post: http://blogs.sciencemag.org/sciencecareers/2011/03/a-genderbiased.html

An Unusual Computational Science Educator

Sometimes crisis propels an existing passion to the forefront of someone's life. This is the one line explanation of how Shodor was founded 15 years ago to advance science education via computational modeling and simulation.

When I first posted about the Interdisciplinary Computing meeting I attended last week, I made a point of mentioning Bob Panoff. Bob is not only a truly interdisciplinary individual but a great person to talk to. So as soon as I could I pried him away from others so that he could speak to me for this report.

If you haven't already looked at his company web site, before you do so, think about what his company name might mean and why he chose it. Don't peek. We'll come back to that. It says a lot about Bob's attitude towards life and work.

You never know where a conversation with Bob will go. It starts at point X and the next thing you know you are somewhere else entirely. But it all makes sense.

It often starts with some interesting comment or question.  He asked me: Do you know what "Quantitative Emotion" is? Given my background, I started thinking about AI. But that was not what he had in mind. He teaches the answer this way: by sending 8th grade students (approx age 13) out into shopping malls to ask people one of two questions.

"is 40% large or small?" Most people respond "it is large".

"is 2/5 large or small?" Most people respond "it is small".

Hmm.... Changing the representation of data makes people feel differently about what something means. Quantitative Emotion. Interesting....

A post-mall conversation with the students (and me!) leads to discussion of multiple representations, and how to present data in different ways - generally through computation and simulation of that data. Bob is all about computational simulation.

One of Bob's very favorite questions, which he also sprung on me, is: "How do I know that it is true?" 

What? Know what is true? Answer: most anything. How do you know that it is true?

This question underpins much of Shodor's work in developing science education materials. "How do you know?" Computation and simulation provide the means to analyse and answer the question. How did Bob arrive at this central question? While in graduate school he taught himself how to use computational simulations to analyse the interactions between pieces of physics problems and to measure the validity of calculations.

Although not a computer science student, he read as many numerical methods books as he could find, most of which had been written in the pre-computer era. (Part of me wondered just where he found these ancient dusty texts, but we were racing the clock against lunch break so I didn't ask). He taught himself to use computing to apply those numerical methods and solve the physics problems.

Pattern recognition and pattern characterization then fueled his interest in simulations. The more he created simulations the more convinced he became that science education in general could be improved through creating effective simulations. How to choose between different approaches in the lab for example, how to compute the properties of materials such as liquid helium, deuterium and solids with impurities. He refers to himself at that period of his life, when he worked in academia, as a computational physicist. Along the way he worked in a supercomputing center. He spent time looking for commonalities between disciplines and how they did use or could use computing, and he worked to share those ideas with other disciplines. In his spare time he started a group  to improve science education. He started by delivering workshops.

Crisis struck in 1994 when he was told he had a kidney tumor and 6 months to live. At this point he decided to follow his true passion with what time he had left. Abandoning formal academia, he incorporated Shodor and went full steam ahead with computational science simulation with the mission of improving science education at all levels.

Short and Dorky. Bob was once called "SHOrt and DORky" by a student, hence the company name. Of course. Creative and humorous and ready to use whatever comes his way.

15 years later Bob Panoff and Shodor are still at it and highly successful. Bob works full time following his passion for computation in the service of science education. As important, his desire to share his work and ideas land him in places like our meeting. Go Bob.

What CS Gains From Interdisciplinary Computing

Following up on last night's post about the reasons why many people engage in interdisciplinary computing work, I'd like to briefly list off some examples that came out in our meeting discussion today. At one point we decided to get specific and share examples of how the computer science discipline has directly benefited from interdisciplinary collaborations.

Here are some of them, written as close to verbatim as I could take notes on the fly. I'm certainly not expert in many of them, so anything I say that is missing an important or interesting piece hopefully someone will chime in and amplify for me. However! this is a classic facet of interdisciplinary collaboration - no one individual can know multiple fields at the same depth and accuracy. That is part of why it is such productive work!

