Some quotes from this week issue of The Economist, on ubiquitous computing (which I started bloggin here), I took them from various articles in the special issue. I find that they nicely exemplify common problems with ubiquitous computing and its slow user-adoption:

“Connecting machines requires giving up control to users, observes Tim Whittaker of Cambridge Consultants, which designs wireless systems. In fact, Orange M2M is criticised for trying to prevent customers from working with other operators. Thus even when mobile firms fall in love with M2M, the technology is suffocated by their embrace. Wireless innovation is more likely to come from smaller companies with a computing background. They are beginning to give machines eyes, ears and a voice.
(…)
Expectations were so high because much of the technology exists already. Yet it is being held back by non-technical factors: the lack of integration among different parts of the industry and the need for companies to change the way they operate. (…) Components from different firms may not work together (…) Mobile network coverage is inconsistent, so relying on just one operator is risky, and for movable things such as vending machines and cars, which may cross national borders, it is unthinkable.
(…)
The list goes on. Back-office software to manage the system has to work with existing corporate software. Someone has to take care of billing and managing the devices. And as everyone takes their cut, the expense grows. “It is a very long value-chain for people to bring this together,”
(…)
But things have not gone as planned. In Japan, where much has been made of vending machines that accept payment via mobile phones, the vast majority are in fact unconnected.
(…)
Part of the reason is the sheer difficulty of getting all the relevant businesses together
(…)
Another question that inhibits take-up, even among those who are interested, is who should pay for the installation“

Why do I blog this? because it’s the first time I read in a broader-audience journal (as opposed to tech journal or scientific publications) a so comprehensive and clear overview of the ubiquitous computing problems. The analyses in this special issue are spot-on the main shortcomings: technological messiness, different business models, different regulations, complex situations, etc.

A definition of technology s-curves drawn from Clayton Christensen (in this paper):

“The technology S-curve has become a centerpiece in thinking about technology strategy. It represents an inductively derived theory of the potential for technological improvement, which suggests that the magnitude of improvement in the performance of a product or process occurring in a given period of time or resulting from a given amount of engineering effort differs as technologies become more mature.
(…)
It states that in a technology’s early stages, the rate of progress in performance is relatively slow. As the technology becomes better understood, controlled, and diffused, the rate of technological improvement increases . But the theory posits that in its mature stages, the technology will asymptotically approach a natural or physical limit, which requires that ever greater periods of time or inputs of engineering effort be expended to achieve increments of performance improvement. “

Why do I blog this? Given that I use this tool more and more often in talks, workshops and work, it’s good to get back to the literature and understand it more thoroughly. In some work recently I mostly used it to describe evolution of certain technologies such as location-aware systems, 3D virtual worlds or mobile gaming. Generally, the point of is to describe a succession of waves starting from an idea as shown on the picture below. For instance, with the “location-awareness” idea, the first wave of mature products was navigation systems (quite often found in cars with garmin and tomtom devices), a second wave concerns place-based annotations systems or people finder (in that case, nothing’s really mature in the same sense as the first wave). Besides, I am well aware of the limits of such curves but they offer a relevant way to discussion diffusion of innovation.

Reading “Manuel de prospective stratégique, tome 1 : Une indiscipline intellectuelle” (Michel Godet), there was an interesting chapter about the most frequent error when doing foresight.

General causes are:
1) Forgetting change (over-estimation) and inertia (under-estimation).
2) “Announcement effect”: some predictions only aim at influence the evolution of the phenomenon and then contribute to its realization
3) Too much information (noise), few strategic information
4) Inaccuracy of data and instability of models (one should always ask whether a small modification in input data will change the output)
5) Error of intrepretation
6) Epistemological obstacles (looking at the tip of the iceberg / or where the light is)

Specific causes:
1) Uncomplete vision (leave behind other variables, disruptions, new trends…)
2) Excluding qualitative variables (that cannot be quantified
3) Thinking variables have static relationships
4) Explaining everything by looking at the past
5) Single future
6) Excessive use of mathematical models (mathematical charlatanry)
7) Conformism to gurus

To complete my notes on the LIFT07 workshop about foresight, there is a very dense and insightful podcast of Bill Cockayne’s talk at Design2.0 (mp3, 15.41 Mb).

