Ash at CIID

Ashwin Rajan's blog while at the Copenhagen Institute of Interaction Design.

Archive for the ‘Computational Design’ Category

People as instruction processors – extended implications

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An exercise I found deeply interesting that we did some weeks back at CIID was called ‘People as instruction processors’. Dennis and Patrick, gestalten at the unique ‘the-product‘, gave us this brief:

“Write down three instructions-sets. These instructions will then be dictated to three other participants. The other participants will process the instruction by drawing on a piece of paper with a red, green or blue marker. The exercise aims at introducing the participants to programming as an everyday exercise, a translation from intention into language into action. The result will be a set of very analog procedural drawings.”

A first-cut instruction I came up with ran something like this:
‘Start in the middle of the page. Mark the point.
Draw a circle with any one point of its circumference lying on the marked point.
Draw a square touching the circle …’ and so on

As you can guess, no sooner than you write out the first set of instructions do you realize that you will need to be much more precise in further iterations. What exactly is the ‘middle’ of the page? Does that refer to the center point on the page as plotted from all four corners? Or, in the second and seemingly more explicit statement – what should the size of the circle be? Each detail provided can set the context and nudge the ‘instruction processor’ to execute results closer to the original intention. When the same instruction set is executed by multiple people, the results can be very similar to, or more often, radically different from one another, depending on the instructions given, and its understanding and execution by the subject.

Some of the results that came out of the exercise looked like this:

People as Instruction Processors - Results 01

People as Instruction Processors - Results 01

People as Instruction Processors - Results 02

People as Instruction Processors - Results 02

I thought this was a very powerful exercise because it communicated the fundamental challenges of providing instructions to processors – whether human or machine – in a manner that achieves intended outcomes, while also underlining the importance of ‘syntax’ or grammar, specificity and detail-orientation, interpretation and translation. The exercise also gave me a fascinating glimpse of how instructions and their interpretation can facilitate (or stifle) emergent phenomena.

Extended implications: I can think of at least two other domains where interaction designers can benefit highly from exploring how people behave as instruction processors: user research, and robotics.

To elucidate the first, here are six examples of contexts from a user research perspective where I can see value in learning how people process instructions:
1. Road signals that control traffic and commuters
2. Call centers: where operators perform (and are evaluated) based on a wide variety of parameters which are essentially instructional in nature, commencing with basic training.
3. e-learning: there is a reason the work of creating e-learning content is called ‘instructional design’.
4. Car Rally: Driving based on navigation instructions
5.  Mass co-ordinated, precision, and time-sensitive operations such as the emergency evacuation of a building by a team of firefighters, or a combat situation – with an extended analogy into virtual worlds of MMORPGs and team gaming; any number of examples can be given here.
6. The patient as an instruction processor who is required to follow the doctor’s prescription of the medication-diet-exercise-lifestyle mix as precisely as possible.

My second connection to this exercise is from a robotics perspective. I will keep it short by pointing you to this video by Rodney Brooks. He pivots a significant bit of his presentation on human-robot interaction, so watch out for that. Half way through his talk, the professor demonstrates how he and his team build artificial intelligence to mimic human instructional processing capabilities by calling a member of audience to the stage. Brooks’ AGI (artificial general intelligence) stance that ‘humans are essentially machines‘ makes for compelling reading. His own take on the singularity contention is neatly summed up in his statement “the singularity will be a period, not an event.”

Visualizing Wind

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This mini-project was executed as part of the Computational Design course at CIID some weeks back. My objective was to use the Nintendo Wii to record simple wind data by suspending the Wii in an open location, and using the recorded data to create visualizations drawn by programming in Processing. More details follow.

The Concept:
This intent is to show  a concept for visualization of simulated wind data as would be available from a sensor located on a windmill.
A real stakeholder – wind data analyst – from a leading Danish consultancy was interviewed to understand key challenges in wind turbine design.
It is common in the Danish wind industry (and elsewhere) to record wind data for very small intervals of time. By understanding wind patterns in terms of its properties such as acceleration and constancy, engineers are able to go beyond physical limitations of turbine design to evolve increasingly efficient and productive systems.
With over 20 sensors recording different parameters on a single windmill, analysts often face a veritable mountain of data (down to individual seconds). In this context, visualization of such data in a manner that facilitates comparison, causality and multivariant evidence becomes key. The poster describes briefly how some of these goals were met.

The delightful ‘Wiimote’ was used in this experiment to mimic the sensor.

Design Context: A real world scenario 
Location: a wind farm out in the North Sea
o   72 turbines
o   20 sensors on each turbine
o   Each sec of wind data recorded
o   Data from 8 years archived for analysis

·      The peak production of windmills of all capacities is 60% of full capacity due to physical limitations
·      Measuring constancy of wind is of most interest to wind analysts and windmills designers
·      Acceleration of wind is most deterrent to wind production as it wears out the material most, and least load on the material comes from constancy of wind
·      The main difficulty in real-world wind turbine design isn’t generating the most electricity at a given speed – it is making blades which will work across a range of wind speeds
·      The design challenge is to be able to measure and visualize wind data in a way that can help engineers interpret the ‘acceleration’, ‘lift’ and ‘orientation’ of wind.

Using the Wii Remote as principle sensor
Wii remote sensors simulate the acceleration of wind by it’s x, y, z acceleration coordinates
·      In order to holistically render the acceleration of wind in a visual manner, we have focused on gathering data in coordinate directions x and y, and rendered the z axis insignificant – by suspending the sensor (in this case, the Wiimote) with a cord of fixed length.
·      Due to the position of the Wii remote when recording the raw wind data, the x and y coordinates are principle axis in this experiment. This has two benefits
o   The z sensor is rendered insignificant in terms of contributing to measuring acceleration, thus rendering it simpler by reducing the number of variables required in its calculating
o   The z axis becomes dedicated to measuring ‘lift’ and denoted here by the small series of arcs at the bottom.

Anatomy of a second - Composite View
Anatomy of a second – Composite View


And another mode of visualizing the same data set …




Written by Ashwin Rajan

November 30, 2008 at 9:00 pm