Perception Overhaul Needed: Good Business IS For Real

It wasn't part of my plan to write a post today. But yesterday, while attending a professional association meeting, I was reminded just how far we have to go with influencing common perceptions of what "computers and society" means.

I am quite used to people reacting to the phrase "computers and society" with comments about how bad it all is. Never once has anyone I have introduced the phrase "computers and society" to assumed anything other than that it refers to negative uses of technology. When introducing the profiles in my book, as I was yesterday to a fellow writer, I am quite used to needing to explain that I wrote there (as I do here) not about bad, but about good uses of computing. Good that is clear to almost everyone and hard to argue with. An all too typical reaction is a deer in the headlights expression accompanied by a confused pause.

Yesterday, I tried to circumvent this reaction by explaining that when I write about business entities, I like to showcase those who are doing good with computers as part of their everyday business. Listening intently, my conversation partner nodded and asked if I knew about a certain organization in Washington D.C. I did not, was interested, and asked what they did. She replied that they went after businesses that were "not doing the right thing" and got them to change their behavior.

Arg. I took a breath, thanked her, and explained that this was not my focus. Ignoring the familiar, growing, puzzled expression, I explained patiently that there are corporate entities out there that have a business model that integrates doing good for society. Not as a side activity, not as a ploy, but because they feel it is the right thing to do AND it is good business. Not necessarily because they set out to save the world, but because their personal ethics are integrated with their professional corporate ethics.

As is often the case, the conversation ended shortly thereafter. I'm not sure how much she believed that you all are out there and are for real.

Why? Perhaps it is partly a reflection of our national lack of civility in public discourse; perhaps it is a  human tendency to gravitate towards the sensational and dwell on suffering. Perhaps it is a tendency of people doing really exciting positive work to focus more on getting the job done than getting the word out. Admirable, but it leaves much of the greater public with the impression that "computers and society" is a negative concept. It leaves the impression that business benefiting society is a side activity, or something that only happens when it is forced upon a company.

Not true. So I keep writing.

Concerned About a Regenerating Doctor? Take Heart: Think Innovation

A friend, colleague and fellow Doctor Who fan announced on Facebook recently that this Spring the current Doctor would be changing. A chorus of "oh no!!!!" popped into Reply boxes from around the world.

Reading nostalgic discussions about The Doctor, it dawned on me there was a connection between accepting technological innovation and global dismay at the prospect of a new Doctor. We earthly humans don't always like change, even when we know it is inevitable and good for us. Since 1963 there have been 11 Doctors. We (his fans) all know that every few seasons he regenerates. New face, new body, new personality, new cohorts. Change is how the show stays fresh and innovative. Yet, each time we mourn and wish to hold onto our favorite for a few more episodes.

We don't always welcome change in technology even when we know the potential for innovation and positive outcomes is grand. An article last year in the CACM about natural user interfaces quoted someone who said "it is not appropriate to start talking about NUIs until you have a complete solution"*. There was more to the statement, but still I wanted to ask: why not? When has technological innovation ever waited for complete solutions? Innovation comes when people keep working, thinking, trying, changing, implementing, deploying, testing, investigating. There are no guarantees, but we forge ahead anyway.

For a period of time I had a pre-adolescent crush of serious proportions on the 4th Doctor. Many years later I had a post-adolescent crush (less serious but still notable) on the 10th Doctor. In each case, when he changed I thought no, it isn't possible to move forward - no! We (I) aren't ready! However, in spite of my personal angst, the interface changed. Tom Baker gave way for Peter Davison (a pretty cool dude too). David Tennant gave way for Matt Smith. Matt Smith will give way for...

There is a reason they call it regeneration. Doctor Who has been around almost 50 years because he upgrades - not always perfect, sometimes not so effective. If we waited around for everything to be complete, standardized, worked out, there would be no adventure. No more planets and people to save - and no opportunity for all the fun along the way.

One of the biggest challenges to technological innovation is moving out of a comfort zone based on familiarity.  A reader of my last post on NUIs commented elsewhere that it is difficult to define "norms". I agreed, and suggested that it is also difficult to define "natural". These are challenges to developing NUIs. But neither one of us suggested that technology should not move forward and strive for positive change. 