In this talk, Bill explains that one of the challenge for take companies/student in engineering schools is to get people understand the bigger context, complexity and big systems. Bill started as a technologist and migrate as a technology-foresisght/strategy person. His point is to ask questions such as “where is it going?“, “why is it going there?“. This is not a matter of being a futurist, not about predicting anything but rather to work on “how do you think about this coming technology?” “how do you think about this coming social change?”. Technology sometimes drive social change, does not, sometimes, maybe but the question is how do you know when?. It’s not predictions, it’s something that comes out of knowing where information comes from.

Beyond tools to design for today’s future (ethnography, brainstorming prototypes) and those for going a little further (scenario planning), the point is to go much further: how do you critically assumptions and build models. Oddly enough this stuff is simple, using 3 tools he describes. My raw notes below:

3 tools: point of view questions, X-Y graphs (out to get there by telling stories, looking for triggers of change, think about to get there when we think about what want to be, being normative (design a better future), defensive (design for a future that is coming but we don’t like it), how to prepare for that kind of things),

1) Simple rule: You won’t get there from here

let’s say you design a toothbrush, you observe current users so you’re going design today for a year from now
as you get out past 7 years that does not work, who are you going to observe?
this is all a POV: get out there with

2) X-Y graph: a structured brainstorming tool
Issues: A versus B. So discuss with your team: What would be the 2 most salient issue? issues being one on X and one on Y. So you have 4 endpoints. What would the salient issues that affect the questions we’re asking in 20 years? It is going to be perception? a social issue? no tech change? Older people (with experience) are better doing this because they’ve seen change (they felt what is 5 years).
After a whole day, you may have 10 good X/Ys. Good = something you learn over time and sth you feel intuitively.
Tell stories when you’re doing it, catchphrases, funny stories…

What you want to look for is whitespots = possibilities, you can make a difference here
either no one is going there because is difficult or it is an opportunity
how might we put something there? a toy, a computer, a social change

3) Then you start building scenarios, like design but way far out, 20 years ahead
what if have 3-5 stories? what would the world be out there?
the most important things about these lines: no changes, lots of changes, one big sweeping change…

tell a story in 5′ and then spend the rest of the afternoon going backwards,
tell me what had to happen all along the way, tell me when it had to happen, give me a timeframe, the trigger, the driver, when does something has to happen is very critical
as you being to go backward, you realize what has to happen (before a product occur, need of having another tech, so another guy has to invent this tech)

As you begin to go further out in time, you have a much harder time to say how close your change drivers are going to be. Then assume that all the decisions you make are too pessimistic and far out. In near term, assume that everything you say is too slow

Long term changes tend to have trigger than is not necessarily in the center of where the change is occurring
when economics are changing is not that because a person stood up and said “Wall Street is going into that directions” it’s more that you watch the housing data, you watch the number of kids that are how many kids are being born, breastfed,… and then you ask where is another change coming further off and how is it going to be its impacts?

The questions were quite interesting. One of the person asked what is the biggest mistake made by companies. Bill argues that most big companies forgot that research existed for two reasons: invent new things and spend a lot of money obtaining patents, the other is to have a bunch of guy who sit around, doing this kind of things he presented in the afternoon, drinking their coffee. Another issue is the fact that None of us read enough, none of us talk to smart people enough.

“Read methodologies and then read WSJ, E, NYT, CSM… daily because you needs to start getting a feel of where data comes from. You may be watching very closely where your products are going to be but something is changing in an area you never even thought but that could infect it, that could be an opportunity. READ MORE

These publications have the broadest range of readers they have op-eds. Get a broad view of business, social, economics, random technology stuff. Take the biggest daily newspaper that don’t focus on news, more like the economist, that look for the analysis, context, why this happened, why A did X… Over time you build up and ability, look for different views, it’s not a bias you’re looking for, but a a different viewpoint“

Why do I blog this? great food for thoughts, methods and ideas about how to structure what I am doing in something more formalized. Besides, the question of “data” in foresight, addressed in the talk, is of great interest to me.