The Folding at Home project involves experts in bio-medicine, distributed computing, bio-technology, high performance computing. As the site implies, computer science has been stimulated in HPC (high performance computing), algorithm development, networking, and simulations. We have expanded boundaries of computational understanding in all these areas.

Distributed computation not only in that project but in other very large scale projects has pushed the boundaries of computational efficiency to new levels as we develop the necessary algorithms to tackle ever more seemingly intractable problems, that are not necessarily so intractable.

Online auctions. I missed the intro to this conversation, but when I picked up, the discussion was about the movement from manual to electronic auctions requiring a change in how economists worked on what turn out to be NP-hard problems. Computer science theory is making advances so that these auctions can function properly. I could use someone helping to fill me in on what I missed in this conversation because it sounds very interesting!

Working with the film industry has driven both the development of 3D graphics and User Interface development. Many of the "old" rules of thumb (meaning circa early 90s) no longer apply and computer science has stepped up to revamp our understanding of what we can do with graphics at very fundamental levels. User Interface theory and application has evolved right along with it. For example, imagine how far we have come from Star Wars  (1977) to  Toy Story (1995) to Avatar (2009).

These advances in computer science from interdisciplinary work with the film industry in turn spurred development on the side of GPUs (Graphic Processor Unit), which were then deployed in so many areas of computing from games to simulations and beyond that they cannot be easily itemized.

Music downloads - the virtually ubiquitous desire to stream music in one form or another, has led to advances in basic streaming technologies and support algorithms that now are used in far flung applications such as digital image processing in medicine and transfer of image data (MRIs for example) to medical service providers on short notice across long distances. There was mention specifically of recognition algorithms as a subset of algorithms that have advanced, - I could use some supplementary information on this one!

Just a few ideas to whet your appetite. Speaking of appetite, I may not have one for a week. Two days of intense cognitive load and equally intense gastric load have left me with wonderful memories and a bit of a tummy ache. Brings a new meaning to "Brain Food".

Interdisciplinary Computing: Finding Common Ground, Experiencing Joy, Tangible Benefits

I spent the day meeting with a group of 15 dynamic people from around the country from academia and industry, who are all passionate about and actively involved in some way with interdisciplinary computing. At the helm of our 2 day meeting: Boots Cassel from  Villanova University Computing Sciences and Ursula Wolz from The College of New Jersey Computer Science. This was the first of several meetings and we were there to begin an ongoing conversation about how to support interdisciplinary computing education. This was our "why are we here and where are we going" meeting. Day 1.

Boots made a comment early on that stuck with me: "We need to give as much as we get".  That phrase reflected the tenor of our conversation even as we wrestled with definitions, perspectives, categories, labels, experiences and visions. 

I previewed the question of what interdisciplinary computing is last week - imagine that conversation magnified, expanded and fueled by 15 never shy, highly experienced practitioners.

That is where the fun started, and continued...Around more delicious food than I can begin to describe (we'll see in a few days if I can still walk upright) we started off by  working to establish common ground and perspectives. Almost immediately we were directed into small breakout groups for sharing our best experiences with interdisciplinary computing, the setbacks we experienced, and what an ideal climate for interdisciplinary computing education would look like. 

It is always nice to start out on a high note, and considering that overall, the day was one big high note (I don't think I'm out on a limb by making that generalization) I'll share this post on part of our pre-lunch breakout conversation - the process of discussing some of our best experiences with interdisciplinary computing. 

Besides, I need to keep my head from exploding with the effort of trying to say too much in too small a space. 

Finding Common Ground. People shared their satisfaction and joy when faculty from different disciplines reached out to successfully work together, overcoming hurdles (different topic) and creating something - a class, a curriculum, a project, an internship, research, a job - that neither could have done without the expertise of the other - as equals.  1 + 1 != 2 Rather, 1 + 1 > 2  (I hope my tired colleagues don't take that literally and think all the food has addled my brain)

Finding Common Ground.  Between combinations (pick any) of universities, K-12, industry, disciplines, departments, various sciences, humanities. It is a cultural issue as well as a content issue.

