Organizational Probes:Exploring Playful Interactions in Work Environment

Playfulness, with non-intrusive elements, can be considered a useful resource for enhancing social awareness and community building within work organizations. Taking inspirations from the cultural probes approach, we developed organizational probes as a set of investigation tools that could provide useful information about employees’ everyday playful experiences within their work organizations. In an academic work environment, we applied our organizational probes over a period of three weeks. Based on the collected data we developed two design concepts for playful technologies in work environments

Round Eye at The Wall: The Power of What We Call Things

I went on a battlefield tour this weekend with Garry Adelman. It was an amazing experience, as any tour with Garry is, because he delves into how we conceptualize landscapes just as much as what happened on those landscapes 150 years ago. My mind was churning the entire time. Of anyone, both those who work for those places and those who just generally love those places, Garry (and his partner in crime Tim Smith) is tops on the list of most effective living time machines. Like always, Garry got me thinking on 15 different levels, and I\u27d wager that the next few weeks\u27 posts will all be inspired by tidbits and nuggets he mentioned at Antietam this past Sunday. [excerpt

Uncertainties in the Algorithmic Image

The incorporation of algorithmic procedures into the automation of image production has been gradual, but has reached critical mass over the past century, especially with the advent of photography, the introduction of digital computers and the use of artificial intelligence (AI) and machine learning (ML). Due to the increasingly significant influence algorithmic processes have on visual media, there has been an expansion of the possibilities as to how images may behave, and a consequent struggle to define them. This algorithmic turnhighlights inner tensions within existing notions of the image, namely raising questions regarding the autonomy of machines, author- and viewer- ship, and the veracity of representations. In this sense, algorithmic images hover uncertainly between human and machine as producers and interpreters of visual information, between representational and non-representational, and between visible surface and the processes behind it. This paper gives an introduction to fundamental internal discrepancies which arise within algorithmically produced images, examined through a selection of relevant artistic examples. Focusing on the theme of uncertainty, this investigation considers how algorithmic images contain aspects which conflict with the certitude of computation, and how this contributes to a difficulty in defining images

On the enumeration of closures and environments with an application to random generation

Environments and closures are two of the main ingredients of evaluation in lambda-calculus. A closure is a pair consisting of a lambda-term and an environment, whereas an environment is a list of lambda-terms assigned to free variables. In this paper we investigate some dynamic aspects of evaluation in lambda-calculus considering the quantitative, combinatorial properties of environments and closures. Focusing on two classes of environments and closures, namely the so-called plain and closed ones, we consider the problem of their asymptotic counting and effective random generation. We provide an asymptotic approximation of the number of both plain environments and closures of size $n$. Using the associated generating functions, we construct effective samplers for both classes of combinatorial structures. Finally, we discuss the related problem of asymptotic counting and random generation of closed environemnts and closures

Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes

I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of Boltzmann and Shannon but in the sense that it allows for compression progress because its regularity was not yet known. This drive maximizes interestingness, the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007 joint invited lectur

Technological Claustrophobia

Tim O'Riley discusses the genesis of his project to examine how technology can be used to construct a speculative model of a physical and spatial experience