Currently completing my PhD program (thesis defense is next week), it gave me the occasion of looking back and think about what interest me. My original background is cognitive sciences (with a strong emphasis on psychology, psycholinguistics and what the french calls ergonomie) and the PhD will be in computer sciences/human computer interaction. In most of my work, I have been confronted to multidisciplinary/interdisciplinarity (even in my undergraduate studies).

It took me a while to understand that my interest less laid in pure cognitive science research (for example the investigation of processes such as intersubjectivity, and its relation to technologies) but rather about the potential effects of technologies on human behavior and cognitive process. In a sense this is a more applied goal, and it led me to take into account diverse theories or methods. Of course, this is challenging since mixing oil and water is often troublesome in academia. Given that my research object is embedded in space (technology goes out of the box with ubicomp) and social (technology is deployed in multi-user applications), there was indeed a need to expand from pure cogsci methods and including methods and theories from other disciplines. The most important issues regarding my work for that matter were the never-ending qualitative versus quantitative methods confrontation (I stand in-between using a combination of both, depending on the purpose) AND the situated versus mentalist approach (to put it shortly: is cognition about mind’s representation? or is it situated in context?). So, this was a kind of struggle in my phd research.

However, things do not end here. Working in parallel of my PhD as a consultant/user experience researcher for some companies (IT, videogames), I had to keep up with some demands/expectations that are often much more applied… and bound to how this research would affect NPD/design or foresight (the sort of project I work on). Hence, there was a need to have a discourse about these 2 issues: design and foresight. No matter that I was interested in both, it was not that easy to understand how the research results/methods can be turned into material for designers or foresight scenarios. SO, three years of talking with designers, developers, organizing design/foresight workshops, conferences helped a bit but I am still not clear about it (I mean I don’t even know how to draw something on paper).

Recently, I tried to clear up my mind about this and the crux issue here is the constant shifting between research and design (or foresight, sorry for putting both in the same bag here but it applies to both). The balance between research that can be reductionist (very focused problem studied, limits in generalizing or time-consuming) and design that needs a global perspective is fundamental. The other day,I had a fruitful discussion with a friend working on consumer insight projects for a big company. Coming from a cognitive science background as this friend, I was interested in his thoughts concerning how he shifted from psychology to management of innovation/design of near-future products/strategy.

I asked him about “turning points” or moments that changed his perspective. He mentioned two highlights. The first one was the paradigm shift in cognitive science in the late 80s when the notion of distributed cognition (Dcog) appeared. Dcog basically posited that cognition was rather a systemic phenomenon that concerned individuals, objects as well as the environment and not only the individual’s brain with mental representation. To him, this is an important shift because once we accept the idea that cognition/problem solving/decisions are not an individual process, it’s easier to bring social, cultural and organizational issues to the table.

The second highlight he described me is when he use to work for a user experience company that conducted international studies, he figure out that the added value not only laid in those studies but also in the cumulative knowledge they could draw out of them: the trend that emerged, the intrinsical motivation people had for using certain technologies, the moment innovation appeared. This helped him change the way he apprehended the evolution of innovations and made him question the fact that they can follows long s-curves.

Why do I blog this? random thoughts on a rainy sunday afternoon about what I am doing. This is not very structured but I am still trying to organize my thoughts about UX/design/foresight and how I handle that. I guess this is a complex problem that can be addressed by talking with people working on design/foresight/innovation. What impresses me is observing how individual’s history helps to understand how certain elements encountered shape each others’ perspective.

The picture simply exemplify the idea that conducting design/foresight projects need a constant change of focus between micro and macro perspectives. This reflects the sort of concern I am interested in by taking into account very focused perspectives (user interface, user experience, cognitive processes) and broader issues (socio-cultural elements, organizational constraints…).