There is clearly something wonderful that happens when people from across disciplines and associated cultures work successfully together. Aside from the practical point that it "looks good", it feels good, judging by the way people were telling their stories. A few of us did a little bit of hopping around in our seats and there was the occasional gesticulating of limbs.

Not only students, but faculty are able to view the world in a new way when a computing person and another disciplinary person successfully collaborate and break new ground. One approach, discussed particularly enthusiastically and with many examples by Bob Panoff from the non-profit Shodor, was the power of harnessing computing technology to find common ways to describe the world through modeling and bringing phenomena to life. Stories form in people's minds and the world takes on new meaning.

Someone pointed out that in these collaborations, we are forced to address issues, details and concepts we never would have thought of otherwise. 

Personally, I would prefer to say that I am "privileged" to address issues, details and concepts I would not have thought of or encountered otherwise. To me,  interdisciplinary computing, especially when it improves people's lives in some way, however small, is a constant exciting exploration and adventure.

Tangible Benefits to Students. Industry jobs are often (always?) interdisciplinary in some way so employers like students who have these skill sets through direct experience. For example, they work better on teams and can more easily shift to new areas within their company. 

Many new terms were tossed up for consideration just within this part of the conversation: convergence; intertwining, cross-disciplinary, multi-disciplinary, inclusiveness. 

A lot of good things to think hard and deeply about.

Charles Babbage the Interactive Auto GPS

Oddly enough, just 2 posts after I wrote about the development of s/w to recognize emotions that started out with a discussion of (imo) annoying auto GPSs, I find a short article and video about a British researcher named Peter Robinson who is developing a quite sophisticated auto GPS system (full story here). The video starts out by echoing my sentiment of how annoying and sometimes inaccurate a car GPS can be, and he actually tosses one in the back seat! I LOVED that moment. And he does it with such poise and non-violence. Just flips it behind him.

Then he discusses why it would be useful to have an interactive conversation with a computer - the computer can read your facial and bodily expressions, determine your emotional state and respond with its own emotion inflected conversation. That is both technically interesting and potentially useful in many areas of society.  I bet you can think of a few areas where this technology could be used productively?

But then he proceeds to discuss how he is testing the system on an auto GPS. Why oh why the fascination with the auto GPS??? Ok I accede to this as a useful example of holding a conversation, where important decisions have to be made on short notice, and there are unpredictable behaviors and circumstances to be dealt with. The creepy part is when Robinson pulls out a custom made head of Charles Babbage (looks a bit rubbery) and props it up at head level in the passenger seat of a car in a driving simulator. The two, Robinson and Babbage, carry on a very polite conversation about road conditions. Robinson ends by saying "Charles, I think this is the beginning of a beautiful relationship".

I have mixed feelings about the idea of driving down the road talking to a life size rubber head with wires coming out the back of it.

Technical information is included in the short video, and there is an interesting bit about how it all works. There are definitely compelling issues of emotional intelligence, neuroscience, psychology, and physiology here to explore. Robinson sees this development as the future of how we will interact with computers. Maybe AI has a not too distant future in realistically simulating life? That would be great progress indeed after years of inching along.

But the rubber head... I don't know about the rubber head. If it made mistakes and the driver got irritated, and tossed it in the backseat, would it start complaining from its disembodied self face down on the seat?

Software and Art Authentication

I spent a few days up in the mountains unplugged. However, technology has an interesting way of creeping in even with the cellphone turned off (reception is unreliable anyway) and the laptop stowed. And I'm not going to talk about the visiting electrician who suggested that I test a 9V battery by licking it (he found this quite amusing, but I declined).

First, I was reading one of my alumni magazines and there was a story about a physicist who used software to examine highly controversial paintings that might or might not have been newly discovered Jackson Pollack works (full story here). The physicist, Richard Taylor, uses software to discover the presence of fractal patterns. Apparently, according to his research, true Pollack paintings are fractal filled. Benoit Mandelbrot agreed. Taylor also determined that fractal patterns reduce our stress levels. Interesting. (Has anyone investigated the effects of meditation while looking at fractals?)