Dr. Who Timeline
* Communications of the ACM, December 2011, Vol. 54, No. 12, p. 15

Why Do We Need NUIs? Assistive Technology

If I had to pick one reason why we need natural user interfaces (NUIs) it would be for use in assistive technology. Technology for increased convenience is all well and good. Technology for improving our ability to multi-task may or may not be good. A growing body of literature demonstrates the more we multi-task the less productive and efficient we actually are. Disturbing to say the least.

However, there are people for whom NUIs have the potential to be life transforming. Jonathan Josephson of Quantum Interface (introduced in my last post) told me about an incredibly moving experience he had observing a quadriplegic trying out a motion based NUI prototype. Apparently, many partially paralyzed people have limited range of motion in their forearm but not their upper arm. After studying what the natural motions were in this situation, QI designed an interface that enabled this individual to interact with his computer using his arm even though he could not lift it. Consider: prior to that moment, this man had been forced to use a straw held in his mouth. Put yourself in his shoes and imagine what that would be like. 

 

How would you feel?

Jonathan's voice choked up as he told me how the user and his family members were virtually in tears. To them, this interface represented freedom and autonomy.

What is holding back full scale development and deployment of this type of NUI? Sensor technology for one thing. We need highly precise 3D pinpointing sensors to locate and track motions, and to enable fine tuned feedback. Fortunately, says Jonathan, these are on the horizon.

So when people get into discussions about what is "natural", there is no single, simple, answer. As pointed out by a LinkedIn reader in response to my last post, there will be cultural differences. There will be differences based upon ability and impaired ability. As we become increasingly sophisticated, and some inventors move into the realm of AI, cognitive issues will become increasingly important. For example, what is "natural" will be different for victims of head trauma.

Our society is undeniably digital and the more people who can access technology easily and naturally, the better. 

There is a ways to go before the assistive technology Jonathan envisions can be produced and marketed widely and affordably, but when it happens the societal impact has enormous potential. At the end of the day, this potential is what drives Jonathan in his NUI work.

Go Natural with Your Interface

Do you sometimes wish you could know what the next big hot thing in technology will be?  Who thought a few years ago that mobile devices would become so dominant? Yet currently, mobile devices are driving software and hardware development pretty much everywhere. Digital designer and thought leader Luke Wrobleski recently wrote a book, "Mobile First", calling for bottom up interface design - design for mobile devices and only then port to non-mobiles. Screen real estate Rules! (pun)

What's next? Some people have a way of zeroing in on these things. I had a conversation recently with Jonathan Josephson, CTO of Quantum Interface,  about Natural User Interfaces (NUIs), which he firmly believes is the way technology is going - and should go.

Jonathan is one of those entrepreneurial think-outside-the-box people who has inspiration at unexpected moments (e.g. driving the sometimes challenging Texas freeways) and rather than just saying "cool idea!" and continuing home, he patents the idea and forms a successful business venture. Hence it was with Quantum Interface's motion interface technology which the company is developing for use ... lots of places.

If you want to run (fast) in a conversation about interesting ideas, Jonathan is your guy. Within minutes of starting our first phone call we were talking about their technology enabling people to walk into the shower and use arm motions to control the water flow (on, off, hotter, colder, pressure increase, pressure decrease), to watch TV (couch potatoes rejoice - you won't have to get up and you won't need all those annoying remotes either), adjust the lighting (come home late at night and can't find the light switch? no problem. Let there be light, says your body, and there it will be just the way you want it).

And then there is assistive technology -

Wait a minute (we'll come back to that last thought) -  What exactly is a NUI?

(try looking it up online. you will get a multitude of unhelpful answers)

Jonathan explains: a Natural User Interface is a user interface that is natural to use. Ok, agreed, but what does "natural" mean (because I just had to ask)? I was mentally screaming ahead with all the examples of how we are so adept at adapting that sometimes it is hard to tell what is natural and what is acquired. As Jonathan and I spoke about body movements (motions) that are natural to us as humans, he agreed with my comment about how unnatural a smart phone is. I felt vindicated: Last fall I wrote a post here about my frustration with (and ultimate defeat by) an Android phone when I first tried to use it. But, as with many items we interact with daily, after you have learned to manipulate a smart phone, it may feel natural. Until, that is, you bruise your fingers from over enthusiastic tapping on it. I mean really - were the ends of our fingers designed to use as mini-sledgehammers day in and day out?

We can adapt. We have opposable thumbs and all that. Why do we need NUIs?  next time...

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.