A good read in the ACM Ubiquity: What if the experts are wrong by Denise Caruso. It’s about how societies prepare themselves to be wrong when creating innovations that can have have important consequences on the world. Some excerpts:

“long-term stewardship” of man-made hazards; that is, how a society prepares to take care of the messes it has made that it can’t get rid of, generations into the future.
(…)
To think that other people might suffer as a result of their actions is not part of the expert’s world, or it gets pushed away in the drive to deploy the technology,” said La Porte. “But what are the consequences if it turns out that all the things they believed in are wrong? That’s really hard. And most technical people can’t talk about this. What they do is theology to them, not science.

Why do I blog this? even though this article addresses tech such as nuclear power and DNA manipulation, the author has a good point about designing new elements/artifacts (given the messiness of the world). And it leads to two questions: is it about designing to avoid future messes or designing in a way that this inherent mess could be taken care of?.

(the picture is a shot I’ve taken last week end: remnants from a restaurant that is refurbished)

Yesterday in the NYT, a pertinent article about PARC strategy. It basically describes PARC’s path to move from being a in-house research lab to a subsidiary form. The article shows on-going projects but more interestingly critiques the fact that PARC is a “lab of missed opportunities”:

Early in the decade, a struggling Xerox Corporation was trying to sell off a stake in its Palo Alto Research Center, which it could no longer afford to support. But with the technology bubble bursting, the price that investors were willing to pay for a piece of PARC, as the center is known, kept going down.

So in 2002, Xerox switched to Plan B: it spun off the center into an independent subsidiary and sought to prove that it could sustain itself by licensing technology and forming partnerships with outside companies. On Friday, PARC is announcing a deal that underscores that strategy. It is licensing a broad portfolio of patents and technology

“There’s no way anyone can top what they did in the past in terms of dramatic research developments,” said the futurist Paul Saffo, a fellow at the Institute for the Future. But Mr. Saffo praised PARC for finding a business model that has allowed it to survive at a time when many research groups at American corporations are being cut.

“This is an organization that has done well at keeping researchers, and spinning out a steady stream of little products,” Mr. Saffo said. “PARC has been a very quiet success.”

I am currently participating in LIFT07 (as a co-organizer). Great speakers, cool audience. This year I chose to take no notes and try to do a write-up afterwards only with what my brain cells would have retained.

After selling its PC division, IBM recently dropped the printer business. At the same time, they explore other paths such as the super-mediatized SL but also other interesting platforms. I have intrigued by Many Eyes. Internet News has a short discussion about it:

IBM (Quote) launched a new social computing site today called Many Eyes, which allows users to upload very large data sets, choose different visual representations for the data sets, and engage in an online discussion of what the data reveals. Each visualization will allow for an active discussion to take place and become a common area to share ideas, add insight and understand the visualization in a group setting.
(…)
an attempt to learn whether the principles of crowd-sourcing can be applied to the analysis of visualized data, in the hopes of generating broader and deeper analysis of data.

Why do I blog this? I find interesting the IBM attempts to try and test new applications in the domain of social computing.

Laurent and I have been interviewed by Fabio Sergio for the Convivio website (a European network for human-centred design of interactive technologies). What is interesting is that Fabio highlighted an aspect that we haven’t really though about when organizing LIFT last year:

Fabio: I am not sure the term really makes sense in this context, but after experiencing LIFT 06 I’d be tempted to say that you applied a Human-Centered approach to the design of the conference last year, and all seems to hint you’re doing it again this year. Is this impression in any way correct?

Nicolas: Even though the human-centered approach nicely reflect what happened, it was actually not that intentional and this mindset emerged from how we thought a conference should be organized and from the type of event we would have liked to attend in the first place. Besides, when you start building a conference from scratch, you don’t have all the needed expertise so you do it with others.