There was a lot of money on the line in this particular situation, and apparently the politicking and public flame throwing were severe. As Taylor said in his article: "For the first time, computers were playing a significant role in determining the fate of artworks". The project was kept secret in order to protect its objectivity as well as Taylor himself. The full story reads a bit like a spy thriller. At one point he was advised not to go outdoors in case a bird dive bomb his head; at another point he was advised to he leave the country for a short while! Eventually his analysis, later supported by other evidence, determined the paintings were fakes. (This was about the time he was told to flee to New Zealand). The process of using software to analyse fractals in artwork is now legitimized and used by many researchers.

Reading this article caused me to think of a similar activity on a smaller scale and closer to home. My father (who passed away in 2009) was an archaeological chemist and spent hours on end sitting at his computer developing FORTRAN code to analyse art. Over the years, he was called upon to authenticate ancient ceramics, paintings from the Middle Ages, and was always glued to stamps (he'd appreciate the little joke). Although the culture of ceramics and artwork is interesting, it was his analysis of stamp forgeries that was most unique as far as I (in my admittedly biased position) could tell. He was always programming programming programming and peering at these little bits of lines and ink. I never got to see his code, and I suspect it is now lost, but I'd give a lot to know more about what he was doing. Over the years I would periodically be ordered off on a mission to obtain some obscure piece of software that he wanted to use, or to locate a specific piece of share-ware that he had somehow learned about. Fortunately he published more articles than I could ever read and a few books, so I can read his results even if I cannot see the code that produced it. If those subjects interest you too, here are a few books: on ceramics (here and here), stamps (here and here)

Do you want to have a fight with your car's software?

Perhaps because it is Sunday and I had promised myself not to work today, yet find myself on the computer (again :) a little bit of irreverence and levity. Besides missing out on the sunny skies to plop myself in this ergonomically correct chair, I am working with 9 fingers, having accidentally smashed one of them into a steel pipe last weekend. Typing has been a bit challenging. But not nearly as much as flossing. Try flossing without both pinkies. It can be done but it takes creativity that would make my dentist proud.

I just listened to the podcast from National Public Radio's Science Friday. The host interviewed the high school pair who won the grand prize in the Siemens Foundation Math, Science and Technology contest  in.... yes... computer science. (The grand prize also went to someone working in astrophysics. NPR did not mention this, but it is on the Siemens Foundation contest web site. See above link) The prize is $100,000.  The host noted afterwards with some regret that he forgot to ask the teenagers what they would do with the money. I don't know...when you were 16, what would you have done with $100,000? That's a lot of [fill in your own blank].

The students developed code to identify emotions from your voice. The NPR host started off by describing how he would sometimes tell his automobile GPS to "shut up". And wondered if this technology would allow the car to adjust if it noted that he was pissed off. My first thought (remember, it is Sunday) was - so turn off the annoying GPS. It is bad enough when Google Maps takes me 5 miles out of my way and dumps me in the far end of a dead end parking lot when my desired destination was half a mile from where I originally got into my car in the first place. I have been in cars with people who cannot seem to drive without their GPS - brain, out the window. Annoying computer voice at the helm. ick....

So I'm not sure that I'd call that particular application of emotional recognition software socially beneficial. I might be more likely to get into an escalating argument with my car as it tried to be soothing and I became more and more annoyed at its cloying fakeness. Then I might find myself and my car in a ditch. Definitely not socially beneficial. Guess I should be clear that I'm pretty sure the car GPS application was the invention of the NPR host, not the students.

The students actually had something much more interesting in mind (assuming that your idea of fun and excitement does not involve fisticuffs with your dashboard). They are now considering creating a wristwatch that can be worn by Autistic children to help them in social situations. The watch will identify the emotion of the person being spoken to and show a picture (smiley face, frown, etc) to help the wearer correctly judge the mood of the other person. Now that is a bright idea and much more interesting I think. I hope they do it.

Meanwhile, I shall attempt to go back to not working....happy Sunday to all!

Socially Meaningful Student Projects

Yesterday I watched some of the student final projects for the APCS Principles pilot class. The students had been given the option of submitting their projects into a contest in which their classmates would vote on the best one. Participation in the contest was completely optional but quite a few students took the plunge. Ten projects were selected for viewing in class and being voted on. They were pretty darned cool if I say so.

This final class assignment (which all students had to do) asked them to create a digital project to communicate their views on some issue facing society; they were given a wide ranging list of topics to choose from and if they thought they had a better topic they could propose it. One topic, acknowledging the large number of psychology students in the class, was to design an interactive psychology experiment.

The contest proceeded in round robin pairings, with students using their by now well finger-smudged clickers to vote. They were able to see the results of each round live. To those who know Alice well it was clear that significant programming sophistication, such as complex looping and list structures, went into some of the projects . A few students figured out how to use concepts that the class did not introduce, such as variables and button input boxes.

One of my favorite projects involved a group of penguins who emerged from an igloo to play and pick up trash. They spun, circled, danced, flapped their little flippers (do penguins technically have wings?), sometimes in a chorus line and sometimes going off to express their individual selves. One happily slid around on its tummy over the ice. Along the way they picked up and tossed trash towards the trash can. Too cute. And there were clearly lists, parameters and other well designed code structures underneath the fun.

Another project was more serious, without being too heavy, which came through as instructor Beth Simon had the honor of "playing it". The tree she was trying to save from destruction did not make it. There were all these objects (zombies, flying anvils) attacking the healthy tree while little bunny rabbits hopped around it. The player had to shoot the nasty objects to save the tree. Unfortunately, the tree withered (i.e. the player missed too many fast moving objects) and the forces of environmental destruction won out ... this time.

One project replicated a famous real psychology experiment in which a group of seminary students on the way to a lecture about ethics and helping people mostly ignored a passerby in distress (it was a "plant" fortunately). The student brought the scenario up to date, using the setting of a student late for class who witnesses a disaster and has to decide if it is worth risking being late for class and possibly hurting their grade by calling for an ambulance. Beth acted as the psychology subject, and asked the class what to do. They ended up running the scenario with both decisions (help and not help).

There were so many more fascinating projects. One used creative graphics to teach quadratic equations, another challenged the user to decide if they should respond to provocation aggressively or attempt reconciliation, another had a kangaroo trying to hop over a tree as it answered questions about tuberculosis, a snowman trying to eat only healthy food, a plea to help out at this holiday time by donating toys to children in poverty, and a fun question and answer animation about sharks facts (ever seen a shark spin around on itself?) and finally a darkly amusing story about a basement hacker who goes phishing and lures in an unsuspecting victim to perform identify theft. This story won the overall class vote, although the stage by stage competition was at times quite close.

Very very few students were texting, or using Facebook in class. This was fun and informational for everyone and a great way to approach the end of the term.

Alice Expert Steve Cooper Makes a Visit

Last week one of the creators of the Alice program, and one of the authors of the most popular textbook on using Alice, Steve Cooper came to San Diego for a visit. He may have thought he was coming for the nice November weather, however he chose the two days when temperatures soared well over 100F : the outdoor thermometer on my cement (heat reflecting) deck read 50C, and the humidity dove to 2 percent.  Welcome to San Diego Steve!

Currently an Associate Professor in the Computer Science Department at Stanford University, Steve has been very active for years in the computing education community. He also spent two years as a National Science Foundation program officer.

Steve came to San Diego to see for himself how the APCS Principles pilot course here was being implemented. He wanted to visit with Beth Simon (the instructor), with the Teaching Assistants, watch a class, and be in the environment. Steve was impressed by what he saw and heard - he pointed out that it is a challenging environment to teach a large number of students, with no definition of "typical" student. He was excited by the ambitious approach to the class that he has been following in many ways, including through this blog.

Steve and I have known each other for many years so I was able to spend some time with him talking about interdisciplinary computing and how he sees Alice contributing to a wider public understanding of what computing is and can be all about.