Laurent: Actually, I feel LIFT06 was deeply human centered, but that was not really intentional, nor did I follow a method or something. It was more me trying to see how I could best accommodate the constraint I had: small budget (LIFT is auto-financed), short notice, small team, no previous experience in organizing events. So we tried to get help from the community as much as we could, as LIFT is a gathering more than anything else.
We asked people to help us with some decisions and suggestions, outsourced a few things to the attendees, and after the event gathered precious feedback via a survey.

Why do I blog this? LIFT06 was really an occasion for us (Laurent, John, Steve and I) to learn how to organize an event based on a low-profile cross-pollinating approach. It’s good when external persons highlight an aspect we haven’t thought of (or maybe because we’re so deeply into user-centered XXXX that we’re shaped by this way of thinking?).

A good quote that I ran across in a paper Fabien wrote:

Bill Buxton’s aphorism: “We need to do smart things with stupid technologies before we do stupid things with smart technologies”.

On of those thing I spot on a regular basis in occidental cities (I took that one in Geneva last week):

Why do I blog this? this is IMHO, one of the most advanced incarnation of what happen when you have “street computing” so far. As a matter of fact, it’s neither the intelligent sidewalk nor the über cool ambient displays. Once again, it’s the dark side of computing. And for those who’re wondering why I keep posting about this sort of things once in a while, I simply think it’s good to ponder what I post here with the other “facets”.

One of the innovation we’re trying to bring forward at LIFT07 (7-8-9 February 2007 Geneva, Switzerland) is to have a more creative and interactive platform during the conference. This is going to be LIFT+, a concept intended as a bridge between creativity and pragmatism, allowing all possible forms of interaction, gathering individuals working freely on a specific topic - DIGITAL FRAGILITY – to evoke the overwhelming presence of connectivity in daily life.

The whole point is to make the conference environment more creative so that people could participate and work out some ideas related to the concept of digital fragility. The website describes some of the project that we plan to have:

• Plotter: Content and selected works will be printed (four plotters will output content uninterrupted) during the conference in the main entrance.
• Double Photo: Build a tiny portrait-studio in the event’s premises, a small world (somewhat exterior to all the fuzz of such a conference) dedicated to shooting portraits.
• Post-it Floor: You post literally – by writing on a post it - some words and its meaning. You’ll have to draw tehm with real pens
• Snake Run: SnakeRun is an actual sized two-player snake game. Each participant controls a virtual snake that is projected on the playing field.
• …

Why do I blog this? as one the organizer, I can tell how stimulating it is to think about how to go beyond sit-and-listen-to-a-talk conference! We’re trying to do our best to do something more innovative.

The LIFT07 program is out (empty slots are being filled progressively no worries):

More explanations later about LIFT+ (surprise) and LIFTcamp (we’re working on digg-like system so that people could propose talks and you could vote for them).

I recently read “Design Research: Methods and Perspectives” (Brenda Laurel, Peter Lunenfeld, Eds.). One of the chapter that I found relevant for my work is the one about Creating a Culture of Design Research by Eric Zimmerman.

The author describes some of the strategies they took at the game development studio called “gameLab“, pushing the boundaries of game companies and cultivating a “design culture”. It’s mostly based on 6 hints:

1. Create a space that encourages design research: “the office space we inhabit is filled to bursting with games, toys, and other play objects”
2. Build a design research library: ” retail game titles, books and graphic novels, DVDs and videotapes, magazines (we have many subscriptions), board and card games, and toys of all kinds.”
3. Attend and create events: “GameLab has attended films, exhibits, conferences, and other events connected to games, design, and popular culture / we also host our own design research affair”
4. Let them teach
5. Encourage side projects: “We encourage our staff to pursue personal projects.”
6. Create contexts for experimentation: “from time to time we create opportunities for our staff to undertake experimental, noncommercial projects as a form of design research. “

Why do I blog this? I was looking for ideas of creative companies, especially in the game industry, I found those highlight relevant and fruitful for future projects. The idea of creating a proper environment, with a culture of design creativity is of interest to me (given that my role in various organization is too nurture designers).