Steves describes the philosophy of Alice as ideal for computing students, whether they are majors or taking their one and only course in the field. Originally Alice was targeted at Pre-CS1 at-risk students. As a pedagogical tool it was envisioned as an innovative way to help students develop intuitions about programming concepts through graphic visualization and animation. Since those early years in the mid 1990s, classes using Alice have spread nationwide and now appear in non-majors classes, pre-CS classes and CS1 classes. Formal research studies have examined the effectiveness of the course. Steve has kept on top of these developments and with his colleagues and team have striven to see that the Alice software meets the broadening needs of the user community.

In addition to use within college and university settings, Alice is used with younger students. Alice has been migrating downwards from 12th grade and Steve is very excited that 9th graders (approximately age 14) are now using it. Steve believes that once Alice is being used regularly in middle school (~ age 12-14) then the broad potential of computing will hit the critical age group where students make decisions about courses to take that ultimately direct their college or other career options. Approximately 1500 K-12 (the pre-college years in the US) teachers belong to the "Alice community" and the number is growing. Community members participate in a listserv, receive special announcements and other opportunities.

As an interdisciplinary tool, Steve talks about how Alice is either being integrated into, or plans are in the works to integrate it into, classes in biology, chemistry, physics and mathematics - courses that are ripe for the use of animation and simulation and that increasingly rely upon solid programming skills. Steve was very animated when he discussed his vision of Alice as a cross-disciplinary tool. In STEM (Science, Technology, Engineering, Math) classes, he points out, Alice can be used for specific units within a broader content course. Creating objects and manipulating them will bring topics alive. For example, Steve suggested that the process of how a desert changes over time can be animated. To create such an animation the student would need to use loops, work with moving objects on top of, in to, and around other objects in the desert environment.  Most of the basic intro programming topics would have a place (functions, methods, event handling, conditionals etc). Therefore, as they create these animations or slide shows, students will learn the computational thinking skills that they need to create robust computer code.

It is all about problem solving, says Steve. Developing intuitions for difficult concepts as opposed to relying on surface level tactics. Learning to speak different languages (the language of computing along with the language of different content fields).

Steve continues to work on the development of curricular materials for Alice and is very excited about the broadening use of it across discipline and age. He is particularly enthusiastic about the possible tie-ins between the goals of the new APCS Principles project,  the new APCS Principles AP exam itself and the ability to introduce the exciting world of computing to younger students.

Contextualized Computing Taken to the Next Level

Something that is clear to me about The Nature of Computing perhaps needs to be tossed out there for wider consideration. Maybe you (being direct here for a minute) haven't thought about it the way I'm about to propose. After all, modern learning theories are all about how people perceive the world differently from one another! I'm talking about that Big Question: "what IS computing?"

If you are like me, sometimes you hear this topic come up and want to sneak out of the room quietly. Stay with me for a minute ok?

As I was writing my last post about the arguments for and against contextualized courses, I was thinking wouldn't it be great if all computing classes from CS1 up through the most advanced theoretical coursework were contextualized? Grounded in exciting real world use?

Then I started thinking about that question concerning "what computing IS". (don't touch that browser button just yet!)

I started mentally playing with the words "contextualize" "interdisciplinary" "integrated", all words I have used in recent posts - words used by different people in different...contexts (sorry, couldn't help that).

What do "contextualize" "interdisciplinary" "integrated" all have in common?

They have to do with making connections. Making connections between content and ... something. Something concrete, something "real", something (by my extension) interesting. Something beyond the abstract interestingness that many computer scientists and computing educators see in the raw content for its own sake.

Making connections throughout the curriculum. I like the sound of that as a catch phrase. That would be a productive way to think about making computing interesting to many more students (parents, legislators?). We know that computing underlies (or overlays!) so much in the world around us. Well, by making connections to it, we can  walk the talk in and outside of class.  Students, the public in general, don't have to take our word for it. From Day 1 it is just there. And it does NOT mean watering down our content or lowering our standards. That is a straw man argument. Really.

Consider: the redesign of a computing curriculum such that every class incorporates making connections to an outside world context.

Consider: that making connections is viewed as integral as any other aspect of the course.

What if a course that does not make significant real world  connections is considered a poorly designed course both from computing and pedagogical perspectives?

Take the above three suggestions as non negotiable givens. Suddenly everything looks different doesn't it? Radical idea?